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15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021

作者： Zhang, Xiujuan Wang, Yongcai Li, Deying Chen, Wenping Ding, Xingjian School of Information Renmin University of China Beijing100872 China School of Computer Science Qufu Normal University Rizhao276826 China School of Software Engineering Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783030931759

Topology control protocol aims at efficiently adjusting the network topology to improve the performance and scalability for networks, for example, spanner topology characteristics can decrease communication links and ensure that the distance of any pair of communication nodes is only within a little increase from that in the original communication graph. This is especially essential to large-scale wireless ad hoc networks, which can be the front-end of ubiquitous Internet of Things (IoT). Since nodes failure is common because of their limited resource, a large number of previous works focused on fault tolerance topologies or fault tolerance spanner topologies. But these topologies are no longer sparse. In this paper, we propose an intuitive solution to construct sparse and robust spanner topology, which builds a sparse spanner topology quickly and reverts to the required spanner topology rapidly without any outside intervention when any node fails. Extensive simulations confirm the effectiveness and efficiency of our solution. © 2021, Springer Nature Switzerland AG.

关键词： Topology

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DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations 39

DearLLM: Enhancing Personalized Healthcare via Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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A simulated annealing method for minimum initial marking estimation in labeled Petri nets: Applications and comparative studies

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Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control engineering 2025年

作者： Yue, Hao Wang, Can Hu, Hesuan Chen, Junjie Wang, Min Pang, Shanchen College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China School of Electro-Mechanical Engineering Xidian University Xian China School of Computer Science and Engineering College of Engineering Nanyang Technological University Singapore Singapore School of Information Engineering Yangzhou University Jiangsu Yangzhou China State Key Laboratory of Chemical Safety Qingdao China

State estimation plays an important role in the field of system supervisory control. With the increase in the scale and complexity of practical systems, problems of state/event estimation for discrete event systems in Petri nets have attracted significant attention. One of the well-studied problems is how to estimate the minimum initial markings (MIMs) in a known labeled Petri net based on the observation of its event sequence. The existing approaches have disadvantages such as (1) much higher computational complexity in order to increase the MIM quantity;and (2) much fewer MIMs in order to decrease the computational complexity. In this paper, a variant of simulated annealing (SA) algorithm is developed to determine the set of MIMs. We take advantage of SA for global search in the problem’s search space to find initial markings that are minimal in their token counts, and then use the selection strategy of SA to determine MIMs from them. Finally, the effectiveness of the proposed method is illustrated through two simulation experiments of industrial applications. Comparisons with existing work are conducted to demonstrate the merits of our method. © IMechE 2025.

关键词： Discrete event systems initial marking estimation labeled Petri nets Petri nets simulated annealing supervisory control

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Correction: CMAE-3D: Contrastive Masked AutoEncoders for Self-Supervised 3D Object Detection(International Journal of computer Vision, 10.1007/s11263-024-02313-2)

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International Journal of computer Vision 2025年第6期133卷 3803-3803页

作者： Zhang, Yanan Chen, Jiaxin Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China

The original version of this article unfortunately contained error in co-author’s affiliation. The first affiliation should be State Key Laboratory of Complex and Critical software Environment, Beihang University, Beijing 100191, China instead of State Key Laboratory of Complex and Critical software Environment, school of computer science and engineering, Beihang University, Beijing 100191, China. Also, the author Yanan Zhang is affiliated with school of computer science and engineering, Beihang University, Beijing 100191, China. The original article has been corrected. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

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Enhancing Robust Text Classification via Category Description

Enhancing Robust Text Classification via Category Descriptio...

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IEEE International Conference on Data Mining (ICDM)

作者： Xin Gao Zhengye Zhu Xu Chu Yasha Wang Wenjie Ruan Junfeng Zhao Key Lab of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University School of Computer Science Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China University of Exeter Exeter UK

Despite the success of deep neural networks on text classification, their large capacity also leads to capturing task-irrelevant patterns such as label noise. Label noise is usually introduced into the data during label collection and causes nontrivial declines in performance due to the memorization effect. Though effort has been devoted to combating the label noise in other systems such as image classification, high-quality input features are necessary for discovering task-relevant patterns before memorizing the label noise. However, such a high-quality input feature requirement is hard to be satisfied for text classification due to the nature of natural language. To combat the label noise with low-quality input features in the text classification, we propose a novel framework that exploits external category descriptions to construct prototypes that can be used to denoise the input representation and alleviate the over-fitting. However, there still remains a challenge that the external category descriptions from other corpora could be semantically discrepant with the underlying task-specific classes in the training corpus. To align their semantics, we propose two regularizers that penalize sample-wise semantic-based deviations at the local level and class-wise structure-based deviations at the global level, respectively. Our extensive experiments across two open datasets and one real-world case study demonstrate that our method is superior to state-of-the-art baselines under various settings of label noise.

关键词： Training Deep learning Text categorization Semantics Neural networks Natural languages Prototypes

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

arXiv

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arXiv 2023年

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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SMT(LIA) Sampling with High Diversity

arXiv

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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： software testing

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A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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

Improved Fireworks Optimization Algorithm for Multi-product ...

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IEEE International Conference on Human-Machine Systems

作者： Feng Guo Xiwang Guo Jiacun Wang Weitian Wang Jinrui Cao Imane Bigaume Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA School of Computing Montclair State University Montclair USA Computer Science Department New Jersey City University Jersey City USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

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.

关键词： Accuracy Human-machine systems Collaboration Programming Stability analysis Whale optimization algorithms Recycling

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All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement

arXiv

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arXiv 2023年

作者： Xu, Xinmeng Tu, Weiping Han, Chang Yang, Yuhong National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Luojia Laboratory China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Monaural speech enhancement (SE) is an ill-posed problem due to the irreversible degradation process. Recent methods to achieve SE tasks rely solely on positive information, e.g., ground-truth speech and speech-relevant features. Different from the above, we observe that the negative information, such as original speech mixture and speech-irrelevant features, are valuable to guide the SE model training procedure. In this study, we propose a SE model that integrates both speech positive and negative information for improving SE performance by adopting contrastive learning, in which two innovations have consisted. (1) We design a collaboration module (CM), which contains two parts, contrastive attention for separating relevant and irrelevant features via contrastive learning and interactive attention for establishing the correlation between both speech features in a learnable and self-adaptive manner. (2) We propose a contrastive regularization (CR) built upon contrastive learning to ensure that the estimated speech is pulled closer to the clean speech and pushed far away from the noisy speech in the representation space by integrating self-supervised models. We term the proposed SE network with CM and CR as CMCR-Net. Experimental results demonstrate that our CMCR-Net achieves comparable and superior performance to recent approaches. © 2023, CC BY.

关键词： Speech enhancement

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