Editorial Procedures
1. Overview. Please find a schematic of our publication procedures here.
2. Submission. Submission to Heliyon is online only using our editorial system EVISE®. You will be guided through the process step by step, and you will be able to track the progress of your manuscript through the system. EVISE® automatically converts the article’s source files into a single PDF, which is used in the peer review process. Please note that we will still need the source files for further processing after acceptance.
3. Article Transfer Service. Heliyon is part of Elsevier’s Article Transfer Service. This means that you can transfer your article from any Elsevier journal to Heliyon, without the need to reformat.
4. Editor and reviewers
4.1. Suggestions. To enable speedy handling of your manuscript, we encourage you to suggest a suitable editor from our Editorial Board and to provide the names and institutional e-mail addresses of several potential referees. Note that the editor retains the sole right to decide whether or not to use the editor or reviewers you suggest.
4.2. Exclusions. You are welcome to exclude a limited number of researchers as potential editors or reviewers of your manuscript. In order to ensure a fair and rigorous peer review process we ask that you keep your exclusions to a maximum of three people. If you wish to exclude additional referees, please explain or justify your concerns – this information will be helpful for editors when deciding whether to honor your request.
5. Peer review
5.1. Manuscript checks. Our in-house editorial team will ensure that you have uploaded all necessary files in usable format and that you have provided all the information we require. If your manuscript does not meet one or more of these requirements, we will return it for further revisions.
5.2. Publishing ethics. All manuscripts submitted to Heliyon are screened using CrossCheck powered by iThenticate to identify any plagiarized content. Your study must also meet all ethical requirements as outlined in our Editorial Policies. If the manuscript does not pass either of these checks we may return it to you for further revisions, or decline consideration of your study for publication.
5.3. Editorial assessment. Once it has passed the initial checks, our editorial team will assign your manuscript to an editorial board member with relevant expertise, who will be responsible for managing the peer review process. Editorial board members as well as the Editor-in-Chief may
reject manuscripts that they deem highly unlikely to pass peer review without further consultation.
5.4. Process. Heliyon operates a single blind review process. The technical quality of the research described in the manuscript is assessed by the editorial board member and a minimum of one additional independent expert reviewer. Manuscripts transferred from another journal will be evaluated in the same way as original submissions, but the editorial board member or the Editor-in-Chief may take a decision without further consultation, if the assessments of at least two independent expert reviewers are available. The Editor-in-Chief is responsible for the final decision regarding acceptance or rejection of articles.
5.5. Confidentiality. All information contained in your manuscript and acquired during the review process will be held in the strictest confidence.
6. Decisions. Your research will be judged on technical soundness only, not on its perceived impact as
judged by editors or referees. There are three possible decisions: Accept (your study satisfies all
publication criteria), Invitation to Revise (more work is required to satisfy all criteria), and Reject
(your study fails to satisfy key criteria and it is highly unlikely that further work can address its
shortcomings). All of the following publication criteria must be fulfilled in order to be accepted for
publication:
6.1. Originality. The study reports original research and conclusions.
6.2. Data availability. All data to support the conclusions have been either provided or are otherwise
publicly available.
6.3. Statistics. All data has been analyzed with the appropriate statistical tests and these are clearly
defined.
6.4. Methods. The methods are described in sufficient detail to be replicated.
6.5. Citations. Previous work has been appropriately acknowledged.
6.6. Interpretation. The conclusions are a reasonable extension of the results.
6.7. Ethics. The study design, data presentation and writing style comply with our Editorial Policies.
7. Revisions. Whenever possible, your revised manuscript will be assessed by the original editor and, if required, by the original reviewers. We request that a response to all reviewer and editor comments is supplied alongside the revised manuscript to allow quick assessment of your revised manuscript. This document should outline in detail how each of the comments was either addressed in the revised manuscript or should provide a rebuttal to the criticism. Please note that Heliyon does not support multiple revisions, a revised manuscript will either be accepted for publication or rejected.
8. Appeals. If you believe that the editorial decision about your manuscript was based on factual errors, you can contact us by email (info@heliyon.com). Please state the manuscript number and describe in detail why you believe the decision was erroneous. Your appeal will be seen again by the handling
editor, who will revisit the previous decision in light of your comments. Please note that we do not allow multiple appeals: a second decision will be final.
9. After acceptance
9.1. Online proof correction. The corresponding author will receive an e-mail with a link to our online proofing system, which lets you annotate and correct proofs online. The environment is similar to MS Word or LaTeX depending on the file format you choose at submission. You can edit text, comment on figures/tables and answer questions that arose during the processing of your article. All instructions for proofing are provided on the online proofing site help pages. We will do everything possible to get your article published quickly and accurately, so please submit all of your corrections within 24 hours. Please check carefully before replying, as we cannot guarantee the inclusion of any subsequent corrections. Please note that we can only publish your article once we have received your proofs or notification that you are happy for us to proceed with publication as is.
9.2. Availability of the accepted article. Heliyon makes articles available online as soon as possible after acceptance, we aim to publish within 72h after receiving your proof corrections.
10. Open Access. Your article will be free for everyone to read – immediately and permanently. You keep the copyright and personal scholarly usage rights, and we’ll have the publishing and distribution rights. Once it is published, people will be able to download, copy and share your article. We provide authors with a choice of publishing under the Creative Commons licenses CC-BY and CC-BY-NC-ND. These licenses will help maximize the impact of your article, getting the most reach and use out of it. Here are the details:
10.1. Creative Commons Attribution (CC-BY): This license lets people distribute and copy your article as long as they credit you. They can create extracts, abstracts and different versions, like translations. Under this license, people can also include your article in a collective work, and mine the text and data – including for commercial purposes. All this is possible, as long as they do not claim that you endorse their adaptation, and do not damage your reputation.
10.2. Creative Commons Attribution – NonCommercial - NoDerivs Alike (CC-BY-NC-ND): This license allows people to distribute and copy your article, as long as it is not done for commercial purposes. Under this license, people cannot change or edit your article for distribution in any way. They are able to distribute and copy your article as long as they give appropriate credit (with the DOI link to the publication), provide a link to the license, and as long as they do not claim that you endorse their adaptation of your work.
11. Article Publishing Charge. Heliyon is an open access journal – everyone can read the research we publish for free. The article publishing charge (APC) that authors, their institutions or funding bodies pay covers all expenses needed to support the publication process. The APC in Heliyon is
$1250 (plus VAT or local taxes where applicable). If you or your institution cannot afford the APC, we will consider your request to waive the fee. We do this on a case-by-case basis, and we may be able to grant waivers in some instances. If you are from a country that is eligible for the Research4Life program, we will give your application priority. Please make any requests to waive the APC upon the submission of your paper to Heliyon.
12. Copyright. Upon acceptance of an article, you will be asked to sign a publishing agreement where you will retain copyright and grant broad publishing and distribution rights to the publisher, including the right to publish the article on Elsevier's online platforms. You can refer to Elsevier’s Copyright Guide for further information.
13. Embargo. Your paper will remain under media embargo until it is published online – until then, there cannot be any public mention of your work, including mentions in mainstream media outlets, blogs, or on social media channels. You are welcome to discuss your work with colleagues in person or at scientific conferences prior to the publication date, however we do request that authors refrain from proactively seeking press attention prior to their paper’s acceptance. Authors may begin discussing their work under the terms of our media embargo with any interested reporters once their paper has been formally accepted. For more information please refer to our Media Policies. If you have any questions regarding our embargo and media policies or if your institution plans to issue a press release, please contact press@heliyon.com.
14. Commenting. Published articles will be available for online commenting. Readers who wish to leave a comment will have to register with their name, email address and affiliation, and their names will be published alongside the comment for transparency. Comments are moderated by the editorial team to ensure they are constructive and do not contain inflammatory language.
15. Errata and Corrigenda. We will publish a correction of your article if a significant error is discovered after publication. An Erratum will be published if we introduced the error; a Corrigendum if the author introduced the error.
16. Retractions. Articles may be withdrawn, retracted, removed or replaced after publication if they contain substantial errors that cannot be corrected by publishing an Erratum or a Corrigendum, or if ethical violations come to light after publication. You can find more information in Elsevier’s article withdrawal policies.
Mark Turner, Keele University
The aim of this document is to provide informal information relating to the major search engines and digital libraries that are frequently used by the software engineering research community. The details provided are particularly focussed towards the needs of evidence-based researchers performing systematic literature reviews but the details may be useful to researchers performing any form of literature survey. Where the information is available, information is provided relating to the resources indexed by each digital library or search engine, and other relevant information concerning the searching facilities provided. It is intended that this document will be updated periodically with information relating to different electronic databases or search engines, or as new information is discovered about the databases already covered.
1. Digital Libraries
The following provides details on the main digital libraries utilised by software engineering researchers, specifically IEEE Xplore, ACM Portal, and ScienceDirect.
1.1. IEEE Xplore
Firstly, it should be noted that IEEE Xplore lists search results ranked by “relevance”, where relevance is calculated by the following algorithm:
• Frequency of the search term in the full abstract/citation record
• Text length of the abstract/citation record
• For AND queries, proximity of the search terms throughout the abstract/citation record
1.1.1. Access restrictions
A list of what can be accessed by subscription level (IEEE Member, Institutional
Subscriber, or Guest) can be seen at the following URL:
http://ieeexplore.ieee.org/guide/g oview access.jsp
1.1.2. Searching
Use of the basic search facility without using field codes to direct where to search, results in the search defaulting to the field code ‘metadata’. The field code ‘metadata’ searches the following:
Document Title, Author, Publication Title, Abstract, Index Terms, Affiliation
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Therefore, the basic search does not search the full text of the indexed resources/PDF files. Full text can be searched when using the basic search facility by including ‘<in>pdfdata’ at the end of the search string. However, a login is required to search the full text of indexed articles.
Logging in allows the user to search specifying certain field codes, as outlined in Table 1:
Abstract ab
Affiliation cs
All fields metadata
Author(s) au
Catalog number ca
CODEN cn
Conference date cy
Document title ti
Editor(s) au
Full text pdfdata
Index term de
ISBN in
ISSN in
Issue number is
Issue part number pt
Meeting date cy
Part number pt
Publication name jn
Publication year pyr
Subject term de
Title ti
Volume vo
Table 1 – Field Codes available in IEEE Xplore
(http://ieeexplore.ieee.org/xplorehelp/Help_fieldcodes.html)
Logging in also allows the user to access the advanced search facility (see Figure 1):
2 Version 5 – January 2010
Figure 1 – Advanced Search Screen in IEEE Xplore
A complete user guide to IEEE Xplore can be accessed at the following URL: http://ieeexplore.ieee.org/guide/g oview guidepdf.jsp
1.1.3. IEEE Computer Society Digital Library
(http://www.computer.org/portal/web/csdl/home)
The IEEE Computer Society Digital Library (CSDL) indexes journals and conferences proceedings published by the IEEE Computer Society. The list of the resources indexed by the CSDL is available at the following link: http://www.computer.org/portal/web/csdl/home
The advanced search facility offers the ability to search particular journals by name, and also to restrict the search to only resources to which the user is subscribed (see Figure 2). It also offers similar search facilities to IEEE Xplore, such as the ability to search via a restricted set of field codes (Full Text, Title, Author, Date, ISSN/ISBN, or Exact Phrase) as well the ability to combine search phrases using Boolean operators.
3 Version 5 – January 2010
Figure 2 – CSDL Advanced Search Facility
A known paper, published by the IEEE Computer Society, was searched for in both the CSDL and IEEE Xplore and the paper was found in both digital libraries (Turner et al., 2003). This result indicates that the CSDL content is a sub-set of the content indexed by IEEE Xplore. Further evidence is provided by the fact that the IEEE Computer Society journals are listed under the content indexed by IEEE Xplore, for example IEEE Transactions on Software Engineering (http://ieeexplore.ieee.org/xpl/periodicals.jsp?letter=S&type=0&list=all).
The IEEE Conference Publishing Services Web site also describes how all conference publications are published in both IEEE Xplore and IEEE CSDL.
“...all CPS published conference publications are automatically included in IEEE Xplore and the IEEE Computer Society (CSDL) Digital Library.” (IEEE Computer Society CPS, 2009).
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1.2. ACM Portal (http://portal.acm.org/dl.cfm)
The ACM portal indexes journals and conference proceedings published by the ACM, along with selected publications from affiliated organizations. A list of publications by affiliated organizations is available:
http://portal.acm.org/browse dl.cfm?linked=1&part=affil&coll=portal&dl=ACM&CFID =65759424&CFTOKEN=68988647
The above link provides the following details:
• ALGOL Bulletin Computer History Museum
• Computational Linguistics MIT Press
• International Journal of Network Management John Wiley & Sons, Inc.
• Journal of Computing Sciences in Colleges Consortium for Computing Sciences in Colleges
• Linux Journal Specialized Systems Consultants, Inc.
• Mobile Networks and Applications Kluwer Academic Publishers
• Personal and Ubiquitous Computing Springer-Verlag
• Proceedings of the VLDB Endowment VLDB Endowment
• The Journal of Machine Learning Research JMLR.org
• The VLDB Journal — The International Journal on Very Large Data Bases Springer-Verlag New York, Inc.
• Wireless Networks Kluwer Academic Publishers
• ACL Workshops: ACL Workshops
• ACL: Annual Meeting of the ACL
• ANLC: Applied Natural Language Conferences
• CASCON: IBM Centre for Advanced Studies Conference
• COLING: International Conference On Computational Linguistics
• CSCL: Computer Support for Collaborative Learning
• EACL: European Chapter Meeting of the ACL
• HLT: Human Language Technology Conference
• ICLS: International Conference on Learning Sciences
• MUC: Message Understanding Conference
• NAACL-ANLP: ANLP/NAACL Workshops
• NAACL: North American Chapter Of The Association For Computational Linguistics
• TINLAP: Theoretical Issues In Natural Language Processing
• VLDB: Very Large Data Bases
In terms of searching, ACM by default searches the full text and all metadata of all indexed articles. The advanced search (Figure 3) is also available without the need to have an account.
5 Version 5 – January 2010
Figure 3 – ACM Advanced Search Screen
1.3. ScienceDirect (http://www.sciencedirect.com/)
ScienceDirect is a digital library that primarily indexes material published by Elsevier.
1.3.1. Indexing
ScienceDirect indexes the following:
• Over 2000 journals published by Elsevier (including Information and Software Technology).
• Plus dynamic linking to journals from around 350 Science/Technology publishers through CrossRef (http://www.crossref.org/).
A full list of titles available through ScienceDirect is available:
http://pts.sciencedirect.com/journalholdings/sd_holdings_guest.asp
The above list can be searched to determine if ScienceDirect indexes a particular journal. Note that both Web of Science and Scopus are also able to search Elsevier journals.
1.3.2. Searching
The basic search allows for searching of the following:
- All Fields (which searches all metadata and the full text of all articles indexed) - Author
- Journal/Book title
- Volume, Issue, Page
The advanced search facility can be accessed only when logged in, which also includes Athens or institutional access. The advanced search, as shown in Figure 4, allows the user
6 Version 5 – January 2010
to search for specific terms within particular fields (‘Abstract, Title and Keywords’, Authors, Specific Author, Source Title, Title, Keywords, Abstract, References, ISBN, ISSN, Affiliation, or Full Text).
