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

作者： Zhao Zhang Chunxiang Xu Man Ho Allen Au School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Single-sign-on authentication (SSO) enables a user to obtain a token from an identity server, and access multiple service providers with the token. In conventional SSO, the identity server can identify the user through the token, which compromises the user’s privacy. Anonymous SSO is proposed to solve this problem. However, the unconditional anonymity precludes identification of fraudulent users and leads to increase in illegitimate activities. In this paper, we propose SONAR, an anonymous single-sign-on authentication protocol that supports fraud detection. The identity server first accesses a user’s trustworthiness using fraud detection. We observe that directly refusing to issue tokens to an untrustworthy user allows the user to immediately realize that he have been detected, which poses security problems. Instead, we postpone the moment the user realizes he has been detected until he attempts to access a service provider. We also illustrate the benefits of this postponement using denial of service attacks as an example. In the proposed SONAR, the identity server issues the user (regardless of his trustworthiness) with a token that contains a covert channel, which is restricted to conveying only a 0/1 bit that is hidden from the user. The restriction of the channel prevents the identity server from tracking the user by embedding specific information in the channel, and meanwhile the 0/1 bit indicates whether the user is trustworthy or not. The token is actually a randomizable signature and can be randomized by the user, while the embedded bit remains unchanged. The user accesses a service provider with a randomized token, which protects the user from being identified as well as informs the service provider whether the user’s access should be permitted. We provide a formal security proof to demonstrate that SONAR is secure, and conduct a performance evaluation to show efficiency of SONAR.

关键词： Performance evaluation Privacy Protocols Sonar Authentication Fraud Servers Security Sonar detection

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Enhancing TCP Congestion Control: A Comparative Analysis, and Algorithmic Modifications using NS2

Enhancing TCP Congestion Control: A Comparative Analysis, an...

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Recent Innovation in Smart and Sustainable technology (ICRISST), International Conference on

作者： Thangam S Trisha K Hima Sree N Srinithya Reddy P Sivaneasan Balakrishnan Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Singapore Institute of Technology Singapore

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The transmission control protocol is essentially a computer network protocol and ensures the reliability of the transmitted data. TCP’s congestion control algorithms support network performance and stability. Network congestion has become a major issue in network analysis. TCP (Transmission Control Protocol) Congestion control algorithms ensure smooth transmission of data across networks. This work examines TCP congestion algorithms. The main objective is to increase its efficiency and modify the algorithm. A detailed performance analysis of the enhanced TCP congestion control algorithms namely Tahoe, New Reno, and Vegas is performed and simulations are used to evaluate its performance under different network conditions such as high latency, constant bit rate (CBR) bandwidth load, and varying traffic loads. Metrics such as latency, throughput, and round-trip time are analyzed under the scenarios using ns-2 simulator.

关键词： TCP Technological innovation Protocols Bit rate Telecommunication traffic Network analyzers Throughput Stability analysis Performance analysis Reliability

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Enhancing the Adaptive Capability of Fuzzy Petri Net by an Improved Whale Optimization Algorithm

Enhancing the Adaptive Capability of Fuzzy Petri Net by an I...

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Chinese Automation Congress (CAC)

作者： Shao-Qiang Ye Azlan Mohd Zain Yusliza Yusoff Wen-Jie Ruan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia School of Information Engineering Hunan Applied Technology University Changde China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Fuzzy Petri Nets (FPNs) are an advanced topological model that extends traditional Petri nets (PNs) by integrating fuzzy theory. However, the generalization capability of FPN models is often limited due to their reliance on expert-driven parameter determination. This paper proposes an improved whale optimization algorithm (IWOA) for optimizing FPN models to address this challenge. The IWOA employs an adaptive weighting strategy, optimal search techniques, population grouping, and the Lévy flight strategy to dynamically adjust the search process, enhancing the WOA's effectiveness in handling complex problems and improving the balance between exploration and exploitation. The IWOA is tested on six classical benchmark functions in the experimental section and applied to the FPN model. The results demonstrate that the IWOA-FPN model exhibits strong generalization capabilities and superior convergence performance.

