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NATURE COMPUTATIONAL science 2024年第11期4卷 877-877页

作者： Feng, Xiaochen Sha, Hao Zhang, Yongbing Su, Yaoquan Liu, Shuai Jiang, Yuan Hou, Shangguo Han, Sanyang Ji, Xiangyang School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Shenzhen International Graduate School Tsinghua University Shenzhen China Institute of Systems and Physical Biology Shenzhen Bay Laboratory Shenzhen China Department of Automation Tsinghua University Beijing China

Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis.

关键词： Silver nanoparticles

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Multimodal attention-driven visual question answering for Malayalam

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Neural Computing and Applications 2024年第24期36卷 14691-14708页

作者： Kovath, Abhishek Gopinath Nayyar, Anand Sikha, O.K. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Visual question answering is a challenging task that necessitates for sophisticated reasoning over the visual elements to provide an accurate answer to a question. Majority of the state-of-the-art VQA models are only applicable to English questions. However, applications such as visual assistance and tourism necessitate the incorporation of multilingual VQA systems. This paper presents an effective deep learning framework for Malayalam visual question answering (MVQA), which can answer a specific natural language question about an image in Malayalam. As there is no available dataset in English–Malayalam VQA, a MVQA dataset was created by translating English question–answer pairs from the visual genome dataset. The paper proposes an attention-driven MVQA model on the developed dataset. The proposed MVQA model uses a deep learning-based co-attention mechanism to jointly learn the attention for images and Malayalam questions. A second-order multimodal factorized high-order pooling is used for multi modal feature fusion. Different VQA models using combinations of classical CNNs and RNNs were experimented on the developed MVQA dataset, and the performance was compared against the proposed attention-driven model. Experimental results show that the proposed attention-driven MVQA model achieves state-of-the-art results as compared to other models for MVQA on the custom Malayalam VQA dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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A Novel Intelligent Task Offloading Scheme for Multi-Controller Environment in Software Defined Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 20018-20029页

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J.F. Javvaji, Pavan Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Anekal562106 India International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Meghalaya 793003 India College of Computer and Information Sciences Department of Computer Engineering King Saud University Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing platform meets these stringent computation requirements, but it has a significant latency that fog computing effectively handles. Software Defined Network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network pre-requisites in an agile manner. Task Offloading (TO) is a crucial issue in Software Defined-IoV (SDIoV), particularly when the vehicle's resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this paper introduces two proactive Intelligent Task Offloading (ITO' and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO' and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7-13.9)% more than the existing scheme. They also reduce average packet loss, delay, Round Trip Time (RTT), and offloaded node time selection by (45.35-21.3)%, (50-33.3)%, (53.9-9.9)% and (43.2-26.06)% which demonstrates the efficacy of the proposed scheme. © 2025 IEEE.

关键词： Fog computing

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QoE Comparison between Multi-User OFDMA and Reliable Groupcast for Wireless LAN Audiovisual Transmission

QoE Comparison between Multi-User OFDMA and Reliable Groupca...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Deguchi, Daiki Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350364866

This paper compares video and audio QoE of OFDMA multi-user transmission and reliable groupcast over wireless LAN. We assume video and audio transmission to several terminals simultaneously. As a reliable groupcast method, we employ Unsolicited Retry, a technique of IEEE 802.11aa GroupCast with Retries. We utilize IEEE 802.11ax for OFDMA transmission. We evaluate application-level QoS by computer simulation. We then assess QoE through a subjective experiment with video and audio streams generated by the output timing obtained from the simulation. We notice that each method has a situation to be applied appropriately. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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MPEG-DASH Multi-View Video and Audio Transmission QoE Over HTTP/2 Stream

MPEG-DASH Multi-View Video and Audio Transmission QoE Over H...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Wakayama, Yamato Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350353983

In this paper, we consider multi-view video and audio streaming using MPEG-DASH, which enables to transmit video tailored to the network conditions over HTTP communication. This paper uses HTTP/2 instead of HTTP/1.1, which the authors previously used. HTTP/2 manages a series of request-response exchanges called a stream, which is assigned a unique stream ID. We perform a subjective experiment under various network conditions to evaluate application-level QoS and QoE. From the evaluation results, we investigate the effect of the HTTP/2 stream on the video and audio quality of the users in the multi-view video transmission scenario. © 2024 IEEE.