Figure 4 – ScienceDirect Advanced Search Screen
2. Search Engines (index multiple sources)
The following provides details about a selection of the other search facilities available which, instead of specialising in a particular publisher, instead index content from multiple digital libraries.
2.1. Web of Science (http://isiknowledge.com)
2.1.1. Searching
The basic search facility in Web of Science allows the user to search using various search terms and Boolean operators. However, the search forces the user to search in a pre¬defined list of fields and, as it only indexes content from other sources, does not allow for the searching of the full text. The field tags that can be used are as follows:
TS=Topic
TI=Title
AU=Author
GP=Group Author
ED=Editor
SO=Publication Name
PY=Year Published
CF=Conference
AD=Address
OG=Organization
SG=Suborganization
7 Version 5 – January 2010
SA=Street Address
CI=City
PS=Province/State
CU=Country
ZP=Zip/Postal Code
FO=Funding Agency
FG=Grant Number
FT=Funding Text
The Boolean operators that can be used are as follows:
AND
OR
NOT
SAME
The advanced search facility allows for searching using the above field tags as well as to combine them, e.g.
TS=(nanotub* SAME carbon) NOT AU=Smalley RE
It also allows for restricting the search to particular years, languages, and different document types. However, like the basic search facility, the advanced search does not appear to allow the user to search the full text of the indexed articles.
2.1.2. Indexing
Web of Science searches five databases:
• Science Citation Index Expanded
• Social Sciences Citation Index
• Arts and Humanities Citation Index
• Index Chemicus
• Current Chemical Reactions
The Science Citation Index Expanded database is of most relevance as it includes Computer Science as a discipline. The SCI-expanded database covers over 8,700 journals, and is maintained by Thomson Scientific. The computer science journals that are covered include IEEE, ACM and IET published journals. A search of the master journal list included:
IEEE Transactions on Software Engineering,
IEEE Software,
IEEE Computer
Empirical Software Engineering,
Information and Software Technology,
IET Software,
Software Practice and Experience.
8 Version 5 – January 2010
The master journal list for Web of Science is available, and can be searched to determine if a particular journal is indexed:
http://www.thomsonscientific.com/cgi-bin/jrnlst/jlsearch.cgi?PC=MASTER
Web of Science authorises journals for inclusion and journals can be submitted to be indexed and, if chosen, are added to the above list and included in the search.
2.2. Scopus (www.scopus.com)
Scopus is similar to Web of Science in that journals are authorised for inclusion and the engine has its own index list. Some universities have institutional access, but for those without institutional access Scopus also offers an Athens login. A full list of the journals that it indexes is available:
http://www.info.scopus.com/detail/what/titles.asp
The list includes:
IEEE Transactions on Software Engineering,
IEEE Software
Empirical Software Engineering,
Information and Software Technology,
IET Software,
Communications of the ACM.
Similar to many other search engines, Scopus has the ability to select where to direct a search (including “All fields”, “Author”, and “References”) but as it only indexes material published elsewhere, it does not appear to have a full text search facility.
2.3. Google Scholar (http://scholar.google.co.uk/)
It is very difficult to find out exactly which journals are indexed by Google Scholar. However, it does index the ACM and IEEE databases and does not appear to include Elsevier journals (Meho & Yang, 2007). Past experience with performing SLRs has however found that there is some crossover between Google Scholar and ScienceDirect, indicating that Google Scholar does search at least some Elsevier journals.
As it is based on a standard ‘web crawler’ method of indexing (as used by Google), it is arguably more suited than the other databases for finding ‘grey literature’ (such as TRs and Masters and PhD theses).
Note that Google Scholar will return several results that are listed as [citation]. These links cannot be clicked on, as according to the Google Scholar help page: “These are articles which other scholarly articles have referred to, but which we haven't found online. A great deal of scholarly literature is still offline, and until these papers are available online, citation-only results can help researchers find as much relevant information as possible.” [Google Scholar Help, 2009].
9 Version 5 – January 2010
It is very difficult to determine if Google Scholar searches full text or exactly where Google scholar gets the results that are returned. As it uses the standard web crawler method, it can be assumed that it will search the full text of PDF files when available, or will search what metadata is freely available from the different indexed sources.
2.4. CiteSeer (http://citeseer.ist.psu.edu/)
CiteSeer is similar to Google Scholar or other search engines in that the indexing is done automatically via a web crawler. The crawler searches for academic publications in PDF, PS, or Microsoft Word format, with filters to remove any documents that do not meet academic standards. Documents can also be submitted for inclusion via a Web form. Links are made via citations and the references included in the indexed documents.
Therefore, it is very difficult to determine exactly what sources are included but, like Google Scholar, it will also include some ‘grey literature’ that would be not be indexed by the digital library search engines.
In terms of the search facilities, according to the help page “CiteSeer indexes the full-text of the entire articles and citations. Full Boolean, phrase and proximity search is supported.” [Citeseer Help, 2009].
Note that Citeseer appears to be in the process of being replaced by CiteseerX (http://citeseerx.ist.psu.edu/).
3. Summary
Table 2 provides a summary of the details of each search engine or digital library covered.
Digital Library (DL) or Search Engine (SE) Login
Required
(Y or N) Full text search
IEEE Xplore DL Y Y (when logged in)
ACM Portal DL Y Y (without logging in)
ScienceDirect DL Y Y (without logging in)
Web of Science SE Y (Institutional access or Athens) N
Scopus SE Y (Institutional access or Athens) N
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Google Scholar SE N Y (although it is assumed this is only possible when the full text is publicly available)
Citseer SE N Y (although it is assumed that this is only
possible
when the
full text is
publicly
available)
Table 2 – Summary of Digital Libraries/Search Engines
References
Introduction
The Processing Procedures outlined in this document focus on specific security capabilities and features of Stacks Inc. (Stacks) and its products including Stacks, Stacks LITE and Stacks Mobile. Stacks provides secure platform services where customers have effective and manageable security to build trusted and secure web and mobile instances for their users. Stacks has a strong security culture and formal security policies, and its products have been used by enterprises over the last decade around the globe.
Security Features
Ensuring your information is safe and secure is paramount for any organization. Stacks is built to prevent the worst from happening by providing a secure CMS and application framework with robust security. Organizations around the world rely on Stacks for portals and applications, testing its security against the most stringent standards and ensuring protection against the most critical internet vulnerabilities in the world.
Procedure
1. What we collect
a. Username and email of each internal user is only collected. If batch loaded - it is the responsibility of the customer/administrator to have obtained the right to load user data on behalf of user.
2. What we control - Stacks has defined the current sources of personal data as:
a. Stacks Internal Users - Stacks has full control of data and is the owner of the data;
b. Integrated User Details - Stacks integrates data in real time via trusted services provided by other vendors. All security specified protocols are followed. Stacks does not retain this data beyond the session and is not the owner and/or controller of the data; and
c. Automated individual decision-making including:
a. MailChimp: Stacks Newsletter
b. WebEx: Stacks product demonstrations and training;
c. Calendly: Stacks product demonstration bookings; and
d. Freshdesk: Stacks Support Desk.
3. Processing Procedure (technical)
a. Integrated Users
i. Information stored during session only
ii. Displayed to user for purposes of identification to support ‘My Account’ functionality
iii. Password hashed
b. Internal Users
i. Information stored during session
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ii. Displayed to user for purposes of identification to support ‘My Account’ functionality
iii. Password hashed
iv. Data stored on the Stacks platform
v. Data stored as per security protocols outlined in Security White Paper and within internal proprietary security procedure
Data Processing Details
ACCESS MANAGEMENT
Access
Management What is your password policy/standard
(e.g. length, complexity, expiration etc), Stacks leverages real-time
integrations with local SSO systems and thus does not store user passwords beyond the session and uses SHA512 with a salt. Stacks runs the hash through PHP's hash function numerous times to
increase the computation cost of generating a password final hash (a security technique called stretching). Stacks internal authentication options employ strong password programs that may be configured on implementation to suit the policies of the customer.
Access
Management How is your password policy enforced? Stacks' password policy is technically enforced to require minimum password length and complexity, as well as password history and duration, with configuration specified by the customer.
Access
Management How many failed login attempts are permitted before the application locks out users? (e.g. Locked out beyond 5 attempts; Locked out after 5 or less attempts; Never locked out, etc) Stacks will lock an account after five (5) failed login attempts by default. This may be adjusted on request. Reset options will be presented.
APPLICATION SECURITY
Application Security What type of encryption, if any, is used for password storage? SHA512 hash with a salt.
Application Security Please describe any and all encryption algorithms your application utilizes and the key sizes employed. Please describe any and all hash functions your application uses. SSL TLS 1.2/1.3 with 2048-bit encryption. Passwords are stored using a SHA512 with a salt. We run the hash through PHP's hash function numerous times to
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increase the computation cost of generating a password final hash (a security technique called stretching).
BACKUP AND DISASTER RECOVERY
Backup and Disaster Recovery What is your backup policy for customer data and supporting systems? Backups are taken care of by the Stacks team and our response times are as follows:
- The Recovery Time Objective (RTO) for Stacks is 6 hours. - The Recovery Point Objective (RPO) for Stacks is 2 hours. These objectives are obtained in part because customers are backed up on the following schedule: - hourly and retained for seven (7) days
- daily and retained for two (2) years
Backup and Disaster Recovery How frequently are backups performed (More than once a day, Weekly, Daily or Monthly)? Stacks backup are taken hourly and retained for serven (7) days, and daily which are retained for two (2) years.
Backup and Disaster Recovery Do you have a disaster recovery plan? If yes, where are your recovery data centers located and what are the RPO (recovery point objective) and RTO (recovery time objective) for services? Yes. The Recovery Time Objective (RTO) for Stacks is six (6) hours. The Recovery Point Objective (RPO) for Stacks is two (2) hours.
Backup and Disaster Recovery Can user data be recovered separately from other customer data? Yes. Each Stacks customer has a dedicated database that contains all of their data and configurations.
Backup and Disaster Recovery How long after termination of the business agreement would you keep copies of user data? No more than 30 days.
DATA PROTECTION
Data Protection How is your environment designed to segregate users data from other customers' data? Each customer is given a dedicated instance in our redundant cloud environment. Each customer therefore also has a dedicated database and file system.
Data Protection Do you encrypt the data at rest or apply other technologies to ensure data confidentiality? If encryption is used, please provide details of the tool and algorithm used. Data at rest is not encrypted, but is hosted in a secure environment and facilitates several options for content protection at a very granular level.
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Data Protection In what countries do you transfer, store, and backup users data? Stacks instances can be hosted in the United States, Europe or Australia.
Data Protection How do you ensure security of data in transmission from user to and within your environment? Do you use any encryption standards? If yes, what encryption strength is used? Stacks has developed and implemented several policies surrounding the protection of data. See our Security Whitepaper. Stacks uses SSL TLS 1.2/1.3 with 2048 bit encryption.
ENDPOINT AND SERVER SECURITY
Endpoint and
Server Security
Endpoint and
Server Security
Please describe your patch deployment and implementation process (including timelines for implementation and how they are prioritized). Does the process differ for critical and non-critical patches?
Please describe the hardware/OS platform (including virtual machine platforms) you have deployed in your environment.
Stacks operates on a continuous deployment model with weekly releases including but not limited to security and non-security related patches, updates, enhancements and bug fixes. These releases do not interfere with user or administrator access or performance with our redundant cloud infrastructure. Hot fixes (critical patches) may be applied in a matter of minutes if required.
The IT infrastructure that Stacks provides to its customers is designed and managed in alignment with security best practices and a variety of IT security standards, including:
l SOC 1/SSAE 16/ISAE 3402 (formerly SAS 70)
l SOC 2
l SOC 3
l FISMA, DIACAP, and FedRAMP
l DOD CSM Levels 1-5
l PCI DSS Level 1
l ISO 9001 / ISO 27001
l ITAR
l FIPS 140-2
l MTCS Level 3
In addition, the flexibility and control allows customers to deploy solutions that meet several industry-specific standards, including:
l Criminal Justice Information Services(CJIS)
l Cloud Security Alliance (CSA)
l Family Educational Rights and Privacy Act (FERPA)
l Health Insurance Portability and Accountability Act (HIPAA)
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l Motion Picture Association of America (MPAA)
Endpoint and Have you deployed host-based Stacks employs a host of Security
Server Security firewalls, antivirus software, and/or protocols and systems, including
host intrusion detection/prevention systems? Please provide details. but not limited to: Preventing XSS, CSRF, and other malicious data entry.
Stacks ensures that data is validated and scrubbed before entry in the database. The system tests that user-entered data--and even the form fields themselves--match prescribed, expected formats and values.
Tokens are injected into each form as it is generated, to protect against potential CSRF attacks.
Database abstraction layer performs additional security checks on data as it is written to and retrieved from the database.
Brute Force Detection - Stacks protects against brute-force password attacks by limiting the number of login attempts from a single IP address over a predefined period of time. Failed login attempts are logged and visible via the administrative interface. Stacks can also be configured to allow administrators to ban individual IP addresses and address ranges.
Mitigating Denial of Service (DoS)
Attacks - Stacks’ extensible cache layer comes pre-configured with basic page, Javascript, and CSS caches. The system supports deep integration with performance technologies such as Memcache, Redis, Varnish, and many popular
CDN services. Individual
components of Stacks are typically cached as well, and granular expiry is a common feature. This multi-layered cache architecture is extremely resistant to high volumes of traffic, and allows for high-traffic websites.
Addresses OWASP Top 10 Risks -
Security feature address all of the
Open Web Application Security
Project’s top ten security risks, a
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list of the most commonly seen risks in practice.
Cookies / User Data - The Stacks platform has session and cookie data is set to expire after three (3) hours. The
following provides a listing of the primary cookies that the Stacks platform uses.
Authenticated users will have a timestamp of their visit recorded in the website logs under their user profile.
Protective Block - Our hosting service has a protective blocking feature that, under certain circumstances, restricts access to web sites with security vulnerabilities. We use this partial blocking method to prevent exploitation of known security vulnerabilities. The protective block is meant for high impact, low complexity attacks.
Endpoint and Do you regularly perform vulnerability Dedicated Platform Security Team
Server Security assessment on your production The Stacks platform is backed by a
environment? If yes, what is the platform security team consisting
frequency of vulnerability assessments? of dozens of experts from around the world which are employed to validate and respond to security issues pertaining to the platform employed by Stacks. Stacks maintains a database of signatures of known security vulnerabilities.
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Appendix A - General Data Information
Performance and Hosting
Stacks production instances are run on a 99.9% uptime-guaranteed managed host with 24/7 support. The managed host also monitors hardware and network connections for reliability purposes. 99.99% uptime is available with Stacks Premium.
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l Enforces the use of highly secure RSA keys for server access and encryption;
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Preventing XSS, CSRF, and other malicious data entry
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attacks. Database abstraction layer performs additional security checks on data as it is written to and retrieved from the database.
