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International Conference on Image and Vision computing New Zealand, IVCNZ

作者： Elham Basunduwah Muhammad Mohzary Sejun Song Baek-Young Choi School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate prediction of crowd mobility dynamics is essential for effective crowd safety management. Traditional models, such as the Random Waypoint Model (RWM), have been widely employed for mobility simulation. However, they suffer inherent limitations, including non-uniform node distribution and speed decay, which reduce their predictive accuracy. Although vision-based crowd mobility tracking methods show promise, movement prediction still faces significant challenges in achieving accuracy, scalability, generalization, and computational efficiency. This paper introduces a novel AI-based multi-visual components analysis approach for crowd dynamics prediction called AIM-CP. This approach combines the YOLOv8 object detection system with a rule-based expert system to enhance prediction accuracy. AIM-CP integrates multi-modal data from face, body, and pose movements across 12 distinct classes, improving real-time processing capabilities and leveraging more scalable and robust AI models. To support this approach, we developed high-resolution datasets, including mall datasets, the CrowdHuman dataset, selected video frames from YouTube, and images from Unsplash. Experimental results demonstrate that AIM-CP is highly effective in detecting mobility dynamics, significantly improving single-modal prediction models with an accuracy of 90.12% in real-world mobility scenarios. These findings suggest that AIM -CP offers a powerful tool for improving crowd safety and mobility management in dynamic environments.

关键词： Accuracy Video on demand Computational modeling Object detection Predictive models Real-time systems Web sites Expert systems Artificial intelligence Faces

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Fast exact synthesis of two-qubit unitaries using a near-minimum number of T gates

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Physical Review A 2023年第4期107卷 042424-042424页

作者： Longcheng Li Cheng Guo Qian Li Xiaoming Sun Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences 100049 Beijing China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences 100049 Beijing China

This paper focuses on exact synthesis of two-qubit unitaries using Clifford and T gates. We propose an ancilla-free synthesis algorithm (i) which uses T gates no more than ten times the minimum possible number of T gates, also known as the T count, and (ii) whose time complexity is linear with the T count and thus instance optimal. Our synthesis algorithm relies on a characterization of the T count of two-qubit unitaries based on Lie group homomorphism, which may be interest of its own. Precisely, we show that for any two-qubit unitary generated by Clifford and T gates, its T count is equivalent to the least denominator exponent of its SO(6) representation up to a factor of at most 10.

关键词： Quantum computation Quantum error correction

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Shared Incentive System for Clinical Pathway Experience

Shared Incentive System for Clinical Pathway Experience

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

作者： Weiqi Dai Wenhao Zhao Xia Xie Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.

关键词： Privacy Hospitals Collaboration Communication channels Data models Security Incentive schemes

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Rough Fermatean fuzzy decision-based approach for modelling IDS classifiers in the federated learning of IoMT applications

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Neural computing and Applications 2023年第30期35卷 22531-22549页

作者： Albahri, O.S. Al-Samarraay, Mohammed S. AlSattar, H.A. Alamoodi, A.H. Zaidan, A.A. Albahri, A.S. Zaidan, B.B. Jasim, Ali Najm Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya12733 Iraq Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Perak Tanjong Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia Iraqi Commission for Computers and Informatics Baghdad10022 Iraq Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Foundation of Alshuhda Baghdad Iraq MEU Research Unit Middle East University Amman Jordan

Intrusion detection systems (IDSs) are commonly employed to mitigate network security threats in various fields, including federated learning applications within the Internet of Medical Things (IoMT). However, IDSs face challenges owing to the sheer volume of network traffic, high-dimensional datasets and the necessity for real-time detection. Although machine learning integration assists IDSs in overcoming these challenges, modelling difficulties persist due to varied evaluation criteria and levels of conflict and importance. Multi-criteria decision-making (MCDM) solutions have been utilised in IoMT and IDS, yet they fall short in capturing the subjective judgements of experts and rely on normalisation approaches, which can impact results. This study seeks to address these issues through the integration of robust MCDM methodologies, namely fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM). Utilising rough Fermatean fuzzy sets (RFFSs), this integration produces precise solutions with reduced uncertainty. Our methodology involves adopting a decision matrix for IDS classifiers based on integrated evaluation criteria, followed by deriving new formulations and developments for RFFSs-based FDOSM and FWZIC for the modelling and weighting of criteria, respectively. Evaluations using datasets involving 125,973 records and 41 features across 17 evaluation criteria revealed that accuracy–security and training time–performance weights yielded the highest scores, whereas false negative rate–security and CPU time–performance criteria received the lowest weights. The random forest emerged as the optimal IDS classifier. Systematic modelling, sensitivity analysis and comparative studies confirmed the robustness of our results. © 2023, Crown.

关键词： Integration

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DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search 32

DU-DARTS: Decreasing the Uncertainty of Differentiable Archi...

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32nd British Machine Vision Conference, BMVC 2021

作者： Lu, Shun Hu, Yu Yang, Longxing Sun, Zihao Mei, Jilin Zeng, Yiming Li, Xiaowei Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Tecent ADlab China

Differentiable Neural Architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency. However, the competition of candidate operations in DARTS introduces high uncertainty for selecting the truly important operation, thus leading to serious performance collapse. In this work, we decrease the uncertainty of differentiable architecture search (DU-DARTS) by enforcing the distribution of architecture parameters to approach the one-hot categorical distribution and by replacing the zero operation with a gate switch. Without any extra search cost, our method achieves state-of-the-art performance with 2.32%, 16.74%, and 24.1% test error on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Moreover, DU-DARTS can robustly find an excellent architecture on NAS-Bench-1Shot1 and NAS-Bench-201, which further demonstrates the effectiveness of our method. The source code is available at https://***/ShunLu91/DU-DARTS. © 2021. The copyright of this document resides with its authors.

