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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Puxeddu, Maria Grazia Rinaldini, Greta Astolfi, Laura University of Rome La Sapienza Department of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Social interactions are woven in the fabric of our lives, from birth to adulthood. Studies show that when social connections are lacking, there is a high probability of developing poor mental and physical health. Conversely, some mental disorders result in a compromised ability to form these interactions, with autism being a prime example. For these reasons, understanding the neural mechanisms underlying social interactions is crucial. Hyperscanning experiments, where brain activity is simultaneously recorded from multiple subjects during social tasks, have emerged as a powerful tool for tackling this issue, and the study of brain-to-brain connectivity has provided significant insights into social neuroscience. However, no agreed-upon methods exist for analyzing and interpreting such data, and most of the analyses still rely on techniques designed for single-subject experiments. Here, we propose a framework to model brain-to-brain connectivity through multi-dimensional networks. Under this conceptualization, both intra- and inter-brain connectivity are viewed as components of a higher-order network that can be studied by extending graph theory indices to a multidimensional case. In this article, we also discuss how these indices can be interpreted in the context of multi-subject networks. Future investigations will apply this framework to hyperscanning datasets to provide new insights into the neural circuits associated with social interactions. © 2024 IEEE.

关键词： Diseases

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Informativeness of Query Answers for Knowledge Bases (Extended Abstract) 37

Informativeness of Query Answers for Knowledge Bases (Extend...

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37th International Workshop on Description Logics, DL 2024

作者： Andolfi, Luca Cima, Gianluca Console, Marco Lenzerini, Maurizio Department of Computer Control and Management Engineering 25 Via Ariosto Rome00185 Italy

This extended abstract summarises our recent work on the informativeness of query answers over Description Logics Knowledge Bases (KBs). We introduce a framework to characterise the information that query answers for KBs can represent and under its lens we study the informative power of current query answering definitions across the literature. Moreover, we also present novel notions of certain and possible answers, showing that they are able to represent a meaningful form of information. Lastly, we study the data complexity properties for relevant problems associated to the answers we introduced. © 2024 Copyright for this paper by its authors.

关键词： Structured Query Language

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Real-time and Energy-aware Scheduling for Edge-to-Cloud Continuum based on Reinforcement Learning 28

Real-time and Energy-aware Scheduling for Edge-to-Cloud Cont...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Panceri, Andrea Mattia, Gabriele Proietti Beraldi, Roberto Sapienza University of Rome Control and Management Engineering "A. Ruberti" Department of Computer Italy

ISBN: (纸本)9798331527211

By spreading out computing workloads over multiple levels, the Edge-to-Cloud continuum paradigm improves the performance of applications that are sensitive to latency. However, real-time scheduling is difficult on the Edge computing layer since it consists of a variety of nodes with varying uptime. In this paper, we address this issue by proposing an online and adaptive scheduling algorithm based on a continuous learning Reinforcement Learning. Our algorithm determines each work request individually, optimizing scheduling policies to meet real-time application requirements while taking environmental energy and battery limits into account. We validate the efficacy of our approach in dynamically assigning tasks, particularly in scenarios where Edge nodes exhibit variable speeds and unpredictable failures, while efficiently managing energy resources and battery constraints through extensive simulations and comparisons with static scheduling strategies. © 2024 IEEE.

关键词： Scheduling algorithms

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Semantic Explanations of Classifiers through the Ontology-Based Data Management Paradigm (Extended Abstract) 37

Semantic Explanations of Classifiers through the Ontology-Ba...

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37th International Workshop on Description Logics, DL 2024

作者： Papi, Laura Cima, Gianluca Console, Marco Lenzerini, Maurizio Department of Computer Control and Management Engineering 25 Via Ariosto Rome00185 Italy

One of the main challenges in modern AI systems is to explain the decisions of complex machine learning models, and recent years have seen a burgeoning of novel approaches. These approaches often rely on some structural components of the models under consideration, e.g., the set of features used for the classification task. As a result, explanations provided by these approaches are expressed in terms of the sub-symbolic information and, therefore, they are hard to interpret for users. In this paper, we argue that, in order to foster interpretability, these explanations should be expressed in terms of the knowledge that the users posses on the underlying application domain rather than on the sub-symbolic components of the model. To this end, our first contribution is the illustration of a novel formal framework for explaining the decisions of machine learning classifiers grounded on the Ontology-Based Data Management paradigm. Within this framework, explanations are defined by logical formulae using the symbols that an ontology defines and, as such, they posses a well-defined semantics. As a second contribution, we provide an algorithm that computes the best explanations that can be expressed in the class of conjunctive queries. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Towards Autonomous computer Networks in Support of Critical Systems 36