Brute Force Detection
Stacks protects against brute-force password attacks by limiting the number of login attempts from a single IP address over a predefined period of time. Failed login attempts are logged and visible via the administrative interface. Stacks can also be configured to allow administrators to ban individual IP addresses and address ranges.
Mitigating Denial of Service (DoS) Attacks
Stacks’ extensible cache layer comes pre-configured with basic page, Javascript, and CSS caches. The system supports deep integration with performance technologies such as Memcache, Redis, Varnish, and many popular CDN services. Individual components of Stacks are typically cached as well, and granular expiry is a common feature. This multi-layered cache architecture is extremely resistant to high volumes of traffic, and allows for high-traffic websites.
Addresses OWASP Top 10 Risks
Security feature address all of the Open Web Application Security Project’s top ten security risks, a list of the most commonly seen risks in practice.
Cookies / User Data
The Stacks platform has session and cookie data is set to expire after three (3) hours. The following provides a listing of the primary cookies that the Stacks platform uses. Authenticated users will have a timestamp of their visit recorded in the website logs under their user profile.
Protective Block
Our hosting service has a protective blocking feature that, under certain circumstances, restricts access to web sites with security vulnerabilities. We use this partial blocking method to prevent exploitation of known security vulnerabilities. The protective block is meant for high impact, low complexity attacks.
Additional Information
Further information can be found in the Stacks Security White Paper and Technical Data Sheet located here https://www.stacksdiscovery.com/specifications/
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Peace Education Australia 2020
Adelaide
June 22-23, 2018
Proposal: to promote and provide Gospel-centred Peace and
Nonviolence Education resources to all Christian faith-based
educational systems and secondary schools in Australia
during the next three years
How might we work together
to engage with this proposal?
Book of Proceedings &
Participant Details
Co-Sponsored by: Pace e Bene Australia
Catholic Education South Australia
the Anglican Schools Commission (Inc.) of Western Australia
Edmund Rice Education Australia
SPONSOR INTRODUCTION
A gathering of 15 people from four Australian states met together from 22nd – 23rd June, 2018 to dream and discuss what peace education initiatives might be possible in Anglican, Catholic and Lutheran schools in 2019 and beyond.
The Anglican Schools Commission WA (ASC), Pace e Bene Australia, The South Australian Catholic Education office and Edmund Rice Education Australia all co-sponsored the meeting.
Participants included Anglican, Catholic and Lutheran Religious Educators, curriculum writers and members of the ecumenical women’s movement, The Grail.
The meeting was inspired by the vision of Brendan McKeague (Pace e Bene) and the Reverend Michael Wood to see peace education (with a Christian foundation) extend through all Christian faith based schools in Australia. The first step in this vision had begun through the writing of a secondary school unit of work by the ASC during 2017, which has started to be trialled in a few schools.
Brendan and Michael facilitated the gathering, sharing their vision, passion and knowledge. As the gathering gained momentum, energy was high and a shared passion for growing peace education was evident. Throughout the 2 days there were many self-organised conversations (in the open-space style) which covered a number of theological, curriculum and practical matters. From these conversations, a number of action plans were created which included:
• Developing a network of peace educators and supporters to continue the work.
• Developing curriculum material to suit primary school aged students.
• Thinking through how to develop professional learning and formation experiences for school staff.
• Having conversations with people in other states.
• Connecting people in an online study/reading group to further knowledge in the area of peace and nonviolence.
Participants departed the gathering feeling inspired, energised and looking forward to what might emerge from the discussions and plans.
Peace Education Australia 2020 2
TOPICS RAISED
SESSION TOPIC TITLE INITIATOR
1A Ongoing communication/collaboration Stuart
1C Peace Education across all year groups Liz Pemberton
2A Beginning the Peace education/nonviolence conversation with Catholic Ed/ school systems Jo
2D The 5 steps to nonviolence Marcia
2 Appreciative Exploration of how nonviolence is explored in broad school curriculum; and identifying gaps Michael Vial
3C Developing a PD Session for Staff – preparing to teach and staff formation Gillian
Interpreting Gospel stories of the “frustrated” Christ Dan Valencic
4A Supporting Schools to Implement Curriculum – challenges of PD for staff Jo, Martina and Michael
4E Gospels and nonviolence Marcia
5D Peace Education and Adults Ruth Crowe
5F The voice of the Child and Nonviolence Michael Vial
5C How is gender addressed in the unit (and other men only voices) Gillian
7D Ideas for funding Brendan
7 SA RE Curriculum and nonviolence Michael Vial
ACTION PLANS (pp22-34)
Developing a network/community of practice from this gathering Jo Hart
Talk with people in Melbourne, Anglican/Ecumenical, re September Visit Michael Wood
Develop lesson material/unit of work appropriate for primary level Liz Pemberton
Incorporating collaborative learning models Gillian Moses
Project continuation & expansion Brendan McKeague
Schools Inquiry on whole school and/or curric exploration on nonviolence Michael Vial
Stimulus Paper on Nonviolence Michael Vial
Learn/read more about peace and nonviolence; with particular interest
in feminist theology Marcia Burgess
Peace Education Australia 2020 4
Topic Title Ongoing Communication / Collaboration Code 1A
Initiator Stuart Recorder Stuart
Present Stuart, Jo, Kath, Brendan
Notes - including summary ideas, suggestions, recommendations, plans for actions....
- Is there an ongoing Leadership Team?
- Are there people who want to facilitate?
- Open Space is handing leadership to the group – No infrastructure of ongoing leadership at the moment
• Either someone passionate stands up
• Funding is sought to support a facilitating role
- What is the need? – to stay connected
• To share new developments
• To share what we are doing?
• To share new resources we have found
• To share how we use it in other curriculum
- Facebook? Another platform? Webinars? Video meetings? Drop Box?
- Act locally, connect regionally, learn globally
- Way forward
• Find a person to take it on
• Find early adopters
- A desire for a physical coming together annually – conference.
• Who is going to facilitate?
• Partnerships? Of systems
• Maybe hold in each state to cut travel costs and increase attendance
- May have to start small and see how it grows
- Systems each contribute to funding certain aspects – Venue, speaker, catering, wine?
- Will we have a summary of these 2 days to take back in schools to use as a resource to help promote the need
- ‘Peace study tour to Philippines’ – a dream
Peace Education Australia 2020 5
Topic Title Peace Education across all year groups Code 1C
Initiator Liz Pemberton Recorder Liz Pemberton
Present Gillian, Teresa, Julian, Marcia
Notes - including summary ideas, suggestions, recommendations, plans for actions....
Where is peace education happening already?
What are the components? (Knowledge and skills)
What are the age appropriate concepts and language to teach this?
Are we unpacking Bible stories intentionally to draw out peace and nonviolence
teaching/themes? Keep revisiting Bible stories – adding more layers of understanding (eg 2
worlds of the text)
Perhaps we could look at language being used in our schools eg bullying. Helpful or
confusing?
Links between reconciliation, forgiveness and peace/nonviolence.
Can we develop a structure which enables developmentally appropriate peace education?
Where is there violence in our school structures that needs observing/noting/challenging?
Look at ACARA – general capabilities (Ethical understandings, intercultural understandings,
etc)
Links / ways in. what religious .... Could we add?
The ability to live and operate peacefully and nonviolently
Knowledge and skills
Questions – How can we resource teachers in other learning areas to make connections with
peace and nonviolence?
School retreat/camps – can we build resource for these?
Peace Education Australia 2020 6
Topic Title Beginning the peace
ed/nonviolence conversation with Catholic ed/school systems Code 2A
Initiator Jo Recorder Jo
Present Ruth, Kay
Notes - including summary ideas, suggestions, recommendations, plans for actions....
- Catholic nonviolence initiative – forming points of entry to parishes through social justice coordinators – not through parish priest
- Could the Plenary 2020 be an opportunity for conversation
- Are Cath Ed systems open to hearing about other visions and articulations of Christology
- Connection of nonviolence education as a response to child abuse royal commission
- Making cross-curricula links, especially – environment/nonviolence – Laudato Si
- Christological approach to Advocacy
- Nonviolence curriculum possibilities documents
- Connections to current school programs ie Restorative justice, anti-bullying programs – connecting to Christological underpinnings
- Bishops onside – Peter Comensoli – Broken Bay
- Relevant to practical topics eg bullying
- Peter Smith - Sydney Archdiocese
Peace Education Australia 2020 7
Topic Title The 5 steps to nonviolence Code 2D
Initiator Marcia Recorder Marcia
Present Gillian, Brendan, Marcia
Notes - including summary ideas, suggestions, recommendations, plans for actions....
Overview: 1. Know self
2. Know culture
3. Know spirituality – faith
4. Skills for the journey
5. Foundational skills to build on to apply
1. Know understand yourself – body awareness system. What happens to you when you prepare for ‘battle’ – physiology
2. Centre yourself – ready for an informed response learning skills to centre – grows overtime. Peace Pebble – circuit breaker – helps connect to heritage. Mantra ‘God with me’, ‘God is here’, God is with us’. Meditation.
3. Listen to truth – ritualizing the intention – listen even if the person is angry, hurt, attacking.
4. Speaking own truth, being honest and open without diminishing/destroying the truth of the other. #3 and 4 are interchangeable
5. Openers to what energises – an openness to the spirit in this work. Mystery to creation. Could name what emerges?
Could be applied to National leaders – doesn’t always work – and it doesn’t have to work
Violence NEVER works.
2 hands of nonviolence
Conflict transformation is about compassion, forgiveness and intention.
Peace Education Australia 2020 8
Topic Title Appreciative Exploration of how nonviolence is explored in broad school curriculum: and identifying gaps Code 2
Initiator Michael Vial Recorder Michael Vial
Present Michael Vial, Julian Klyuge, Liz pemberton, Dan Valencic, Terese
Notes - including summary ideas, suggestions, recommendations, plans for actions....
- Appreciative exploration:
- Restorative practice approaches to behaviour education, backed up in pastoral care lessons
using various resources/packages (eg Bounceback, child protection, curriculum, etc)
- Social Justice initiatives, eg White Ribbon, Day support
- Circle time practices
- Ecological conversion curriculum and practice
- RE Curriculum units eg
- Heroes/ heroines of the church (who worked) - Ethics
- Religion and peace (Yr 12)
- Justice
- Reconciliation
- Evil and suffering
- Student Leadership Formation
- Student Retreats
- Pastoral care Counselling : to students/families suffering violence, domestic violence
- Online violence
- Christian Meditation / contemplative prayer practice
- English / HASS / Media studies units, Economics etc
- ‘dialogue School’ in the enhancing catholic school identity project
- Indigenous
What are the gaps?
1. Staff formation (Spiritual /religious)
2. Staff Professional Learning
3. ANZAC and western front/Gallipoli immersion trip. Traditions: transformation required
4. Australia Day and Anzac Day – a new way to talk about
5. Explicit RE Curriculum that explore nonviolent Jesus nnd the nonviolent way of being human
6. Schools naming themselves as places of peace / shalom – deep relationship and communion (nonviolent)
7. Interreligious dialogue
8. Engagement of parents and education and their experience of violence
Ideas for moving forward:
a. Inquiry professional learning approach around nonviolent/shalom approach (inviting schools)
b. Stimulus paper
Peace Education Australia 2020 9
Topic Title Developing a PD Session for Staff – preparing to teach; staff formation Code 3C
Initiator Gillian Recorder Gillian
Present Jo, Martina, Liz, Brendan, Michael
Notes - including summary ideas, suggestions, recommendations, plans for actions....
What are the easiest elements to communicate to staff?
- Do we opt in or make it all staff?
Pre-existing framework out of which unit emerged (4 pillars)
- Knowing yourself and what is my bigger narrative where I come from, ancestors (genetic memory). Naming activity and brings our bigger stories who the place and know own capacity for violence
- Know culture and how dominant culture works, vocab (shared) around culture, how dominant culture forms us around fragmentation etc, myth and redemptive violence
- Knowing my spiritual source from which I operate and what is my commitment to
peacebuilding and nonviolence, for us it’s the Jesus story, what culture/narrative was Jesus embedded in? Here you can use wink stories from Bible, Inner work that is required (shadow work)
- Skills we need to sustain commitment to nonviolence (least important part). 5 steps in conflict transformation
2 Dimensions; outer work, inner work.
- Outer – how it shows up in school community
- Inner – understanding own capacity for violence
Staff situation varies from school to school. Specialist or nonspecialist teachers. RE as last but of teacher load.
Reality is we can probably only bring in elements of this with whole staff
Schools are busy and PD is prescriptive usually and is this curriculum or formation? Staff come in and out.
How do we use elements on their own without diluting integrity of the underpinning framework.
Start where you are at with what you’ve got! If peace education in school is long term goal, have to start small – anything we do around ‘de-enemyising’ is worth doing!
Can we start with a small group in the school who are been and meet with see what we can do.
Identifying what we are already doing – how is peace already a value in a school?
Power of word “nonviolence” end justice, peace or other familiar words.
Parker Palmer’s work – quiet days for staff and power of small group to change culture, circle work/circle of trust, community of practice.
Peace Education Australia 2020 10
Picking a pain point in school that might traditionally be addressed by leadership and instead invite talking circle to address it – name iot peacebuilding practices in school.
Power of personal stories and especially stories of violence / nonviolence / peace building
Discipline of not fixing, advising, rescuing, saving!
Running regular meetings as circle work
Wellbeing and violence – self care etc
Anglican Schools – “God in an hour” retreats – 2 hours/wk over 6 weeks
Systemic violence Negative peace Positive peace how we value busyness but not silence
Coaching processes – creating space for deep listening coaching is valued (see ATSIL Standards)
St Marks school – 5 sessions of 2 hours ed of school day offered “coaching skills” workshops
40 teachers 5 hr and middle leaders
Great take up
Practice of nonviolence framed as coaching (form of spiritual direction)
High levels of systemic violence in school systems
Culture of “Big Questions” which are allowed to shape school year
Appreciative Inquiry again
Short video from yesterday followed by paper as part of PD Day
Difference between information and formation
Can we offer 2 or more steams on combined PD Day – one stream on nonviolence, and others. How do we enable choice and invitation.
Open space for PD Days
Ethos Committee meeting as an opening into talking about PD / Staff days
How do we do conflict well? – practice of peace
Resisting the urge to focus on outcomes ie doing something because it works, not just because it’s the right thing.
Peace Education Australia 2020 11
Topic Title Interpreting Gospel stories of the “frustrated” Christ Code ?
Initiator Dan Valencic Recorder Dan
Present Kay, Therese, Ruth, Christina, Martina
Notes - including summary ideas, suggestions, recommendations, plans for actions....
A ‘just’ anger can occur in a Christian life in response to unjust situations
Provoking others to gain insight into unjust practices
Being frustrated with unjust situations is a natural reaction
The distinction between anger and violence – feelings and emotions should shape Christian policy.
A reaction when the people of god are not who they are meant to be.
Difficult when Christian teachings re not evident in society / individuals / church structures
Are modern institutions in the same crisis as itj was in the Temple story
Lay people have the right to show that level of anger at the structures in our Church – in the same way Christ reacted in the Temple
Does the “money lenders” story give us permission to be angry at our Church structure?