关键词： computer vision

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Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

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ICT Express 2025年第3期11卷 542-546页

作者： Gupta, Brij B. Gaurav, Akshat Alhalabi, Wadee Arya, Varsha Alharbi, Eman Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul South Korea Ronin Institute Montclair NJ United States International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung 413 Taiwan Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Hong Kong Metropolitan University Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems. © 2025

关键词： CNN model Distributed optimization Federated learning IoT attack detection Siberian Tiger Optimization (STO)

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READFake: Reflection and Environment-Aware DeepFake Detection

READFake: Reflection and Environment-Aware DeepFake Detectio...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Muhammad Mohzary Elham Basunduwah Sejun Song Baek-Young Choi Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper presents a novel Reflection and Environment-Aware DeepFake (READFake) detection technique. Using reflections on various body parts (e.g., eyes, nose, cheeks, etc.) and environmental factors, we validate the hypothesis that the existing DeepFake creation methods, including reenactment, replacement, and synthesis, fail to coordinate their counterfeits with the reflective components along with the given environmental mapping. We detect various features from the specular highlight images, including color components, shapes, and textures, to check the coordination with the surrounding environmental factors, such as indoor/outdoor, bright/dark backgrounds, and light strength. We have conducted extensive experiments to evaluate the performance of READFake using various input parameters and advanced Deep Neural Network (DNN) architectures on multiple public DeepFake datasets. The empirical results show that READFake achieves high accuracy (99.00%) in detecting sophisticated DeepFake images.

关键词： Deepfakes Accuracy Shape Image color analysis Visual communication Nose Artificial neural networks Feature extraction Reflection Environmental factors

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Auction Theory and Game Theory Based Pricing of Edge computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China 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 Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Design and Simulation of Multi-tiered Heterogeneous Memory Architecture

Design and Simulation of Multi-tiered Heterogeneous Memory A...

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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems (MASCOTS)

作者： Jinyuan Hu Haikun Liu Hai Jin Xiaofei Liao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Heterogeneous memory systems have become increasingly popular in recent years. Because heterogeneous storage media often show significantly different characteristics in terms of bandwidth, latency, capacity, and energy consumption, it is still challenging to best utilize them for cost-efficient and energy-efficient heterogeneous memory systems. In this paper, we propose a simulation framework for multi-tiered heterogeneous memory architectures based on GEM5 and DRAMsim3 simulators. We design a heterogeneous memory controller to architect Non-Volatile Memory (NVM) as main memory, and architect both Dynamic Random Access Memory (DRAM) and High-Bandwidth Memory (HBM) as a hybrid cache of NVM. Specifically, HBM, DRAM, and NVM are managed in a single (flat) address space. However, we use an address remapping table to maintain the mappings between NVM pages and HBM/DRAM pages, and logically manage HBM/DRAM/NVM as a three-tiered hybrid memory system. We also design a hardware-supported hot page monitor based on Majority Element Algorithm (MEA) to identify the hottest pages in the DRAM, and a dynamic threshold adjustment scheme for hot page migration to balance the memory bandwidth between DRAM and HBM. Our multi-tiered heterogeneous memory architecture can take advantage of the large capacity of NVM, the low latency of DRAM, and the high bandwidth of HBM concurrently. Experimental results show that our tiered memory architecture can improve application performance by an average of $2.5\times$ compared with an NVM-only architecture, and up to 57.4% compared with a DRAM-only architecture. Moreover, the performance gap between our HBM/DRAM/NVM architecture and a HBM-only architecture is less than 10%.

关键词： Analytical models Nonvolatile memory Heuristic algorithms Computational modeling Memory architecture Random access memory computer architecture

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CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture

arXiv

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arXiv 2022年

作者： Li, Huize Jin, Hai Zheng, Long Liao, Xiaofei Huang, Yu Liu, Cong Xu, Jiahong Duan, Zhuohui Chen, Dan Gui, Chuangyi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The attention-based neural network attracts great interest due to its excellent accuracy enhancement. However, the attention mechanism requires huge computational efforts to process unnecessary calculations, significantly limiting the system’s performance. To reduce the unnecessary calculations, researchers propose sparse attention to convert some dense-dense matrices multiplication (DDMM) operations to sampled dense-dense matrix multiplication (SDDMM) and sparse matrix multiplication (SpMM) operations. However, current sparse attention solutions introduce massive off-chip random memory access since the sparse attention matrix is generally unstructured. We propose CPSAA, a novel crossbar-based processing-in-memory (PIM)-featured sparse attention accelerator to eliminate off-chip data transmissions. First, we present a novel attention calculation mode to balance the crossbar writing and crossbar processing latency. Second, we design a novel PIM-based sparsity pruning architecture to eliminate the pruning phase’s off-chip data transfers. Finally, we present novel crossbar-based SDDMM and SpMM methods to process unstructured sparse attention matrices by coupling two types of crossbar arrays. Experimental results show that CPSAA has an average of 89.6×, 32.2×, 17.8×, 3.39×, and 3.84× performance improvement and 755.6×, 55.3×, 21.3×, 5.7×, and 4.9× energy-saving when compare with GPU, FPGA, SANGER, ReBERT, and ReTransformer. Copyright © 2022, The Authors. All rights reserved.

关键词： Memory architecture

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