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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Weng, Shaowei Chu, Shu-Chuan Nguyen, Trong-The Dao, Thi-Kien School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

The development of effective drug therapies is a complex and multifaceted problem involving various biological, chemical, and computational challenges. Traditional drug development methods are often time-consuming and costly, necessitating innovative approaches. Multi-objective genetic algorithms (MOGAs) have emerged as a powerful tool for optimizing drug design by simultaneously considering multiple conflicting objectives, such as efficacy, safety, and cost. This paper reviews the application of MOGAs in drug discovery, highlighting their principles, advantages, and case studies, and discussing future directions in the field. Enhancing MOGAs in drug discovery are also outlined, emphasizing the integration with machine learning, advancements in computational infrastructure, and the potential for personalized medicine. The paper concludes by affirming the significant promise of MOGAs in accelerating the development of safe and effective drug therapies, thereby addressing critical needs in modern pharmacology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

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Classification of Underwater Fish Image Based on Supervised Contrastive Learning

Classification of Underwater Fish Image Based on Supervised ...

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Intelligent Robotics and Systems (ISoIRS), International Symposium on

作者： Qiankun Yan Xiujuan Du College of Computer Qinghai Normal University Qinghai Provincial Key Laboratory of IoT Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (数字)9798350364798

ISBN: (纸本)9798350364804

Classification of underwater fish images is of great scientific research significance for assessing the ocean's health. However, compared with land-based optical images, underwater optical images are characterized by color distortion, low contrast, and high noise intensity, making it challenging to perform effective feature extraction and train a highly accurate recognition model for underwater images. This paper proposes a supervised contrastive learning-based classification method for underwater fish images. First, the residual idea is integrated to improve the representation of the last four stages of the residual network to obtain the stage residual network, which more fully utilizes the shallow features and reduces the risk of network overfitting at the same time; second, the channel attention mechanism is incorporated at the end of the last four stages of the backbone network, which prompts the model to pay more attention to the feature channels with high contribution; finally, the contrastive learning loss function is improved by the introduction of labels, and realizes the supervised contrastive learning. Dividing the backbone network and the classifier into two stages of training effectively closes the intra-class distance and distances the inter-class distance and improves the model's performance. The experimental results show that the accuracy, precision, recall, and F1 value of this paper's model on the public benchmark dataset Fish4Knowledge are 99.28%, 98.17%, 94.15%, and 95.71%, and it can realize the classification of underwater fish images under extreme noise conditions more efficiently.

关键词： Training Accuracy Oceans Contrastive learning Optical distortion Fish Optical imaging

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Enhanced polysulfide redox kinetics by niobium oxynitrides via insitu adsorptive and catalytic effect in wide temperature range

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Nano Research 2022年第7期15卷 6200-6207页

作者： Benben Wei Chaoqun Shang Qiao Cu Le Hu Xuelian Fu Guofu Zhou Xin Wang School of Materials Science and Engineering&Hubei Key Laboratory of Plasma Chemistry and Advanced Materials Wuhan Institute of TechnologyWuhan 430205China Faculty of Materials Science and Chemistry China University of GeosciencesWuhan 430074China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology&South China Academy of Advanced Optoelectronics South China Normal UniversityGuangzhou 510006China School of Materials Science and Engineering&State Key Laboratory of Material Processing and Die and Mold Technology Huazhong University of Science and TechnologyWuhan 430074China

The development of Li-S batteries(LSBs)is hindered by the low utilization of S species and sluggish redox reaction *** metal oxides always possess high adsorption to polar S species,while conductive metal nitrides show fast electron transport and ensure fast redox reaction of S *** combination merits of metal oxides and metal nitrides in one provide an effective strategy to improve the electrochemical performance of *** this work,defect design of niobium oxynitrides highly dispersed on graphene(NbON-G)is evaluated as effective trapper and catalyst for S *** to the effective structural merits including enriched active sites,alleviated volume variation,defect modulated electronic property,and in-situ chemisorption and catalytic conversion of soluble lithium polysulfides(LiPSs),the LSBs with NbON-G modified separator show remarkably enhanced performance compared to NbN-G and Nb_(2)O_(5)-***,even at low temperature of−40°C,the LSBs with NbON-G can operate for 1,000 cycles with 0.04%capacity decay per cycle(Rate:2 C).