Gospels should be – History / Story / Current application – allowing all stories to be used to promote peace and justice
Jesus needs to be re-represented as counter-cultural in our school communities
Stories like the ‘Cleansing of the Temple’ allow and justify us to respond to the structures that frustrate Christian adherents.
Peace Education Australia 2020 12
Topic Title Supporting Schools to implement Curriculum – challenges of PD for staff Code 4A
Initiator Jo, Martina and Michael Recorder
Present Ruth, Gillian
Notes - including summary ideas, suggestions, recommendations, plans for actions.... Challenges facing school realities – revolving door of untrained teachers
Modify unit to enable input by a trained person and teachers facilitate activities in which they learn with their students.
- Could use a flipped classroom approach of 1 or 2 expert teachers to create the shared thematic inputs
- Brendan and Michael – offer workshops around the country – day 1 for teachers, day 2 for consultants – train the trainer
Video chunks of content
Skype/phone coaching
Circles of wisdom – collaborative learning of teachers and students
Facebook page to share ideas, ask questions
Develop a community of practice at school level of those teaching the unit
National gathering to reconnect, story sharing, what are we the learnings.
Taster session in Perth
Across systems in region – practitioner workshops
- Possibly 2019
Making connections across catholic systems
Peace Education Australia 2020 13
Topic Title Gospels and nonviolence Code 4E
Initiator Marcia Recorder Marcia
Present Michael, Liz, Dan, Martina, Kay, Teresa
Notes - including summary ideas, suggestions, recommendations, plans for actions.... Gospel stories – The forgiving Father – love not rejection
Walter Wink – resources, turn the other cheek. Examples of Jesus acting nonviolently and
challenges systems of nonviolence
- Nonviolent resistance
Stoning of the adulterous woman. “Let he who is without sin”
Woman at the Well – scapegoating
The good Samaritan
Where is violence in the Gospels?
- Jesus fleeing as a baby to Egypt
- Jesus arrest in the garden
- Jesus in the temple – upturning tables
- Jesus on the cross – Jesus non-violent accepting his fate
Jesus in the desert – inner struggle
Jesus’ response to Peter after the trial
The Beatitudes
The time Jesus spent with sinners
Connection between Jesus healing and innter peace – forgiveness and reconciliation
Young people – mid-late teens – choosing vegetarian and vegan – can we connect them with Gospel stories?
Peace Education Australia 2020 14
Topic Title Peace Education and Adults Code 5D
Initiator Ruth Crowe Recorder Ruth
Present Dan Valencie, Martina Cooper
Notes - including summary ideas, suggestions, recommendations, plans for actions....
ACH Contacts
Notre Dame
Mentoring young teachers
P&F masterclasses!!
Peace and non-violence strategy
Collaborate Dan and Martina and Grail (Social Justice Groups)
Schools in WA have a lot of training
Pace Bene contacts
And Edmund Rice Centre Homebush
SOCIAL JUSTICE GROUPS
MARIST BROTHERS’ CONTACTS
Peace Education Australia 2020 15
Topic Title The voice of the child and nonviolence Code 5F
Initiator Michael Vial Recorder Michael
Present Michael V, Terese, kay, Brendan
Notes - including summary ideas, suggestions, recommendations, plans for actions.... Kay: The voice of the child within the adult, teacher, leader
Brendan: pictorial ‘faces of the enemy’ in Bagdad, the children in the “enemy”
Musical South Pacific “you have to carefully taught to hate all those your family hate...before
you are 6, 7 or 8”
MV: Seeking to listen to children’s experience and ideas about curriculum / nonviolence / etc
- An inquiry approach
- Co-designing a unit
- A pedagogy of listening
Brendan: a co-construction of the curriculum in an Engineering example - Not normal KPIs but
1. What did you notice?
2. What did you learn?
3. How will you use that learning
ie learning how to learn
- Around a question worth asking and invite people interested and affected
Therese: Importance for teachers to know where they come from (eg Interplay)
- Their stress of holding it all together
- How to be sensitive to students and where they’re at
- What do they need to have done themselves with their own inner work
- “unless you become like children”
MV: teacher formation so important for transformation / conversion about nonviolence
- in order to be an example/witness to it (in a similar way to RE)
- essentially ‘nonviolent education’ is ‘religious education’
Kay: the anger that is within
- inner and outer work
- Brendan: ok to say “part of me is really angry.. and another part of me...” (c/- George Trippe) (a self-talk tool)
Domestic Violence and the Child
- Attachment parenting
- All behavior has meaning (Kent Hoffman and circle of security)
Brendan: child protection (and be wary of strangers) – welcome the other, the different, some contradictions, eg, forgiveness. Sin vs Sinner
Peace Education Australia 2020 16
Topic Title How is gender addressed in the unit (and other minority voices) Code 5C
Initiator Gillian Recorder Gillian
Present Marcia, Christina, Liz, Jo, Michael
Notes - including summary ideas, suggestions, recommendations, plans for actions....
Resources – women’s stories of peacebuilding eg in Nashville video, Fatouma’s story in soldiers of Peace other videos
Not a topic that is explored explicitly
Women writing about these topics eg Carly Osborne
Book – Athena Doctrine – qualities we describe as masculine or feminine and critiquing our systems and structures. Is there space to discuss this in the unit?
Mothers who stand for Peace – picture book (Grandmothers – who stood to change the world)
Hunger Project – educating women to empower the world
Principles – what is the violent culture in which we are embedded?
- How does gospel of Jesus speak into this?
- Who are the feminist/post – colonial theologians writing about Peace? Also
feminist Jewish scholars
How do feminist theologians engage with Girard’s idea of the forgiving victim (also post colonial period)
How is pornography addressed
Girard on desire as really useful paradigm – how does my desire enslave me?
Dignity and offering positive vision of human relationships
Wisdom of the victim
James Alison as gay person writing on Girard ‘On Being Liked’
Asking a Facebook group for their recommendations of feminist theologians writing on this issue
Peace Education Australia 2020 17
Topic Title Ideas for funding Code 7D
Initiator Brendan Recorder Brendan
Present Jo, Martina, Liz
Notes - including summary ideas, suggestions, recommendations, plans for actions.... Artfinc Grant - $6000; 2017-2018
Current Co-sponsors
- SA CEO
- WA ASC
- EREA
- P & B Australia
In the past – Religious congregations might have funded something like this. Diminishing
opportunities
Who to ask? Diocesan funds perhaps?
Prepare a clear vision of what we want to achieve / Jo?
- Coalition approach across the Christian ‘establishment’ landscape – reflected in our current partnerships
- Personal passion – Brendan – would love 2 days pa for 2 years to continue researching, networking and training
- How would schools be engaged?
- ‘Train the trainer” approach, local initiatives – regional
- CARITAS / BROKEN BAY INSTITUTE / ACID / NDA
BBI – infrastructure, Technology / networks a teaching level unit for Teachers /others
Connect to meet all of Divinity – possible?
Development of an online deliverable program
MCCA – peace studies/creation 6 – ecumenical
BLAIR FOUNDATION may have grants available
National Council of Churches Australia
Asking Catholic Ed/ ASC to prioritise resources/ in-kind/ secondment to assist with work
Support for specific events, future projects for peace education conference
Expend/the next steps
- Continue
Heads of catholic congregations in education
- New body – how do we engage with them?
- Opportunities to explain our vision
Peace Education Australia 2020 18
Topic Title SA RE Curriculum and nonviolence Code 7
Initiator Michael V Recorder Michael v
Present Michael v, Marcia B and Christina J
Notes - including summary ideas, suggestions, recommendations, plans for actions....
Developmental aspects of ‘nonviolence’ Component elements
- This is a foundation for considering how crossways can better embrace.
- Eg knowledge, skills and disposition which together comprise ‘nonviolence’
Marcia: 5 steps are very similar to restorative justice (see handout from Thursday workshop)
The ‘wisdom’ skills and disposition strand
Connects with:
- self awareness (communal and self identity)
- dialogue
- Interpretation – their, societal, historical. – interpret scripture
- Discernment and engagement
Purpose Statement: 4th element could include “...a just and nonviolent world”
Knowledge Strands
- Some ‘Enduring Understandings” about nonviolence (indirectly and directly)
Integrates Concept
“Nonviolence”
Peace Education Australia 2020 19
Topic Title Biblical, Theological and
Hermeneutic touchstones for M.V Code 6A
Initiator Michael Wood Recorder Michael Wood
Present Everyone
Notes - including summary ideas, suggestions, recommendations, plans for actions....
I begin with the principal that God is a God of Love. When working with children, I ask “where is the ‘God of Love’ in this story?” (ie God intervenes in the near sacrifice of Isaac)
Jesus shows me to God who is Love
The move from God being the ambassador of the right tribe. Jesus comes along and talks about what the Kingdom of God is like – Not about who is in or out, or what you do (make the right sacrifices or taking on the Romans), its about treating people with respect. Inviting people up the mountain to hear some more. Love your enemies.
Jesus challenges the obsessive following of the law when it gets in the way of compassion. The upside down messiah. (The ‘turnip’ Messiah)
‘Love one another as I have you’
The Beatitudes
Seeing the face of Christ in everyone – connecting with everyone, just as Jesus did. (eg The woman at the Well)
God walking with Adam and Eve in the Garden (Companion god) – Ruth/Naomie story
The prophets as Companions of God
Jesus as Companion with whom we are co-creators (Feeling the partnership)
Garden of Gethsemane – ‘put away the sword – those who live by the sword will die by the sword’ (A different way of being). Instead of raising an army to strike down ones enemies.
Jesus died because he was a radical trying to change the world (not because he died for our sins)
Jesus on the cross – sometimes part of us needs to die before we can live authentically – or for some new life to emerge (“unless a grain falls to the ground and dies...”)
Evolution could not happen if there was not death (Life requires experimentation)
Jesus did not need to die. He died because of the way he lived.
Salvation is when I can say “I will die rather than kill” (new life and sign of the kingdom)
Detachment from “clinging to Life”
Story of Muslim command to marry women raped and bring up children as own. (Bosnean war)
Peace Education Australia 2020 20
Susan Connelly – Josephite in Sydney did PhD on a Girard related subject
Flip Chart Insert
Two ‘voice’ in scripture – the voice of the persecutor and the voice of the victim
- The vengeful victim (Abel’s Blood crying from ground)
- The guilty victim (Exiles)
- The victim who calls for vindication (Psalms/Job)
- The forgiving victim (Jesus) (2nd Isiah – suffering servant) ( Joseph Saga)
- Barth – Bonhoeffers critique of natural law (estate church)
Christ as the primary WORD who provides the interpretive key to scripture (Shalom – Forgiveness) Moltman – The Christlike God
St Paul The Law, apparently good in itself is ‘weaponised’ (by SIN) The WORD/Law which kills
(Problem of Zeal – Phineas) Grace and Paul’s experience of being a forgiven/transformed murderer.
N.V Atonement + The economy of exchange
Anselm – God’s offended honour
Calvinism – God’s wrath against sin
St. Paul – Romans
1. Grace trumps Law (personal salvation) – supercessionism
2. New Perspective (incorporation of the gentiles)
3. Post New Perspective (apocalyptic in breaking/revelation – Barth)
Orthodox – incorporation into TRINITY DIVINISATION
Girardian influenced
Gil Baillie – Violence unveiled (Catholic)
James Allison (Catholic)
- Raising Abel
- On Being Liked (and many others)
Walter Wink (Protestant)
- Engaging the powers
Michael Hardin (Protestant)
- The Jesus Driven Life
- Reading the Bible with Rene Girard
- Mimetic Theory and Biblical interpretation
Brad Jersak (Orthodox, from Canada)
- A More Christlike God
- Her Gates will never be shut: Hope, Hell and the New Jerusalem
- Stricken by God? Nonviolent identification and the victory of Christ (series of essays)
S. Marklteim – Saved from Sacrifice
Peace Education Australia 2020 21
Action Plan
Action Area Developing Network/C of P from this gathering
Covered by: Jo
Offers of Support:
Name Email Ph
Christina Jonas christina.jonas@cesa.catholic.edu.au
lpemberton@asc.wa.edu.au
0402 066 934
L Pemberton 0413 896 118
Gillian Moses g.moses@staidans.qld.edu.au
0414 373 151
Martina Cooper mcooper@waverley.nsw.edu.au
0415 194 805
Kay Hunt kay.hunt@optusnet.com.au
0417 884 395
Terese Sheridan tereshe@bigpond.net.au
0413 558 171
Ruth Crowe ruthcrowe@ozemail.com.au
0408 291 429
Dan Valencic dan.valencic@cbhslewisham.nsw.edu.au 0410 456 804
Immediate next steps - Create communication base - facebook?
Action Plan
Action Area Conversations in Melb. (Anglican/Ecurenical)
(Who can I meet with?)
Covered by: Michael Wood
Offers of Support:
Name Email Ph
Jo Hart jo.hart@erea.edu.au
0429 431 206
Next step (s) Talk with people in Melbourne
By when: During Sept
Action Plan
Action Area Develop lesson material/unit of work appropriate for primary level
Covered by: Liz Pemberton
Offers of Support:
Name Email Ph
Brendan 0429 448 090
Marcia marcia.burgess@cesa.catholic.edu.au
0421 640 061
Next step (s) Develop an outline of possible unit of
work/ideas/resources to include
By when: End August
Peace Education Australia 2020 22
Action Plan
Action Area
Covered by:
Offers of Support:
Name
Martina Cooper mcooper@waverley.nsw.edu.au
0415 194 805
Liz Pemberton lpemberton@asc.wa.edu.au
0413 896 118
Michael Wood spacemaker62@gmail.com
0435 065 326
Brendan mckeaguebrendan@gmail.com
0429 448 090
Next step (s) To look at PBL proposal being developed
By when: End of 3rd term
Action Plan
Action Area Project continuation & expansion
Covered by: Brendan
Offers of Support:
Name Email Ph
Liz Pemberton lpemberton@asc.wa.edu.au
0413 896 118
Michael Vial michael.vial@cesa.catholic.edu.au
0424 996 612
Michael Wood spacemaker62@gmail.com
0435 065 326
Next step (s) Forward
Brendan will prepare a proposal for circulation
Action Plan
Action Area Schools Inquiry on whole school a/or curric exploration on nonviolence
Covered by: Michael Vial
Offers of Support:
Name Email Ph
Brendan mckeaguebrendan@gmail.com
0429 448 090
Christina Jones christina.jonas@cesa.catholic.edu.au
0402 066 934
Maria Burgess marcia.burgess@cesa.catholic.edu.au
0421 640 061
Jo Hart jo.hart@erea.edu.au
0429 431 206
Michael Wood spacemaker62@gmail.com
0435 065 326
Next step (s) CESA people to arrange a teleconference for larger group
By when: 3 July 2018
Peace Education Australia 2020 23
Action Plan
Action Area Stimulus Paper on Non Violence
Covered by: Michael Vial
Offers of Support:
Name Email Ph
Brendan 0429448090
Next step (s) Michael V to talk to Brendan about how to link this to 'Project continuation and expansion action.
