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作者： Zhang, Haotong Lin, Weiwei Xie, Rong Li, Shenghai Dai, Zhiyan Wang, James Z. School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China Department of New Network Technologies Peng Cheng Laboratory Shenzhen China School of Information Guangdong University of Finance & Economics Guangzhou China School of Computing Clemson University ClemsonSC United States

Emerging computing paradigms provide field-level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform-independent, low-resource-consumption operating environment for edge service. The image-pulling time of Docker is a crucial factor affecting the start-up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node-local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote-cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. © 2023 John Wiley & Sons, Ltd.

关键词： Containers

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Privacy-Preserving Scheme Against Collusion Attacks Based on Federated Learning

Privacy-Preserving Scheme Against Collusion Attacks Based on...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Longxiang Qu School of Computer Science Wuhan Vocational College of Software and Engineering (Wuhan Open University) Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid growth and increasing complexity of data, there are significant challenges to security and efficiency when using the traditional centralized machine learning model to deal with large-scale data. However, as a distributed learning mechanism, federated learning also has potential threats to handle data applications. For example, attackers can attempt to infer sensitive information of data providers by analyzing model or gradient information. Therefore, we propose a privacy-preserving scheme against collusion attacks based on federated learning to mitigate the above-mentioned threats. Specifically, combined with the federated learning mechanism, the local gradients are blinded by random numbers, and the local data security are ensured by aggregating the blinded gradients. Through experimental analysis on real data sets, we validate the performance of our scheme, which incurring minimal computational overhead, effectively achieves resistance against collusion attacks.

关键词： Training Resistance computer aided instruction Federated learning Distance learning Computational modeling Signal processing Data models Performance analysis Cryptography

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Improved Fireworks Optimization Algorithm for Multi-product Human-robot Collaborative Hybrid Disassembly Line Balancing 4

Improved Fireworks Optimization Algorithm for Multi-product ...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Feng Guo, Xiwang Wang, Jiacun Wang, Weitian Cao, Jinrui Bigaume, Imane Liaoning Petrochemical University Information And Control Engineering Fushun China Monmouth University Computer Science And Software Eng. Wes Long BranchNJ United States Montclair State University School Of Computing Montclair United States New Jersey City University Computer Science Department Jersey City United States

ISBN: (纸本)9798350315790

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model's validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem. © 2024 IEEE.

关键词： Optimization

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OptEx: expediting first-order optimization with approximately parallelized iterations 24

OptEx: expediting first-order optimization with approximatel...

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

作者： Yao Shu Jiongfeng Fang Ying Tiffany He Fei Richard Yu Guangdong Lab of AI and Digital Economy (SZ) China College of Computer Science and Software Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China and School of Information Technology Carleton University Canada

ISBN: (纸本)9798331314385

First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence. In response, we introduce first-order optimization expedited with approximately parallelized iterations (OptEx), the first general framework that enhances the optimization efficiency of FOO by leveraging parallel computing to directly mitigate its requirement of many sequential iterations for convergence. To achieve this, OptEx utilizes a kernelized gradient estimation that is based on the history of evaluated gradients to predict the gradients required by the next few sequential iterations in FOO, which helps to break the inherent iterative dependency and hence enables the approximate paral-lelization of iterations in FOO. We further establish theoretical guarantees for the estimation error of our kernelized gradient estimation and the iteration complexity of SGD-based OptEx, confirming that the estimation error diminishes to zero as the history of gradients accumulates and that our SGD-based OptEx enjoys an effective acceleration rate of θ(√N) over standard SGD given parallelism of N, in terms of the sequential iterations required for convergence. Finally, we provide extensive empirical studies, including synthetic functions, reinforcement learning tasks, and neural network training on various datasets, to underscore the substantial efficiency improvements achieved by OptEx in practice. Our implementation is available at https://***/youyve/OptEx.