关键词： Adaptation models Automation Heuristic algorithms Petri nets Benchmark testing Search problems Whale optimization algorithms Convergence

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Depth first search approach with multi-variable heuristic function for enhancing routing protocol in 802.11 sensor network 8

Depth first search approach with multi-variable heuristic fu...

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8th International Conference on computing for Sustainable Global Development, INDIACom 2021

作者： Madasamy, Anand School of Computing SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544434

Wireless Sensor Networks (WSNs) are spread broadly due to their commonsense use in various applications and zones;this prompted pervasiveness remote sensor networks all over the place. Vitality utilization is considered as the greatest test to decide the wSNs lifetime, because of the restricted force source in the batteries that are coordinated into these sensor hubs. This paper proposes steering convention dependent on DFS calculation. Reenactment results show that the proposed convention is effective as far as decreasing vitality utilization and increment the wSNs life expectancy and accomplishes preferred execution over notable conventions as far as transmission postponement, throughput, and parcel conveyance proportion. In future, update with another artificial intelligence convention will be done. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Heuristic algorithms

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Zoom-shot: Fast and Efficient Unsupervised Zero-Shot Transfer of CLIP to Vision Encoders with Multimodal Loss

arXiv

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arXiv 2024年

作者： Shipard, Jordan Wiliem, Arnold Thanh, Kien Nguyen Xiang, Wei Fookes, Clinton Queensland University of Technology Australia Sentient Vision Systems Australia School of Computing Engineering and Mathematical Sciences La Trobe University Australia

The fusion of vision and language has brought about a transformative shift in computer vision through the emergence of Vision-Language Models (VLMs). However, the resource-intensive nature of existing VLMs poses a significant challenge. We need an accessible method for developing the next generation of VLMs. To address this issue, we propose Zoom-shot, a novel method for transferring the zero-shot capabilities of CLIP to any pre-trained vision encoder. We do this by exploiting the multimodal information (i.e. text and image) present in the CLIP latent space through the use of specifically designed multimodal loss functions. These loss functions are (1) cycle-consistency loss and (2) our novel prompt-guided knowledge distillation loss (PG-KD). PG-KD combines the concept of knowledge distillation with CLIP’s zero-shot classification, to capture the interactions between text and image features. With our multimodal losses, we train a linear mapping between the CLIP latent space and the latent space of a pre-trained vision encoder, for only a single epoch. Furthermore, Zoom-shot is entirely unsupervised and is trained using unpaired data. We test the zero-shot capabilities of a range of vision encoders augmented as new VLMs, on coarse and fine-grained classification datasets, outperforming the previous state-of-the-art in this problem domain. In our ablations, we find Zoom-shot allows for a trade-off between data and compute during training;and our state-of-the-art results can be obtained by reducing training from 20% to 1% of the ImageNet training data with 20 epochs. All code and models are available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Novel Abnormal Data Correction Method Based on GA-RBF Neural Network Considering Power Balance 8

Novel Abnormal Data Correction Method Based on GA-RBF Neural...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Li, Xiwang Yan, Shun He, Hai Lv, Li Cai, Ming Cui, Jia Li, Chaoran Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China State Grid Liaoning Electric Power Co. Ltd. Anshan Electric Power Supply Company Anshan Liaoning China School of Electrical Engineering Shenyang University of Technology Liaoning Shenyang China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

In view of the shortcomings of the methods for correcting abnormal data in actual power grids, this paper proposes to use power balance method and GA-RBF neural network algorithm to correct abnormal data. Firstly, the power balance method is used to correct single abnormal data because of its simple principle and high accuracy. Secondly, the RBF neural network algorithm optimized by genetic algorithm is used to correct multiple interrelated abnormal data, which just makes up for the shortcomings of the previous correction method, and has the advantages of convenient operation and high calculation efficiency. Finally, the two methods are simulated and analyzed through a practical case. © 2024 SPIE.