By when: 3 July 2018
Action Plan
Action Area Learn/read more about peace and nonviolence
Particular interest in feminist theology
Covered by: Marcia
Offers of Support:
Name Email Ph
Christina Jonas christina.jonas@cesa.catholic.edu.au
0402 066 934
Michael Vial michael.vial@cesa.catholic.edu.au
0424 996 612
Gillian Moses g.moses@staidans.qld.edu.au
0414 373 151
Michael Wood spacemaker62@gmail.com
0435 065 326
Teresa Sheridan tereshe@bigpond.net.au
0413 558 171
Liz Pemberton lpemberton@asc.wa.edu.au
0413 896 118
Kay Hunt kay.hunt@optusnet.com.au
0417 884 395
Ruth Crowe ruthcrowe@ozemail.com.au
0408 291 429
Dan Valencic dan.valencic@cbhslewisham.nsw.edu.au 0410 456 804
Jo Hart jo.hart@erea.edu.au
0429 431 206
Next step (s) an email group and explore a yammer space
By when:
Peace Education Australia 2020 24
IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 1, Ver. I (Jan. 2014), PP 32-36
www.iosrjournals.org
Clustering and Classification of Cancer Data Using Soft
Computing Technique
Mr.S.P.shukla and Mrs. Ritu Dwivedi
Abstract: Clustering and classification of cancer data has been used with success in field of medical side. In this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of the result. this paper address the problem of learning to classify the cancer data with two different method and information derived from the training and testing .various soft computing based classification and show the comparison of classification technique and classification of this health care data .this paper present the accuracy of the result in cancer data.
Keywords: clustering, classification,
I. Introduction
Cancer data classification and clustering have been the focus of critical research in the area of medical and artificial intelligence. Health care is now days very important for human being. Now everyone is health care unit of system are there to monitor and analyze health status of a human being .As we know health is wealth, if health of a particular is food individual will grow hence society and nation will go a health .This is the reason why soft computing based health care system is to be developed, which has proved it efficiency and performance with conventional system.
Cancer has become one of the major causes of mortality around the world and research into its diagnosis and treatment has become an important issue for the scientific community. The most important issue in classification and clustering of cancer data is deciding what criteria is to be classify against, for example suppose it is desirous to classify cancer disease in describing cancer one will look at its type ,spot, stages and duration and so on .many of these feature are fuzzy and qualitative in nature. For this classification some criterion is to be decided. One can classify cancer on the basis of its type, its human parts of manifestation i.e. mouth, thought, tongue, intestine, image, liver or similar other parts of the body.
The popular method of classification is very well-known as fuzzy C-means (FCM),so named because of its close analog in the crisp word, this method uses concept in n-dimensional Euclidean space to determine the geometric closeness or classes and the determining the distance between the clusters.
In this piece of research work two very important application of research work two very important application, classification and clustering are use on cancer data. It is well known that classification and clustering are the technique to separate same type of data together, classification is a supervisee way to separate same type of data to put similar type of data together. Classification and clustering technique can apply on cancer dataset and find the accuracy of classification and clustering.
The rest of the paper is organised as follows: The 2 section outlines the reason for using ear as biometric for newborn. This section is followed by details of database acquisition in section 3. Covariates of newborn ear is explained in section 4 followed by automated ear masking in section 5. The details of feature extraction and matching are explained in section 6 and this section also explains proposed methodology for ear recognition. Section 7 describes performance evaluation of different algorithms on newborn ear. Finally section 8 and 9 present future direction and key conclusion.
II. Cancer detection algorithm and concept
Our cancer detection system adopts a two FCM and K-means algorithms. In this process we show the class of cancer that mean cancer is benign (2) and malignant(4) .these frames are derived from UCI repository dataset. Which is use to compare the classification and clustering technique and find the accuracy of the result.
2.1 Data Description
In data description number if instances are 699(as of 15 July 1992) that are used for research work. This cancer data contain 10 attribute and their id number value between 1 to 10 .the last attribute of the data are class that has been moved to last column .attribute class are use two value 2 for benign and 4 for malignant.
2.2 MATLAB Software Working
The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy
access to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB engines
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Clustering and Classification of Cancer Data Using Soft Computing Technique
incorporate the LAPACK and BLAS libraries, embedding the state of the art in software for matrix computation. MATLAB is the tool of choice for high-productivity research, development, and analysis. MATLAB Toolboxes are comprehensive collections of MATLAB functions (M-files) and our research paper is based on this function. In this research work data set convert in M-file, after the creating M-file MATLAB toolbox perform the comprehensive study of booth.
2.3 Clustering and classification with their algorithm
Clustering can be considered the most important unsupervised learning problem; so as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.
Classification same as classify the cancer data set but it is the type of supervised way and in this process training data has to specify what we are trying to learn so data classification is data reduction technique and data present in class from so classification contain the similar type value in group.
This research paper work are address to feed forward neural network are address to feed forward neural network which is work in based on supervised learning .this topic work in this technique ,that have three layer
• Input layer(multiple input data )
• Hidden layer(multiple or one layer)
• Output layer(one layer)
Classification side the three layer are available .first layer input layer have input data in matrix from ,second layer hidden layer ,some process feed forward neural network contain one layer and some contain multiple layer and last layer is output layer which is the resultant layer.
In clustering process the output layer will be hiding so in this condition that process is unsupervised process. Feed forward neural network use some activation function like
• sigmoid activation function
( )
hyperbolic activation function
( )
• linear activation function
( )
Data classification in clustering side use FCM algorithm .it converts the input matrix in output from.
III. Experimental Work for Cancer Detection
In the present work the soft computing technique one used for cancer affected object classification purpose .the soft computing technique derived their power due to their clustering and classification an ability to learn in experiment. In experiment work neural network related classification can be used for fairly accurate classification of input data into categories, provided they are previously trained to do so .the accuracy of the classification depend on the efficiency of training. The knowledge gained by the learning experience is stored in the form of connection weights.
The issues need to be settled in designing an ANN for specific application topology of the network training algorithm neuron activation function with weights and bias.
In our topology, the number of neurons in the input layer is 9 by the ANN classifier. The output layer was determined by the number of the class designed .the output are type1 therefore; the output layer of consist of one neurons. The hidden layer is consisting of 12 neurons. Before the training process is started, all the weights and bias are initialized. The training set used LEARNGDM adoption learning function and TRAINLM training function it is work in three layer and after the processing the experimental graph show the 100epochs .in this experiment work ,the training set was formed by choosing 160 data set for the testing process.
Cancer data is classify into one of two type object using the feed forward neural network classification in error back propagation algorithm .after the classification of the cancer object correct and incorrect classification are computer. The next step of classification algorithm is creating the performance matrix.
Same as work perform in clustering side and classify the cancer data find the accuracy of data set but in clustering side only two layer working network because it in type of unsupervised learning so the output layer are hide and find the accuracy of cancer data use fuzzy C-means algorithm and create the performance matrix .
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Clustering and Classification of Cancer Data Using Soft Computing Technique
IV. Training and Testing
The proposed network was trained with 240 data samples. These 240 samples are fed to the network with 9 input neurons, one hidden layer of 12 neurons and one output neuron.MATLAB software version 8 is used to implement the software in current work. When the training process is completed for the training data set the last weights of the network were saved to be ready for the testing process.
The testing process in done for 160 samples, the 160 samples are fed to the proposed network and their output is recorded for calculating of accuracy of data.
In second type clustering use 240 training data set only. It isn’t work for testing and the accuracy of data is less with the comparison of classification and finds the performance matrix.
V. Data Accuracy and Performance
The accuracy of cancer detected data was evaluated by computing the percentages of right classified cancer data .in classification data show the simple number 2 for benign and 4 foe malignant in training and `testing so we remake it data are classify or misclassify when target and actual class are same or differ
The related confusion matrix show the result of EBPA network after training and testing 160 out of 165 samples of benign class are classified correctly while 5 samples are misclassified similarly 73 out of 75 sample of malignant class are classified correctly while 02 samples are misclassified similarly in testing process perform in 160 samples.
In clustering only input the data value and match the target and work the membership function in C1 and C2 (C1 and C2 are benign and malignant class) if output value are high in C1 class for example data membership in C1 is 0.9746 and C2 is 0.0053 so data belong to benign class.
VI. Results and Discussion
Figure 1show the training curve with 100 epochs and figure 2 show the bar chart of performance matrix after the comparison between training and testing session the overall performance of classification are decrease in testing session .in training session correct classification of sample are 96.96% and 97.33% but in testing session the classification parameter are reduced and the percent are 94.54% and 94.00%.
Fig 1: Training curve with 100 epochs
Fig 2: bar chart of performance matrix
Fig 3 show the clustering of cancer data after applying FCM algorithm .In this session data perform only
training so in which 156 out of 165 sample of benign class are classified correctly which 09 samples are miss
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Clustering and Classification of Cancer Data Using Soft Computing Technique
classified. Similarly 71 out of 75 samples of malignant class are classified correctly which 04 samples are misclassified.
Fig 3: clustering of cancer data after applying FCM algorithm
At last comparison between both EBPA and FCM as simulated above the result is tabulated in table 1.1 from which it is clear that correct classified % in case of EBPA is 97.13% which in case of FCM it is 94.03% which clearly indicate that EBPA algorithm is performing well for classification of cancer related health cancer data.
Table 1.1: Comparison table
Class EBPA FCM
Correct Incorrect Correct Incorrect
Benign 96.96% 3.04% 94.54% 5.46%
Malignant 97.33% 2.67% 94.66% 5.34%
Average 97.14% 2.85% 94.6% 5.4%
VII. Conclusions
This paper presented a clustering and classification method for classify the cancer data and find their accuracy .this paper is compare on clustering and classification of soft computing with the area of health care data i.e. cancer data .As a comparison research that author of the current dissertation took bi-direction approach to the problem .In one direction the research studies the supervised manner on classification .the approach lead to constriction of intelligent, less error high performance network due to feed forward and layer architecture of paper.
In second direction, the research studied the unsupervised manner on clustering the approach lead to constriction of perceptional system which is based on fuzzy logic. In this paper exposed the problem of the result and proposed the solution of system by pointing out the attributes of cancer data.
Supervised and unsupervised are the two apposite techniques for classification of data but with the help of MATLAB software 8 used to implement the software in the current work. Supervised need both only input pattern. The EBPA and FCM are compared in terms of performance .the EBPA performance accuracy is 97.14% which in case of FCM accuracy is 94.6%which in case of FCM is having low performance due to unsupervised manner of classification.
References
[1] MATLAB software in URL address:\\www.mathworks.com\\The Math Works,
[2] Using Cancer data set from UCI repository data set ,the URL address: \\WWW.UCI.com\\
[3] George j.klir / boyuan “fuzzy set and fuzzy logic” theory and application, year 2003, pages 50-61,
[4] MATLAB software in URL address: \\www.mathworks.com\\The Math Works, MATLAB 7.5.0(R2007b) help file.
[5] Zadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing," Communications of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.
[6] Takagi, H.: “Fusion Technology of Fuzzy Theory and Neural Networks: Survey and Future Directions” IIZUKA90: International Conference on Fuzzy Logic and Neural Networks. pp. 13-26, Iizuka, Japan 1990.
[7] Tanaka, Makoto: “Application of The Neural Network and Fuzzy Logic to The Rotating Machine Diagnosis” Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications. CRC Press LLC, CRC Press LLC, Boca Raton, FL, USA 1999.
[8] Lee, S. and E. Lee: “Fuzzy Sets and Neural Networks” Journal of Cybernetics. Volume 4, No. 2, pp. 83-013, 1974.
[9] Zadeh, Lotfi: “The Role of Soft Computing and Fuzzy Logic in the Conception, Design, Development of Intelligent Systems” Plenary Speaker, Proceedings of the International Workshop on soft Computing Industry. Muroran, Japan, 1996.
[10] Zadeh, Lotfi: “What is Soft Computing” Soft Computing. Springer-Verlag Germany/USA 1997.
[11] Kacpzyk, Janusz (Editor): Advances in Soft Computing. Springer-Verlag, Heidelberg, Germany, 2001.
[12] Learning internal representations by error propagation by Rumelhart, Hinton and Williams (1986).
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Clustering and Classification of Cancer Data Using Soft Computing Technique
[13] Vikram Chandramohan and Tuan D. Pham James Cook University School of Math, Physics and IT. Of published ” Cancer Classification using Kernelized Fuzzy C-means” research paper
[14] Xiao Ying Wang, Jon Garibaldi, Turhan Ozen Department of Computer Science and Information Technology ,The University of Nottingham, United Kingdom of published ” Application of the Fuzzy C-Means Clustering Method on the Analysis of non Preprocessed FTIR Data for Cancer Diagnosis ” Research paper.
[15] Dave Anderson and George McNeill Kaman “ARTIFICIAL NEURAL NETWORKS TECHNOLOGY” developed by Sciences Corporation address of 258 Geneses Street Utica, New York
[16] Aleksander, Igor and H. Morton: An Introduction to Neural Computing Chapman and Hall, London, UK 1990
[17] Bonissone, Piero: “Soft Computing: The Convergence of Emerging Reasoning Technologies” Soft Computing. Springer-Verlag, Germany/USA 1997.
[18] Lee, S. and E. Lee: “Fuzzy Sets and Neural Networks” Journal of Cybernetics. Volume 4, No. 2, pp. 83-013, 1974.
[19] Gurney, Kevin: An Introduction to Neural Networks. UCL Press, London, UK 1999.
[20] Fausett, Laurene: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice Hall, NJ, USA 1994.
[21] Hertz, J.A., Krogh, A. & Palmer, R. Introduction to the Theory of Neural Computation (Addison-Wesley, Redwood City, 1991)
www.iosrjournals.org 36 | Page
MLaaS: Machine Learning as a Service
Mauro Ribeiro, Katarina Grolinger, Miriam A.M. Capretz
Department of Electrical and Computer Engineering
Western University, London, Ontario, Canada N6A 5B9
{mribeir5, kgroling, mcapretz}@uwo.ca
Abstract—The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time.
Keywords—Machine Learning as a Service, Supervised Learn¬ing, Regression, Prediction, Service Oriented Architecture, Ser¬vice Component Architecture, Platform as a Service
I. INTRODUCTION
The amount of data generated has been continuously grow¬ing from global data sources like Web sites, social media, mobile applications, news networks, weather, political insti¬tutes, society and the economy. No matter how big the data are, they may be useless without proper preparation and processing. Many different machine learning algorithms have been used to extract valuable knowledge from data, e.g., for scientific modeling, consumer behavior, energy consumption forecasting, related article recommendation and user trends.
At the same time, with the popularization of sensors and mobile devices able to connect to a network (e.g., the Internet of Things), it is becoming viable to collect more data from specific contexts at higher levels of detail. By connecting global and context specific data, it is possible to extract even more detailed information and build richer knowledge using machine learning algorithms.
Large companies have enough resources to invest in their own machine learning solutions. However, small companies, developers and researchers in general have difficulties when facing the steep learning curve of how machine learning works and when building their own solutions or integrating with third-party ones. In addition, machine learning can require computational resources with impracticable costs. How could these users have access to affordable machine learning ser¬vices?
