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Reconfiguration of vertex-disjoint shortest paths on graphs

Journal of Graph Algorithms and Applications 2024年第3期28卷 87-101页

作者： Saito, Rin Eto, Hiroshi Ito, Takehiro Uehara, Ryuhei Graduate School of Information Sciences Tohoku University Sendai Japan School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

We introduce and study reconfiguration problems for (internally) vertex-disjoint shortest paths: Given two tuples of internally vertex-disjoint shortest paths for fixed terminal pairs in an unweighted graph, we are asked to determine whether one tuple can be transformed into the other by exchanging a single vertex of one shortest path in the tuple at a time, so that all intermediate results remain tuples of internally vertex-disjoint shortest paths. We also study the shortest variant of the problem, that is, we wish to minimize the number of vertex-exchange steps required for such a transformation, if exists. These problems generalize the well-studied Shortest Path Reconfiguration problem. In this paper, we analyze the complexity of these problems from the viewpoint of graph classes, and give some interesting contrast. © 2024, Brown University. All rights reserved.

关键词： Graph theory

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

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computers, Materials & Continua 2024年第9期80卷 3485-3506页

作者： Tajim Md.Niamat Ullah Akhund Waleed M.Al-Nuwaiser Department of Computer Science and Engineering(CSE) Daffodil International UniversityDhaka1216Bangladesh Graduate School of Science and Engineering Saga UniversitySaga8408502Japan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

关键词： Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test

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Metaphor Detection with Additional Auxiliary Context 16

Metaphor Detection with Additional Auxiliary Context

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Hayashi, Takuya Sasaki, Minoru Graduate School of Sci. and Eng. Ibaraki Univ. Major in Computer and Information Sciences Hitachi Japan College of Engineering Ibaraki Univ. Department of Computer and Information Sciences Hitachi Japan

ISBN: (纸本)9798350377903

'Metaphor detection' refers to the detection of figurative expressions and metaphors in sentences and text data. Metaphor expressions are expressions that cannot be interpreted literally and their meaning needs to be inferred. Traditionally, linguistic features such as word classes have been used as input for specific machine learning models to infer Metaphor content. In previous studies, detection has been performed on a single input sentence, which has the problem that the context before and after the sentence cannot be properly captured. Therefore, in order to perform effective Metaphor detection for sentences with little context information, this study proposes a method to generate and add sentences that complement the context using ChatGPT. Using this method, we compared the predictions made by the Metaphor detection model for both the dataset to which the complementary sentences were added and the original dataset. The results show that adding supplementary context to the dataset improves the accuracy of Metaphor detection. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Reinforcement Learning for Optimizing Multi-hop Distributed Collaborative Task Offloading in R2X

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9533-9548页

作者： Zhao, Wei Weng, Tangjie Cheng, Yu Liu, Zhi Kato, Nei Anhui University of Technology School of Computer Science and Technology Anhui Ma'anshan243032 China University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Tohoku University Graduate School of Information Sciences Sendai Japan

Roadside units (RSUs) with strong sensing abilities enhance the viability of the RSU-to-Everything (R2X) paradigm, offering crucial infrastructure support for Mobile Edge Computing (MEC) that enables real-time data processing and reduced delay. Since RSUs collect a large volume of data but have limited computing capability, data analysis tasks are usually offloaded to other network nodes, such as the cloud, other RSUs, or even vehicles. The multi-hop distributed collaborative task offloading scheme is expected to achieve high resource utilization efficiency and low task delay in this scenario, despite increasing energy consumption in data transmission. However, the highly dynamic nature of the R2X network topology makes it challenging for a node to independently select the next hop and collaboratively allocate tasks to neighbors in a multi-hop transmission path. Specifically, offloading decisions made by an individual node are influenced not only by its immediate neighbors but also by other nodes along the multi-hop path, referred to in this paper as the effect value. Additionally, the heterogeneity in computing resources and link delays among network nodes further increases the difficulty. To address these challenges, we first apply a Long Short-Term Memory (LSTM) model to predict and update the neighbors for each node while considering effect values, allowing them to independently adapt to environmental changes. Then, we design a two-layer Deep Reinforcement Learning (DRL) algorithm for network nodes to make decisions. The first-layer DRL algorithm is implemented by RSUs to determine task offloading modes. When an RSU decides to offload tasks to multiple vehicles for collaborative computing, the second-layer DRL algorithm is used by a vehicle to select its next hop vehicle and allocate tasks. Simulation results show that our proposed approach effectively adapts to topology changes in complex and highly dynamic network environments. Compared with existing methods,

