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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Zhao, Hong Tian, Weiquan An, Ding Mi, Shan School of Computer Science and Communication Lanzhou University of Technology Lanzhou China Cccc Big Data Technology Co. Ltd. Lanzhou China

ISBN: (纸本)9798350360240

Multi-label text classification is a fundamental task in the field of natural language processing. Currently, there are issues in the Chinese multi-label text classification tasks, such as insufficient extraction of text label features and a lack of learning about the correlations between labels. To address these challenges, we proposed a Multi-label Text Classification Model based on DistilBERT and Word-Label Probabilities (DBWL). The model consists of a metric space module that integrates word-label probabilities and a deep neural network module based on DistilBERT. Firstly, the trained Labeled Latent Dirichlet Allocation (Labeled-LDA) model is utilized to obtain the probabilities of words associated with labels, which are then used to calculate the text mapping vectors and map them into the same metric space as the labels. Similarity measurement is employed to extract features between text-label pairs and label-label pairs. Then, in the deep neural network module, DistilBERT is employed to jointly embed texts and labels, capturing rich semantic information about text labels, followed by feature extraction using TextCNN. Finally, the features extracted from both modules are weighted and fused for label prediction. Experimental results on two benchmark datasets demonstrate that the proposed model outperforms current mainstream multi-label classification models in terms of key performance metrics. © 2024 IEEE.

关键词： Semantics

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing 38

TreeVI: Reparameterizable Tree-structured Variational Infere...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xiao, Junxi Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Mean-field variational inference (VI) is computationally scalable, but its highly-demanding independence requirement hinders it from being applied to wider scenarios. Although many VI methods that take correlation into account have been proposed, these methods generally are not scalable enough to capture the correlation among data instances, which often arises in applications involving graphs or explicit constraints among instances. In this paper, we developed the Tree-structured Variational Inference (TreeVI)2, which uses a tree to capture the correlation among latent variables in the posterior. We show that samples from the tree-structured posterior can be reparameterized efficiently and parallelly, making its training cost just 2 or 3 times that of VI under the mean-field assumption. To capture correlation with more complicated structure, the TreeVI is further extended to the multiple-tree case. Furthermore, we show that the underlying tree structure can be automatically learned from training data. With experiments on synthetic datasets, constrained clustering, user matching and link prediction, we demonstrate the efficacy of TreeVI in capturing instance-level correlation in posteriors and enhancing the performance of downstream applications. © 2024 Neural information processing systems foundation. All rights reserved.

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Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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Tsinghua science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo College of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ***,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling *** some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing *** study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud *** on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this *** the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection *** results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling

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Financial Fraud Defense Strategy based on Gradient Compensated Asynchronous Federated Learning 30

Financial Fraud Defense Strategy based on Gradient Compensat...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Liu, Tongrui Zhou, Yizhi Song, Zhipeng Jia, Xibei Wu, Shan Qi, Heng Dalian University of Technology School of Computer Science and Technology Dalian China Shenzhen Audaque Data Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798331515966

Asynchronous federated learning (AFL) allows participants to immediately submit trained models without waiting for other participants, building upon the federated learning (FL). Due to privacy concerns, FL is more susceptible to financial fraud, compounded by the gradient delay issues introduced by asynchronous submissions, making defense against financial fraud more challenging. Motivated by the above finding, we propose a secure and privacy-preserving AFL defense method for image datasets with an implanted backdoor via filtering redundant neurons (BDAFL), enhancing its resilience against such attacks without compromising privacy. We utilize gradient compensation to mitigate the impact of delays introduced by asynchrony. To counter financial fraud, we employ an anomaly detection algorithm based on neurons' weights and ensemble distillation to eliminate the affected neurons implanted with the backdoor, rendering the attack ineffective. Extensive experiments demonstrate the effectiveness and superiority of our approach. © 2024 IEEE.

关键词： Federated learning

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Permeability Model for Fractal Porous Media with Rough Capillary Walls

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International Journal of High Speed Electronics and Systems 2025年第0期

作者： Zhao, Yibo School of Data and Computer Science Shandong Women's University Jinan China

The pore space of a porous medium is typically considered to consist of a collection of curved capillaries with rough walls. A model describing roughness is established by applying fractal theory. As a result, a modified permeability model is derived theoretically, which is closely related to roughness. To validate this model, a new form of Kozeny-Carman (KC) equation is also developed for estimating permeability on a set of benchmark rock samples, and a good agreement was achieved in terms of small relative error between measured and predicted permeabilities. Therefore, we can conclude that pore roughness should not be ignored in predicting permeability. Although the improved KC equation can predict permeability effectively too, it is not practical as the KC constant is difficult to determine. © 2025 The Author(s).