One way to meet this demand is by creating a functional and ready-to-use Machine Learning as a Service (MLaaS) platform. Because multiple users will be using the same platform, computational resources can be shared or allocated
on demand, reducing overall costs. By specifying a well defined interface, users can have access to machine learning process efficiently from anywhere, at any time. Users must not be concerned with implementation and computing resources, focusing mainly on the data itself.
This paper proposes a novel approach for machine learning, providing a scalable, flexible, and non-blocking platform as a service based on the service component architecture. This platform facilitates the creation, validation and execution of machine learning models. By taking advantage from service oriented architecture, the proposed approach becomes easily scalable and easy to adapt by adding, removing, changing and linking any component. This also makes the system more flex¬ible for handling multiple data sources and different machine learning algorithms at the same time. In addition, a graphical user interface is presented to facilitate the comparison between different models.
The proposed framework source code is available1 as an open-source project to facilitate its use for various prediction modeling tasks and to enable it to be adapted for other purposes.
The following sections of this paper are organized as follows: Section II gives an overview of machine learning, service component architecture and the main related works on machine learning as a service; Section III describes the proposed architecture for MLaaS; Section IV explains the MLaaS process; Section V presents the case study; and finally, Section VI concludes the paper.
II. RELATED WORKS
A. Machine Learning
Machine Learning is one of the fastest growing fields in computer science [1]. It is a collection of statistical techniques for building mathematical models that can make inferences from data samples (known as a training set). Machine learning is a part of artificial intelligence: it must adapt itself to a changing environment.
Figure 1 roughly illustrates how to choose between the main categories of machine learning. There are three main types of learning [1]: (a) Supervised Learning, when the training set is labeled (i.e., it contains the attribute that the model is trying to estimate); (b) Unsupervised Learning, when the training set is not labeled, and (c) Reinforced Learning, when the learned results lead to actions that change the environment.
1https://github.com/mauro0x52/mlaas
M. Ribeiro, K. Grolinger, M.A.M Capretz, MLaaS: Machine Learning as a Service, International Conference on Machine Learning and Applications, 2015. c 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Fig. 1: Machine learning methods categorization
The labels in supervised learning can be discrete or con-tinuous, which are handled by classification and regression algorithms respectively. Classification is used mostly for pre¬diction, pattern recognition and outlier detection, whereas regression is used for prediction and ranking. Unsupervised learning is known as density estimation in statistics and is represented mainly by clustering algorithms. Classification, regression and clustering are widely used in data mining (applications of machine learning to large databases), whereas reinforced learning is mostly used in decision-making prob¬lems (e.g., a computer playing chess).
Independently of the applications just described, machine learning techniques work in a similar way: the model learns from a training set and then becomes able to make inferences for a new data set. This abstraction inspires the creation of a generic architecture to support any machine learning algorithm. This paper will focus on regression predictive modeling, although the approach can be adapted for other algorithms.
In predictive modeling, once rules have been extracted from past data (the training set), the model can make accurate prediction for new instances of data (the predictor set) if the future is similar to the past. Spam filtering, investment risk and energy consumption forecasting are some examples of predictive modeling. Predictive modeling approaches in¬clude: Artificial Neural Networks for energy consumption [2], Support Vector Machines for energy consumption [2] and K-Nearest Neighbors for wind power [3].
Validation for predictive models has a twofold importance:
(a) choosing the most accurate algorithm and parameters; and
(b) estimating the expected error for new predictions [1]. Ac¬
curacy can be related with errors, which can be calculated by comparing the estimated results from the model with the real measured results. A popular and reliable validation technique for predictive models is the K-Fold Cross-Validation. The data set is split randomly into K parts of the same size. One of the K folds is used to calculate the errors using the other K¬1 folds to train the algorithm. The same process is repeated K times each time using different fold for validation. This method guarantees that the entire data set is validated with statistical significance.
Different models can perform better or worse, depending on the used algorithms, parameters and data set. However, there is no such a thing as the best learning algorithm [1]. For any algorithm, there are data sets that perform very accurately and others that perform very poorly. For the same data set, different algorithms can perform differently because of their own nature. MLaaS helps the user to run multiple algorithms and compare their performances, so the most suitable algorithm can be chosen.
B. Service Component Architecture
A service component architecture (SCA) [4] is a modeling specification for composing systems according to the princi¬ples of Service-Oriented Architecture (SOA).
SCA separates implementation concerns into three artifacts: (a) components implement its business function; (b) compos¬ites assemble various components together to create business solutions, and (c) services create an interface for remote access to component and composite functions. In a system, composites, services, and their relations with components are defined in a dynamic XML descriptor file.
Because SCA is built on top of SOA, it inherits all SOA’s advantages — for example, intrinsic interoperability, inherent reuse, simplified architecture and solutions, and organizational agility [5]. In addition, whereas SOA focuses on building an architecture to design individual components, SCA focuses on assembling multiple components into a composite and facilitating design, implementation, and deployment. SCA sys¬tems have been successfully used, for example, in geographic information systems [6] and smart home systems [7] [8].
This research aims to build a platform which is capable of providing various machine learning algorithms to build different predictive models which will run at the same time. Adding a new algorithm must be simple. The system must provide well-defined APIs which can be remotely accessed over the Web by any external system. SCA provides enough artifacts to meet these requirements.
C. Machine Learning as a Service (MLaaS)
The increasing demand for machine learning is leveraging the emergence of new solutions. In this section, various machine learning platforms are reviewed.
PredictionIO [9] was launched in 2013. It is an open-source platform with an architecture that integrates multiple machine learning processes into a distributed and horizontally scalable
system based on Hadoop. In addition, PredictionIO provides access through web APIs and graphical user interface (GUI).
Baldominos et al. [10] also proposed a platform built on top of Hadoop. Its implementation was capable of handling up to 30 requests at one time while maintaining a response time of less than one second.
OpenCPU [11] is another open-source platform, launched in 2014, that creates a Web API for R [12], a popular statistical analysis software environment. However, because it is practically a middleware for accessing R functions, it does not take into account many non-functional requirements like scalability and performance.
In the industry context, Google, Microsoft, and Amazon have been releasing their own proprietary platforms. Google released its Prediction API2 in 2014. Also in 2014, Microsoft launched Azure Machine Learning3. Most recently in 2015, Amazon released AWS Machine Learning4. Their sales can prove that the demand exists. Unfortunately, the designs and implementation specifications of these products are not pub¬licly available.
PredictionIO, OpenCPU, and Baldominos’ platforms are built on top of a specific analytical tools and suffer from its restrictions. This means less flexibility for adding new machine learning algorithms, for data storage, and for deployment. Although Hadoop and R are open-source projects, it is not a trivial challenge to adapt them to a new approach. The same happens with the industry players and their proprietary solutions when external developers cannot have access to the code to add new algorithms.
The MLaaS proposed in this paper focuses on predictive modeling. As an architecture based on SCA specifications, the architecture facilitates the addition of new algorithms, its improvement, and its adaptation to other machine learning applications. Even the revised platforms mentioned above can be attached to proposed architecture to build prediction models.
III. ARCHITECTURAL DESIGN
This section describes the proposed MLaaS architecture, which is designed to support machine learning by gathering data from multiple sources and building multiple models using different algorithms. The approach focuses on predictive modeling, but it is adaptable to other applications.
The scope of this architecture deals with the machine learning itself, ignoring the front-end aspects such as the user interface. In a Model-View-Controller (MVC) perspective, this architecture focus on the model layer while the controller and view layers are only implemented as part of the case study.
The SCA diagram in Figure 2 depicts a high level overview of the architecture.
The Modeler composite is responsible for building new predictive models. A predictive model is an instance of Modelµ composite, running a specific algorithm. The cardinality 0..N
2https://cloud.google.com/predictio
3http://azure.microsoft.com/en-us/services/machine-learning 4http://aws.amazon.com/pt/machine-learning
shows that MLaaS can run multiples instances of Model-µ composite at the same time, through the Build, Train, Test and Predict services. The model property shows that each instance can run with different settings.
The architecture works as follows: the Machine Learning as a Service composite receives raw data from data sources through its Send Training Set service. First, data are received and prepared by the Data Gatherer composite. The Modeler composite then receives the prepared data to train a Model¬µ instance. When receiving a predictor set from the Send Predictor Set service, the Model-µ instance calculates the prediction and serves it to external modules through the Get Prediction service.
The specified services provide well defined interfaces that increase the architecture’s flexibility to new inputs and outputs: the Send Training Set and Send Predictor Set services enable the inclusion of various data sources that will be merged by Data Gatherer; the Build, Train, Test and Predict consumers enable the architecture to be pluggable with different Model¬µ instances; and the Get Report, Get Test and Get Prediction services enable different user interfaces and external systems to consume the data.
The following subsections describe each of the composites shown in Figures 2 and 3.
A. Data Gatherer Composite
The Data Gatherer composite is responsible for receiving data, pre-processing it, and feeding it to the model. One instance is created for each Send Training Set, Send Test Set or Send Predictor Set services, so that they can run in parallel and independently. The Data Gatherer composite is made up of three components arranged in a pipeline as illustrated in Figure 3; they can be described as follows:
• The Merger component merges all received data (single data points or batches) from different data sources (e.g., sensors or databases). Data sets with different schema are joined into a single multicolumn schema by related at¬tributes (e.g., time-stamp for time-series data, categories, identifiers, etc). When finished, it forwards the data to the Outliers Remover component.
• The Outliers Remover component removes outliers (e.g., missing values, zeros, extremely high values, etc.). Once finished, it forwards the cleaned data to the Pre-Processor component.
• The Pre-Processor component modifies the data set by re-sampling, creating columns, getting the maximum, minimum, or average values, etc. When it is finished, it sends the pre-processed data to the destination component in the Modeler composite.
B. Modeler Composite
This is the core composite in the architecture, because it is responsible for building, training, testing, and running the Model-µ instances. It is made up of five components as illustrated in Figure 2, which can be described as follows:
Fig. 2: MLaaS architecture using SCA notation
Fig. 3: Data Gatherer composite
• The Builder component receives from Build Model the parameters (e.g., algorithm and property values) to build and deploy a new model (a Model-IL instance) for the Build consumer. When the instance is created, Builder sends the model identifier back to the consumer and forwards it to the Learner and Predictor components.
• The Learner component receives the pre-processed data from the Train service and forwards them to the destined Model-IL instance. When it receives the training report from the Model-IL instance through the Train consumer callback, it forwards it to Reports Storage.
• The Reports Storage component receives the report from the Learner component through the Store Report service and serves it to external consumers through the Get Report service.
• The Predictor component receives the predictor set from the Predict service and forwards it to the Model through the Predict consumer, which will return the prediction through a callback. The prediction will be returned to the Predict requester and also forwarded to Predictions Storage. Predictor is also responsible for forwarding the testing set.
• The Predictions Storage component receives and stores
the predictions and tests from the Store Prediction and Store Test services and provides them to external con¬sumers through the Get Prediction and Get Test services.
C. Model-IL Composite
The Model-IL composite is an architecture for building different models. It holds all the implemented algorithms source codes (e.g., Multilayer Perceptron), but only one must be loaded. The algorithm to be loaded and its parameters should be specified when calling the Build service. In other words, for each Build Model service request, a new instance of a Model-IL composite is created.
The model property describes how the model needs to be built and executed. It is composed of four sub-properties: modelId: is the model unique identifier, algorithm: specifies which algorithm is going to be used by the model, parameters: adjust the algorithm behavior, and k: the number of folds to use in the K-Fold Cross-Validation.
The Train, Test, and Predict service specifications enable the Modeler composite to interact with any Model-IL instance.
The Model-IL composite is made up of four components, which can be described as follows:
• The Constructor component is responsible for loading the right algorithm and setting the properties of the model in
stance using the Build service request parameters. When the instance is set up and running, it is ready to provide Train, Test and Predict services.
• The Trainer component receives the training set from the Train service and forwards it to Validator and Predictor components through the Validate and Train services re¬spectively. When validation is finished, the Trainer com¬ponent receives the validation report from the Validate service callback and returns it to the consumer through Train service callback.
• The Validator component receives the training set from the Validate service, feeds it to the model and validates the model (e.g., K-Fold Cross-Validation), returning a report.
• The Predictor component receives the training set from the Train service to feed the model for future prediction requests. When receiving predictor sets through the Pre¬dict service, it calculates and returns the predictions.
The implemented algorithms source code must be responsi¬ble only for training and predicting. Testing and validating do not depend on the algorithm itself, but on the results, which can be found by using the algorithm’s training and pre¬dicting functions. Therefore, testing and validating functions are responsibilities of Validator and Predictor components, increasing standardization and reducing the effort when adding a new algorithm.
IV. MLAAS PROCESS
The diagram in Figure 4 illustrates the main interaction flow between the Consumer, the Modeler and the Model-IL composites. To simplify, the earlier stage related to the Data Gatherer composite is ignored by assuming that data have already been pre-processed. The term Consumer in the following discussion refers to a generic consumer using the Modeler component.
The main flow is divided into three stages:
• Building: it starts with the Consumer requesting the Builder component to build a new model through the Build Model service. The Builder component will then create and configure a new Model-IL instance. When the building operation is complete, the Builder component sends the new model identifier to the Learner and Pre¬dictor components and to the Consumer.
• Training: the Consumer is now able to train the instan¬tiated model. It sends the already pre-processed training set to the Learner component through the Train service, which will forward the training set to the Trainer com¬ponent of the Model-IL instance. The Trainer component will make two requests at the same time: one to the Validator component to validate the model (e.g., K-Fold Cross-Validation) and another to the Predictor compo¬nent to be trained for future prediction requests. When validation is complete, the Validator component responds to Trainer component with the validation report, which contains information such as error measurements. The
Predictor
Fig. 4: Communication flow for the three machine learning stages
report will be stored into Reports Storage component for future retrievals.
• Predicting: the model is ready to predict. The Consumer sends the predictor set to the Modeler composite’s Pre¬dictor component, which will forward to the Model-IL instance’s Predictor component, where the prediction is calculated and returned to the Modeler. The predictions are sent to the Predictions Storage to be stored and served.
In Training and Predicting stages, the Consumer receives the report and prediction identifiers as soon as the Learner and Predictor components receive the request, so it is not necessary to keep the connection while the entire request is be processed. When the report or prediction is ready, it can be accessed from Reports Storage and Predictions Storage components, using the specific identifier.
A Training Stage can also be considered and works similarly to the Predicting Stage. The main difference is the final result, which contains testing information such as errors.
V. CASE STUDY
The goal of this case study is to forecast energy demand based on past electricity demand data data for an office build¬ing, using different machine learning algorithms and finding the best-performing one. This experiment focuses mainly on the Modeler and Model-IL composites.
