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ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM

作者： Haiyu Song Pengjie Wang Xiongfei Li Houjie Li Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China The Research Institute of Computer Graphics and Image Processing Dalian Nationalities of University Dalian Liaoning China

CAD drawings reuse is of great importance in promoting design efficiency and productivity. The ability to retrieve CAD drawings is the core of drawings reuse task. Most of the existing drawings retrieval systems rely only on text descriptions. Due to complexity of visual feature, a little system is based on content. A simplified topological relationship is proposed to describe visual features for dimension reduction. An effective topological relationship vector is constructed by graph spectra. A novel retrieval system based on visual contents, including topological relationships, shape, and directional relationships, is proposed. The experiments show that the proposed approach and system are superior to the text-based and traditional content-based retrieval systems.

关键词： Shape Feature extraction Topology Indexing Design automation Data mining Visualization

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Shortcut Learning in In-Context Learning: A Survey

arXiv

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arXiv 2024年

作者： Song, Rui Li, Yingji Shi, Lida Giunchiglia, Fausto Xu, Hao College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Information Engineering and Computer Science University of Trento Italy

Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of Large Language Models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel perspective to review relevant research on shortcut learning in In-Context Learning (ICL). It conducts a detailed exploration of the types of shortcuts in ICL tasks, their causes, available benchmarks, and strategies for mitigating shortcuts. Based on the corresponding observations, we summarize the unresolved issues in existing research and attempts to outline the future research landscape of shortcut learning. © 2024, CC BY.

关键词： Contrastive Learning

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Medicine composition concentration analysis based on least square support vector machine

Medicine composition concentration analysis based on least s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Xin-Chen Guo Zhou-Yi Chen Li-Rong Teng Chun-Guo Wu Tian-Bao Du Jia-Huilu Qing-Fan Meng Yan-Chun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun China College of Science Northeast China Institute of Electric Power Engineering Jilin China EAAF of PLA Institute of General Development and Evaluation of Equipment Beijing China College of Life Science Jilin University Changchun China

This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation.

关键词： Least squares methods Support vector machines educational institutions Performance analysis Algorithm design and analysis knowledge engineering Linear regression Spectral analysis Quadratic programming Lagrangian functions

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Searching for closed itemset with formal concept analysis

Searching for closed itemset with formal concept analysis

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hong Qi Da-You Liu Cheng-Quan Hu Ming Lu Liang Zhao Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Jilin University Changchun Jilin CN Coll. of Comput. Sci. & Technol. Jilin Univ. Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)0780384032

The problem of mining frequent patterns plays an essential role in many important data mining tasks. However, it often generates a very large number of frequent itemsets. The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. An algorithm Closearcher based on formal concept analysis for closed itemset searching is proposed. This algorithm divides the whole search space of closed itemsets into several subspaces in accordance with criterions prescribed ahead, and introduces an efficient scheme to recognize the valid ones, in which the search for closed itemsets is bounded. An intermediate structure is employed to judge the validity of a subspace and search closed itemsets more efficiently. The algorithm is experimental evaluated and compared with the famous NextClosure algorithm proposed by Ganter for random generated data, as well as for real application data. The results show that our algorithm performs much better than the later.

关键词： Itemsets Data mining Machine learning algorithms Pattern analysis Frequency Algorithm design and analysis Databases educational institutions Computer science educational technology

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Sparse synthesis of concentric circular antenna array via multi-objective evolutionary computation

Sparse synthesis of concentric circular antenna array via mu...

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IEEE Vehicular Technology Conference

作者： Geng Sun Yanheng Liu Zhaoyu Chen Shuang Liang Qianao Ju Ying Zhang Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Computer Science and Technology Changchun China Department of Information Technology Changchun Vocational Institute of Technology Changchun China Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

ISBN: (纸本)9781538663592;9781538663585

The sparse synthesis of the concentric circular antenna array (CCAA) is a very important technology because it is able to reduce the cost of the antenna array. In this paper, we first formulate a multi-objective optimization problem to jointly reduce the maximum sidelobe level (SLL) and the number of the switched-on elements of the CCAA. Then, we propose a novel enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) to solve this problem. ENSGA-II introduces a hierarchy mechanism to improve the population utilization of the conventional non-dominated sorting genetic algorithm, thereby enhancing the accuracy and the convergence rate of the algorithm. Simulation results show that ENSGA-II obtains a lower maximum SLL with the similar number the switched-off elements compared with other algorithms. Moreover, ENSGA-II has a faster convergence rate.

