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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yu’Ang Zhang Yujie Song Yu Wang Yue Cao Xuefeng Ren Fei Yan Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China Huali iSmartWays Technology Inc Wuhan China

Vehicular Ad Hoc Networks (VANETs) rely heavily on trustworthy message exchanges between vehicles to enhance traffic efficiency and transport safety. Although cryptography-based methods are capable of alleviating threats from unauthenticated attackers, they can not prevent attacks from those legitimate network participants. This paper proposes a trust model to deal with attackers from the latter case, who can tamper with their received messages and deliberately decrease the trust value of benign vehicles. The trust evaluation process is formed by two stages: (i) the local trust evaluation at vehicles and (ii) trust aggregation on Road Side Units (RSUs). In the local trust evaluation stage, vehicles detect attacks and calculate the trust value for others in a distributed manner. Also, the social metrics of vehicles are calculated based on interaction records and trajectories. In the trust aggregation stage, each RSU collects local data from nearby vehicles and derives aggregation weights from the eigenvector centrality of the local trust network and social metrics. Then the RSU broadcasts the aggregated trust value towards vehicles in proximity. These vehicles can thus obtain a more accurate and comprehensive view. Vehicles with trust value below a preset threshold will be considered malicious. Extensive simulations based on the ONE simulator show that the proposed model (TECS) outperforms another benchmark model (IWOT-V) regarding the malicious vehicle detection and the delivery rate of authentic messages.

关键词： Measurement Privacy Road side unit Benchmark testing Trajectory Safety Time factors

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Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

arXiv

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings. © 2022, CC BY.

关键词： Reinforcement learning

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Bulk properties of the system in Au–Au collisions at 3 GeV and their dependence on collision centrality and particle rapidity

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Results in Physics 2024年 64卷

作者： Waqas, M. Hassan, Belqees Alnakhlani, Ali Ajaz, M. Altalbe, Ali Ghodhbani, Refka Ismail, A. Haj School of Mathematics Physics and Optoelectronic Engineering Hubei University of Automotive Technology Department of Physics College of Science Qassim University Department of Physics Faculty of Science Ibb university Department of Physics Abdul Wali Khan University Mardan Department of Computer Science. Prince Sattam Bin Abdulaziz University Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Department of Mathematics and Science Ajman University

We analyze the transverse momentum spectra of proton (p), deuteron (d), triton (t), and helium (3He and 4He) in different centrality intervals in various rapidity slices in gold–gold (Au–Au) collisions at sNN=3 GeV.... 详细信息

We analyze the transverse momentum spectra of proton (p), deuteron (d), triton (t), and helium (³He and ⁴He) in different centrality intervals in various rapidity slices in gold–gold (Au–Au) collisions at s_NN=3 GeV. We apply the blast-wave model with Tsallis statistics (TBW) to transverse momentum spectra of the particles, which fits well the data of STAR Collaboration. The freezeout parameters such as kinetic freezeout temperature, transverse flow velocity, and kinetic freezeout volume are extracted. Our findings reveal that central collisions exhibit higher values for the kinetic freezeout temperature, transverse flow velocity, and kinetic freezeout volume (which means that the central collisions are more hot and expansive), and observed that they decrease from central to peripheral collisions, and the former two parameters decrease from backward rapidity regions to mid-rapidity. This indicates a rapid expansion of the system in central collisions and in backward rapidities. The above parameters are mass-dependent, and the former increases, while the latter two decrease for heavier particles, reflecting the multiple kinetic freezeout and volume differential freezeout scenarios. Besides, the entropy parameter (q) is extracted, and it increases from peripheral to central and from backward to mid-rapidity regions. © 2024 The Author(s)

关键词： Energy transfer Kinetic freezeout temperature Kinetic freezeout volume Light nuclei Phase transition Transverse flow velocity Transverse momentum spectra

