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IEEE International Joint Conference on Biometrics (IJCB)

作者： Yanliang Guo Xianxu Hou Feng Liu Linlin Shen Lei Wang Zhen Wang Peng Liu Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Qualcomm Technologies Inc

Facial expression editing has a wide range of applications, such as emotion detection, human-computer interaction, and social entertainment. However, existing expression editing methods either fail to allow for fine-grained editing, resulting in unnatural and unrealistic facial expressions, or generate artifacts and blurs, leading to poor image quality. In this paper, we propose a novel framework called StyleAU, which is based on StyleGAN and facial action units, to address these problems. Our framework leverages the pre-trained StyleGAN prior knowledge to enable action unit editing of the face in the StyleGAN latent space, allowing precise expression editing. In addition, we use an encoder to extract multi-scale content features to achieve high-fidelity image reconstruction. Our approach qualitatively and quantitatively outperforms competing methods for action unit manipulation and expression editing.

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SMS Spam Filtering Using Machine Learning Technique 3rd

SMS Spam Filtering Using Machine Learning Technique

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3rd International Conference on Communications and Cyber Physical engineering, ICCCE 2020

作者： Vishwakarma, Arvind Kumar Ansari, Mohd Dilshad Rai, Gaurav ZEBEC Software Technologies Private Limited DehradunUttrakhand India Department of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad India Gurukula Kangri Vishwavidyalaya Haridwar India

ISBN: (纸本)9789811579608

Sending and receiving SMS is very ordinary thing for any individual’s daily life. But when at the moment, we receive undesirable SMS frequently that waste our time and money as well and consequently this moment gives us unpleasant feeling. If undesirable messages are to be sent to a huge volume of recipients erratically have resulted in displeasure by consumers but gives large profit to spammers. There are lots of reasons like high internet speed, very cheap smart phones and user friendly interface of mobile web and mobile applications that attracts a huge volume of mobile phone users. These are the key factors expected to shape the future of the market. This paper focuses on SMS Spam filtering techniques and compared their performance. We compared the machine learning model’s performance and finally result indicates that the Logistic Regression model performed well with accuracy reaching up to 96.59%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Quantum Forgery Attacks on COPA, AES-COPA and Marble Authenticated Encryption Algorithms

arXiv

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arXiv 2023年

作者： Xu, Yinsong Liu, Wenjie Yu, Wenbin School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China

Since the classic forgery attacks on COPA, AES-COPA and Marble authenticated encryption algorithms need to query about 2n/2 times and their success probability are not high. To solve this problem, the corresponding quantum forgery attacks on COPA, AES-COPA and Marble authenticated encryption algorithms are presented. In the quantum forgery attacks on COPA and AES-COPA, we use Simon’s algorithm to find the period of the tag generation function in COPA and AES-COPA by querying in superposition, and then generate a forged tag for a new message. While in the quantum forgery attack on Marble, Simon’s algorithm is used to recover the secret parameter L, and the forged tag can be computed with L. Compared with classic forgery attacks on COPA, AES-COPA and Marble, our attack can reduce the number of queries from O(2n/2) to O(n) and improve success probability close to 100%. © 2023, CC BY.

关键词： Marble

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Fast Division Algorithm for Finite Real Number 16

Fast Division Algorithm for Finite Real Number

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Chen, Ting Li, Fenfen Lin, Jinbao National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Huai'an China

ISBN: (纸本)9781665405539

The data representation and division for finite real number in computers has always been a hot topic in the fields of scientific research and engineering technology. With the research of existing real number division algorithm, design, improvement and adjustment for data structure, algorithm and programming language, and realize the fast division for finite real number. The algorithm can apply to the engineering requirement of finite real number of 506 bits. It replaces the commonly used trial division method with a comparison method, reducing the time complexity of the algorithm. The experimental results demonstrate the correctness and efficiency of the algorithm. The division of the algorithm uses comparison instead of trial division, which reduces the time complexity of the algorithm from O(n2) to O(n). The experimental results show that the algorithm is correct and efficient. © 2021 IEEE.

关键词： Computational complexity

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mRS-DBFT: An improved consensus mechanism suitable for end-edge-cloud hybrid trust models

mRS-DBFT: An improved consensus mechanism suitable for end-e...