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LSNSCDA: Unraveling CircRNA–Drug Sensitivity via Local Smoothing Graph Neural Network and Credible Negative Samples

LSNSCDA: Unraveling CircRNA–Drug Sensitivity via Local Smoo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyu Fan Yuanpeng Zhang Yahan Li Zeyu Zhong Lei Deng School of Computer Science and Engineering Central South University Changsha China School of Software Xinjiang University Wulumuqi China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associations are resource-intensive, we propose LSNSCDA, a novel prediction algorithm that integrates Local Smoothing Graph Neural Networks (LS-GNN) and Credible Negative Sampling (CNS) to improve prediction accuracy. Our approach overcomes the challenges of fixed-length propagation in graph neural networks and the unreliability of randomly sampled negative instances. Experimental results show that LSNSCDA outperforms existing models, providing more reliable predictions and valuable insights into cancer treatment. Extensive evaluation confirms the effectiveness of each component of our model, while case studies further demonstrate its practical applicability. The source code and dataset are available at https://***/ZiyuFanCSU/LSNSCDA.

关键词： Drugs Sensitivity Smoothing methods Accuracy RNA Precision medicine Predictive models Prediction algorithms Graph neural networks Reliability

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Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?

arXiv

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arXiv 2024年

作者： Saha, Soumit Kanti Rabbi, Fazle Wang, Song Yang, Jinqiu Computer Science and Software Engineering Concordia University Montreal Canada Lassonde School of Engineering York University Toronto Canada

Large Language Models (LLMs) are increasingly being applied across various domains, including code-related tasks such as code translation. Previous studies have explored using LLMs for translating code between different programming languages. Since LLMs are more effective with natural language, using natural language as an intermediate representation in code translation tasks presents a promising approach. In this work, we investigate using NL-specification as an intermediate representation for code translation. We evaluate our method using three datasets, five popular programming languages, and 29 language pair permutations. Our results show that using NL-specification alone does not lead to performance improvements. However, when combined with source code, it provides a slight improvement over the baseline in certain language pairs. Besides analyzing the performance of code translation, we also investigate the quality of the translated code and provide insights into the issues present in the translated code. © 2024, CC BY.

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Fuzzy Overlapping Community Guided Hierarchical Self-Attention Pooling

Fuzzy Overlapping Community Guided Hierarchical Self-Attenti...

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Intelligent Computing and Human-computer Interaction (ICHCI), International Conference on

作者： Xin Liu Yao Xiao Zuping Zhang School of Computer Science and Engineering Central South University Changsha China School of software Xinjiang University Urumqi China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Graph pooling aims at extracting vital information for graph coarsening, and thus helping graph neural networks to improve their graph representation ability. However, existing methods either compress similar nodes by a clustering algorithm or discard unimportant nodes by an importance score, resulting in the coarsened graph suffering from different issues. Therefore, aiming at the goal of breaking through their limitation, this paper develops a fuzzy overlapping community guided hierarchical self-attention pooling, namely FOCoHSAP. FOCoHSAP performs node selection within every detected community rather than the entire graph, which allows for an explicit preservation of both topology hierarchy and detailed node information. Especially, FOCoHSAP introduces fuzzy overlapping community detection algorithm to explore those fine-grained relationships between nodes and communities, allowing for a more promising learning of graph representation that can be utilized for accurate graph classification.

关键词： Human computer interaction Accuracy Clustering algorithms Graph neural networks Topology Data mining Detection algorithms

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Thermal Simulation Computing Network Based on Deep Learning

Thermal Simulation Computing Network Based on Deep Learning

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International Conference on Digital Home (ICDH)

作者： Ruomei Wang Yi Teng Yaoquan Zhang Fan Zhou School of Software and Engineering School of Computer Science Guangdong University of Education Guangzhou China National Engineering Research Center of Digital Life School of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