关键词： Audio streaming

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Improving the Learning Efficiency of Hierarchical Reinforcement Learning SUHRL Using Transition Information Between Subgoals 13

Improving the Learning Efficiency of Hierarchical Reinforcem...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kurodo, Yuya Manabe, Yusuke Graduate School of Information and Computer Science Chiba Institute of Technology Japan Chiba Institute of Technology Department of Cognitive and Information Sciences Japan

ISBN: (纸本)9798350373332

The hierarchical reinforcement learning approach incorporates intrinsic motivation into reinforcement learning. In this approach, the agents are hierarchically organized into a higher-level strategy that learns the application order (plan) of sub goals and a lower-level strategy that learns the sequence of actions up to the subgoals. This incorporation has been shown to facilitate problem-solving even in environments with sparse rewards. Stepwise Unified Hierarchical Reinforcement Learning (SUHRL) has been proposed as a hierarchical reinforcement learning algorithm. SUHRL enables autonomous learning without the need for prior knowledge or predefined subgoals. However, learning efficiency is low, and, in some cases, problem solving is not possible. Therefore, we propose the enhancement of SUHRL by constraining the exploration of sub goals based on the number of steps required to transition between them. This improvement is based on Hierarchical Reinforcement Learning with a k-step Adjacency Constraint, a method that can achieve efficient learning when sub goals are predefined. Experimental results using the Frozen Lake environment show that the proposed method exhibits a better learning efficiency than SUHRL. © 2024 IEEE.

关键词： Adversarial machine learning

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Enhancing Energy Efficiency in IoT Networks Through Fuzzy Clustering and Optimization

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Mobile Networks and Applications 2024年第5期29卷 1594-1617页

作者： Javadpour, Amir Sangaiah, Arun Kumar Zaviyeh, Hadi Ja’fari, Forough Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal International Graduate School Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Economics and Statistics Guangzhou University Guangzhou510006 China Department of Computer Engineering Sharif University of Technology Tehran Iran

Wireless Sensor and Internet of Things (WSIoT) networks are characterized by nodes scattered throughout the environment, posing a significant challenge when it comes to battery replacement. The task of developing an algorithm that effectively reduces energy consumption in IoT networks through the utilization of artificial intelligence and fuzzy logic is a formidable one. Heuristic algorithms emerge as valuable tools in this context, capable of swiftly addressing non-deterministic polynomial problems or approximating optimal solutions with remarkable accuracy. Conversely, mathematical optimization approaches often falter due to their sluggishness or inefficiency, rendering them less suited for the dynamic demands of IoT networks. This study embarks on a novel approach, leveraging the synergy of fuzzy clustering to intricately connect sensors to the network and particle optimization to approximate the initial values of head clusters within the realm of WSIoT. A comprehensive evaluation of our proposed strategy is meticulously conducted through extensive simulations utilizing the NS2 simulator. In this evaluation, we distinguish our algorithm from existing methods, including IoTbWSN and REcsIoT. The results of this comparative analysis are illuminating. In contrast to other methodologies, our proposed approach demonstrates a notable 9.57% improvement in network throughput and a substantial 8.47% reduction in energy consumption. This signifies a remarkable leap forward in the quest to address the challenges posed by energy consumption and battery replacement within the complex landscape of IoT networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Energy utilization

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Development of a Visual Analysis Tool for Seabird Behavior 1

Development of a Visual Analysis Tool for Seabird Behavior

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Onuki, Tomoya Misue, Kazuo University of Tsukuba Graduate School of Science and Technology Ibaraki Japan University of Tsukuba Department of Computer Science Ibaraki Japan

ISBN: (纸本)9798350335569

Biologging data are used to analyze animal behavior. Visualization of such data facilitates analysis;however, the data are often large, and designing visual representations and interactions is challenging. Another problem is that few tools can comprehensively support analysis. The authors aim to develop an integrated analysis tool to support seabird researchers in data analysis. To support the analysis of large datasets, they developed an automatic behavior classification function and visual representation of seabird behavior in a spatiotemporal manner. The usefulness of the tool was confirmed through interviews with seabird researchers. © 2023 IEEE.

关键词： Data visualization

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