关键词： Genetic algorithms

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A Data-Centric Accelerator for High-Performance Hypergraph Processing 22

A Data-Centric Accelerator for High-Performance Hypergraph P...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Qinggang Wang Long Zheng Ao Hu Yu Huang Pengcheng Yao Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue 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 and Zhejiang Lab Hangzhou 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 Engineering University of New South Wales Sydney NSW Australia

ISBN: (纸本)9781665462723

Hypergraph processing has emerged as a powerful approach for analyzing complex multilateral relationships among multiple entities. Past research on building hypergraph systems suggests that changing the scheduling order of bipartite edge tasks can improve the overlap-induced data locality in hypergraph processing. However, due to the complex intertwined connections between vertices and hyperedges, it is almost impossible to find a locality-optimal scheduling order. Thus, these task-centric hypergraph systems often suffer from substantial off-chip *** this paper, we first propose a novel data-centric Load-Trigger-Reduce (LTR) execution model to exploit fully the locality in hypergraph processing. Unlike a task-centric model that loads the required data along with a task, our LTR model invokes tasks as per the data used. Specifically, once the hypergraph data is loaded into the on-chip memory, all of its relevant computation tasks will be triggered simultaneously to output intermediate results, which are finally reduced to update the final results. Our LTR model enables all hypergraph data to be accessed once in each iteration. To fully exploit the LTR performance potential, we further architect an LTR-driven hypergraph accelerator, XuLin, which features with an adaptive data loading mechanism to minimize the loading cost via chunk merging at runtime. XuLin is also equipped with a priority-based differential data reduction scheme to reduce the impact of conflicting updates on performance. We have implemented XuLin both on a Xilinx Alveo U250 FPGA card and using a cycle-accurate simulator. The results show that XuLin outperforms the state-of-the-art hypergraph processing solutions Hygra and ChGraph by 20.47× and 8.77× on average, respectively.

关键词： hypergraph data-centric accelerator

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hBP-Fi: Contactless Blood Pressure Monitoring via Deep-Analyzed Hemodynamics

hBP-Fi: Contactless Blood Pressure Monitoring via Deep-Analy...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yetong Cao Shujie Zhang Fan Li Zhe Chen Jun Luo School of Computer Science and Technology Beijing Institute of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Intelligent Networking and Computing Research Center and School of Computer Science Fudan University China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Blood pressure (BP) measurement is significant to the assessment of many dangerous health conditions. Apart from invasively inserting catheters into arteries, non-invasive approaches typically rely on wearing devices on specific skin areas with consistent pressure. However, this can be uncomfortable and unsuitable for certain individuals, and the accuracy of these methods may significantly decrease due to improper device placements and wearing states. Recently, contactless methods leveraging RF technology have emerged as a potential alternative. However, these methods suffer from the drawback of overfitting deep learning (DL) models without a sound physiological basis, resulting in a lack of clear explanations for their outputs. Consequently, such limitations lead to skepticism and distrust among medical experts. In this paper, we propose hBP-Fi, a contactless BP measurement system driven by hemodynamics acquired via RF sensing. In addition to its contactless convenience, hBP-Fi is superior to other RF sensing approaches in i) grounding on hemodynamics as the key physical process of heart-pulse activities, ii) exploiting beam-steerable RF devices to achieve a super-resolution scan on the fine-grained pulse activities along arm arteries, and iii) ensuring the trustworthiness of system outputs via an explainable (decision-understandable) DL model. Extensive experiments with 35 subjects demonstrate that hBP-Fi can achieve the error of -2.05±6.83 mmHg and 1.99 ± 6.30 mmHg for monitoring systolic and diastolic blood pressures, respectively.

关键词： Radio frequency Accuracy Superresolution Blood pressure Skin Hemodynamics Sensors

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract)

A Survey on Modern Deep Neural Network for Traffic Predictio...

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International Conference on Data engineering

作者： David Alexander Tedjopurnomo Zhifeng Bao Baihua Zheng Farhana Murtaza Choudhury A. K. Qin School of Computing Technologies RMIT University Australia School of Computing and Information Systems Singapore Management University Singapore School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare; making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.

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