关键词： Fractal dimension

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching 9

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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9th International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2024

作者： Hu, Tao Zhang, Zan Bu, Chenyang Li, Lei Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350373554

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency. © 2024 IEEE.

关键词： Efficiency

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Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification 30

Context-Aware Non-Autoregressive Document-Level Translation ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yu, Hao Huang, Kaiyu Zhao, Anqi Liu, Junpeng Huang, Degen School of Computer Science and Technology Dalian University of Technology China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University China

ISBN: (纸本)9782493814104

Previous studies employ the autoregressive translation (AT) paradigm in the document-to-document neural machine translation. These methods extend the translation unit from a single sentence to a pseudo-document and encode the full pseudo-document, avoiding the redundant computation problem in context. However, the AT methods cannot parallelize decoding and struggle with error accumulation, especially when the length of sentences increases. In this work, we propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification (SA-CTC) loss for document-level neural machine translation. In particular, the SA-CTC loss reduces the search space of the decoding path by fixing the positions of the beginning and end tokens for each sentence in the document. Meanwhile, the context-aware architecture introduces preset nodes to represent sentence-level information and utilizes a hierarchical attention structure to regulate the attention hypothesis space. Experimental results show that our proposed method can achieve competitive performance compared with several strong baselines. Our method implements non-autoregressive modeling in Doc-to-Doc translation manner, achieving an average 46X decoding speedup compared to the document-level AT baselines on three benchmarks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Neural machine translation

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Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models 38

Hot or Cold? Adaptive Temperature Sampling for Code Generati...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Zhu, Yuqi Li, Jia Li, Ge Zhao, YunFei Jin, Zhi Mei, Hong Academy of Military Sciences China Ministry of Education School of Computer Science Peking University Beijing China Advanced Institute of Big Data Beijing China

ISBN: (纸本)1577358872

Recently, Large Language Models (LLMs) have shown impressive abilities in code generation. However, existing LLMs' decoding strategies are designed for Natural Language (NL) generation, overlooking the differences between NL and programming languages (PL). Due to this oversight, a better decoding strategy for code generation remains an open question. In this paper, we conduct the first systematic study to explore a decoding strategy specialized in code generation. With an analysis of loss distributions of code tokens, we find that code tokens can be divided into two categories: challenging tokens that are difficult to predict and confident tokens that can be easily inferred. Among them, the challenging tokens mainly appear at the beginning of a code block. Inspired by the above findings, we propose a simple yet effective method: Adaptive Temperature (AdapT) sampling, which dynamically adjusts the temperature coefficient when decoding different tokens. We apply a larger temperature when sampling for challenging tokens, allowing LLMs to explore diverse choices. We employ a smaller temperature for confident tokens avoiding the influence of tail randomness noises. We apply AdapT sampling to LLMs with different sizes and conduct evaluations on two popular datasets. Results show that AdapT sampling significantly outperforms state-of-the-art decoding strategy. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Temperature

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Preliminary analysis of traffic related pollution factors around primary and secondary schools during commuting in winter: A case study in Jinan city

Preliminary analysis of traffic related pollution factors ar...

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Healthy Buildings 2023: Asia and Pacific Rim

作者： Ren, Lulu Fu, Sifang Sun, Teng Liu, Jiying School of Thermal Engineering Shandong Jianzhu University Jinan China School of Data and Computer Science Shandong Women's University Jinan China

ISBN: (纸本)9781713890850

In recent years, the rapid development of urbanization and the increase in vehicle emissions have exacerbated urban air pollution. During commuting, the number of vehicles around primary and secondary schools has increased, and students' exposure to pollutants emitted by motor vehicles has also increased significantly. Traffic related pollutants around schools are affected by the layout of buildings, street characteristics, traffic volume, and traffic planning around schools, but there is less research on related aspects. This study selected 20 typical primary and secondary schools in Jinan to comprehensively analyze factors such as the architectural layout, street characteristics, traffic volume, and traffic planning around the school. Then, the method of fixed-point testing is used to test the traffic pollutants in different time periods and spatial locations around the school to obtain the exposure level of traffic pollutants. The results show that the concentration of pollutants around primary and secondary schools is related to traffic flow, road congestion, and the characteristics of surrounding buildings. The main time period during commuting time when the concentration of particulate matter increases due to traffic is 7:00-7:40 a.m. This study proposes methods to effectively alleviate the exposure level of air pollutants around schools, mitigate the impact of motor vehicle emissions on student health, and ensure the health and quality of life of primary and secondary school students, providing a reference for school site selection and planning and design. © 2023 Healthy Buildings 2023: Asia and Pacific Rim. All rights reserved.

关键词： Site selection

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