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IEEE International Conference on Web Services (ICWS)

作者： Jingyu Liang Zihan Feng Han Gao Ying Chen Jiwei Huang Hong-Linh Truong Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China Department of Computer Science School of Science Aalto University Espoo Finland

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development of 5G technology, the service demand in various application scenarios is continuously increasing. Mobile edge computing (MEC) has become a popular computing paradigm by placing services and corresponding computing resources to edge servers to satisfy the low latency demands of users. However, edge servers lack a stable infrastructure for protection and limited storage space and computing power. Considering the reliability and stability of the edge system, efficiently placing resources and offloading tasks to the edge servers has become an urgent challenge. In this paper, we consider resource placement and task offloading strategies under different time scales to optimize the service response time in a dynamic edge system environment. We established the Markov model to obtain a quantitative relationship between system reliability and latency, and analyze the time required for resource and task offloading. Then, we propose the resource placement and task offloading (RPTO) algorithms under different time scales based on deep reinforcement learning (DRL) techniques with the aim of minimizing the cost of service providers in the long term. The experimental results demonstrate that our approach effectively tackles the challenges of joint resource placement and task offloading in the MEC.

关键词： Analytical models Costs Web services Dynamic scheduling Deep reinforcement learning Stability analysis Servers Reliability Time factors Protection

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Query-guided Prototype Evolution Network for Few-Shot Segmentation

arXiv

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arXiv 2024年

作者： Cong, Runmin Xiong, Hang Chen, Jinpeng Zhang, Wei Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries. The evolution of the foreground prototype is accomplished through a support-query-support iterative process involving two new modules: Pseudo-prototype Generation (PPG) and Dual Prototype Evolution (DPE). The PPG module employs support features to create an initial prototype for the preliminary segmentation of the query image, resulting in a pseudo-prototype reflecting the unique needs of the current query. Subsequently, the DPE module performs reverse segmentation on support images using this pseudo-prototype, leading to the generation of evolved prototypes, which can be considered as custom solutions. As for the background prototype, the evolution begins with a global background prototype that represents the generalized features of all training images. We also design a Global Background Cleansing (GBC) module to eliminate potential adverse components mirroring the characteristics of the current foreground class. Experimental results on the PASCAL-5i and COCO-20i datasets attest to the substantial enhancements achieved by QPENet over prevailing state-of-the-art techniques, underscoring the validity of our ideas. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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Frogmen Formation Voice Communication technology Based on Fast ICA

Frogmen Formation Voice Communication Technology Based on Fa...

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2021 OES China Ocean Acoustics, COA 2021

作者： Zhang, Xiao Liu, Xiaomeng Jilin University College of Computer Science and Technology Chang Chun China College of Underwater Acoustic Engineering Harbin Engineering University Acoustic Science and Technology Laboratory Key Laboratory of Marine Information Acquisition and Security Harbin China

ISBN: (纸本)9781728181905

Frogmen formation voice communication has become a mega trend nowadays. Among various methods of modulation, single side band (SSB) multi-user voice communication system holds the advantages of remaining speakers' tones, however with no ability of resisting interference from other users in the same frequency band. Considering the frequency band utilization of the system, there is a need for an underwater voice communication system with many frogmen working in the same frequency band simultaneously. In this work, a multi-user voice communication technology based on fast independent component analysis (Fast ICA) is proposed, which is applied to noise-free cases as well as the environment with noise. For simulation, it is demonstrated that non-noise algorithm can almost recover sources with similarity coefficient approaching 1.0 and the improved algorithm for noisy cases is able to lessen distortion and reduce interference effectively. In the experimental scenario, the average similarity coefficient between sources and separated voices can reach above 0.88 in two-user and three-user systems, which indicates that the proposed voice communication technology is able to realize multi-user transmission in the same frequency band. © 2021 IEEE.

关键词： Independent component analysis

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Gesture Builder: Flexible Gesture Customization and Efficient Recognition on VR Devices

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2025年第2期9卷 1-37页

作者： Yang Zou Yanguang Wan Yonghao Zhang Xiao Zhou Jian Cheng Cuixia Ma Chun Yu Xiaoming Deng Hongan Wang Institute of Software Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology and University of Chinese Academy of Sciences Beijing China Key Laboratory of System Software Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Institute of Software Chinese Academy of Sciences Beijing China

In the realm of VR/MR interactions, gestures serve as a critical bridge between users and interfaces, with custom gestures enhancing creativity and providing a personalized immersive experience. We introduce a novel gesture definition and recognition framework that allows users to customize a wide array of gestures by demonstrating them just three times. A major challenge lies in effectively representing gestures computationally. To address this, we have pre-trained a hand posture representation model using a Vector Quantized Variational Autoencoder (VQ-VAE) with a codebook of adaptive size, allowing hand postures defined by 23 joint positions of the hand to be projected into a latent space. In this space, different postures are formed into clusters, and a testing posture can be assigned to a cluster by a specific distance metric. The dynamic gestures are then represented as sequences of discrete hand postures and wrist positions. Employing a straightforward sequence matching algorithm, our framework achieves highly efficient recognition with minimal computational demands. We evaluated this system through a user study that includes 16 pre-defined gestures and 106 user-defined gestures. The results confirm that our system can provide robust real-time gesture recognition and effectively supports the customization of gestures according to user preferences. Our approach surpasses previous methods by enhancing gesture diversity and reducing constraints on gesture customization. Project page: https://***/GestureBuilder/.

