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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

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ACS Chemical Biology 2023年第3期18卷 476-483页

作者： Jian, Bo-Siyuan Chiou, Shao-Lun Hsu, Chun-Chia Ho, Josh Wu, Yu-Wei Chu, John Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 10675 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 10675 Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei 10675 Taiwan

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms. © 2023 The Authors. Published by American Chemical Society.

关键词： Algorithms Bacteria Bioinformatics and computational biology Peptides and proteins Pharmaceuticals

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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TSC: A Simple Two-Sided Constraint against Over-Smoothing

arXiv

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arXiv 2024年

作者： Peng, Furong Liu, Kang Lu, Xuan Qian, Yuhua Yan, Hongren Ma, Chao Institute of Big Data Science and Industry School of Computer and Information Technology Shanxi University Shanxi Taiyuan China College of Physics and Electronic Engineering Shanxi University Shanxi Taiyuan China HOPERUN Information Technology Jiangsu Nanjing China

Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN by leveraging information from high-order neighbors. However, the increase of the network depth will induce the over-smoothing problem, which can be attributed to the quality and quantity of neighbors changing: (a) neighbor quality, node’s neighbors become overlapping in high order, leading to aggregated information becoming indistinguishable, (b) neighbor quantity, the exponentially growing aggregated neighbors submerges the node’s initial feature by recursively aggregating operations. Current solutions mainly focus on one of the above causes and seldom consider both at once. Aiming at tackling both causes of over-smoothing in one shot, we introduce a simple Two-Sided Constraint (TSC) for GCNs, comprising two straightforward yet potent techniques: random masking and contrastive constraint. The random masking acts on the representation matrix’s columns to regulate the degree of information aggregation from neighbors, thus preventing the convergence of node representations. Meanwhile, the contrastive constraint, applied to the representation matrix’s rows, enhances the discriminability of the nodes. Designed as a plug-in module, TSC can be easily coupled with GCN or SGC architectures. Experimental analyses on diverse real-world graph datasets verify that our approach markedly reduces the convergence of node’s representation and the performance degradation in deeper GCN. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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MAC-Based Symmetric Key Protocol for Secure Traffic Forwarding in Drones 6th

MAC-Based Symmetric Key Protocol for Secure Traffic Forwardi...

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6th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2022

作者： Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Ma, Junchao Al Sibahee, Mustafa A. Khalefa, Mustafa S. Honi, Dhafer G. Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China Faculty of Biological and Physical Sciences Tom Mboya University Homabay40300 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq

ISBN: (纸本)9783031151002

Unmanned aerial vehicles have been deployed for surveillance in highly sensitive domains such as in the military. As such, the data exchanged between the operators and these aerial vehicles must be protected as any malicious access may lead to leakages and adversarial control of the drones. To achieve this, many schemes have been developed based on techniques such as blockchains, elliptic curve cryptography, dynamic keys, physically unclonable function, asymmetric and symmetric cryptography among others. However, majority of these protocols have been shown to be inefficient for deployment in this environment, while others have security holes that be exploited by attackers to cause mayhem in these networks. In this paper, a protocol that leverages on quadratic residues and Chinese remainder theorem is developed. Its security analysis shows that it offers mutual authentication, non-repudiation, unlinkability, identity privacy and traceability for misbehaving drones. It is also resilient against impersonation, forgery and replay attacks. In terms of performance, this protocol has the least execution time and relatively lower bandwidth requirements. © 2022, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Drones

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs

arXiv

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arXiv 2024年

作者： Yang, Boyang Tian, Haoye Ren, Jiadong Zhang, Hongyu Klein, Jacques Bissyandé, Tegawendé F. Le Goues, Claire Jin, Shunfu School of Information Science and Engineering Yanshan University China Jisuan Institute of Technology Beijing JudaoYouda Network Technology Co. Ltd. China School of Computer Science Carnegie Mellon University United States SnT University of Luxembourg Luxembourg School of Big Data and Software Engineering Chongqing University China

Large language models (LLMs) have demonstrated remarkable capabilities on a broad spectrum of downstream tasks. Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks are however generally overlooking the need to reason about the logic behind code changes, beyond syntactic patterns in the data. High-performing fine-tuning experiments also usually come at very high computational costs. With MOREPAIR, we propose a novel perspective on the learning focus of LLM fine-tuning for program repair: we not only adapt the LLM parameters to the syntactic nuances of the task of code transformation (objective 1), but we also specifically fine-tune the LLM with respect to the logical reason behind the code change in the training data (objective 2). Such a multi-objective fine-tuning will instruct LLMs to generate high-quality patches. We apply MOREPAIR to fine-tune four open-source LLMs with different sizes and architectures. Experimental results on C++ and Java repair benchmarks show that the implemented fine-tuning effectively boosts LLM repair performance by 7.6% to 10% in Top-10 repair suggestions. We further show that our fine-tuning strategy yields superior performance compared to the incumbent state-of-the-art in fine-tuned models for program repair, Fine-tune-CoT and RepairLLaMA. Copyright © 2024, The Authors. All rights reserved.

关键词： Repair

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Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Lon-Chen Hung Chun-Cheng Wei Hung-Jen Chen Chih-Fu Yang Wei-Min Cheng Department of Electronic Engineering Lunghwa University of Science and Technology School of Software and Big Data Changzhou College of Information Technology Department of Computer Information and Network Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781665454209

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.

关键词： Uncertainty Target tracking Trajectory tracking Simulation Neural networks Manipulators Stability analysis

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Enhancement of Mechanical Performance and Low-Temperature Strengthening Mechanism: High-Entropy Alloy/Graphene Composite Material

SSRN

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SSRN 2024年

作者： Zhou, Shaocong Liang, Yongchao Pu, Yuanwei Zhou, Yu Zhou, Lili Tian, Zean Institute of Advanced Optoelectronic Materials and Technology School of Big Data and information engineering Guizhou University Guiyang550025 China Department of Information Engineering Gannan Medical University Ganzhou341000 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

High-entropy alloy (HEA) /graphene (Gr) composites hold vast potential for various applications owing to their exceptional mechanical properties. However, the elucidation of their micro-mechanical mechanisms remains elusive, impeding their advancement. Therefore, this study utilizes molecular dynamics (MD) simulations to explore the tensile behavior of CoCrFeMnNi HEA and HEA/Gr composites, aiming to investigate the strengthening mechanism of the reinforcing phase and deformation behavior across different temperatures. The insertion of Gr enhances the yield strength and elasticity modulus of HEA/Gr by 57.5% and 19%, respectively, compared to pure HEA. The hindering effect of the Gr layer interface on dislocation propagation fosters the generation of numerous immobile dislocations, leading to significant disparities in atomic stress and strain distribution, thereby markedly enhancing mechanical properties. Moreover, the hindrance posed by the Gr layer facilitates out-of-plane deformation, and the fracture of Gr significantly impacts material properties. Low-temperature stretching induces FCC-BCC phase transitions in the composites, with transformation-induced plasticity (TRIP) triggering a synergistic effect on material strength and plasticity. Throughout the plastic deformation phase, phase transition promotes dislocation slip and transfer, thereby extending dislocation plasticity and augmenting material plasticity and strength. The results provide important insights into the strengthening mechanism of HEA/Gr at low temperatures, illustrating the key role of reinforcement in mechanical properties from the dual perspectives of Gr insertion and fracture. © 2024, The Authors. All rights reserved.

关键词： High-entropy alloys

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