关键词： Deep reinforcement learning

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in engineering & sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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Imitation Game for Adversarial Disillusion With Chain-of-Thought Reasoning in Generative AI

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IEEE Access 2025年 13卷 95085-95093页

作者： Chang, Ching-Chun Chen, Fan-Yun Gu, Shih-Hong Gao, Kai Wang, Hanrui Echizen, Isao National Institute of Informatics Information and Society Research Division Tokyo101-8430 Japan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan School of Multidisciplinary Sciences Kanagawa240-0193 Japan The University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviors. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalizes and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. Experimental results demonstrate that the proposed framework consistently neutralizes both deductive and inductive adversarial illusions across diverse white-box and black-box attack scenarios. © 2013 IEEE.

关键词： Intelligent systems

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J. F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Key Laboratory of Computing Power Network and Information Security Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Ministry of Education Shandong Computer Science Center Jinan250353 China

Intelligent supply line surveillance is critical for modern smart grids (SGs). Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the SG. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage (ESL), and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using elliptic curve cryptography (ECC) and physical unclonable functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the random or real (ROR) model. Furthermore, informal security analysis demonstrates the protocol’s robustness in rigorously resisting various attacks, including physical tampering, impersonation, ESL and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol’s superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Energy Efficient Routing for Fso-Rf Space-Air-Ground Integrated Network: A Deep Reinforcement Learning Approach 8

Energy Efficient Routing for Fso-Rf Space-Air-Ground Integra...

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8th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Guo, Qi Kato, Nei Tang, Fengxiao Graduate School of Information Sciences Tohoku University Japan School of Computer Science and Engineering Central South University China

ISBN: (纸本)9798350317923

Space-Air-Ground Integrated Network (SAGIN) is a promising architecture for next-generation wireless networks. It combines satellite networks, aerial networks, and terrestrial networks, offering ubiquitous global network services to ground users and enhancing connectivity for a wide range of wireless communications. Additionally, free-space optical (FSO) communication has gained more attention recently because of its energy efficiency and exceptional high-speed data delivery capabilities. However, the data transmission efficiency in SAGIN remains limited due to the dynamic and time- varying network topology and long-distance transmission link. Moreover, efficiently utilizing the limited power resources in satellites and aircraft to achieve effective data routing is still a crucial challenge. To address these issues, we construct an FSO/radio frequency (RF) space-air-ground integrated network that aims to enable large-scale data trans-mission as well as degrading the burden on terrestrial networks. Furthermore, a decentralized deep-Q network-based reinforcement learning with two hops node information is proposed to execute dynamic energy-efficient hybrid routing while ensuring system load balancing. The experimental results demonstrate that the proposed method reaches significant network performance improvement compared with baseline methods. © 2023 IEEE.

关键词： Reinforcement learning

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A Novel Intelligent Task Offloading Scheme for Multicontroller 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 Alliance University Department of Computer Science and Engineering Alliance College of Engineering and Design Anekal562106 India National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Yunlin Douliu64002 Taiwan Sunway University School of Engineering and Technology Subang Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Mohali140413 India VIT-AP University School of Computer Science and Engineering Amaravati522237 India National Institute of Technology Department of Computer Science and Engineering Shillong793003 India King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing (CC) platform meets these stringent computation requirements, but it has a significant latency that fog computing (FC) 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 prerequisites in an agile manner. Task offloading (TO) is a crucial issue in software defined-IoV (SD-IoV), 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 article introduces two proactive intelligent TO (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. © 2014 IEEE.

关键词： Fog computing

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