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VENUS: A Geometrical Representation for Quantum State Visualization

arXiv

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arXiv 2023年

作者： Ruan, Shaolun Yuan, Ribo Guan, Qiang Lin, Yanna Mao, Ying Jiang, Weiwen Wang, Zhepeng Xu, Wei Wang, Yong School of Computing and Information System Singapore Management University Singapore Department of Computer and Information Sciences University of Delaware United States Department of Computer Science Kent State University United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Computer and Information Science Department Fordham University United States Electrical and Computer Engineering George Mason University United States Computational Science Initiative Brookhaven National Laboratory United States

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Safeguarding Privacy for Cloud computing's Encrypted Data Using Association Rule Mining Algorithm and FP Growth Algorithm

Safeguarding Privacy for Cloud Computing's Encrypted Data Us...

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Delhi Section Conference (DELCON), IEEE

作者： D. MohanaPriya P. Anu N. Kalyani S. Rekha R. Praba S. Sasikala Department of Computer Science PSG College of Arts & Science Coimbatore Tamilnadu India School of Computing SASTRA Deemed to be University Thanjavur Tamilnadu India Department of Information Technology Dr. N. G. P. Arts and Science College Coimbatore Tamilnadu India Department of Data Analytics (PG) PSGR Krishnammal College for Women Coimbatore Tamilnadu India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

In an effort to support data privacy, safeguarding privacy guidelines for association mining techniques have recently been presented. Nevertheless, the algorithms are unable to conceal data frequency and have an extra expense when inserting fictitious objects or transactions. In this research, we present a connection constraint mining technique for cloud computing encrypted data that protects confidentiality. In order to accomplish the data mining operations using the two cloud servers, the strategy first uses propagated encryption, which impairs the cloud infrastructure’ encryption capabilities and shields the server from active attacks. Second, our approach uses combinations, coincidence masking, and cybernetic transactions in tandem to conceal the information while mining in order to safeguard the privacy of the association rule utilized in the extraction procedure. We use the FP growth algorithm for rule-mining for associations avoiding the need for extra fraudulent transactions. Therefore, the suggested technique can hide data frequency while ensuring query and data privacy. We show that, with regard to the time requirements of the Mining Association, the recommended strategy operates roughly three to five times better than the current algorithm.

关键词： Data privacy Cloud computing Privacy Encryption Association rule learning Servers Cybernetics Guidelines

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Multi-View Mutual Learning Network for Multimodal Fake News Detection

SSRN

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SSRN 2023年

作者： Cui, Wei Zhang, Xuerui Shang, Mingsheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China School of Electronic Information and Communication Engineering Chongqing Aerospace Polytechnic Chongqing400021 China

Multimodal fake news is more deceptive than unimodal content and often has adverse social and economic impacts. However, most existing methods learn modal features from a single perspective, without considering simultaneously learning and sharing knowledge across modalities from different perspectives. Our work presents a novel Multi-View Mutual Learning (MVML) network for multimodal fake news detection, which explores the semantic relationship between words and scenes, as well as words and objects, and investigates the semantic connection that exists between global concepts and local objects from multiple perspectives. We first construct text-scenes and text-objects graphs respectively, and perform intra-graph inference to obtain multi-view features. Then, the multi-view fusion layer based on the cross-view attention mechanism interactively models the cross-view dependencies between the image and text. The extracted features before feeding into the final classifier are further processed via a feed-forward attention module, which adaptively reweights and aggregates the features for redundancy reduction. In addition, to exploit the respective advantages of two multi-view classifiers, we propose a mutual learning mechanism that allows them to perform knowledge distillation and align the learning targets. The proposed MVML is thoroughly assessed on four publicly available benchmark datasets, and the findings demonstrate that it outperforms the existing standard approaches. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Federated Linear Bandit Learning via Over-the-air Computation

Federated Linear Bandit Learning via Over-the-air Computatio...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiali Wang Yuning Jiang Xin Liu Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China Automatic Control Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.

