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Journal of Network Intelligence 2024年第2期9卷 959-970页

作者： Yue, Liya Hu, Pei Zhu, Jiulong Fanli Business School Nanyang Institute of Technology Nanyang473004 China School of Computer and Software Nanyang Institute of Technology Nanyang473004 China

Educational data mining explores the features that are related to students’ academic performance. To improve the prediction accuracy in college English, we propose a model based on feature selection and particle swarm optimization (PSO). Since feature selection is a typical binary optimization, to make the model solve the problem, an improved binary PSO (IBPSO) balances global and local search abilities through a new transfer function. A novel learning method develops the position update of PSO to facilitate the algorithm to search for solutions in more space, while advanced social cognitive factors accelerate convergence. IBPSO outperforms the original binary PSO, henry gas solubility optimization, and binary sine cosine algorithm on the benchmark datasets from UCI. In our proposed academic performance model, these algorithms predict the college English grades of sophomores at a university. IBPSO significantly improves classification accuracy, and the experiments illustrate that demographic features are the main factors affecting students’ academic performance in college English. © 2024, J. Network Intell. All rights reserved.

关键词： Students

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Optimizing local search-based partial MaxSAT solving via initial assignment prediction

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Science China(Information Sciences) 2025年第2期68卷 59-73页

作者： Chanjuan LIU Guangyuan LIU Chuan LUO Shaowei CAI Zhendong LEI Wenjie ZHANG Yi CHU Guojing ZHANG School of Computer Science and Technology Dalian University of Technology School of Software Beihang University Institute of Software Chinese Academy of Sciences School of Computer Science Peking University

Partial maximum satisfiability(PMS) is a significant generalization of Boolean satisfiability(SAT) and maximum satisfiability(MaxSAT), by introducing hard clauses and soft clauses. Compared with SAT and MaxSAT, the PMS problem has more real-world applications where both hard and soft constraints are involved. Local search is an effective incomplete method for solving PMS and is useful for important domains where good-quality solutions are desired within reasonable *** local search PMS solvers, the approach for initial assignment generation is crucial because its effectiveness significantly affects practical performance. In this study, we propose a novel initial assignment prediction approach, called InitPMS. When predicting an assignment for PMS, InitPMS considers the specific structure of PMS instances, i.e., distinguishing hard and soft clauses. Our experiments on extensive PMS instances from MaxSAT evaluations(MSEs) 2020 and 2021 show that InitPMS significantly boosts the performance of five state-of-the-art local search PMS solvers, demonstrating its generality. In addition,our results indicate that incorporating InitPMS could improve the performance of one of the best incomplete PMS solvers in MaxSAT Evaluation 2021, indicating that InitPMS might help advance the state of the art in PMS solving.

关键词： MaxSAT local search initial assignment incomplete method assignment prediction

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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A Deep Video Hashing Algorithm Based on CNNs 13

A Deep Video Hashing Algorithm Based on CNNs

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Wang, Baosheng School of Computer and Software Nanyang Institute of Technology Nanyang China

ISBN: (纸本)9798350305463

This article proposes a new deep video hashing algorithm to improve video retrieval speed. This method utilizes the powerful feature extraction ability of neural networks (CNNs) to extract representative video and temporal features using the spatiotemporal information after stacking convolutional pooling layers, and then calculates compact binary codes. In the loss function, a dual objective loss function is used, which aims to maximize the inter class distance of video feature pairs and minimize the intra class distance, as well as minimize the loss between real valued codes and binary codes. This training process uses the Siamese network. Finally, calculations were conducted on databases CCB and JHMDB, and compared with other hash methods. The experimental results showed the effectiveness of the method. © 2024 IEEE.

