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2024 International Conference on Telecommunications and Intelligent systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Enveloping, frictionless, planar grasping

Enveloping, frictionless, planar grasping

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IEEE International Conference on Robotics and Automation (ICRA)

作者： J. Trinkle J. Abel R. Paul GRASP Laboratory Department of Systems Engineering University of Pennsylvania Philadelphia PA Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia PA GRASP Laboratory Department of Computer and Information Science University of Pennsylvania Philadelphia PA

Grasping by a two-dimensional hand comprised of a palm and two hinged fingers is studied. The mathematics of frictionless grasping is presented and used in the development of a planner/simulator, The simulator computes the motion of the object using an active constraint set method and assuming exact knowledge of the physical properties of the polygonal object, hand, and support.

关键词： Fingers Grasping Friction Laboratories Computational modeling Grippers Jacobian matrices Mechanical engineering information science systems engineering and theory

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Filling fuzzy ontologies with people knowledge using fuzzy ontologies and group decision making methods

Filling fuzzy ontologies with people knowledge using fuzzy o...

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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Juan Antonio Morente-Molinera Ignacio Javier Pérez Francisco Javier Cabrerizo Sergio Alonso Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Information Science University of Cádiz Cádiz Spain Department of Software Engineering and Computer Systems National Distance Education University (UNED) Madrid Spain Department of Languages and Computer Systems University of Granada Granada Spain

Internet has experienced a deep change. Now, people can communicate among themselves and share information and experiences thanks to Web 2.0 technologies. One way of storing all these information in a sorted way implies the use of Fuzzy Ontologies. In this paper, a novel and automatic method that is capable of retrieving and storing information coming from Internet users has been designed. Thanks to our method, it is possible to create knowledge databases that store knowledge coming from the consensus of a set of users. Thanks to group decision making methods, it is possible for all these users to debate and reach an agreement. Fuzzy Ontologies provide us with a trustful environment that we can use to store all the retrieved information. They also allow other users to consult and get advantage of the stored knowledge. Multi-granular fuzzy linguistic modelling methods are used in order to create an user-friendly interface for the users to debate and perform queries.

关键词： Ontologies Decision making Pragmatics Internet Electronic mail Data mining computer science

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NOMA-Based Statistical QoS Provisioning for Wireless Ad-Hoc Networks With Finite Blocklength

NOMA-Based Statistical QoS Provisioning for Wireless Ad-Hoc ...

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IEEE Global Communications Conference

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Non-orthogonal multiple access (NOMA) has been designed to significantly enhance spectral efficiency for massive connections of mobile devices. Moreover, to guarantee the short latency requirements for 5G multimedia wireless services, researchers have designed statistical delay-bounded quality-of-service (QoS) provisioning for supporting video transmissions over statistically varying wireless channels. Accordingly, researchers have proposed finite blocklength coding (FBC) techniques to model the relationship between data transmission rate and channel capacity in the non-asymptotic regime while supporting short-packet communications under QoS constraints. Due to its potential to significantly improve spectral efficiency and reduce the transmission latency, a NOMA system can be exploited while being integrated with FBC to guarantee the QoS requirements for both latency and reliability over mobile wireless ad-hoc networks. However, because of the complexity of the effective-capacity maximization problem in the non-asymptotic regime, the FBC based NOMA scheme imposes new challenges for determining the convexity of the optimization problem and deriving optimal resource allocation policies for NOMA subject to statistical delay-bounded and error-rate bounded QoS constraints. In order to solve the above-mentioned problems, we define a new concept of ε-effective capacity and propose a corresponding system architecture model for NOMA systems under FBC. In particular, we characterize the FBC based NOMA system models in wireless ad hoc networks. Considering the statistical delay-bounded and error-rate bounded QoS constraints, we formulate and solve the max-min fairness problem under FBC. Simulation results are included, which evaluate and validate our proposed FBC based NOMA scheme subject to statistical delay-bounded and error-rate bounded QoS constraints.

关键词： NOMA Quality of service Ad hoc networks Error probability Wireless communication Delays Interference

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Neyman-Pearson Criterion Based Optimal Hierarchical Caching Over D2D Wireless Ad-Hoc Networks

Neyman-Pearson Criterion Based Optimal Hierarchical Caching ...

