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41st International Conference on Machine Learning, ICML 2024

作者： Ji, Tianying Luo, Yu Sun, Fuchun Zhan, Xianyuan Zhang, Jianwei Xu, Huazhe Department of Computer Science and Technology Tsinghua University China Institute for AI Industry Research Tsinghua University China Shanghai Artificial Intelligence Laboratory China Department of Informatics University of Hamburg Germany Institute for Interdisciplinary Information Sciences Tsinghua University China Shanghai Qi Zhi Institute China

Learning high-quality Q-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms. Previous works primarily focus on addressing the value overestimation issue, an outcome of adopting function approximators and off-policy learning. Deviating from the common viewpoint, we observe that Q-values are often underestimated in the latter stage of the RL training process, potentially hindering policy learning and reducing sample efficiency. We find that such a long-neglected phenomenon is often related to the use of inferior actions from the current policy in Bellman updates as compared to the more optimal action samples in the replay buffer. To address this issue, our insight is to incorporate sufficient exploitation of past successes while maintaining exploration optimism. We propose the Blended Exploitation and Exploration (BEE) operator, a simple yet effective approach that updates Qvalue using both historical best-performing actions and the current policy. Based on BEE, the resulting practical algorithm BAC outperforms state-of-the-art methods in over 50 continuous control tasks and achieves strong performance in failure-prone scenarios and real-world robot tasks. Benchmark results and videos are available at https://***/BEE/. Copyright 2024 by the author(s)

关键词： Deep reinforcement learning

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer Science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Integrated Sensing and Communications With Reconfigurable Intelligent Surfaces: From signal modeling to processing

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IEEE Signal Processing Magazine 2023年第6期40卷 41-62页

作者： Chepuri, Sundeep Prabhakar Shlezinger, Nir Liu, Fan Alexandropoulos, George C. Buzzi, Stefano Eldar, Yonina C. Indian Institute of Science Department of Electrical and Computer Engineering Bengaluru560012 India Ben-Gurion University of the Negev School of Electrical and Computer Engineering Be'er Sheva84105 Israel Southern University of Science and Technology Shenzhen518055 China School of Sciences National and Kapodistrian University of Athens Department of Informatics and Telecommunications Athens15784 Greece University of Cassino and Lazio Meridionale Cassino03043 Italy Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel

Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mm-wave) and terahertz (THz) frequency bands. However, establishing wireless connections at these high frequencies is quite challenging, mainly due to the penetrating path loss that prevents reliable communication and sensing. Another emerging technology for next-generation wireless systems is reconfigurable intelligent surface (RIS), which refers to hardware-efficient planar structures capable of modifying harsh propagation environments. In this article, we provide a tutorial-style overview of the applications and benefits of RISs for sensing functionalities in general, and for ISAC systems in particular. We highlight the potential advantages when fusing these two emerging technologies, and identify for the first time that 1) joint sensing and communications (S&C) designs are most beneficial when the channels referring to these operations are coupled, and that 2) RISs offer the means for controlling this beneficial coupling. The usefulness of RIS-aided ISAC goes beyond the obvious individual gains of each of these technologies in both performance and power efficiency. We also discuss the main signal processing challenges and future research directions that arise from the fusion of these two emerging technologies. © 1991-2012 IEEE.

关键词： Signal processing

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Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL 41

Offline-Boosted Actor-Critic: Adaptively Blending Optimal Hi...

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41st International Conference on Machine Learning, ICML 2024

作者： Luo, Yu Ji, Tianying Sun, Fuchun Zhang, Jianwei Xu, Huazhe Zhan, Xianyuan Department of Computer Science and Technology Tsinghua University China Department of Informatics University of Hamburg Germany Institute for Interdisciplinary Information Sciences Tsinghua University China Shanghai Qi Zhi Institute China Shanghai Artificial Intelligence Laboratory China Institute for AI Industry Research Tsinghua University China

Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously collected data for policy learning. However, most existing off-policy RL algorithms fail to maximally exploit the information in the replay buffer, limiting sample efficiency and policy performance. In this work, we discover that concurrently training an offline RL policy based on the shared online replay buffer can sometimes outperform the original online learning policy, though the occurrence of such performance gains remains uncertain. This motivates a new possibility of harnessing the emergent outperforming offline optimal policy to improve online policy learning. Based on this insight, we present Offline-Boosted Actor-Critic (OBAC), a model-free online RL framework that elegantly identifies the outperforming offline policy through value comparison, and uses it as an adaptive constraint to guarantee stronger policy learning performance. Our experiments demonstrate that OBAC outperforms other popular model-free RL baselines and rivals advanced model-based RL methods in terms of sample efficiency and asymptotic performance across 53 tasks spanning 6 task suites1 Copyright 2024 by the author(s)

关键词： Contrastive Learning

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An Exploratory Case Study on Effort Estimation in Microservices 49

An Exploratory Case Study on Effort Estimation in Microservi...

