The Fourth International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024 will be held at Mahindra University, Hyderabad on Aug 22-24, 2024. This is technically co-sponsored by the IEEE Computational Intelligence Society (CIS) as in the prior editions. This conference aims to highlight the evolution of topics, frontline research and multiple applications in the domain of Computational Intelligence, from the mainstream foundations to novel investigations and applications. The conference comprises of one day of tutorial sessions followed by two days of Keynote Lectures by invited international experts from Industry and Academia, and technical paper presentations. Also, the conference hosts several special sessions on emerging technologies and applications related to computational intelligence. In addition to tutorials by experts from the academia, the conference is also expected to have industry-relevant tutorials by experts from top industries operating in this field.
More information on the Conference may be found at https://www.ietcint.com/
ICETCI 2024 invites submissions that are original, previously unpublished innovative work in any area of Computational Intelligence, both emerging topics which form the theme of the conference as well as more foundational areas.
The three main tracks of the Conference are:
• Deep Learning
• Sequence Modelling and
• General Topics in Computational Intelligence
List of Conference Topics can be seen at https://ietcint.com/user/instructions
Paper Submission
Manuscripts for ICETCI 2024 should be submitted electronically at https://edas.info/N31894
online Course: Introduction to Artificial Intelligence for Public Service Interoperability
1. Course overview and introduction
2. Introduction to AI and its components
3. Introduction to interoperability
4. AI supporting IOP in public administration in the EU Member States
5. Conclusions and next steps
Quiz
Certificate of Completion
https://academy.europa.eu/courses/introduction-to-artificial-intelligence-for-public-service-interoperability
The "AI in Education: Policy Starter Resources'' launches in April 2024. This resource includes one-pagers, policy considerations, and talking points - created to assist leaders in crafting policies for teaching with and about AI.
You can learn more at TeachAI.org.
Currently, the development and analysis of accident progression event trees (APETs) are performed in a manner that is computationally time-consuming, difficult to reproduce, and phenomenologically inconsistent. A software tool is presented for automated APET generation using the concept of dynamic event trees. The tool determines the branching times from a severe accident analysis code based on user-specified criteria for branching. It assigns user-specified probabilities to every branch, tracks the total branch probability, and truncates units based on the given pruning/truncation rules to avoid an unmanageable number of scenarios. While the software tool could be applied to any systems analysis code, the MELCOR code is used for this illustration. A case study is presented involving a station dimming with the loss of an auxiliary feedwater system for a pressurized water reactor.
The celebration of the AI Literacy Day is April 19.
Organizations or school districts wanting to learn more or engage in National AI Literacy Day can learn more at ailiteracyday.org.
Event-B is a modelling language and a formal methods approach for correct construction of software. This paper presents our work on code generation for Event-B, including the definition of a syntactic translation from EventB to JML-annotated Java programs, the implementation of the translation as the EventB2Java tool, and two case studies on the use of EventB2Java. The first case study is on implementing an Android application with the aid of the EventB2Java tool, and the second is on testing an Event-B specification of the Tokeneer security-critical system. Additionally, we have benchmarked our EventB2Java tool against two other Java code generators for Event-B
Support for distributed transactions across heterogeneous storage technologies is nonexistent or suffers from poor operational and performance characteristics. In contrast, OLEP is increasingly used to provide good performance and strong consistency guarantees in such settings.
1st Meeting of the Vienna Network of Music and Cognition Research (ViNoMaRe), an *interdisciplinary networking event that brings together research groups from Vienna* and beyond.
Supported by the Vienna Cognitive Science Hub, this event aims to leverage and harvest the potential of expertise by bringing a multitude of perspectives together, from life sciences, humanities and arts.
On June 6th 2024, from 17:30 - 19:30In Lecture Hall 1 in the Department of Musicology at the University of Vienna, Austria
You can register here*: https://cogsci.univie.ac.at/vinomare/
<https://massmailer.univie.ac.at/action/mlr/lk?&idx=622484&url=nUE0pUZ6Yl9wo2qmL2xhqJ5cqzyyYzSwYzS0Y3Mcoz9gLKWyYj&cid=15206&uid=0&cks=94a9790d>
Watch the top North America entrepreneur finalists in 2024 present their innovative AI-powered solutions to achieve SDGs to the honorary US judges in this Innovation Factory pitching session.
The AI for Good session on "Meet The Top North America Entrepreneur Finalists 2024" will take place on April 17th, 2024 at 17:30 Geneva time CE(S)T!
Visit here https://neuralnetwork.aiforgood.itu.int/event/ai-for-good/register?registerAsParticipant=true&externalId=22686
In this paper, authors have shown how to use the Kohonen maps as a complement of Factorial Correspondence Analysis (FCA) methods classically used in lexicometry,
- to improve the information provided by the different projections of the FCA,
- to make the Kohonen maps more robust concerning the randomness of the SOM algorithm by distinguishing stable neighbor pairs from fickle pairs,
- to build graphs of connections between fickle words which are difficult to analyze by both FCA and Kohonen map alone
The rapid increase in insider attacks and proprietary and sensitive data leakage emphasizes the need for more secure and trusted user-access strategies. In general, the user access to classified data follows hierarchical role-based, task-based, MAC or DAC models, which does not require human approval, so once the access is permitted, the user gets a free ride on the requested resource. The access logs and activity logs can provide the usage statistics of a file with specific sensitivity.
The building of a concrete overlay on a two-lane rural roadway presents certain unique problems; nevertheless, with proper planning, these obstacles may be overcome to the satisfaction of both the contractors and the affected populace.
In order to create future abilities, this study analyzed which elements of Education 4.0 are being employed in 21st century frameworks and which teaching and learning approach works best. The findings show that: (a) case studies and teaching and learning strategies are highlighted in the literature on frameworks; (b) comprehensive studies have been conducted on the skills and knowledge learning dimensions, and there is room for further research on the development of character and meta-learning skills; (c) the most popular teaching and learning strategies are those that set the trends for educational innovation; (d) learning methods and competencies are the most frequently addressed components of Education 4.0; and (e) there is a noticeable lack of frameworks targeted at teachers and managers and strategies to strengthen educational innovation in schools.
Geat artical showing different elements and how to utilize them in healthcare. It also offers practical suggestions for developing an AI strategy to assist the digital healthcare transition.
Cooperative AI Research Grants
The Cooperative AI Foundation is seeking proposals for research projects in Cooperative AI. Anyone is eligible to apply, and we welcome applications from disciplines outside of computer science. This call will remain continuously open throughout 2024, and will have four deadlines after which applications will be processed: January 14, March 17, July 28, and October 13.
Check the application process here https://www.cooperativeai.com/grants/cooperative-ai
Hub