CALL FOR PAPERS, TUTORIALS, PANELS
COGNITIVE 2022, The Fourteenth International Conference on Advanced Cognitive Technologies and Applications
General page: https://www.iaria.org/conferences2022/COGNITIVE22.html
Submission page: https://www.iaria.org/conferences2022/SubmitCOGNITIVE22.html
Event schedule: April 24 – 28, 2022
Contributions:
– regular papers [in the proceedings, digital library]
– short papers (work in progress) [in the proceedings, digital library]
– ideas: two pages [in the proceedings, digital library]
– extended abstracts: two pages [in the proceedings, digital library]
– posters: two pages [in the proceedings, digital library]
– posters: slide only [slide-deck posted at www.iaria.org]
– presentations: slide only [slide-deck posted at www.iaria.org]
– demos: two pages [posted at www.iaria.org]
Submission deadline: January 10, 2022
Extended versions of selected papers will be published in IARIA Journals: https://www.iariajournals.org
Print proceedings will be available via Curran Associates, Inc.: https://www.proceedings.com/9769.html
Articles will be archived in the free access ThinkMind Digital Library: https://www.thinkmind.org
The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.
All tracks are open to both research and industry contributions.
Before submission, please check and comply with the editorial rules: https://www.iaria.org/editorialrules.html
COGNITIVE 2022 Topics (for topics and submission details: see CfP on the site)
Call for Papers: https://www.iaria.org/conferences2022/CfPCOGNITIVE22.html
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COGNITIVE 2022 Tracks (topics and submission details: see CfP on the site)
NEW DIRECTIONS: Hot topics on cognitive science
Neuroscience; Brain connectivity; Brain-like Computing; Neuromorphic computing; Neuromorphic devices; Brain machine interface; Interfacing via brain waves; Spiking hierarchical models; Vision sensors; Sensory modalities; Temporal approach to object recognition; Hierarchical temporal memory; Spatio-temporal event recognition; Reasoning with relative directions; Web-navigation via Cognitive Models; Mining cognitive patterns; Bag-of-Features for retrievals; Computing with symmetries; Periphery of Knowledge; Emotions and cooperation levels
BRAIN: Brain information processing and informatics
Cognitive and computation models; Human reasoning mechanisms; Modeling brain information processing mechanisms; Brain learning mechanisms; Human cognitive functions and their relationships; Modeling human multi-perception mechanisms and visual, auditory, and tactile information processing; Neural structures and neurobiological process; Cognitive architectures; Brain information storage, collection, and processing; Formal conceptual models of human brain data; Knowledge representation and discovery in neuroimaging; Brain-computer interface; Cognition-inspired complex systems; Aesthetic emotions
COGNITION: Artificial intelligence and cognition
Expert systems, knowledge representation and reasoning; Reasoning techniques, constraint satisfaction and machine learning; Logic programming, fuzzy logic, neural networks, and uncertainty; State space search, ontologies and data mining; Games, planning and scheduling; Natural languages processing and advanced user interfaces; Cognitive, reactive and proactive systems; Ambient intelligence, perception and vision; Pattern recognition
MACHINE LEARNING: Advanced topics in Deep/Machine learning
Distributed and parallel learning algorithms; Collective cognition; Image and video coding; Deep learning and Internet of Things; Deep learning and Big data; Data preparation, feature selection, and feature extraction; Error resilient transmission of multimedia data; 3D video coding and analysis; Depth map applications; Machine learning programming models and abstractions; Programming languages for machine learning; Visualization of data, models, and predictions; Hardware-efficient machine learning methods; Model training, inference, and serving; Trust and security for machine learning applications; Testing, debugging, and monitoring of machine learning applications; Machine learning for systems.