关键词： lithium polysulfides Li-S batteries niobium oxynitrides catalyst low temperature

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Solving Constrained Multi-objective Optimization Problems with Passive Archiving Mechanism 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Jia, Huijuan Zhang, Kai Shen, Chaonan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430065 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

During these years, many advanced constrained multi-objective evolutionary algorithms (CMOEAs) have been developed to solve constrained multi-objective optimization problems (CMOPs). However, some existing constrained multi-objective algorithms do not use an archiving mechanism, which leads to easy loss of the searched feasible solutions during the search process. In addition, even if some algorithms use an archiving mechanism, an unsuitable archiving update strategy can cause difficulty in balancing the diversity and convergence of the archive set. To address these challenges, a two-stage constrained multi-objective evolutionary algorithm based on a passive archiving mechanism (PA-CMOEA) is proposed in this paper. In the first stage, the constrained multi-objective problem is modeled as an unconstrained multi-objective problem, which allows the population to fully explore the decision space. The archive set is passively updated in a constraint-domination principle (CDP) criterion using population information. The goal of this stage is for the archive set to converge to the constrained Pareto front region of the problem with the help of population information. In the second stage, a local search is performed on the archive set. Specifically, the convergence and diversity of the archived solution set is improved while ensuring that the feasibility of the archived set is not worse. Finally, the algorithm outputs the archive set. To estimate the performance of the proposed algorithm, experiments are conducted on the LIRCMOP and CF benchmarks in comparison with some of the most popular algorithms. The experimental results demonstrate that the proposed algorithm has a competitive advantage compared with the comparative algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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Analysis of vocational education dynamics evolution based on topic modeling 14

Analysis of vocational education dynamics evolution based on...

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14th International Conference on information technology in Medicine and Education, ITME 2024

作者： Wang, Xinhua Yu, Xiaomei Jiang, Yunmeng Zheng, Xiangwei Li, Wei Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (纸本)9798350356670

Clarifying the focus of vocational education dynamics is crucial for understanding the current situation and future development trends. However, comprehensive exploration on vocational education dynamics is inadequate presently. In this paper, textual information on vocational education is analyzed with Latent Dirichlet Allocation, BERTopic, and the Dynamic Topic Model. Two key issues are thoroughly investigated as follows: (1) What are the main aspects of the current development of vocational education;(2) How to learn the embeddings of vocational education text and further to promote the formulation of policy. The experiments are conducted and the experimental results show that LDA and BERTopic model achieve effective performance in clustering the texts of vocational education dynamics, and the DTM captures the topics trend of vocational education, which facilitate the government to guide the formulation and implementation of policies. © 2024 IEEE.

关键词： Apprentices

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GHTO: Improving Byzantine Robustness in Distributed Machine Learning Through Gradient Heterogeneity Twofold Optimization 6

GHTO: Improving Byzantine Robustness in Distributed Machine ...

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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Li, Caiyun Yang, Ming Cao, Jian Wu, Xiaoming He, Yunpeng Wang, Xin Jinan250014 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China Shandong Evayinfo Technology Co. Ltd Jinan250101 China

ISBN: (纸本)9798331541750

Heterogeneous datasets induce local gradient variance and global gradient variance in distributed learning, collectively referred to as gradient heterogeneity. Gradient heterogeneity increases the system's vulnerability to Byzantine attacks, affecting performance. To address this issue, the paper proposes a Gradient Heterogeneity Twofold Optimization (GHTO) algorithm to enhance the Byzantine robustness of the system in non-independent and identically distributed environments. Specifically, a Local Gradient Correction (LGC) algorithm is proposed, which aims to reduce the local gradient variance. By further integrating LGC with momentum and bucketing techniques to achieve dual optimization of local and global gradient variance. Experimental results demonstrate the proposed LGC algorithm diminishes local gradient variance, and the GHTO method optimizes both local and global gradient variance, which further strengthens the robustness of the system against Byzantine attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Visible-infrared person re-identification via specific and shared representations learning

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Visual Intelligence 2023年第1期1卷 1-12页

作者： Zheng, Aihua Liu, Juncong Wang, Zi Huang, Lili Li, Chenglong Yin, Bing Information Materials and Intelligent Sensing Laboratory of Anhui Province the Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei 230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China iFLYTEK Research No. 666 West Wangjiang Road Anhui Hefei China

The primary goal of visible-infrared person re-identification (VI-ReID) is to match pedestrian photos obtained during the day and night. The majority of existing methods simply generate auxiliary modalities to reduce the modality discrepancy for cross-modality matching. They capture modality-invariant representations but ignore the extraction of modality-specific representations that can aid in distinguishing among various identities of the same modality. To alleviate these issues, this work provides a novel specific and shared representations learning (SSRL) model for VI-ReID to learn modality-specific and modality-shared representations. We design a shared branch in SSRL to bridge the image-level gap and learn modality-shared representations, while a specific branch retains the discriminative information of visible images to learn modality-specific representations. In addition, we propose intra-class aggregation and inter-class separation learning strategies to optimize the distribution of feature embeddings at a fine-grained level. Extensive experimental results on two challenging benchmark datasets, SYSU-MM01 and RegDB, demonstrate the superior performance of SSRL over state-of-the-art methods. © The Author(s) 2023.