Towards Autonomous Computer Networks in Support of Critical ...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Sacco, Alessio Marchetto, Guido Politecnico di Torino Department of Control and Computer Engineering Italy

ISBN: (纸本)9781665477161

A recent trend dictating evolution of management and orchestration of computer networks is constituted by the softwarization and virtualization of them, which have drastically simplified the deployment and real-time reconfiguration of network functions, allowing them to continuously adapt and to deal with dynamic demands in an automated way. Alongside, recent management and orchestration approaches for softwarized networks employ Artificial Intelligence (AI) and Machine Learning (ML) to further reduce reaction time and improve the accuracy of decisions, where the network operations can be automated to the point of realizing autonomous driving networks. However, while automating operations can improve the overall system (it is acknowledged that 70% of network faults are caused by manual errors), AI/ML methods are not the panaceas, and we are still far from having a fully operating and efficient automated architecture. In this dissertation, we present a novel class of software network solutions that share the goal of enabling intelligent and autonomous computer networks, exploring how to exploit the power of AI/ML to handle the growing complexity of critical systems. We start with a new network management scheme for adaptive routing and autonomous scaling of virtual network resources. Then, acting on the hosts, we propose to adjust the TCP congestion control with a ML-based solution, whose goal is to select the proper congestion window learning from end-to-end features and (when available) network signals. We believe that the proposed solutions, and their combination, can lay the foundation for automated systems that better suit modern edge environments and cellular networks by providing unprecedented flexibility and adaptation to even unseen and unknown network conditions. © 2023 IEEE.

关键词： Artificial intelligence

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs 46

User Input Search - Custom Motion Estimation Algorithm Optim...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Benjak, J. Hofman, D. Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia

ISBN: (纸本)9789532331042

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data - the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested. © 2023 MIPRO Croatian Society.

关键词： Real time systems

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High-Capacity DWDM Transmission System for Free-Space Optical Network 6

High-Capacity DWDM Transmission System for Free-Space Optica...

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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Rahman, M.T. Jahid, M.A. Anha, S.Y. Rahman, Md. Shohanur Azmi Orpy, F. Bakibillah, A.S.M. Department of Computer Science and Engineering Bangladesh Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo152-8552 Japan

ISBN: (纸本)9798350380040

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems. © 2024 IEEE.

关键词： Cost effectiveness

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Vaccination, Booster Doses and Social Constraints: A Steady State and an Optimal Transient Approaches to Epidemics Containment

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SN computer Science 2024年第1期5卷 1-17页

作者： Di Giamberardino, Paolo Iacoviello, Daniela Department of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Rome 00185 Italy

The problem of the definition of control actions to contain epidemic diseases is crucial in case of high infectivity, dangerous or fatal consequences, large inhabited areas involved. Unfortunately, during the last three years, the COVID-19 pandemic has represented a critical situation all over the world. On the basis of the experiences for known diseases and the literature on the epidemic modeling, various strategies have been proposed and applied in different countries, someone using all the possible efforts, some others just maintaining the global health compact within acceptable levels. The effectiveness of the approaches has been always measured on the basis of the reproduction number R_t , which is intrinsically a steady-state evaluation, since it does not takes into account the control variations. In the present paper, with reference to a mathematical model which takes into account the different level of vaccination in the population, an optimal condition based approach is adopted to define the actions of intervention, bringing to a switching optimal control scheme based on the time by time evolution of the disease. The two approaches are developed and compared, showing that the use of partial information can bring to counter-intuitive situations and supporting the necessity of a feedback action to better adapt the containment measure to the situation. Numerical simulations are performed to better show the claimed results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Booster doses COVID-19 Epidemic modeling Optimal control Social behavior Vaccination

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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System 4

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Ovono, Gerald Zuva, Travos Otunniyi, Temidayo Faculty of Applied and Computer Sciences Department of Computer South Africa VaalUniversity of Technology Faculty of Engineering and Technology Department of Process Control and Computer Systems South Africa

ISBN: (纸本)9798350387988

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems. © 2024 IEEE.

关键词： Algorithms Evaluation Metrics Novelty Recommender Systems Scholarly Research Serendipity

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