关键词： Gesture customization Virtual Reality

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FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain system

arXiv

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arXiv 2024年

作者： Liu, Chao Hou, Cankun Jiang, Tianyu Ning, Jianting Qiao, Hui Wu, Yusen Fujian Provincial Key Laboratory of Data Intensive Computing the Key Laboratory of Intelligent Computing and Information Processing Fujian China The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Department of Computer Science University of Miami MiamiFL33146 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China The Faculty of Data Science City University of Macau China

Data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system1 offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

关键词： Access control

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U-HRMLP: Refining Segmentation Boundaries in Histopathology Images

U-HRMLP: Refining Segmentation Boundaries in Histopathology ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Tao Wang Kai Zhang Weijie Wang Mingrui Ma Ye Zhang He Zhao Guixia Liu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Information Science and Engineering Shandong Normal University China Department of Information Engineering and Computer Science University of Trento Italy Faculty of Information Engineering and Automation Kunming University of Science and Technology China School of Information Science and Technology Northeast Normal University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Segmenting regions of interest in histopathology images is a critical prerequisite for biomedical applications. CNN-based models have advanced medical image segmentation, yet accurately delineating boundaries is still challenging. This challenge arises from the loss of spatial resolution due to downsampling and the complexity of capturing contextual information when upsampling to the original resolution. To address these issues, we design U-HRMLP, a U-shaped network based on a hybrid combination of HRNet and Multi-Layer Perceptron (MLP). Specifically, we propose a multi-scale feature fusion module to avoid the loss of image resolution in the encoding stage as much as possible. Simultaneously, we propose an MLP-based decoding block to recover image details and resolution progressively, alleviating the issues related to the inadequate segmentation of smaller objects and the blurring of tissue boundaries. Results on the MoNuSeg and GlaS datasets demonstrate that U-HRMLP outperforms conventional or transformer-based methods and achieves state-of- the-art performance.

关键词： Measurement Image segmentation Histopathology Biological system modeling Semantics Refining Transformers

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Improved Underwater Acoustic Voice Communication Based on Blind Separation 3

Improved Underwater Acoustic Voice Communication Based on Bl...

引用

3rd IEEE International Conference on Communications, information system and computer Engineering, CISCE 2021

作者： Zhang, Xiao Liu, Xiaomeng College of Computer Science and Technology Jilin University Chang Chun China Acoustic Science and Technology Laboratory Key Laboratory of Marine Information Acquisition and Security College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9780738112152

Frogman underwater voice communication system is an indispensable equipment for underwater frogman combat. Among various methods of modulation, SSB voice communication system holds the advantages of high stability and better retention of speaker tone, however, with no capability of suppressing the underwater multichannel effect and being susceptible to noise. With the advancements in ocean development, there is a need for a voice communication system with both low voice distortion and robust transmit performance. In this paper, an acoustic voice communication technology based on blind separation is proposed, which uses complex cepstrum characteristic peak filter and minimum phase decomposition to calculate the amplitude and phase components of multichannel respectively. The performance of the improved voice communication system is verified by the simulated case and experiment data. In the experimental scenario, the average similarity coefficient between transmitted voice and processed voice can reach above 0.85, which reveals that the improved voice communication system can cope with the underwater multichannel effects. © 2021 IEEE.

关键词： Speech communication

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Factored Trace Lasso Based Linear Regression Methods: Optimizations and Applications 5th

Factored Trace Lasso Based Linear Regression Methods: Optimi...

引用

5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhang, Hengmin Du, Wenli Liu, Xiaoqian Zhang, Bob Qian, Feng School of Information Science and Engineering Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Department of Computer and Information Science University of Macau 999078 China

ISBN: (纸本)9789811623356

Consider that matrix trace lasso regularized convex p -norm with p= 1, 2 regression methods usually have the higher computational complexity due to the singular value decomposition (SVD) of larger size matrix in big data and information processing. By factoring the matrix trace lasso into the squared sum of two Frobenius-norm, this work studies the solutions of both adaptive sparse representation (ASR) and correlation adaptive subspace segmentation (CASS), respectively. Meanwhile, the derived models involve multi-variable nonconvex functions with at least two equality constraints. To solve them efficiently, we devise the nonconvex alternating direction multiplier methods (NADMM) with convergence analysis satisfying the Karush-Kuhn-Tucher (KKT) conditions. Finally, numerical experiments to the subspace clustering can show the less timing consumptions than CASS and the nearby performance of our proposed method when compared with the existing segmentation methods like SSC, LRR, LSR and CASS. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces

Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensiona...

引用

International Conference on Software Engineering (ICSE)

作者： Haoxiang Jia Ming Wen Zifan Xie Xiaochen Guo Rongxin Wu Maolin Sun Kang Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab. Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System School of Informatics Xiamen University China School of Computer Science and Technology Huazhong University of Science and Technology China

Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex and large-scale Java programs, Just-In-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such nov-elties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.

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