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40th IEEE International Conference on data engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Demand Response Scheme: Achieving Sustainability in Distributed data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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TIGER: A Generating-Then-Ranking Framework for Practical Python Type Inference

arXiv

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arXiv 2024年

作者： Wang, Chong Zhang, Jian Lou, Yiling Liu, Mingwei Sun, Weisong Liu, Yang Peng, Xin College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China School of Software Engineering Sun Yat-sen University China

Python’s dynamic typing system offers flexibility and expressiveness but can lead to type-related errors, prompting the need for automated type inference to enhance type hinting. While existing learning-based approaches show promising inference accuracy, they struggle with practical challenges in comprehensively handling various types, including complex parameterized types and (unseen) user-defined types. In this paper, we introduce TIGER, a two-stage generating-then-ranking (GTR) framework, designed to effectively handle Python’s diverse type categories. TIGER leverages fine-tuned pre-trained code models to train a generative model with a span masking objective and a similarity model with a contrastive training objective. This approach allows TIGER to generate a wide range of type candidates, including complex parameterized types in the generating stage, and accurately rank them with user-defined types in the ranking stage. Our evaluation on the ManyTypes4Py dataset shows TIGER’s advantage over existing methods in various type categories, notably improving accuracy in inferring user-defined and unseen types by 11.2% and 20.1% respectively in Top-5 Exact Match. Moreover, the experimental results not only demonstrate TIGER’s superior performance and efficiency, but also underscore the significance of its generating and ranking stages in enhancing automated type inference. © 2024, CC BY.

关键词： Contrastive Learning

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A Survey of Internet of Things in Healthcare 29th

A Survey of Internet of Things in Healthcare

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Chen, Shuwen Wang, Jiaji Song, Zhijun School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031449468

With the rapid development of computers, Internet of Things (IoT) technology is becoming more and more closely integrated with the healthcare sector. This article introduces two major applications of IoT in healthcare: hospital information management and remote healthcare. In digitizing the healthcare industry, it is a great challenge to avoid the leakage of private medical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Why not Just Look for Answers? Using a More Direct Way for API Recommendation

Why not Just Look for Answers? Using a More Direct Way for A...

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Asia-Pacific Conference on Software engineering

作者： Changxin Liu Ling Xu Wenhan Mu Rui Qin 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)9798331534011

ISBN: (纸本)9798331534028

Automated API recommendation technology can help developers quickly find target APIs that meet their require-ments. Some retrieval-based API recommendation approaches, such as BIKER and CLEAR, adopt an indirect API recommen-dation strategy. Although these approaches have shown good performance, we observed two limitations in these approaches: (1) These approaches heavily relies on the coverage of the collected question titles proposed by developers; (2) These approaches ignore the API source code information. To overcome the two limitations, we propose an approach named AnsAPIRec (Answer- directed API Recommendation), which leverages a pre-trained model and joint-attention mechanism in this paper. In addition to the fully qualified name of the API, AnsAPIRec extracts the corresponding source code from the JDK as an additional feature. AnsAPIRec adopts a direct API recommendation strategy, which directly calculates the similarity between user queries and API features, i.e., API fully qualified name and API source code, to find the target API, alleviating the reliance on question titles. To further enhance AnsAPIRec's ability to understand the semantics of the inputs, we propose a joint-attention mechanism to learn the interdependent representations between the queries and API features. This deep semantic fusion mechanism enables AnsAPIRec to perform well in understanding user intent and API functionality. We reused the APIBench-Q dataset, containing 5885 queries for training and 653 queries for testing. The experimental result shows that AnsAPIRec achieved an MRR of 0.473, significantly outperforming the baseline approaches, BIKER and CLEAR.

关键词： Training Source coding Semantics Feature extraction Testing Software engineering

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MECNet: Multi-Scale Exposure-Consistency Learning via Fourier Transform for Exposure Correction

MECNet: Multi-Scale Exposure-Consistency Learning via Fourie...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gaofei Qiao Zhibin Zhang Liqiang He Key Laboratory of Wireless Networks and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia University Hohhot China Geomechanica Inc Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the real world, due to various challenging lighting conditions such as low light, underexposure, and overexposure, captured images often exhibit undesirable appearances. Given that images with different exposure levels require different correction processes, a single neural network struggles to produce satisfactory results. We propose a coarse-to-fine exposure correction model for learning exposure consistency representation to address underexposure and overexposure issues. Building upon the bilateral activation mechanism, we introduce the Fourier transform to capture global information and fuse it with locally extracted information through convolution to achieve superior feature representation. Additionally, we employ Laplacian pyramids to decompose the source image into different spatial frequency bands, then the image details are enhanced by denoising high-frequency layers. Experimental results on the MSEC and SICE datasets demonstrate the superiority of our proposed method over current state-of-the-art approaches. Our code will be made available on GitHub.

关键词： Representation learning Laplace equations Fourier transforms Image color analysis Plants (biology) Noise reduction Neural networks Lighting Feature extraction Software development management

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Learning to Learn Transferable Generative Attack for Person Re-Identification

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IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Efficient Heuristic Algorithm for Precedence-Constrained Multi-Robot Task Assignment

Efficient Heuristic Algorithm for Precedence-Constrained Mul...

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第43届中国控制会议

作者： Xiaoshan Bai Baode Li Fuqiang Liu Li Qiu Guangzhong Cao College of Mechanical and Control Engineering Shenzhen University Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University College of Mechanical and Vehicle Engineering Chongqing University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Due to the widespread application of multi-robot systems, the efficient task assignment for a team of robots has become particularly critical for performing complex tasks. This paper studies the task assignment problem for multiple robots to visit a group of target locations with precedence constraints, which determine the order/sequence in which certain target locations need to be visited before others. A marginal-cost-based heuristic algorithm is developed to minimize the time for the robots to visit the last target location. The algorithm first uses a timestamp strategy to update the anticipated visiting time of each assigned target and the corresponding earliest time that each successor target can be visited under the precedence constraints. Then, it utilizes the topological sorting technique and the marginal-cost-based mechanism to insert the feasible target that induces the minimum marginal cost into the robots' current routes. Numerical simulations demonstrate the effectiveness of the proposed algorithm compared with the popular greedy algorithm and the existing simple iterative auction algorithm.

关键词： Multi-robot task assignment Precedence constraints Heuristic algorithm Marginal cost

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