AGENTS: Agent-based adaptive systems
Agent frameworks and development platforms; Agent models and architectures; Agent communication languages and protocols; Cooperation, coordination, and conversational agents; Group decision making and distributed problem solving; Mobile, cognitive and autonomous agents; Task planning and execution in multi-agent systems; Security, trust, reputation, privacy and safety in agent-based systems; Negotiation brokering and matchmaking in agent-oriented protocols; Web-oriented agents (mining, semantic discovery, navigation, etc.; SOA and software agents; Economic agent models and social adoption
AUTONOMY: Autonomous systems and autonomy-oriented computing
Self-organized intelligence nature-inspired thinking paradigms; Swarm intelligence and emergent behavior; Autonomy-oriented modeling and computation; Coordination, cooperation and collective group behavior; Agent-based complex systems modeling and development; Complex behavior aggregation and self-organization; Agent-based knowledge discovery and sharing; Autonomous and distributed knowledge systems; Autonomous knowledge via information agents; Ontology-based agent services; Knowledge evolution control and information filtering agents; Natural and social law discovery in multi-agent systems; Distributed problem solving in complex and dynamic environments; Auction, mediation, pricing, and agent-based market-places; Autonomous auctions and negotiations
APPLICATIONS
Agent-oriented modeling and methodologies; Agent-based interaction protocols and cognitive architectures; Emotional modeling and quality of experience techniques; Agent-based assistants and e-health; Agent-based interfaces; Knowledge and data intensive classification systems; Agent-based fault-tolerance systems; Learning and self-adaptation via multi-agent systems; Task-based and task-oriented agent-based systems; Agent-based virtual enterprise; Embodied agents and agent-based systems applications; Agent-based perceptive animated interfaces; Agent-based social simulation; Socially planning; E-Technology agent-based ubiquitous services and systems
Call for paper-Neural Computing and Applications, Topical Collection on Interpretation of Deep Learning
November 11th, 2021 
Daniela Lopez de Luise Topical Collection on Interpretation of Deep Learning: Prediction, Representation, Modeling and Utilization
https://www.springer.com/journal/521/updates/19187658
Aims, Scope and Objective
While Big Data offers the great potential for revolutionizing all aspects of our society, harvesting of valuable knowledge from Big Data is an extremely challenging task. The large scale and rapidly growing information hidden in the unprecedented volumes of non-traditional data requires the development of decision-making algorithms. Recent successes in machine learning, particularly deep learning, has led to breakthroughs in real-world applications such as autonomous driving, healthcare, cybersecurity, speech and image recognition, personalized news feeds, and financial markets.
While these models may provide the state-of-the-art and impressive prediction accuracies, they usually offer little insight into the inner workings of the model and how a decision is made. The decision-makers cannot obtain human-intelligible explanations for the decisions of models, which impede the applications in mission-critical areas. This situation is even severely worse in complex data analytics. It is, therefore, imperative to develop explainable computation intelligent learning models with excellent predictive accuracy to provide safe, reliable, and scientific basis for determination.
Numerous recent works have presented various endeavors on this issue but left many important questions unresolved. The first challenging problem is how to construct self-explanatory models or how to improve the explicit understanding and explainability of a model without the loss of accuracy. In addition, high dimensional or ultra-high dimensional data are common in large and complex data analytics. In these cases, the construction of interpretable model becomes quite difficult and complex. Further, how to evaluate and quantify the explainability of a model is lack of consistent and clear description. Moreover, auditable, repeatable, and reliable process of the computational models is crucial to decision-makers. For example, decision-makers need explicit explanation and analysis of the intermediate features produced in a model, thus the interpretation of intermediate processes is requisite. Subsequently, the problem of efficient optimization exists in explainable computational intelligent models. These raise many essential issues on how to develop explainable data analytics in computational intelligence.
This Topical Collection aims to bring together original research articles and review articles that will present the latest theoretical and technical advancements of machine and deep learning models. We hope that this Topical Collection will: 1) improve the understanding and explainability of machine learning and deep neural networks; 2) enhance the mathematical foundation of deep neural networks; and 3) increase the computational efficiency and stability of the machine and deep learning training process with new algorithms that will scale.
