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IEEE Conference on Global Communications (GLOBECOM)

作者： Minyang Li Xiangyun Tang Tao Zhang Guangyuan Liu Yu Weng Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Minzu University of China China School of Software Engineering Beijing Jiaotong University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets.

关键词： Training Accuracy Federated learning Distributed databases Robustness data models Servers Global communication Standards Faces

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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International Conference on data Engineering

作者： Yuqi Chen Hanyuan Zhang Weiwei Sun Baihua Zheng School of Computer Science & Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach.

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Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints

Reinforcement Learning Framework for Delivery Capacity Sched...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Zhongneng Ma Zhuorui Qin Shuxia Shi Qingyao Wu South China University of Technology Guangzhou China School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage,

关键词： Processor scheduling Prevention and mitigation Neural networks Reinforcement learning Real-time systems Scheduling Digital twins Smart grids Resilience Multi-agent systems

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Analysis and Optimization of Ancient Building Lighting Based on DIALux 32

Analysis and Optimization of Ancient Building Lighting Based...

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第32届中国控制与决策会议

作者： Dou Jiaming Ma Hongyan Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to provide a better interior visual experience for visitors in ancient buildings, insufficient illumination and non-uniformity need to be solved urgently. DIALux software is used to simulate an ancient building to find out its problems, like non-uniformity and north-east corner illumination lack. By the use of controlling variable method, six different cases were proposed to optimize the lighting environment. The results show that case 3 and case 4 are chosen as the best cases to solve the defects. It is verified that the overall environment has better illumination and uniformity.

关键词： Ancient Building Lighting Environment Optimization DIALux

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Lyapunov-guided Deep Reinforcement Learning for Semantic-aware AoI Minimization in UAV-assisted Wireless Networks

arXiv

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arXiv 2024年

作者： Long, Yusi Gong, Shimin Sun, Sumei Lee, Gary Li, Lanhua Niyato, Dusit The School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen Campus Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A*STAR Singapore The College of Computing and Data Science Nanyang Technological University Singapore

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Stable and Feature Decoupled of Deep Mutual Information Maximization Based on Wasserstein Distance

SSRN

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SSRN 2023年

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University PR Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University PR Guiyang550025 China Guizhou Big Data Academy Guizhou University PR Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University PR Guiyang550025 China

Deep learning techniques usually require large amounts of expensive labeled data to train networks, and the extracted deep representations are usually mixed with multiple attributes having uninterpretability, which limits the application and development of deep learning techniques. Therefore, it is crucial to investigate the stability of training models by unsupervised learning methods and to learn feature decoupled representations. Although the method based on depth mutual information maximization(Deep InfoMax or DIM) has achieved good results in the field of computer vision, it is extremely prone to "gradient explosion" or "gradient disappearance", which makes the training model unstable. At the same time, it is difficult to decouple the features learned by the encoder. In order to solve these problems, we stable and feature decoupled of deep mutual information maximization based on Wasserstein distance, which we call the method called WDIM. The method first addresses the instability of the model trained by the DIM method and proposes to measure the distance between the encoder output and the prior distribution based on the Wasserstein distance, which is a good guide to the stability of the model training. Second, to address the problem that the features learned by the encoder are difficult to be decoupled, the mutual information between the features learned by the hidden layer of the encoder and the intermediate layer is required to be minimized during the training of the encoder so that the features learned by each filter are as uncorrelated as possible to achieve feature decoupling. Finally, the WDIM method is validated on the CIFAR-10, CIFAR-100, STL-10, and FashionMNIST datasets. Experiments show that our proposed WDIM method is more stable for model training, faster model convergence, and more significant feature decoupling. © 2023, The Authors. All rights reserved.

关键词： Network coding

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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Computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Source Code Summarization in the Era of Large Language Models

Source Code Summarization in the Era of Large Language Model...

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International Conference on Software Engineering (ICSE)

作者： Weisong Sun Yun Miao Yuekang Li Hongyu Zhang Chunrong Fang Yi Liu Gelei Deng Yang Liu Zhenyu Chen College of Computing and Data Science Nanyang Technological University Singapore Singapore State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering University of New South Wales Sidney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of coderelated tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs, which covers multiple aspects involved in the workflow of LLMbased code summarization. Specifically, we begin by examining prevalent automated evaluation methods for assessing the quality of summaries generated by LLMs and find that the results of the GPT-4 evaluation method are most closely aligned with human evaluation. Then, we explore the effectiveness of five prompting techniques (zero-shot, few-shot, chain-of-thought, critique, and expert) in adapting LLMs to code summarization tasks. Contrary to expectations, advanced prompting techniques may not outperform simple zero-shot prompting. Next, we investigate the impact of LLMs' model settings (including top_p and temperature parameters) on the quality of generated summaries. We find the impact of the two parameters on summary quality varies by the base LLM and programming language, but their impacts are similar. Moreover, we canvass LLMs' abilities to summarize code snippets in distinct types of programming languages. The results reveal that LLMs perform suboptimally when summarizing code written in logic programming languages compared to other language types (e.g., procedural and object-oriented programming languages). Finally, we unexpectedly find that CodeLlamaInstruct with 7B parameters can outperform advanced GPT-4 in generating summaries describing code design rationale and asserting code properties. We hope that our findings can provide a comprehensive understanding of code summarization in the era of LLMs.

关键词： Computer languages Codes Systematics Source coding Large language models Object oriented modeling Software Prompt engineering Object oriented programming Software engineering

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Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

arXiv

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arXiv 2024年

作者： Xiong, Qi Tang, Kai Ma, Minbo Zhang, Ji Xu, Jie Li, Tianrui School of Computing and Artificial Intelligence Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China School of Computing The University of Leeds LeedsLS2 9JT United Kingdom

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by 1.47% to 9.19% and change value errors by 4.57% to 15.78%, highlighting its substantial performance improvements. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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Learning Unified Embeddings for Recommendation via Meta-path Semantics 21

Learning Unified Embeddings for Recommendation via Meta-path...

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29th ACM International Conference on Multimedia, MM 2021

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781450386517

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance;2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Embeddings

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