关键词： Binary codes

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Study of stability and object tracking of traffic video image for smart cities

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Personal and Ubiquitous Computing 2024年第2期28卷 431-442页

作者： Yongfeng, Xing Luo, Zhong Xian, Zhong School of Computer Science and Technology Wuhan University of Technology 122 Luoshi Road Wuhan430070 China School of Software Nanyang Institute of Technology Nanyang473000 China

In order to solve the problem that image information is not effectively utilized because of unclear traffic video images and random jitter between image sequences, this paper has studied how to achieve stability of traffic video images and proposed an improved Mean Shift algorithm about how to conduct object centroid registration in compensation for the deviations in space localization and on this basis, how to select kernel window width to eliminate the errors in scale positioning. This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications;removes or relieves the impact motion has on imaging, improves the quality of the video image information obtained, automatically adjusts the size of window according to the scale changes of moving object in the image, and effectively enhances the stability and real-time of object tracking. The experiment has proven that the algorithm of this paper is practical to some extent. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Stability

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COMPLEX PRODUCT QUALITY DIAGNOSIS FRAMEWORK BASED ON ASSOCIATION RULE MINING 14

COMPLEX PRODUCT QUALITY DIAGNOSIS FRAMEWORK BASED ON ASSOCIA...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Xu, Erbao Shan, Pingping Zou, Fangfang School of Computer and Software Nanyang Institute of Technology Nanyang China

ISBN: (纸本)9781837242672

Quality diagnosis serves as a critical tool in quality control, playing a pivotal role in identifying potential quality issues and thereby enhancing the overall quality management of enterprise products. However, conventional quality diagnosis methods often underutilize the valuable data relationships between process parameters and quality indicators, resulting in challenges in providing a clear diagnostic foundation for quality anomalies, thereby complicating quality control efforts. To address this issue within the context of modern manufacturing data characteristics, this study proposes a product quality diagnosis method based on association rule mining. The methodology begins with the application of the Partitioning Around Medoid (PAM) algorithm for data pre-processing, including clustering and discrete coding. Subsequently, the Frequent Pattern Growth (FP-Growth) algorithm is employed to mine rules from the discretely coded data. Finally, the Whale Optimization Algorithm (WOA) is leveraged to extract optimal rules and establish a comprehensive rule library for effectively tracing the root causes of anomalies, thereby enabling precise quality diagnosis of products. © The Institution of Engineering & technology 2024.

关键词： Data quality

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A Knowledge Representation Learning Model Integrating Multiple Information 24

A Knowledge Representation Learning Model Integrating Multip...

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2024 International Conference on Image Processing, Intelligent Control and computer Engineering, IPICE 2024

作者： Fu, Zijian School of Computer and Software Nanyang Institute of Technology China

ISBN: (纸本)9798400710285

Most of the current knowledge representation techniques only consider triplet structure information in knowledge graph, but ignore the rich potential semantic information contained in knowledge graph, such as entity neighborhood information and semantic category information. In this paper, based on the classical TransE method, we propose a knowledge representation learning model by integrating the entity’s neighborhood semantic information, using Bi-LSTM neural network and attention mechanism to obtain the path information. Our model takes entity as the center, constructs the mixed neighborhood representation, and fuses entity semantic class information to extend entity semantic class to mixed neighborhood semantic class. Then, the entity and relational embedding are obtained by the neighborhood semantic model are used as the initialization vector, and the corresponding initialization vector of the entity relation pair in the relational path is spliced as the input. The semantic association between the entities in the relational path is captured through the Bi-LSTM network. Finally, multiple relational paths are mixed and represented by the attention mechanism. In relation prediction and link prediction tasks, the comparative experiments show that our model outperforms other models in both MAP and MRR. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Improving Fully Non-Autoregressive Translation with Pre-Trained Language Models 24

Improving Fully Non-Autoregressive Translation with Pre-Trai...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Fu, Zijian Wang, Shuheng School of Computer and Software Nanyang Institute of Technology Henan Nanyang China

ISBN: (纸本)9798400712692

In recent years, Non-Autoregressive Translation (NAT) has received lots of attention because of its outstanding decoding speed. However, there is a certain gap between the NAT model and its autoregressive comparator. It, how to effectively narrow this gap, remains to be further investigated. In this work, we use BERT to improve the performance of NAT model. Specifically, we add a CTC model on the top of BERT for extracting semantic and learning alignment. In order to test the effect of the model, we conducted the experiments on three public translation datasets. The experimental results show that our model can significantly outperforms existing non-autoregressive baselines. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Neural machine translation

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