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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Multi-Classification of Cancer Samples Based on Co-Expression Analyses

Multi-Classification of Cancer Samples Based on Co-Expressio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Qiang Huang Liang Chen Zhi Li Ying Xu Huiyan Sun Yi Chang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Engineering Key Laboratory of Intelligent ManufacturingTechnology of Ministry of Education Shantou University Shantou China Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province The First Hospital of China Medical University Shenyang China The China-Japan Union Hospital Cancer Systems Biology Center Jilin University Changchun China The China-Japan Union Hospital School of Artificial Intelligence Cancer Systems Biology Center Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multi-classification of differential gene-expressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multi-classification technique, based on co-expression patterns specific to individual subclasses in provided training data as co-expression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive co-expression patterns in individual samples, which is solved through comparing co-expression patterns within a subclass and those in the subclass plus a new sample. Compared with the state-of-the-art gene expression-based classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the co-expressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.

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A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation

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Journal of Physics: Conference Series 2021年第1期1746卷

作者： Minghao Yin Yanheng Liu Xu Zhou Geng Sun College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms.

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Sure: Screening Unlabeled Samples for Reliable Negative Samples Based on Reinforcement Learning

SSRN

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SSRN 2022年

作者： Li, Ying Sun, Hang Fang, Wensi Ma, Qin Han, Siyu Wang-Sattler, Rui Du, Wei Yu, Qiong Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Department of Biomedical Informatics Ohio State University Lane Ave ColumbusOH43210 United States TUM School of Medicine Technical University of Munich Ismaninger Straße 22 Bavaria Munich81675 Germany Helmholtz Center Munich German Research Center for Environmental Health Ingolstadter Landstraße 1 Bavaria Neuherberg85764 Germany Department of Epidemiology and Biostatistics School of public health Jilin University Qianjin Street Jilin Changchun130012 China

For many classification tasks, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a great challenge for using supervised machine learning. To address this problem, we propose a novel deep reinforcement learning based model to screen reliable negative samples from unlabeled samples, named SURE. SURE has two modules: sample selector and sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies. The sample inspector classifies samples and provides rewards to the sample selector. In this paper, we focus on one popular issue in the field of bioinformatics: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The Experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a webserver (***/sure) is available offering the NPI prediction refined by SURE. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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An unmanned aerial vehicle pose estimation system based on SLAM

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IOP Conference Series: Materials Science and engineering 2019年第4期569卷

作者： Xinglei Dou Yuzhou Huo Yongchang Liu Xin Wang College of Software Engineering Jilin University Changchun Jilin 130012 China College of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin 130012 China

This paper presents an unmanned aerial vehicle (UAV) pose estimation system based on monocular simultaneous localization and mapping (SLAM) guided by the desired shot. The system enables UAV to automatically adjust the pose to achieve a shot close to the desired shot provided by the user. The SLAM module in the system includes ORB feature-based visual odometry and Levenberg-Marquardt method-based optimizer. To ensure the reliability of the camera pose estimation result, the bag of words model is used to select an image which has enough good matches with the desired shot. The experimental results prove that the system is valid and effective.

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Effective pruning algorithm for least squares support vector machine classifier

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2007年第7期44卷 1128-1136页

作者： Yang, Xiaowei Lu, Jie Zhang, Guangquan School of Mathematical Sciences South China University of Technology Guangzhou 510641 China Faculty of Information Technology Sydney University of Technology NSW 2007 Australia Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China

A well-known drawback in the least squares support vector machine (LS-SVM) is that the sparseness is lost. In this study, an effective pruning algorithm is developed to deal with this problem. To avoid solving the primal set of linear equations, the bottom to the top strategy is adopted in the proposed algorithm. During the training process of the algorithm, the chunking incremental and decremental learning procedures are used alternately. A small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using the support vector set, one can construct the final classifier. In order to test the validation of the proposed algorithm, it has been applied to five benchmarking UCI datasets. In order to show the relationships among the chunking size, the number of support vector machine, the training time, and the testing accuracy, different chunking sizes are tested. The experimental results show that the proposed algorithm can adaptively obtain the sparse solutions without almost losing generalization performance when the chunking size is equal to 2, and also its training speed is much faster than that of the sequential minimal optimization (SMO) algorithm. The proposed algorithm can also be applied to the least squares support vector regression machine as well as LS-SVM classifier.