关键词： antenna arrays genetic algorithms Antenna arrays Genetic Algorithm (GA) Loop antennas side lobe level Elements Concentric Small B-Cell Lymphocytic Lymphoma evolutionary algorithm

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Using TCM in mobile terminals in internet of vehicles

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Dongbei Daxue Xuebao/Journal of Northeastern University 2011年第SUPPL.1期32卷 107-110页

作者： Gao, Wen-Bin Liu, Yan-Heng Wang, Jian College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

A trusted cryptography module (TCM) is introduced into vehicular electronic systems to solve security issues of a mobile terminal in the Internet of vehicles, such as authentication, integrity assessment and so on. TCM has many security features, including integrity measure, integrity report and trusted storage, which can make a vehicular electronic system achieve a trusted boot process and encrypt data and information for storage protection, thereby improving the privacy and security of vehicular platform information. Referring to the TNC(trusted network connect)architecture, we design a kind of vehicular terminal access model compatible with the TNC to authenticate and authorize the trusted vehicular platform with TCM to access Internet of vehicles. The vehicular terminal access model proposed will improve the credibility and security of the Internet of vehicles.

关键词： Authentication

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Genetic algorithm with affinity propagation

Genetic algorithm with affinity propagation

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International Conference on Information and Automation (ICIA)

作者： Chunguo Wu Hao Gao Lianjiang Yu Yanchun Liang Rongwu Xiang College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun China School of Basic Courses Shenyang Pharmaceutical University Shenyang China

ISBN: (纸本)9781424457014

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems, e.g., aerodynamic design optimization, qualitative model learning in bioinformatics. To address this problem, we present a combination between genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy.

关键词： Genetic algorithms Clustering methods Evolutionary computation Design optimization Optimization methods Design automation Computer science educational technology Pharmaceutical technology knowledge engineering

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A fuzzy adaptive multi-population parallel genetic algorithm for spam filtering

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Journal of Convergence Information Technology 2011年第2期6卷 172-182页

作者： Zhao, Zheng-dong Wang, Gang Zhao, Wei Chen, Hui-ling Zhao, Wei Li, Lu-lu Network Information Center Changchun University of Science and Technology Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Department of Information Engineering Jilin Police College China Branch Institute of information technology Changchun Vocational Institute of Technology Changchun China

Nowadays, e-mail is one of the most inexpensive and expeditious means of communication. However, a principal problem of any internet user is the increasing number of spam, and therefore an efficient spam filtering method is imperative. Feature selection is one of the most important factors, which can influence the classification accuracy rate. To improve the performance of spam prediction, this paper proposes a new fuzzy adaptive multi-population parallel genetic algorithm (FAMGA) for feature selection. To maintain the diversity of population, a few studies of multi-swarm strategy are reported, whereas the dynamic parameter setting has not been considered further. The proposed method is based on multiple subpopulations and each subpopulation runs in independent memory space. For the purpose of controlling the subpopulations adaptively, we put forward two regulation strategies, namely population adjustment and subpopulation adjustment. In subpopulation adjustment, a controller is designed to adjust the crossover rate for each subpopulation, and in population adjustment, a controller is designed to adjust the size of each subpopulation. Three publicly available benchmark corpora for spam filtering, the PU1, Ling-Spam and Spam Assassin, are used in our experiments. The results of experiments show that the proposed method improves the performance of spam filtering, and is significantly better than other feature selection methods. Thus, it is proved that the multi-population regulation strategy can find the optimal feature subset, and prevent premature convergence of the population.

关键词： Data mining

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Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays

arXiv

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arXiv 2023年

作者： Li, Jiahui Sun, Geng Duan, Lingjie Wu, Qingqing The College of Computer Science and Technology Jilin University Changchun130012 China Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore487372 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms and also show that the proposed method can reduce time and energy costs significantly

关键词： Multiobjective optimization

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Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data

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computational Intelligence and Neuroscience 2022年第0期2022卷 4940401页

作者： Wang, Ying He, Xin Wang, Hongji Sun, Yudong Wang, Xin College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China College of Artificial Intelligence Jilin University Changchun130012 China

With the rapid development of e-commerce, recommendation system has become one of the main tools that assists users in decision-making, enhances user's experience, and creates economic value. Since it is difficult to explain the implicit features generated by matrix factorization, explainable recommendation system has attracted more and more attention recently. In this paper, we propose an explainable fast recommendation model by combining fine-grained sentiment in review data (FSER, (Fast) Fine-grained Sentiment for Explainable Recommendation). We innovatively construct user-rating matrix, user-aspect sentiment matrix, and item aspect-descriptive word frequency matrix from the review-based data. And the three matrices are reconstructed by matrix factorization method. The reconstructed results of user-aspect sentiment matrix and item aspect-descriptive word frequency matrix can provide explanation for the final recommendation results. Experiments in the Yelp and Public Comment datasets demonstrate that, compared with several classical models, the proposed FSER model is in the optimal recommendation accuracy range and has lower sparseness and higher training efficiency than tensor models or neural network models;furthermore, it can generate explanatory texts and diagrams that have high interpretation quality. © 2022 Ying Wang et al.

关键词： Matrix algebra

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