关键词： Cross-modality Person Re-identification (Re-ID) Shared Representations Specific Representations

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Research on Multi-modal Multi-objective Path Planning by Improved Ant Colony Algorithm 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Jing, Juan Zhang, Ling Shen, Chaonan Zhang, Kai School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430065 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

A research about the path planning problem has been popular topic nowadays and some effective algorithms have been developed to solve this kind of problem. However, the existing algorithms to solve the path problem can only find a single optimal path, cannot satisfactorily find multiple groups of optimal solutions at the same time, and it is very necessary to propose as many solutions as possible. So this paper carries out a research on the Multi-modal Multi-Objective Path Planning (MMOPP), the objective is to find all sets of Pareto optimal path solutions from the start point to the end point in a grid map. This paper proposes a multi-modal multi-objective ant colony path planning optimization algorithm based on matrix preprocessing technology and Dijkstra algorithm (MD-ACO). Firstly, a new method of storing maps that reduces the size of the map and reduces the size of the decision space has been proposed in this paper. Secondly, using the characteristics of the Dijkstra algorithm that can quickly find the optimal path, generate an initial feasible solution about the problem, and improve the problem that the initial pheromone of ant colony algorithm is insufficient and searching for solutions is slow. Thirdly, a reasonable threshold is set for the pheromone to avoid algorithm getting stuck in local optimal solution. Finally, the algorithm is tested on the MMOPP test sets to evaluate the performance of the algorithm, and the results show that MD-ACO algorithm can solve MMOPP and get the optimal solution set. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization

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Overview of CCL24-Eval Task1: Chinese Frame Semantic Parsing Evaluation 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Yang, Peiyuan Li, Juncai Yan, Zhichao Su, Xuefeng Li, Ru School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Shanxi Taiyuan030006 China School of Modern Logistics Shanxi Vocational University of Engineering Science and Technology Shanxi Jinzhong030609 China

ISBN: (纸本)9780000000002

Chinese Frame-semantic Parsing (CFSP) aims to extract fine-grained frame-semantic structures from texts, which can provide fine-grained semantic information for natural language understanding models to enhance their abilities of semantic representations. Based on the CCL-23 CFSP evaluation task, we introduce construction grammar to expand the targets, as basic units activating frames in texts, from word-style to construction-style, and publish a more challenging CFSP evaluation task in CCL-2024. The evaluation dataset consists of 22,000 annotated examples involving nearly 695 frames. The evaluation task is divided into three subtasks: frame identification, argument identification, and role identification, involving two tracks: close track and open track. The evaluation task has attracted wide attention from both industry and academia, with a total of 1988 participating teams. As for the evaluation results, the team from China University of Petroleum won the first place in the closed track with the final score of 71.34, while the team frome Suzhou University won the first place in the open track with the final socre of 48.77. In this article, we reports the key information about the evaluation task, including key concepts, evaluation dataset, top-3 results and corresponding methods. More information about this task can be found on the website of the CCL-2024 CFSP evaluation task 1 © 2024 China National Conference on Computational Linguistics.

关键词： Semantics

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Combined Representation Learning for Uncertain Knowledge Graphs 2

Combined Representation Learning for Uncertain Knowledge Gra...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Qiao, Rui Zhong, Xiaolei Fu, Yang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Dianchi College Kunming China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350386639

Uncertain knowledge graphs store the relations between entities in the real world in the form of weighted triples, where the weights indicate the confidence scores of the triples to be true. However, there is limited research on the inference of uncertain knowledge graphs, especially in the integration of symbolic and numerical methods. Rules are explicit knowledge. Encoding the rules directly can provide more knowledge graph semantic information and can make full use of the interpretability and accuracy of logical rules. Unfortunately, none of the existing methods directly encode rules for uncertain knowledge graphs, but only use rules to calculate the confidence of unseen triples in training data. In this paper, we propose a new reasoning model CRLUKG for uncertain knowledge graphs, that combines the advantages of symbolic and numerical reasoning. We directly encode rules mined from the knowledge graph as one of the inputs of the CRLUKG model. Compositional representation learning with triple and rule embedding is implemented for optimizing objective function for triples and relations pairs. Experimental results demonstrate that our proposed method enhances the accuracy of reasoning, and furthermore, CRLUKG exhibits superior performance in comparison to baseline models when handling multi-task scenarios. © 2024 IEEE.

关键词： Knowledge graph

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