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IEEE Global Communications Conference

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Caching in mobile devices is a new paradigm to revolutionize the traditional data content (i.e., files) sharing methods in wireless cellular networks. By caching popular data content items in mobile devices and disseminating them through device-to-device (D2D) communications, the wireless cellular network can improve its area spectral efficiency, save the base station bandwidth consumption, and reduce the transmission delay of downloading. Caching is closely related to data popularity prediction, but how to accurately predict the future popularity profile has not been well understood. Moreover, although the hierarchical caching architecture has been shown to yield more benefits than flat caching strategies, the challenge of designing an optimal hierarchical caching scheme has not been thoroughly addressed. In this paper, we use a Neyman-Pearson hypothesis testing mechanism to predict the future data popularity, and also develop optimal hierarchical caching schemes over D2D wireless ad-hoc networks. The key of Neyman-Pearson hypothesis testing in our proposed scheme is to derive a closed form expression for the decision threshold, which is a function of costs of choosing the incorrect hypothesis. We formulate these costs as sums of time durations for content placement and content delivery phases. We derive closed form expressions for these two phases respectively and obtain a closed form of an optimal decision rule, which maximizes the cache hitting probability and upper-bounds the prediction error probability. Finally, we evaluate and validate our proposed Neyman-Pearson hypothesis testing based hierarchical caching schemes through numerical analyses.

关键词： Testing Ad hoc networks Mobile handsets Device-to-device communication Wireless communication Delays Error probability

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AI for social science and social science of AI: A Survey

arXiv

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arXiv 2024年

作者： Xu, Ruoxi Sun, Yingfei Ren, Mengjie Guo, Shiguang Pan, Ruotong Lin, Hongyu Sun, Le Han, Xianpei School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent. © 2024, CC BY-NC-ND.

关键词： Behavioral research

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

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Procedia computer Science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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MMHSV: A Multimodal Handwritten Signature Verification Fusing Dynamic and Static Feature

MMHSV: A Multimodal Handwritten Signature Verification Fusin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qixiang Li Zhaoya Wang Lianwen Jin Nurbiya Yadikar Kurban Ubul School of Software Xinjiang University China School of Electronic and Information Engineering South China University of Technology China School of Computer Science and Technology Xinjiang University China Key Laboratory of Xinjiang Multilingual Information Technology China

In recent years, significant progress has been made in the field of handwritten signature verification through methods based on deep learning. However, due to the high intra-class variability and high inter-class similarity of signature samples, achieving high accuracy and security in handwritten signature verification systems remains challenging. In this paper, we propose a deep learning-based multimodal handwritten signature verification framework, MMHSV, to fuse dynamic and static features by utilizing the complementary signal components of signature images and pen stroke sounds, and explore the feasibility of multimodal signature verification. MMHSV consists of an innovative joint-embedded feature representation method based on multi-task learning and a dual-path network model for signature representation extraction. To support our method, we have curated a multimodal signature dataset, serving as a benchmark for the proposed technique. Preliminary findings suggest that our approach offers a pioneering solution in the realm of handwritten signature verification and security metrics that surpass the benchmarks set by the unimodal state-of-the-art methods.

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Optimal QoS-Driven Power Allocation for Energy Harvesting Wireless Ad-Hoc Networks Using FBC

Optimal QoS-Driven Power Allocation for Energy Harvesting Wi...

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IEEE Global Communications Conference

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In the last few decades, the statistical delay-bounded quality-of-service (QoS) technique has been proposed and investigated to support delay-bounded 5G multimedia wireless services In addition, energy harvesting (EH) based systems, which enable mobile devices to harvest energy from various external sources, have been designed to address the energy scarcity problem. Researchers have recently integrated EH based systems with short-packet communications for their potential benefits of small payloads and low latency to support reliable multimedia data transmissions. Under the short delay requirements for the low-latency 5G multimedia wireless services, it is not ideal to apply the traditional Shannon capacity for modeling the data rate under the error-rate bounded in the non-asymptotic regime. Accordingly, researchers have developed the finite wireless data transmission rate in the non-asymptotic regime. However, it is challenging to determine the convexity of the effective-capacity maximization problem subject to the statistical delay-bounded and error-rate bounded QoS constraints due to the complexity of analyzing the maximization problem in the non-asymptotic regime. To overcome these challenges, we design the EH based cross-layer optimization scheme in supporting the statistical delay-bounded and error-rate bounded QoS requirements in the finite blocklength regime in the following steps. First, we characterize the EH based system models under finite blocklength coding (FBC). Second, we derive and analyze the convexity of the e-effective capacity maximization problem for our proposed EH scheme in the finite blocklength regime. Finally, we conduct several simulation evaluations which validate our proposed EH scheme under the statistical delay-bounded and error-rate bounded QoS constraints in the finite blocklength regime.

关键词： Quality of service Ad hoc networks Wireless communication Data communication Error probability Energy harvesting 5G mobile communication

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