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49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

作者： Unlu, Huseyin Hacaloglu, Tuna Omural, Neslihan Kucukates Caliskanel, Neslihan Leblebici, Onur Demirors, Onur Izmir Institute of Technology Computer Engineering Izmir Turkey Atilim University Information Systems Engineering Ankara Turkey Middle East Technical University Informatics Institute Ankara Turkey Univera Inc. Izmir Turkey

ISBN: (纸本)9798350342352

Software project management plays an important role in producing high-quality software, and effort estimation can be considered as a backbone for successful project management. Size is a very significant attribute of software by being the only input to perform early effort estimation. Even though functional size measurement methods showed successful results in effort estimation of traditional data-centric architectures such as monoliths, they were not designed for today's architectures which are more service-based and decentralized such as microservices. In these new systems, the event concept is highly used specifically for communication among different services. By being motivated by this fact, in this study, we looked for more microservice-compatible ways of sizing microservices using events and developed a method accordingly. Then, we conducted an exploratory case study in an organization using agile methods and measured the size of 17 Product Backlog Items (PBIs) to assess how this proposed method can be useful in effort estimation in microservices. The implication from the case study is that despite performing a more accurate effort estimation using the proposed size measurement than COSMIC, we were unable to significantly outperform using the total number of events. However, our suggested approach demonstrated to us a different way to use software size in terms of events, namely, to determine the coupling complexity of the project. This finding can be beneficial specifically when evaluating the change requests. © 2023 IEEE.

关键词： Project management

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication Engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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OMPO: A Unified Framework for RL under Policy and Dynamics Shifts 41

OMPO: A Unified Framework for RL under Policy and Dynamics S...

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41st International Conference on Machine Learning, ICML 2024

Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge. Existing works often overlook the distribution discrepancies induced by policy or dynamics shifts, or rely on specialized algorithms with task priors, thus often resulting in suboptimal policy performances and high learning variances. In this paper, we identify a unified strategy for online RL policy learning under diverse settings of policy and dynamics shifts: transition occupancy matching. In light of this, we introduce a surrogate policy learning objective by considering the transition occupancy discrepancies and then cast it into a tractable min-max optimization problem through dual reformulation. Our method, dubbed Occupancy-Matching Policy Optimization (OMPO), features a specialized actor-critic structure equipped with a distribution discriminator and a small-size local buffer. We conduct extensive experiments based on the OpenAI Gym, Meta-World, and Panda Robots environments, encompassing policy shifts under stationary and non-stationary dynamics, as well as domain adaption. The results demonstrate that OMPO outperforms the specialized baselines from different categories in all settings. We also find that OMPO exhibits particularly strong performance when combined with domain randomization, highlighting its potential in RL-based robotics applications1 Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward

Foundation Models in Healthcare: Opportunities, Risks & Stra...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Thieme, Anja Nori, Aditya Ghassemi, Marzyeh Bommasani, Rishi Andersen, Tariq Osman Luger, Ewa Microsoft Health Futures United Kingdom Massachusetts Institute of Technology United States Stanford University United States Department of Computer Science University of Copenhagen Denmark Institute of Design Informatics The University of Edinburgh United Kingdom

ISBN: (纸本)9781450394222

Foundation models (FMs) are a new paradigm in AI. First pretrained on broad data at immense scale and subsequently adapted to more specific tasks, they achieve high performances and unlock powerful new capabilities to be leveraged in many domains, including healthcare. This SIG will bring together researchers and practitioners within the CHI community interested in such emerging technology and healthcare. Drawing attention to the rapid evolution of these models and proposals for their wide-spread adoption, we aim to demonstrate their strengths whilst simultaneously highlighting deficiencies and limitations that give raise to ethical and societal concerns. In particular, we will invite the community to actively debate how the field of HCI - with its research frameworks and methods - can help address some of these existing challenges and mitigate risks to ensure the safe and ethical use of the end-product;a requirement to realize many of the ambitious visions for how these models can positively transform healthcare delivery. This conversation will benefit from a diversity of voices, critical perspectives, and open debate, which are necessary to bring about the right norms and best practices, and to identify a path forward in devising responsible approaches to future FM design and use in healthcare. © 2023 Owner/Author.

关键词： Ethical technology

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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