The proposed architecture was implemented using elec-tricity demand data from Powersmiths’ office building, in Brampton, ON, Canada. The data set were pre-processed before feeding them to the system. This data set was made up of 13 daily attributes: the energy demand peak, six weather attributes and six time attributes. The six weather attributes were: maximum temperature, minimum temperature, average temperature, maximum humidity, minimum humidity and av¬erage humidity. The six time attributes were: year, month (from 1 to 12), day of the month (from 1 to 31), day of the year (from 0 to 365), weekDay (from 0 for Sunday to 6 for Saturday) and dayType (0 for a business day, 1 for a weekend and 2 for a holiday).
The system was built using Node.js because of its ease and agility for coding and deploying Web services and handling JSON. Because there are currently no SCA frameworks for Node.js, one had to be implemented. JSON was used for Web service communication, data storage and the SCA artifact descriptor file. A simple user interface was developed to generate effective illustrations of the results obtained.
The source code is available in a public repository 5.
A. Algorithms
To evaluate the architectural flexibility of running different machine learning models at the same time, Model-IL composite was implemented to support the following algorithms:
• Multi-Layer Perceptron (MLP): one of the most used techniques when evaluating machine learning models, and one of the most used for electrical consumption problems [2]. It was implemented using the Synaptic package6.
• Support Vector Regression (SVR): also one of the most used techniques for electrical consumption problems [2]. It was implemented using the Node-SVM package7.
•K-Nearest Neighbors (KNN): easy to understand, to code, and to debug. This algorithm was coded for this experi¬ment.
A generic Algorithm class was coded under object-oriented programming structure, defining the standard interface for train and predict function calls. A new algorithm can be implemented simply by inheriting the Algorithm class and making minor adaptations. In this case study, the KNN Al¬gorithm class was implemented first to test and validate the Model-IL composite. Later, using the same code structure, MLP Algorithm and SVR Algorithm classes were coded and imported into Model-IL composite.
5https://github.com/mauro0x52/mlaas
6http://synaptic.juancazala.com
7https://github.com/nicolaspanel/node-svm
When a Model-IL instance is built, the algorithm with the parameters (both specified in the model property) is loaded.
The test and validate functions are performed by Predictor and Validation components respectively, and not by the Algo¬rithm class. Both functions use the results from Algorithm’s train and predict calls.
The Validator component implements de K-Fold Cross-Validation method to validate the model, calculating the mean absolute errors and the mean square errors. The number of folds K can be defined to the model property when building a new model.
The architectural design and the dynamic artifacts descriptor file make it possible to create new Model-IL instances dynam¬ically. After the new Model-IL instance is deployed and the artifacts descriptor file is updated, the new Model-IL instance will be available without the need to recompile or restart the system.
B. Results
Three different models were created by instantiating the Model-IL composite. Table I shows the parameters used for each model. The models were requested to predict using a test set, which contains all the 13 attributes including the real measured daily electricity demand peaks. For the K-Fold Cross-Validation, K = 10 was fixed for all the models. The models were also requested to run a prediction using a different predictor set.
Figure 5 shows a screenshot of the MLaaS graphical user interface (GUI). Through the navigation bar, the user can access models (list, create and remove), train, test and predict models and consult a graphical summary of the results. The first row of charts shows the validation performance, with three graphics showing the mean absolute errors, mean square errors, and the execution time for each of the three models. The second row shows the test performance, comparing the mean absolute errors, mean square errors, and execution time for the three models. The third row is a chart comparing the three models’ test results with the real measured data from the test set. Finally, the last row shows the results of a prediction.
TABLE I: Model Parameters
Algorithm Parameter Value
k 10
KNN
max distance 2
nodes per layer 12, 14, 1
learning rate 0.1
MLP
max iterations 1000
min error 0.0001
gamma 0.125, 0.5, 1
c 8, 16, 32
SVR
epsilon 0.001, 0.125, 0.5
retained variance 0.995
Fig. 5: MLaaS screenshot comparing KNN, MLP, and SVR.
The SVR model showed better accuracy – it had the lowest mean absolute errors and mean square errors – both in validation and in testing. Although the KNN model had better accuracy in validation than the MLP model, it had the worse mean square error in testing.
The KNN model performed much faster during validation and could finish executing even while the SVR and MLP mod¬els were still running. The SVR model finished the validation last. On the other hand, during testing, MLP model finished first and KNN model was the last. In other words, one model’s processing did not block the CPU as it would have on a single-threaded server.
VI. CONCLUSIONS
With the growing amount of data available, companies and researchers are demanding feasible and affordable ways to
extract knowledge from all this data. This paper has presented a novel architecture for a scalable, flexible, and non-blocking machine learning as a service based on SCA and focusing on predictive modeling. The proposed architecture can support multiple data sources and create various models with different algorithms, parameters, and training sets.
To prove the concept, the system was built to predict electricity demand using real-world data. Once the main architecture is working and at least one algorithm coded, it is simple to implement other algorithms. It is possible to execute multiple models concurrently.
For future research, MLaaS can be adapted to machine learning applications other than predictive modeling, for ex¬ample, pattern recognition, outlier detection, ranking and clus¬tering.
ACKNOWLEDGE
This research was supported in part by an NSERC CRD at Western University (CRDPJ 453294-13). Additionally, the authors would like to acknowledge the support provided by Powersmiths.
REFERENCES
[1] E. Alpaydin, Introduction to machine learning. MIT press, 2014.
[2] a. S. Ahmad, M. Y. Hassan, M. P. Abdullah, H. a. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable and Sustainable Energy Reviews, vol. 33, pp. 102–109, 2014. [Online]. Available: http://dx.doi.org/10.1016/j.rser.2014.01.069
[3] M. Yesilbudak, S. Sagiroglu, and I. Colak, “A new approach to very short term wind speed prediction using k-nearest neighbor classification,” Energy Conversion and Management, vol. 69, pp. 77–86, 2013.
[4] Service Component Architecture Assembly Model Specification Version 1.1. Accessed: 30-04-2015. [Online]. Available: http://docs.oasis-open. org/opencsa/sca-assembly/sca-assembly-1.1-spec-cd03.html
[5] T. Erl, Service-Oriented Architecture: Concepts, Technology, and De¬sign. Pearson Education India, 2005.
[6] F.-C. Lin, L.-K. Chung, W.-Y. Ku, L.-R. Chu, and T.-Y. Chou, “Service component architecture for geographic information system in cloud computing infrastructure,” in Advanced Information Networking and Applications (AINA), 2013 IEEE 27th International Conference on. IEEE, 2013, pp. 368–373.
[7] T. Calmant, J. C. Am´erico, D. Donsez, and O. Gattaz, “A dynamic sca-based system for smart homes and offices,” in Service-Oriented Computing-ICSOC 2012 Workshops. Springer, 2013, pp. 435–438.
[8] C.-C. Lo, D.-Y. Chen, and K.-M. Chao, “Dynamic data driven smart home system based on a service component architecture,” in Computer Supported Cooperative Work in Design (CSCWD), 2010 14th Interna¬tional Conference on. IEEE, 2010, pp. 473–478.
[9] S. Chan, T. Stone, K. P. Szeto, and K. H. Chan, “PredictionIO: a distributed machine learning server for practical software development,” in Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013, pp. 2493–2496.
[10] A. Baldominos, E. Albacete, Y. Saez, and P. Isasi, “A scalable machine learning online service for big data real-time analysis,” in Computational Intelligence in Big Data (CIBD), 2014 IEEE Symposium on. IEEE, 2014, pp. 1–8.
[11] J. Ooms, “The OpenCPU System: Towards a Universal Interface for Scientific Computing through Separation of Concerns,” arXiv:1406.4806, no. 2000, pp. 1–23, 2014. [Online]. Available: http://arxiv.org/abs/1406.4806
[12] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013, ISBN 3-900051-07-0. [Online]. Available: http://www.R-project. org/
DISSERTATION POLICIES & PROCEDURES
This document presents the various steps involved in arriving at a finished dissertation, from forming a committee, to writing and defending the proposal, to making changes (if necessary) to the dissertation’s contents and to the constitution of the committee, all the way to composing and defending the dissertation.
I. COMMITTEE
A. General
By the end of the Autumn Quarter of his or her Fourth Year in the graduate program, a student is to assemble a dissertation committee.
The student is to constitute a committee with expertise appropriate to the chosen field of study and, in so doing, he or she should solicit advice from a broad range of faculty.
In particular, students should consider and consult with faculty members who have published or taught seminars devoted to topics or figures that are central to a student’s selected field of study regarding the composition of an appropriate dissertation committee.
In addition, students should also consider and commit to developing an ongoing rapport with the members of the committee that is founded on substantive constructive critique and development.
The formation of the committee is to be the result of a consensus of all those involved.
B. Composition
The dissertation committee will consist minimally of three members, all of whom must be permanent, full-time members of the DePaul's Department of Philosophy. A director, or sometimes two co-directors, must be stipulated. Other members of DePaul faculties, or philosophers and scholars from outside the University, whose expertise is pertinent to the topic of the dissertation, may serve as extra readers upon the consent of the dissertation director(s) and the Director of Graduate Studies.
At least one of the Director(s) of the committee must be a tenured (Associate Professor or Professor) member of the Department of Philosophy. However, where the subject matter of the dissertation project warrants an untenured faculty member serving as the Director, the student may petition the Graduate Affairs Committee for an exception to this requirement.
The Readers may be tenured or untenured members of the Department of Philosophy.
Where the subject matter of the dissertation project warrants, and with the approval of the dissertation Director, the third Reader may be a faculty member from another department or from another institution.
C. Committee Responsibilities
The responsibilities of the dissertation committee are to advise the student on the formulation of an appropriate research topic and plan, assess the student’s relevant skills (e.g., language preparation, current knowledge of the field, etc.), and review the progress of the student’s ongoing research.
In pursuit of these obligations, the committee, under the guidance of the dissertation Director, shall assist the student in preparing a Dissertation Proposal and, when agreed, shall conduct an oral defense of the proposal (see section entitled Proposal for all requirements concerning the proposal).
Once the proposal has been successfully defended, the student is to prepare and submit a brief report of research progress to the entire dissertation committee once a year, corresponding with the Graduate Student Reviews (and the
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document should be included with the review documents). This report is to be between 300 and 900 words and it is to describe both the work accomplished since the preceding submission and that which is projected to be completed in the foreseeable future. It may also include any issues or problems that have arisen in the course of conducting research or in writing.
When the student and the committee agree that the project is complete, the dissertation committee shall conduct a public, oral defense of the dissertation (see Dissertation Policies and Procedures, Section III. Dissertation for requirements concerning the dissertation).
Throughout the entire process, the committee shall have the authority, under the guidance of the dissertation Director, to require supplemental research as well as the revision or rewriting of any portion or of all of the Dissertation Proposal or of the dissertation itself.
D. Constitution
Once the dissertation committee has been agreed upon, the student is to submit a Dissertation Committee Constitution Form to the Director of Graduate Studies for review and possible comment by the Graduate Affairs Committee. A student making reasonable progress through the program will have defended his or her proposal by the end of his or her Fourth Year, and by the very latest by the Winter Quarter of his or her Fifth Year.
The Dissertation Committee Constitution Form should be submitted, with the signatures of all committee members, at least two weeks prior to the projected date of the proposal defense. As stated on the form, the student should also submit at this time an approx. 200 word précis of the dissertation project (a short summary of the projected dissertation's main thesis and argument, including the central figures and texts it will cover), as well as a brief chapter outline. Once it has been submitted, the Director of Graduate Studies will distribute the materials to the Graduate Affairs Committee, solicit its feedback, and communicate to the student and his or her dissertation Director any suggestions or advice about the committee or about the execution of the project that the Graduate Affairs Committee may have..
The Graduate Affairs Committee will then register the dissertation committee and finalized form and, upon the completion of this process, the dissertation committee will be officially constituted.
E. Changes to a Constituted Committee
In unusual and rare circumstances, the composition of the dissertation committee may be changed:
a. Changes in Readers
Should either a student or a Director come to believe that a Reader serving on a duly constituted dissertation committee is no longer participating in the work of the committee in a constructive critical fashion or that the focus of the project has changed such that the Reader is no longer deemed, by the student or a Director, to be best suited for the project, then the Director, in consultation with the student, is to seek to mediate and resolve any differences.
If a genuine effort has been made to affect this result, but to no avail, then the dissertation Director is to notify the Director of Graduate Studies in writing that a change in the composition of the dissertation committee is desired, providing an explanation for this action, and submitting a replacement candidate.
Should a Reader serving on a duly constituted dissertation committee come to believe that he or she is no longer able to participate in the work of the committee in a constructive critical fashion or that the focus of the project has changed such that he or she is no longer best suited for the project, then the Director, in consultation with the student, is to seek to mediate and resolve any differences.
If a genuine effort has been made to affect this result, but to no avail, then the dissertation Director is to notify the Director of Graduate Studies in writing that a change in the composition of the dissertation committee is desired, providing an explanation for this action, and submitting a replacement candidate.
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The Director of Graduate Studies is to report any of these changes to the Graduate Affairs Committee for review and, where the Committee deems appropriate, any advice about the change will be communicated to the Director and the student.
In those cases where the committee member in question is also a member of the Graduate Affairs Committee, the faculty member must recuse him- or herself from the discussions of the Committee as it reviews the notice.
After any desired consultation by the Graduate Affairs Committee with the dissertation Director, the new composition of the committee will be registered and considered official.
In this situation, the student and the committee may continue his or her work from where it had been suspended. b. Changes in Director
i. Student Initiated
Should a student change the focus of their research or come to believe that the Director is not participating in the work of the committee in a constructive critical fashion or that the relationship between the student and the Director has become unworkable, then the student may request that the Director of Graduate Studies and the Chair of the Department seek to mediate and resolve any differences.
If the dissertation Director is also the Director of Graduate Studies or the Chair of the Department, then the Director must recuse him- or herself from this process. The other administrative officer is then solely in charge of the mediation process.
If a genuine effort has been made to affect this result, but to no avail, then the student is to notify the Director of Graduate Studies in writing that a change of Director is desired, providing an explanation for this action, and submitting a replacement candidate.
The Director of Graduate Studies is to report this change to the Graduate Affairs Committee for review and, where the Committee deems appropriate, any advice about the change will be communicated to the Director and the student.
In those cases where the dissertation Director in question is also a member of the Graduate Affairs Committee, the faculty member must recuse him- or herself from the deliberations of the Committee as it reviews the notice.
After any desired consultation by the Graduate Affairs Committee with the student, the new composition of the committee will be registered and considered official.
In this situation, the previously constituted committee is considered dissolved and the student must begin the process of assembling a new committee again, including preparing and defending a Dissertation Proposal under the guidance of the new Director and the newly constituted committee.
ii. Director Initiated
Should a Director come to believe that he or she cannot participate in the work of the committee in a
constructive critical fashion or that the relationship with the student or other committee members has become unworkable or that the focus of the project has shifted so significantly that he or she believes that he or she is no longer the best faculty member suited to direct the project, then the Director may request that the Director of Graduate Studies and the Chair of the Department seek to mediate and resolve any differences.
If the dissertation Director is also the Director of Graduate Studies or the Chair of the Department, then the Director must recuse him- or herself from this process. The other administrative officer is then solely in charge of the mediation process.
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If a genuine effort has been made to affect this result, but to no avail, then the Director may notify the Director of Graduate Studies in writing that a change of Director is desired, providing an explanation for this action.
