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SSRN 2022年

作者： Ma, Xintao Dong, Liyan Wang, Yuequn Li, Yongli Liu, Zhen Zhang, Hao 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 School of Information Science and Technology Northeast Normal University Changchun130117 China Graduate School of Engineering Nagasaki Institute of Applied Science 536 Aba-machi Nagasaki851-0193 Japan

Nowadays, recommendation system incorporating social networks has attracted a lot of research attention and is widely used to understand user preferences regarding social relations. Besides, graph neural networks(GNNs) have proven to learn graph-structured data effectively. Thus, integrating GNN into social recommendations has become one challenge. Many approaches encode both the user-item interaction and social relations to learn user preference. However, the implicit relations are also crucial to understanding the features of users and items. Thus, we propose the attentive implicit relation embedding for social recommendation(SR-AIR), which models the user-item interaction and social networks, utilizing a graph attention mechanism on implicit relations of users and items. We evaluate our framework on two real-world datasets and demonstrate that our framework performs the best compared with state-of-the-art baselines © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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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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BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

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Chinese Journal of Electronics 2019年第4期28卷 797-804页

作者： BAI Tian GE Yan YANG Changqing LIU Xiaohua GONG Leiguang WANG Ye HUANG Lan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Yantai Intelligent Information Technologies Ltd. Department of Computer Science and Technology Zhuhai College of Jilin University

To facilitate the search of rapidly growing biomedical knowledge in literature, we developed a Biomedical entity-relationship search tool(BERST). It is also a biomedical knowledge integration framework, which presently contains six popular databases represented in terms of a network of concepts and relations extracted from these knowledge sources. Users search the integrated knowledge network by entering keywords, and BERST returns a sub-network matching and representing the keywords and their relationships. The resulting graph can be navigated interactively allowing users to explore specific paths between any two nodes representing potentially interesting relationships between them. A graphical UI was developed to provide a more intuitive and overall view of the information being searched and studied. BERST framework can be naturally expanded to integrate other biomedical knowledge sources. BERST is implemented as a Java web application.

关键词： Biomedical ontology Relationship mining Relationship searching tool

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Vertex-Disjoint Paths in BCDC Networks

Vertex-Disjoint Paths in BCDC Networks

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Xi Wang Jianxi Fan Suzhou Institute of Industrial Technology Suzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun P.R. China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9781665496407

For any integer $n\geq 2,\ B_{n}$ , the $n$ -dimensional BCDC network, based on a class of BC networks, crossed cube, is a server-centric and high-performance data center network for mega data centers with manageable cabling complexity. Implementing vertex-disjoint paths has great value of application in fault-tolerance and multi-path routing of data center networks. In this paper, we construct $n$ vertex-disjoint paths joining every two distinct vertices in $B_{n}$ , whose maximum length is limited by $\lceil\frac{n}{2}\rceil+5$ , where the diameter of $B_{n}$ is $\lceil\frac{n+1}{2}\rceil+1$ . Our results are fundamental and essential for fault-tolerance and multi-path routing in BCDC networks.

关键词： Data centers Fault tolerance Fault tolerant systems Programming Routing Parallel architectures Complexity theory

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Curvature regularization to prevent distortion in graph embedding 34

Curvature regularization to prevent distortion in graph embe...

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34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Pei, Hongbin Wei, Bingzhe Chang, Kevin Chen-Chuan Zhang, Chunxu Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Department of Computer Science University of Illinois Urbana-Champaign United States Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving proximity, may distort in the ambient embedding Euclidean space, and hence to detect them becomes difficult for machine learning models. To address the problem, we propose curvature regularization, to enforce flatness for embedding manifolds, thereby preventing the distortion. We present a novel angle-based sectional curvature, termed ABS curvature, and accordingly three kinds of curvature regularization to induce flat embedding manifolds during graph embedding. We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Topology

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A two-stage HV-driven adaptive multi-objective evolutionary algorithm and its application in fixed polarity reed-muller circuits

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Expert Systems with Applications 2025年 288卷

作者： Lu Yang Shengsheng Wang Ruyi Dong Zihao Fu 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 College of Information and Control Engineering Jilin Institute of Chemical Technology Changchun 130012 China

To achieve a balance between convergence and diversity, we proposed a two-stage HV-driven adaptive multi-objective evolutionary algorithm (TSAMEA). TSAMEA employs a sinusoidal decreasing parameter adjustment method to enhance exploration pace in the first stage. An adaptive parameter control mechanism utilizes historical memory pools and an HV-driven degree adjustment strategy to achieve better exploitation in the second stage. Extensive experimental data demonstrate that TSAMEA outperforms nine other compared MOEAs. The component analysis illustrates the efficacy of each component of TSAMEA. In addition, area and power optimization are now the main limitations in chip design, TSAMEA is applied to area and power optimization for Fixed Polarity Reed-Muller (FPRM) logic circuits and perform well, which further verifies the ability of the TSAMEA to solve practical problems.

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ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction 28

ToHRE: A Top-Down Classification Strategy with Hierarchical ...

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28th International Conference on computational Linguistics, COLING 2020

作者： Yu, Erxin Han, Wenjuan Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China College of Biological and Agricultural Engineering Jilin University China Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)9781952148279

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation;2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem. © 2020 COLING 2020 - 28th International Conference on computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Classification (of information)

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Object Attribute Matters in Visual Question Answering

arXiv

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arXiv 2023年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model’s robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method. © 2023, CC BY.

关键词： Distillation

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Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wu, Qingqing Wang, Jiacheng Niyato, Dusit Liu, Yuanwei The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Parallel generated method of transcriptional regulatory networks

Parallel generated method of transcriptional regulatory netw...

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作者： Liu, Shuai Ta, Na Lu, Mengye Liu, Gaocheng Bai, Weiling Li, Wenhui College of Computer Science Inner Mongolia University Hohhot010012 China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot010012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China

Generated method of transcriptional regulatory networks remains an important research in biology. Many approaches have been proposed to construct transcriptional regulatory networks. However, with the increase of ChIP-seq and RNA-seq data, the speed of constructing transcriptional regulation networks is still a challenge. Moreover, parallel computing lacks application in constructing gene regulatory networks through the analysis of the relationships between transcription factors (TFs) and target genes (TGs). Therefore, in this paper, a parallel generated method of transcriptional regulatory networks was proposed. First, two datasets, Michigan Cancer Foundation – 7 (MCF-7) and Cardiomyocytes (CM) were applied. Then, a parallel method was used to generate transcriptional regulatory network with their transcription factors (TFs) and target genes (TGs). Finally, experimental results showed that 61% regulatory relations in MCF-7 were validated in the Gene Expression Omnibus (GEO), while 29% results needed further experimental verification. Besides, 56% regulatory relations in CM were consistent with GEO, while 33% results were not yet verified. Furthermore, speed of parallel algorithm was faster than traditional serial algorithm in generating transcriptional regulatory networks. © 2018 John Wiley & Sons, Ltd.

关键词： Transcription factors

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