November 2021 - RoboCup-worldwide

PhD/MS Openings in Human-Robot Interaction at University of Pittsburgh's School of Information and Computing
by Michael Lewis 16 Nov '21

16 Nov '21

The PhD programs in Informatics and Networked Systems or Intelligent Systems provide interdisciplinary training in robotics, AI, and HRI. The available position(s) at ULab (http://www.pitt.edu/~cmlewis/research.html) comes with a 2-5 year Graduate Assistantship to study human interaction with AI and robotic systems. You will work in close collaboration with Prof. Sycara's laboratory at Carnegie Mellon University ( http://www.ri.cmu.edu/person.html?person_id=304) to study Theory of Mind models of Artificial Social Intelligence, Human-Autonomy Teams (HATs) and metrics for evaluating them. Applicants at either bachelors or masters level are invited to apply. Please contact Michael Lewis cmlewis(a)pitt.edu to discuss possible assistantship and instructions for application.

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CPS&IoT'2022 - the 10th International Conference on Cyber-Physical Systems and Internet-of-Things
by Jozwiak, Lech 12 Nov '21

12 Nov '21

Special Focus: Green CPS&IoT for Green World Special Theme of CPS&IoT’2022: Artificial Intelligence, Edge Computing, Architectures, Methods and Tools for Autonomous Robots, Vehicles, Assistive, Environmental and other advanced CPS&IoT Dear Colleague, Please find the Call for Papers of CPS&IoT’2022 - the 10th International Conference on Cyber-Physical Systems and Internet-of-Things, Budva, Montenegro, 7-10 June, 2022 attached to this e-mail. CPS&IoT’2022 will be held in the Conference Venue (Hotel Budva) and online. All papers that will be accepted after the conference review process will be published in the CPS&IoT’2022 Conference Proceedings regardless of the authors' physical presence at the conference and will be presented by their authors either in the Conference Venue or online. The CPS&IoT’2022 Conference will host several prestigious keynote speakers from industry and academia. Extended journal versions of selected high-quality papers will be published in a special issue of the ISI indexed journal “Microprocessors and Microsystems” (MICPRO) https://www.journals.elsevier.com/microprocessors-and-microsystems<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.journ…>. The CPS&IoT'2022 Conference<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fembeddedc…> is collocated with the CPS&IoT’2022 Summer School<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fembeddedc…> and MECO'2022 Conference<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fembeddedc…> - the 11th Mediterranean Conference on Embedded Computing. Together they constitute a major Europe based conference event in CPS and IoT. Please also find attached the Call for Workshops and Special Sessions of CPS&IoT’2022. You are encouraged to submit your papers, as well as, Workshop and Special Session Proposals to the CPS&IoT’2022 Conference, and to participate in the collocated CPS&IoT’2022 Summer School. Paper Submission Deadline: 31 January 2022. Workshop and Special Session Proposal Submission Deadline: 30 November 2021. Best regards, Lech Jozwiak Program Chairman of the CPS&IoT’2022 Conference and Radovan Stojanovic Managing Chairman of the CPS&IoT’2022 Conference

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[Journal] IJAIT Call for Special Issue on Explainable Machine Learning in Methodologies and Applications
by Pub Conference 09 Nov '21

09 Nov '21

Computational Intelligence Journal Special Issue on Explainable Machine Learning in Methodologies and Applications https://onlinelibrary.wiley.com/page/journal/14678640/homepage/special_issu… This special issue 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 Special Issue 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 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

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Call for paper-Neural Computing and Applications, Topical Collection on Interpretation of Deep Learning
by Pub Conference 08 Nov '21

08 Nov '21

Neural Computing and Applications <https://www.springer.com/journal/521/> 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(a)udc.edu Jian Wang, China University of Petroleum (East China), Qingdao, China, wangjiannl(a)upc.edu.cn Leszek Rutkowski, Czestochowa University of Technology, Poland, leszek.rutkowski(a)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’.

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Call for Special Offers (RoboCup Humanoid League)
by Maike Paetzel-Prüsmann 05 Nov '21

05 Nov '21

========================================================= Call for Special Offers (Humanoid League) ========================================================= Research institutes and companies that produce and sell relevant hardware for RoboCup humanoid teams are encouraged to send their proposals for special offers to the Technical Committee of the Humanoid League. A special offer is limited in time and enables teams and institutes participating in RoboCup 2022 to buy a hardware product used for the RoboCup 2022 competition for a reduced price or under other special conditions. Hardware products can either be complete robot platforms or relevant hardware parts needed for robot construction. Special offers will be published on the humanoid league website and sent out via the RoboCup email list. Special offers are *not* considered an official sponsoring of the RoboCup event. Important dates: ----------------------------------------------------------- * Deadline: November 29th 2021 * Notification: December 3rd 2021 Submission guidelines and procedure ---------------------------------------------------------- Send an email with a the following information to the Humanoid League Technical Committee <rc-hl-tc(a)lists.robocup.org> : 1. Name of institution or company and contact information of a person responsible for the offer 2. Description of the special offer as it shall appear on the website (max 100 words). This MUST include information about the product, the price and a point of contact 3. Explanation of relevance to the league (if not obvious from the description) 4. Date of expiry for the special offer All proposals for special offers will be reviewed by the Technical Committee and checked for relevance to the league. The responsible person for the special offer will be notified on December 3rd about the decision. Kind regards, Maike (for the Humanoid Legue Technical Committee) -- Dr. Maike Paetzel-Prüsmann Postdoctoral Researcher Foundations of Computational Linguistics Laboratory University of Potsdam, Germany Pronouns: she / her More information: https://www.maike-paetzel.de/

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