关键词： Support vector machines

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Collaborative WSN-UAV Data Collection in Smart Agriculture: A Bi-objective Optimization Scheme

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ACM Transactions on Sensor Networks 1000年

作者： Geng Sun Xiaoya Zheng Jiahui Li Hui Kang Shuang Liang 1) College of Computer Science and Technology Jilin University 2) Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University China College of Information Science and Technology Northeast Normal University China

Smart agriculture which integrates the agriculture with Internet of Things (IoT) has attracted attention since it contributes to increase the productivity and quality of crops, reduce energy consumption and facilitate the farmers. Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) are two most commonly deployed devices that are used for enabling the smart agriculture. In this paper, we design a collaborative WSN-UAV system, wherein different clusters of sensor nodes form different sensor-based virtual antenna arrays (SVAAs) to transmit the collected data towards different receiver UAVs via adopting collaborative beamforming (CB), then the receiver UAVs will take the collected data back to the ground control station (GCS). We formulate a transmission rate and battery energy bi-objective optimization problem (TRBEBOP) to simultaneously maximize the total transmission rate of the sensor-based CB clusters and the total remaining battery energy of the selected sensor nodes, by selecting appropriate sensor nodes in each cluster that can form a predominant SVAA, determining suitable receiver UAVs and optimizing the excitation current weights of the selected sensor nodes. To handle the formulated TRBEBOP that is demonstrated to be non-convex and NP-hard, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) with several specific designs is presented. Simulation results validate the effectiveness of the proposed ENSGA-II for solving the formulated TRBEBOP. Moreover, compared with other benchmark algorithms, the superiority of the proposed ENSGA-II is demonstrated. In addition, the impacts of several fortuitous circumstances on the system are estimated, and the results illustrate the robustness of the proposed scheme. Finally, the discussion about several mechanisms to deal with the interference induced by the sidelobe levels and the impact of UAV movement on receiving rate are provided.

关键词： Smart agriculture wireless sensor networks unmanned aerial vehicles collaborative beamforming multi-objective optimization problem.

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Drive: An Adjustable Parallel Architecture Based on Evidence Awareness for Fake News Detection

SSRN

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SSRN 2024年

作者： Cong, Mou Liu, Lu Han, Jiayu Zhu, Beibei Yuan, Xiaosong Peng, Tao College of Computer Science and Technology Jilin University 2699 Qianjin Street Jilin Changchun130012 China College of Software Jilin University 2699 Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University 2699 Qianjin Street Jilin Changchun130012 China Department of Linguistics University of Washington SeattleWA98195 United States

Fake news detection becomes a critical challenge in NLP. Batch processing with attention-derivative models is a popular solution for fake news detection. Models with attention mechanisms could mitigate the long-distance dependence problem. However, we find that extracting local dependencies using convolutional networks is as important as extracting global dependencies using attention mechanisms. Besides, fake news datasets often suffer from problems such as imbalance and are hard to understand by current models. To address these problems, we propose DRIVE, which is short for an adjustable parallel architecture based on evidence awareness for fake news detection to alleviate the above problems, in which we introduce Claim as a supporting clue to enhance the model’s comprehension ability. Specifically, we propose a novel method for unbalanced datasets and design adjustable parallel branches, using Claim Additives Network (ClaimAddNet) and the MLP with convolutional gating mechanism (CPMLP) to capture global and local information in distinct branches, respectively. Experiments conducted on 5 public datasets demonstrate that DRIVE outperforms all baselines, improving accuracy by approximately 1.7 %. Furthermore, we implement an anti-noise interference test and evaluate the weighted average of the two branches along with ablation studies. © 2024, The Authors. All rights reserved.

关键词： Parallel architectures

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Multilocus association analysis into susceptibility genes of schizophrenia

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第SUPPL. 2期38卷 205-212页

作者： Zhang, Hong-Li Liu, Yuan-Ning Zhang, Hao Yu, Ya-Qin Yu, Qiong Kou, Chang-Gui Shi, Jie-Ping Meng, Xiang-Fei Tao, Lu Qu, Ting College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China Research Center for Genomic Medicine School of Public Health Jilin University Changchun 130021 China

A model based on multilocus interaction of schizophrenia was designed. Using the program of MATLAB through a C-language interface, combined with Fisher's exact test, a total of 18 SNPs that came from 9 susceptibility genes of the cPLA2 and PNPLA8 family were analyzed to detect positive sites of schizophrenia in multilocus association analysis. Results show that 4 positive sites at rs3816533, rsl668589, rsl356410 and rs40848 were detected in 13 SNPs, which did not deviate from the Hardy-Weinberg equilibrium (P>0.05). The haplotype significance test of these positive sites indicates that 6 haplotypes passing through rs3816533, rsl668589, rsl356410 and 2 haplotypes passing through rs40848 are associated with schizophrenia.

关键词： MATLAB

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