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Frontiers technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Xue Zhai Shanchen Pang Sibo Qiao Shihang Yu Qingdao institute of software college computer science and technology China University of Petroleum (East China) Qingdao China School of Mechanical Engineering Tiangong University Tianjin China

As a three-terminal collaborative architecture of terminal, edge and cloud, end-edge-cloud is jointly participated by multiple institutions or organizations, and has the characteristics of a hybrid trust architecture of “partially trustworthy, globally untrustworthy”. Traditional consensus algorithm designs are mostly used in relatively unified trust model environments and do not provide a mechanism for cross-organizational cooperation. Therefore, they cannot effectively solve the trust and consensus issues between organizations. We propose an improved DPoS-PBFT consensus mechanism based on reputation scoring (mRS-DBFT). The core idea of the mRS-DBFT is to add a reputation scoring mechanism based on the DPOS-PBFT consensus algorithm. The reputation score is calculated based on factors such as the node’s historical behavior, contribution, and credibility. Nodes with higher reputation score have greater influence and decision-making power in the consensus process. This mechanism can solve the problem of mutual distrust among nodes of different organizations in the end-edge-cloud architecture, and each node participates in the consensus process based on its reputation score, thus forming a credible and consistent system, achieving cross-organizational consensus, and ensuring node trust and data consistency in an open end-edge-cloud environment. The experimental results demonstrate that the mRS-DBFT consensus algorithm helps select nodes with higher stability to participate in the consensus by evaluating the reputation of computing devices, it can reduce fluctuations in delay and improve the stability of the consensus process.

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Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

Distributionally Robust Optimization for Computation Offload...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Northwestern Polytechnical University School of Software Xi'an710072 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024 IEEE.

关键词： Computation offloading

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Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations

Interpretable Pulmonary Disease Diagnosis with Graph Neural ...

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Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT), International Conference on

作者： Jiahong Li Yiyuan Chen Yichi Wang Yiqiang Ye Min Sun Hao Ren Weibin Cheng Haodi Zhang College of Computer and Software Engineering Shenzhen University HKUST(GZ) Guangzhou China dept. Computer Science Cornell University New York USA College of Computer and Software Engineering Shenzhen University Shenzhen China China Mobile Information Technology Co. Limited Shenzhen China Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou China

ISBN: (数字)9798350373257

ISBN: (纸本)9798350373264

With recent fast development of artificial intelligence and related techniques, computer-aided diagnosis is increasingly emerging. During the process of automated diagnosis, the importance of explainable decision making has grown significantly. Traditional black-box models have often hindered the practical implementation of automated diagnosis algorithms. To address these challenges, we propose a novel framework that seamlessly integrates prototype learning with Graph Neural Networks (GNN) while also providing post-hoc explanations. On one hand, our model achieves intrinsic interpretability by reasoning based on the similarity calculations with prototypes for each disease. On the other hand, it employs counterfactual reasoning on graphs to pinpoint the most significant features for post-hoc explanations, supporting the diagnosis process. Building on these strengths, we integrate vision-language models to effectively harness multimodal patient information, thereby capturing a more detailed understanding of their medical condition. Our experiments on real Chinese EMRs of Pulmonary diseases demonstrate that our method not only delivers precise diagnoses but also accurately identifies medical findings substantiating the diagnoses.

关键词： Pulmonary diseases Computational modeling Decision making Prototypes Graph neural networks Cognition Sensors

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Study on Potability Water Quality Classification Based on Integrated Learning 16

Study on Potability Water Quality Classification Based on In...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Fen, Li Fen Lei, Zhou Ting, Chen Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. The effect of set classifier is higher than that of simple classifier. © 2021 IEEE.

关键词： Water quality

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A Resource-efficient Load Balancing Algorithm for Network Virtualization

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Chinese Journal of Electronics 2023年第4期20卷 667-670页

作者： LIAO Shengquan WU Chunming YANG Qiang WANG Baojin JIANG Ming College of Computer Science and Technology Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China China National Digital Switching System Engineering and Technological Research Center Zhengzhou China Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou China

Most recently much effort has been made to address Internet “ossification” by exploiting network virtualization technique. It allows multiple virtual networks to share the physical substrate for delivering customized services via heterogenous protocols or mechanisms. Recent advances enable virtual routers to flexibly migrate across multiple physical routers without interrupting network operation. With this recognition, this paper takes a further step by proposing a novel algorithmic approach to optimize network load balancing as well as resource utilization. The suggested approach is assessed through extensive simulation experiments against two other algorithmic solutions for a range of network scenarios (e.g. dense and sparse networks). The numerical result shows its effectiveness in providing the best trade-off of network load balancing and resource utilization whilst minimizing the communication overheads.

关键词： Load management Routing protocols Numerical models Internet Resource management Virtualization Substrates

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