The heat and moisture transfer simulation about human body and clothing is a method to simulate the regulation mechanism of human body, and heat and moisture performance of clothing in thermal environment by using computer technology. According to the simulation results, it can be used to the monitor and predicate the key physiological data of human body, and the functional clothing design. Some mathematical models about this area have been built. However, a large number of differential equations are involved to be used to the numerical computation in the simulation process, which leads to high computational complexity, large memo-ry consumption and long computing time. With the expansion of computing scale, these problems will become more obvious and directly affect the practical application of simulation models. The main work of this paper is to study the efficient solution method based on neural network, and proposed a deep learning-based differential equation computing network. The network uses differential equation as the constraint of the loss function of neural network, and integrates the differential equation into the training and prediction process of the network. The experimental results show that the proposed network can reduce the computational complexity of the simulation model and improve the computational efficiency.

关键词： Training Computational modeling Clothing Neural networks Moisture Differential equations Mathematical models Numerical models Computational complexity Heat transfer

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DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion 39

DAPoinTr: Domain Adaptive Point Transformer for Point Cloud ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Yinghui Zhou, Qianyu Gong, Jingyu Zhu, Ye Dazeley, Richard Zhao, Xinkui Lu, Xuequan School of Information Technology Deakin University Australia Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China School of Software Technology Zhejiang University Ningbo China Department of Computer Science and Software Engineering The University of Western Australia Australia

ISBN: (纸本)157735897X

Point Transformers (PoinTr) have shown great potential in point cloud completion recently. Nevertheless, effective domain adaptation that improves transferability toward target domains remains unexplored. In this paper, we delve into this topic and empirically discover that direct feature alignment on point Transformer’s CNN backbone only brings limited improvements since it cannot guarantee sequence-wise domain-invariant features in the Transformer. To this end, we propose a pioneering Domain Adaptive Point Transformer (DAPoinTr) framework for point cloud completion. DAPoinTr consists of three key components: Domain Query-based Feature Alignment (DQFA), Point Token-wise Feature alignment (PTFA), and Voted Prediction Consistency (VPC). In particular, DQFA is presented to narrow the global domain gaps from the sequence via the presented domain proxy and domain query at the Transformer encoder and decoder, respectively. PTFA is proposed to close the local domain shifts by aligning the tokens, i.e., point proxy and dynamic query, at the Transformer encoder and decoder, respectively. VPC is designed to consider different Transformer decoders as multiple of experts (MoE) for ensembled prediction voting and pseudo-label generation. Extensive experiments with visualization on several domain adaptation benchmarks demonstrate the effectiveness and superiority of our DAPoinTr compared with state-of-the-art methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Motif-driven Subgraph Structure Learning for Graph Classification

arXiv

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arXiv 2024年

作者： Zhou, Zhiyao Zhou, Sheng Mao, Bochao Chen, Jiawei Sun, Qingyun Feng, Yan Chen, Chun Wang, Can College of Computer Science Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China School of Computer Science and Engineering Beihang University China

To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numerous GSL methods, the progresses in this field mostly concentrated on node-level tasks, while graph-level tasks (e.g., graph classification) remain largely unexplored. Notably, applying node-level GSL to graph classification is non-trivial due to the lack of find-grained guidance for intricate structure learning. Inspired by the vital role of subgraph in graph classification, in this paper we explore the potential of subgraph structure learning for graph classification by tackling the challenges of key subgraph selection and structure optimization. We propose a novel Motif-driven Subgraph Structure Learning method for Graph Classification (MOSGSL). Specifically, MOSGSL incorporates a subgraph structure learning module which can adaptively select important subgraphs. A motif-driven structure guidance module is further introduced to capture key subgraph-level structural patterns (motifs) and facilitate personalized structure learning. Extensive experiments demonstrate a significant and consistent improvement over baselines, as well as its flexibility and generalizability for various backbones and learning procedures. The code is available in the https://***/OpenGSL/OpenGSL. © 2024, CC BY.

关键词： Structural optimization

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