The Director of Graduate Studies is to report this change to the Graduate Affairs Committee for review and, where the Committee deems appropriate, any advice about the change will be communicated to the Director and the student.
In those cases where the dissertation Director in question is also a member of the Graduate Affairs Committee, the faculty member must recuse him- or herself from the deliberations of the Committee as it reviews the notice.
After any desired consultation by the Graduate Affairs Committee with the Director, the committee will be considered officially dissolved.
In this situation, the student must begin the process of assembling a new committee, including preparing and defending a Dissertation Proposal under the guidance of the new Director and the newly constituted committee.
c. Departure of Faculty Member from the Department
In the event that either the Director or a Reader leaves the department, he or she may, if willing and able, continue to serve on a duly constituted dissertation committee for up to one year after the end of his or her employment at DePaul. After which time, he or she may serve as an outside reader only. According to the already articulated policies regarding a change in director, this requires another official proposal and official defense. However, this requirement can be waived at the discretion of the new director.
II. PROPOSAL
Ideally, the dissertation proposal should be defended by the Winter Quarter of the Fourth Year, but in any case no later than the Winter Quarter of the Fifth Year. The student should speak with his or her Director concerning the aims, format, and length of the proposal. Different Directors will ask for different elements in the proposal, and there may even be requirements that are specific to a given project or student.
That being said, the following are some general guidelines concerning dissertation proposals.
A. The Aims of the Proposal
The dissertation proposal has two basic aims, as it relates to its two audiences—i.e., the student’s committee members and the student him- or herself during the subsequent research and writing of the thesis.
On the one hand, from the proposal, the committee should come to understand very clearly the following five things:
(1) the basic issue or question to be addressed in the dissertation;
(2) the method that will be employed to address the issue;
(3) a table of contents or chapter outline, presenting in some detail the contents of each chapter;
(4) the scope that will be covered by the project;
(5) the current state of the question in relevant scholarly discussion; and
(6) the precise contribution the project will make to that discussion.
On the basis of these six pieces of information, the committee will be able to evaluate the intellectual merit, the viability, and the marketability of the proposed dissertation project.
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On the other hand, the successful dissertation proposal will also serve to direct the student in his or her execution of the project. Because of the length of time over which this project must be carried out, this is an extremely important function of the proposal. The student will likely return to the proposal again and again, both to touch base with the original intention and structure of the project, as well as to make self-conscious alterations, additions, and subtractions to that project. The student should, thus, construct the proposal accordingly, keeping in mind that it will have to serve this vital, orienting function during the writing process.
B. Elements and Format of the Proposal
Although, as mentioned above, the elements required in a proposal may well differ to some extent according to the Director, the student, and even the project itself, a typical dissertation proposal will accomplish the above-stated purposes by including the following elements:
• Brief Abstract
The abstract should be approximately 200-350 words. It should summarize the thesis and main argument of the project.
• Outline
The outline should include all of the main steps of the argument, though not every single step. It should be clear from this what each chapter accomplishes as a unit, what it contributes to the argument, and why it contains the subsections it does.
• Summary Presentation
The summary of the entire project should be approximately 4500-6000 words. It should lay out, in clear terms, the single, unifying claim that the dissertation will make, situating that claim both in the broader philosophical discussion and clarifying the argument, chapter by chapter, that will be set out in support of it.
As stated above, the proposal should be sure to make perfectly clear, (1) the basic issue or question to be addressed in the dissertation; (2) the method that will be employed to address the issue; (3) the scope that will be covered by the project; (4) the current state of the question in relevant scholarly discussion; and (5) the precise contribution that this project will make to that discussion.
• Bibliography
The bibliography should include full bibliographic data for all primary and main secondary sources that the student anticipates using in the course of his or her research on the project.
As with the basic plan of the dissertation, this initial bibliography will surely change during the researching and writing according to the specific exigencies and interests of the student’s project. However, at this point, the student must show that he or she is familiar with all the most important and central works pertaining to this subject.
C. The Proposal Defense
Once the proposal has been written, it should be reviewed by the Director and by all members of the committee.
Once the members have had the opportunity to request any changes or clarifications, a defense is to be scheduled in which the committee and the student will come together to assess the merit and viability of the project.
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The student is to submit the final draft of the proposal to all members of his or her committee at least 2 weeks prior to the meeting, unless otherwise directed.
At the defense, the student will generally give a 10-minute summary presentation of the project, reviewing its central aim, argument, and contribution to the scholarly discussion.
The members of the committee will then ask questions and raise issues that they believe remain outstanding concerning the project. The student is required to address these concerns to the full satisfaction of the committee.
The proposal defense will typically last between 1.5 and 2 hours.
At its conclusion, the committee will determine whether or not the project is defined sufficiently to begin work on it, whether it is worthy of pursuing, and whether it can be completed in an acceptable time-span (between 2-5 years).
If the judgment is positive, the committee will sign off on the project and the student may begin work.
If outstanding issues remain in the judgment of the committee, then the student will be required to revise the proposal before the committee signs the required forms, and if the committee decides that there are serious reservations about the project or the student’s ability to complete it, the student will be required to propose again and would then undergo another proposal defense.
III. DISSERTATION
A. The Completed Dissertation
The dissertation should be approximately 200-275 pages, including scholarly apparatus. The length of the dissertation will depend on the demands of the topic and should be determined in discussion with the dissertation Director. Ideally the defense should take place at the end of the Sixth Year but no later than the end of the Tenth Year. Exceptions will be granted on a case-by-case basis.
B. The Dissertation Defense
i. Planning of the Defense
Once the dissertation has been written, it should be submitted to the Director, whose approval is necessary for the setting of a defense date. This approval does not guarantee that the Director will vote for the dissertation’s passing at the defense but it does imply that the Director deems the dissertation ready to be judged by the entire committee. The dissertation Director might call for revisions prior to the setting of a dissertation defense. All other degree requirements (course work, language exams, etc.) must be met prior to the setting of the defense date.
Before setting a date, either the Director or the graduate student in consultation with the Director should consult with the other members of the committee, ensuring that they are available to read the dissertation and participate in the defense (this participation can be electronic although it is not recommended that more than one committee member participate electronically). If a committee member can participate neither in person nor electronically, comments can be sent to the Chair ahead of time (only one such absent member is permitted).
The committee should be given at least a month to read the dissertation. The graduate student must submit the dissertation electronically to the committee members by the agreed upon date.
6
Revised 10/24/17
ii. Format of the Defense
The defense is typically 2 -2.5 hours long. It is a public event and should be announced to the entire philosophy department.
The defense begins with a 5-10 minute statement from the graduate student, offering a brief summary of the project and, typically, some comments about future research that will follow from it. This statement has a dual audience, both the committee and other attendees who may not have read the dissertation. The defense will then proceed with each committee member asking questions of the graduate student. The order of the speakers will be decided by the Director, with the Director typically going last. The Director will also decide whether committee members can ask follow up questions to each other’s questions or whether they should complete their comments before another member joins in. Once all committee members have completed their remarks, the floor should be opened for other defense attendees to ask questions.
Once all questions have been asked or the time limits have been reached, the Director will conclude the discussion and the committee members will begin their private deliberations. The committee then votes, with a majority needed for any outcome (see below for possible outcomes). If there are 4 committee members a tie is not sufficient to determine any given outcome, so a majority vote must be negotiated. If a committee member is unable to participate, and so has sent in comments, a vote should be submitted ahead of time to the Director.
iii. Possible Outcomes
There are five possible outcomes:
a. the dissertation can be passed
b. it can be passed with honors
c. it can be passed with the need for revisions
d. the committee can ask for revisions needed for further review, prior to a decision being made on whether it will be passed
e. it can be failed without the possibility of revisions
Once the committee’s deliberations are completed, the members sign the Dissertation Defense Form, and then they announce the result to the graduate student.
If the committee has either passed the dissertation or a passed it with honors (a or b), the student should follow the procedures outlined by the LAS Graduate Office for its electronic submission, and graduation (http://las.depaul.edu/CurrentStudents/GradStudentSupport/index.asp). This includes submitting a dissertation abstract. The dissertation Director should change the student’s PHL 699, Dissertation Research course grade from an incomplete to an A.
If the dissertation has been passed with revisions required (c), the revisions should be completed within a year and the revised dissertation submitted to the Director, whose approval is required before it can be submitted to the college. If revisions are required prior to a decision being made on whether the dissertation passes (d), the revised dissertation should be submitted to the Director and the other members of the committee within a year. The committee will review the revised dissertation and will, in a timely fashion, meet and judge the work (all 5 outcomes are once again possible). Only one attempt at a revision is allowed. If the attempted revision is rejected by the committee, this entails option (e) above.
If the dissertation is failed (e), and it should be noted that this is a rare occurrence, the student should meet with his or her Director in order to decide whether further work on the dissertation can bring it
7
Revised 10/24/17
into line with the committee’s expectations. This would require a further dissertation defense. Only one further defense is allowed.
While the decision of the committee is made independently, any concerns that the graduate student might have should be brought to the Director of Graduate Studies (or to the Chair if the Director of Graduate Studies is on the committee; if both are on the committee such concerns can be brought to any other member of the Graduate Affairs Committee) who can relay them to the Graduate Affairs Committee.
iv. Celebrations
The department recommends that all successful dissertation defenses be appropriately celebrated, although, unfortunately, there are no department funds available.
8
Revised 10/24/17
Proof of identity (currently residing overseas)29. Justice of the Peace, Commonwealth Representative, Judge or Commission of Oath to complete
is section s ould only be com leted if you are living overseas i.e. you are outside of e ealand at t e time of com leting t is form. It needs to be com leted by eit er a udge (of the country’s judicial system), ustice of t e Peace, Commission of Oat Common ealt Re resentative mbassador/Hig Commission , or any erson aut orised by la of t at country outside of e ealand to administer an oath for t e ur ose
of udicial roceedings. e Proof of Identity referee will certify the applicant’s identity by completing this section of the form. e a licant is not
re uired to rovide any certified co ies of t e identification documents to t e Education Council unless re uested.
Name of applicant (print full name) dk
ic t e t o forms of identification resented to you in erson. One form of identification must be from Category and one must
be from Category (refer to the table below). t least one of t e acce table forms of identification documents must be
otogra ic. ot documents must be originals. e a licant must be t e resenter of t e documents.
Category A Tick Category B Tick
Overseas Pass ort it or
immigration visa/ ermit it out e ealand Overseas Drivers icence
e ealand Pass ort e ealand Drivers icence
ational Police Certificate issued in t e last 6 mont s
by t e Country t e a licant as been residing in for
more t an 6 mont s
Identification documents resented to you in erson by t e a licant must be from t e list in t e above table one document from Category and one document from Category . e documents must be originals, current and not e ired and issued by an
aut orised agency as outlined in t e Com letion uide. If a licable, ere names or ot er identity information are different on eit er document Category and , lease confirm you ave sig ted acce table evidence of name c ange a e ealand Marriage Certificate is acce table but a Particulars of Marriage document is not . See Completion Guide for more information and t e full list of acce table identification documents. Please rovide details in t e s aces belo about t e identification documents you ave verified.
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Category A
Category B
Name change
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ustice of t e Peace
Commission of Oat Common ealt Re resentative mbassador/Hig Commission
erson aut orised by la of t at country to administer
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ame of identity referee:
rint full name
ddress:
You must apply an official stamp or seal below.
Email address:
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I declare that (Identity referee please tick)
I ave sig ted t o forms of identification one from Category and one from Category
and I verify t at t e erson in t e oto is t e erson ose name is rinted in full above.
Name Change: I ave sig ted evidence of t e name c ange (if applicable).
Identity referee’s signature Date / /
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Completing proof of identity section (currently residing overseas)
e Education Council re uires t e Proof of Identity referee to be eit er a Judge of the country’s judicial system, a ustice of t e Peace, Commission of Oat , Common ealt Re resentative mbassador/Hig Commission or any erson aut orised by la of t at Country to administer an oath for t e ur ose of udicial roceedings.
The identity referee must:
• verify t e t o forms of identification re uired in t is section one from Category and one from Category of ic one form of identification must be otogra ic
• verify t at t e a licant o must also be t e resenter of t e identification documents, is t e same erson as identified in t e document from Category and Category
• com lete all sections of t e form and a ly your official stam or seal
• not be related to, or be a artner or s ouse to t e a licant
• not reside at t e same address as t e a licant Identification verification:
• e a licant must rovide in erson t o forms of identification documents to be certified by an acce table roof of identity referee. ese must be original documents.
• ey must include one Primary identification document/record and one Secondary identification document record issued by an a roved agency. See Category and in t e table belo .
• One identification document from eit er Category or must be otogra ic
Category A: Primary identification document/record Issuing agency
e ealand Pass ort De artment of Internal ffairs
Overseas ass ort it or
immigration visa/ ermit it out e ealand Relevant ut ority in country of issue visa/ ermit to be
issued by t e Ministry of usiness, Innovation and
Em loyment Immigration e ealand
Overseas ass ort Relevant ut ority in country of issue
Category B: Secondary identification document/record Issuing agency
e ealand Driver icence e ealand rans ort gency
Overseas Driver’s Licence Relevant ut ority in country of issue
ational Police Certificate issued in t e last 6 mont s by t e
Country t e a licant as been residing in for more t an 6
mont s
Evidence of name change or other changes to identity information
It is im ortant t at t e evidence of identity resented to t e identity referee covers all identity information rovided in t e sections of t e a lication form under t e eadings of Personal details, Applicant declaration and Proof of Identity.
If t e identity documentation t at you rovide to t e identity referee s o s t at t ere is a difference in t e name or ot er as ects in t e identity related information t at t ose documents ere issued under, you ill need to rovide to t e identity referee evidence from t e table belo in Category C to verify t e difference. or e am le if t e document t at you rovide to t e identity
referee from Category as issued under a name t at is different from t e name t at t e document you rovide from Category
, you ill need to rovide a document from Category C t at verifies t at difference in name for bot documents. ote: If t ere are inconsistencies across t e identification documents or identity verification you ave rovided to t e Education Council, you may be as ed to rovide furt er information.
Category C: Evidence of name Change Issuing agency
C ange of ame by Statutory Declaration De artment of Internal ffairs
C ange of ame by Deed Poll De artment of Internal ffairs
e ealand ame C ange Certificate De artment of Internal ffairs
e ealand Marriage Certificate not a Particulars of Marriage document De artment of Internal ffairs
e ealand Civil nion Certificate De artment of Internal ffairs
e ealand irt Certificate issued after 1998 De artment of Internal ffairs
e ealand Divorce Pa ers Ministry of ustice
Certificate of nnulment Ministry of ustice
Overseas Pass ort – must be current Relevant ut ority in country of issue
Overseas irt Certificate or Marriage Certificate Relevant ut ority in country of issue
POI Overseas (Version 2016-08-26) Physical Address: Postal Address:
Level 12, 80 Boulcott Street PO Box 5326
Page of 2 of 2 Wellington 6011 Wellington 6145
enquiries@educationcouncil.org.nz Telephone: (04) 471 0852
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