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Online and semi-online scheduling on two hierarchical machines with a common due date to maximize the total early work

arXiv

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arXiv 2022年

作者： Xiao, Man Liu, Xiaoqiao Li, Weidong Chen, Xin Sterna, Malgorzata Blazewicz, Jacek Yunnan University School of Mathematics and Statistics Kunming China Liaoning University of Technology School of Electronics and Information Engineering Jinzhou China Poznan University of Technology Institute of Computing Science Poznan Poland European Centre for Bioinformatics and Genomics Polish Academy of Sciences Poznan Poland

In this study, we investigated several online and semi-online scheduling problems on two hierarchical machines with a common due date to maximize the total early work. For the pure online case, we designed an optimal online algorithm with a competitive ratio of √2. For the case when the total processing time is known, we proposed an optimal semi-online algorithm with a competitive ratio of 4/3. Additionally, for the cases when the largest processing time is known, we gave optimal algorithms with a competitive ratio of 6/5 if the largest job is a lower hierarchy one, and of √5−1 if the largest job is a higher hierarchy one, respectively. © 2022, CC BY.

关键词： Scheduling

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In Defense of Clip-based Video Relation Detection

arXiv

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arXiv 2023年

作者： Wei, Meng Chen, Long Ji, Wei Yue, Xiaoyu Zimmermann, Roger The Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam999077 Hong Kong The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Kowloon999077 Hong Kong The School of Computing National University of Singapore 117417 Singapore The School of Electrical and Information Engineering The University of Sydney CamperdownNSW2006 Australia

Video Visual Relation Detection (VidVRD) aims to detect visual relationship triplets in videos using spatial bounding boxes and temporal boundaries. Existing VidVRD methods can be broadly categorized into bottom-up and top-down paradigms, depending on their approach to classifying relations. Bottom-up methods follow a clip-based approach where they classify relations of short clip tubelet pairs1 and then merge them into long video relations. On the other hand, top-down methods directly classify long video tubelet pairs. While recent video-based methods utilizing video tubelets have shown promising results, we argue that the effective modeling of spatial and temporal context plays a more significant role than the choice between clip tubelets and video tubelets. This motivates us to revisit the clip-based paradigm and explore the key success factors in VidVRD. In this paper, we propose a Hierarchical Context Model (HCM) that enriches the object-based spatial context and relation-based temporal context based on clips. We demonstrate that using clip tubelets can achieve superior performance compared to most video-based methods. Additionally, using clip tubelets offers more flexibility in model designs and helps alleviate the limitations associated with video tubelets, such as the challenging long-term object tracking problem and the loss of temporal information in long-term tubelet feature compression. Extensive experiments conducted on two challenging VidVRD benchmarks validate that our HCM achieves a new state-of-the-art performance, highlighting the effectiveness of incorporating advanced spatial and temporal context modeling within the clip-based paradigm. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Mengers Authentication for efficient security system using Blockchain technology for Industrial IoT(IIOT) systems

Mengers Authentication for efficient security system using B...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Daxa Vekariya Ajay Rastogi R. Priyadarshini Manoj Patil M. Sunil Kumar Bhasker Pant Department of CSE Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India College of Computing Science and Information Technology Teerthanker Mahaveer University Moradabad Uttar Pradesh India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Research and Development Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Wardha India Department of CSE School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

The Internet of Things (IoT), which allows computers from different settings to connect and cooperate on a same activity while ensuring better levels of privacy and safety for some of the connected equipment, must manage the whole process as manufacturing procedures grow considerably more complex. Some well-liked things that solve important issues and provide third-party security. Since the problem is still open, privacy and security must come first. According to this research, Mengers identification, a unique and potent security technique made possible by blockchain, would foster confidence in a variety of industries. In this instance, access control carries out security. To allow authentication across linked devices while protecting device privacy, the Mengers authenticating mechanism is defined. Before delivering keys to linked parties, an appropriate path must first be established. The findings show improved performance during the verification process when the simulation is run in the simulation environment.

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