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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hua, Xiaoqi Chen, Jian Zhang, Peiyun Fu, Zhangjie Zhu, Haibin Lu, Kezhong Ren, Jigang School of Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics of Ministry of Education The School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China

ISBN: (纸本)9781665410205

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio. © 2024 IEEE.

关键词： Blockchain

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A Naming Pattern Based Approach for Method Name Recommendation

A Naming Pattern Based Approach for Method Name Recommendati...

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International Symposium on software Reliability engineering (ISSRE)

作者： Yanping Yang Ling Xu Meng Yan Zhou Xu Zhongyang Deng Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Method names in software projects are significant for developers to understand the method functionality. Existing state-of-the-art automated approaches tend to explore tokens composing method names from method contexts. However, the method name is not a simple combination of tokens, as it is structured and contains many repetitive naming patterns (e.g. “get __”, “create __”). Through a large-scale empirical analysis on 15M methods from 14K real software projects developed with Java codes, we found repetitive naming patterns in method names. In addition, the names of two function-similar methods usually have the same naming pattern. Based on our empirical study, we propose a naming pattern-based approach for method name recommendation, named Nam-Pat. Specifically, for a target method, NamPat first retrieve the most similar method from the training data by estimating their body code similarity. Then, the name of the most similar method is used as the pattern guider to provide the naming pattern, and NamPat combines it with the context information of the target method to perform method name recommendation. To verify the effectiveness of the proposed approach, we conducted experiments on 17M methods from a widely used Java dataset. Experimental results show that compared with Code2vec, Code2seq, MNire, and Cognac, NamPat improves the state-of-the-art approaches in precision (5.8%-27.1%), recall (11.1%-60.1 %), and F-score (8.5 %-43.9%), which proves the effectiveness of our proposed approach.

关键词： Java Codes Source coding Semantics Training data software Generators

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A Blockchain-Based Multi-Cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing data centers Costs Blockchains Behavioral sciences

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WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

arXiv

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arXiv 2023年

作者： Huo, Dongjian Zhang, Zehong Su, Hanjing Li, Guanbin Fang, Chaowei Wu, Qingyao School of Software and Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Peng Cheng Laboratory China Tencent Wechat Department School of Computer Science and Engineering Sun Yat-sen University China Xidian University China

Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches——one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks;precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multiscale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach’s remarkable superiority, surpassing existing state-ofthe-art methods by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

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Model Degradation Hinders Deep Graph Neural Networks

arXiv

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arXiv 2022年

作者： Zhang, Wentao Sheng, Zeang Yin, Ziqi Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of CS & Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Beijing Institute of Technology China

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers. As a result, most GNNs only have shallow architectures, which limits their expressive power and exploitation of deep neighborhoods. Most recent studies attribute the performance degradation of deep GNNs to the over-smoothing issue. In this paper, we disentangle the conventional graph convolution operation into two independent operations: Propagation (P) and Transformation (T). Following this, the depth of a GNN can be split into the propagation depth (Dp ) and the transformation depth (Dt ). Through extensive experiments, we find that the major cause for the performance degradation of deep GNNs is the model degradation issue caused by large Dt rather than the over-smoothing issue mainly caused by large Dp. Further, we present Adaptive Initial Residual (AIR), a plug-and-play module compatible with all kinds of GNN architectures, to alleviate the model degradation issue and the over-smoothing issue simultaneously. Experimental results on six real-world datasets demonstrate that GNNs equipped with AIR outperform most GNNs with shallow architectures owing to the benefits of both large Dp and Dt, while the time costs associated with AIR can be ignored. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples

arXiv

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arXiv 2023年

作者： Zhao, Xiaotong Li, Mian Wang, Bo Song, Enbin Chang, Tsung-Hui Shi, Qingjiang The School of Software Engineering Tongji University Shanghai201804 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen518172 China The Wireless Network RAN Algorithm Department Xi’an Huawei Technologies Co. Ltd. Xi’an710000 China The College of Mathematics School of Aeronautics and Astronautics Sichuan University Sichuan Chengdu610064 China The Shenzhen Research Institute of Big Data Shenzhen518172 China

Recently, the decentralized baseband processing (DBP) paradigm and relevant detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local computing fabric. However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells. Moreover, in the DBP architecture, linear minimum mean-square error (LMMSE) detection methods rely on the estimation of the noise covariance matrix through averaging distributedly stored noise samples. This presents a significant challenge for decentralized LMMSE-based equalizer design. To address this issue, this paper proposes decentralized LMMSE equalization methods under colored noise scenarios for both star and daisy chain DBP architectures. Specifically, we first propose two decentralized equalizers for the star DBP architecture based on dimensionality reduction techniques. Then, we derive an optimal decentralized equalizer using the block coordinate descent (BCD) method for the daisy chain DBP architecture with a bandwidth reduction enhancement scheme based on decentralized low-rank decomposition. Finally, simulation results demonstrate that our proposed methods can achieve excellent detection performance while requiring much less communication bandwidth. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Learning Beams Adaptive to the Environment: An RSRP-based Codebook Design

Learning Beams Adaptive to the Environment: An RSRP-based Co...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Xinzhi Ning Shutao Zhang Ye Xue Xi Zheng Qingjiang Shi Tsung-Hui Chang School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

The design of an effective codebook is crucial for the fifth generation (5G) systems, particularly in the context of massive multiple-input-multiple-output (MIMO) and millimeter wave communication. However, existing codebook design methods that rely on the instantaneous channel impulse response (CIR) face challenges in adapting to dynamic channels that undergo small-scale fading changes. To address this issue, this paper proposes a novel two-stage codebook design framework based solely on the reference signal receiving power (RSRP), which provides large-scale statistical information about environment attenuation. The proposed approach involves two stages: first, the angular power spectrum (APS) is estimated through sparse Bayesian learning (SBL), which uses the statistical relationship between APS and RSRP. Second, an iterative clustering and optimization algorithm is used to determine the optimal codebook based on the estimated APS. Experimental results demonstrate that the proposed codebook design framework with a smaller number of beams can adapt to localized environment well and achieves better performance than the classical DFT codebook.

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning

Research on Multi-AGVs dynamic scheduling based on deep rein...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Zhuorui Qin Xiaoqian Wu Yimin Zheng Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes.

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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