Potential topics include but are not limited to the following:
- Interpretability of deep learning models
- Quantifying or visualizing the interpretability of deep neural networks
- Neural networks, fuzzy logic, and evolutionary based interpretable control systems
- Supervised, unsupervised, and reinforcement learning
- Extracting understanding from large-scale and heterogeneous data
- Dimensionality reduction of large scale and complex data and sparse modeling
- Stability improvement of deep neural network optimization
- Optimization methods for deep learning
- Privacy preserving machine learning (e.g., federated machine learning, learning over encrypted data)
- Novel deep learning approaches in the applications of image/signal processing, business intelligence, games, healthcare, bioinformatics, and security
Guest Editors
Nian Zhang (Lead Guest Editor), University of the District of Columbia, Washington, DC, USA, nzhang@udc.edu
Jian Wang, China University of Petroleum (East China), Qingdao, China, wangjiannl@upc.edu.cn
Leszek Rutkowski, Czestochowa University of Technology, Poland, leszek.rutkowski@pcz.pl
Important Dates
Deadline for Submissions: March 31, 2022
First Review Decision: May 31, 2022
Revisions Due: June 30, 2022
Deadline for 2nd Review: July 31, 2022
Final Decisions: August 31, 2022
Final Manuscript: September 30, 2022
Peer Review Process
All the papers will go through peer review, and will be reviewed by at least three reviewers. A thorough check will be completed, and the guest editors will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 30% difference from the original works).
Submission Guidelines
Paper submissions for the special issue should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).
Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx.
Authors should select “TC: Interpretation of Deep Learning” during the submission step ‘Additional Information’.
Special Issue “Learning-based Digital Image and Video Compression” in Frontiers in Signal Processing
November 11th, 2021 Daniela Lopez de Luise We would like to invite you to submit a paper for this
Special Issue. Both comprehensive reviews and original articles are welcome. You may find a short description and the technical scopes of this Special Issue at https://www.frontiersin.org/research-topics/26497/learning-based-digital-image-and-video-compression**Submission Deadlines**
Manuscript: 14 January 2022
Looking forward to hearing from you and your team.
Kind regards,
Guest Editors
Dr. Wassim Hamidouche
Dr. Sid Ahmed Fezza
Dr. Giuseppe Valenzise
Dr. Shuyuan Zhu
Live e-Lecture by Prof. Cees Snoek: “Real-World Learning”, 23rd November 2021 17:00-18:00 CET. Upcoming AIDA AI excellence lectures
November 11th, 2021 Daniela Lopez de Luise Lecture by Prof. Cees Snoek (University of Amsterdam, Netherlands), a prominent AI researcher internationally, will deliver the e-lecture:
‘Real-World Learning’, on Tuesday 23rd November 2021 17:00-18:00 CET (8:00-9:00 am PST), (12:00 am-1:00am CST),
see details in: http://www.i-aida.org/event_cat/ai-lectures/
You can join for free using the zoom link: https://authgr.zoom.us/s/96903010605 & Passcode: 148148
The International AI Doctoral Academy (AIDA), a joint initiative of the European R&D projects AI4Media, ELISE, Humane AI Net, TAILOR and VISION, is very pleased to offer you top quality scientific lectures on several current hot AI topics.
Lectures are typically held once per week, Tuesdays 17:00-18:00 CET (8:00-9:00 am PST), (12:00 am-1:00am CST). Attendance is free.
Other upcoming lectures:
1. Prof. Marios Polycarpou (University of Cyprus, Cyprus), 7th December 2021 17:00 – 18:00 CET.
2. Prof. Bernhard Rinner (Universität Klagenfurt, Austria), 11th January 2021 17:00 – 18:00 CET.
More lecture infos in: https://www.i-aida.org/event_cat/ai-lectures/?type=future
The lectures are disseminated through multiple channels and email lists (we apologize if you received it through various channels).
If you want to stay informed on future lectures, you can register in the email lists AIDA email list and CVML email list.
Best regards
Profs. M. Chetouani, P. Flach, B. O’Sullivan, I. Pitas, N. Sebe
17th International Conference on Intelligent Autonomous Systems – IAS-2022
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