Article
Computer Science, Information Systems
Joojo Walker, Fengli Zhang, Ting Zhong, Fan Zhou, Edward Yellakuor Baagyere
Summary: In this paper, a recommendation model called CORE-VAE is proposed to address the complexity and sparsity of social networks by utilizing a social-aware similarity function and a graph convolutional network. The model generates cold-start resistant rating vectors by producing robust social-aware user representations and explores user rating information using an expressive variational autoencoder. Experimental results demonstrate that CORE-VAE outperforms competitive models on real-world datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jia Xu, Hongming Zhang, Xin Wang, Pin Lv
Summary: To address the problem of user cold-start recommendation, a novel adaptive meta-learning model based on user relevance (AdaML) is proposed. This model identifies related users with similar preferences and utilizes their information to improve user cold-start recommendations.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Meizi Li, Weiqiao Que, Ziyao Geng, Maozhen Li, Zuliang Kou, Jisheng Chen, Chang Guo, Bo Zhang
Summary: In this paper, a novel Heterogeneous Graph Embedding Learning based Skip-Gram (HSG) model is proposed to mitigate the cold-start problem of items. The HSG model constructs heterogeneous graphs and generates meaningful sequences of nodes, and uses a Skip-Gram model to learn item representations. Extensive experiments on publicly available datasets demonstrate the effectiveness of the HSG model.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Chuan Shi, Yuanfu Lu, Linmei Hu, Zhiyuan Liu, Huadong Ma
Summary: This paper proposes a relation structure-aware HIN embedding model called RHINE, which captures the structural characteristics of heterogeneous relations in HINs and learns node and relation embeddings using relation-specific projection matrices. Experimental results demonstrate that RHINE outperforms existing methods in four tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Green & Sustainable Science & Technology
Jia Wang, Zhiping Wang, Ping Yu, Peiwen Wang
Summary: This paper investigates the important role of individuals in social networks and their impact on epidemic transmission. By constructing a nonlinear coupling dynamic model on the hyper network, a dynamic evolutionary coupling mechanism between the social network and epidemic transmission is established. The results show that an individual's recovery state is influenced by their social ability and the degree of information forgetting.
Article
Computer Science, Artificial Intelligence
Vineet K. Sejwal, Muhammad Abulaish
Summary: Recommender systems aim to estimate item ratings and recommend items based on users' interests. However, traditional recommender systems face limitations such as data sparsity, black-box recommendation, and cold-start problems. To address these issues, this study presents RecTE, a hybrid recommendation technique that combines rating data and topic embedding. Empirical evaluation shows that RecTE outperforms other recommendation techniques on three real-world datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Biomedical
Nanlin Shi, Xiang Li, Bingchuan Liu, Chen Yang, Yijun Wang, Xiaorong Gao
Summary: This study reduces the calibration effort for steady-state visual evoked brain-computer interfaces (SSVEP-BCIs) by using cross-dataset model adaptation, while maintaining high prediction ability.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Information Systems
Yiding Zhang, Xiao Wang, Nian Liu, Chuan Shi
Summary: This article introduces a novel HIN embedding model HHNE, which uses hyperbolic spaces to project HIN and believes that hyperbolic spaces better reflect the properties of HIN. Experimental results demonstrate the effectiveness of the proposed models.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2022)
Article
Computer Science, Artificial Intelligence
Huizhi Liang, Thanet Markchom
Summary: Temporal dynamics are important in information networks, but existing network embedding learning methods fail to consider multiple temporal factors. This paper proposes a time-aware network representation learning framework called TNE, which incorporates temporal nodes and relations to construct a time-aware network. It uses a meta-path based random walk method to create a hybrid context considering both semantic and temporal factors. Self-supervised representation learning approaches are employed to preserve both semantic and temporal factors in embeddings.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Fu Jie Tey, Tin-Yu Wu, Chiao-Ling Lin, Jiann-Liang Chen
Summary: This paper presents a social network-based recommender system that aims to exclude products disinterested by users according to user preferences and their friends' shopping experiences, ultimately improving recommendation accuracy through indirect relations between friends and sentinel friends. Simulation results have proven the proposed mechanism to be efficient in enhancing recommendation accuracy.
JOURNAL OF BIG DATA
(2021)
Article
Computer Science, Artificial Intelligence
Huiting Liu, Lei Wang, Peipei Li, Cheng Qian, Peng Zhao, Xindong Wu
Summary: To address the cold-start problem and improve the generalization performance of meta-learning, we propose a relation-propagation meta-learning method on explicit preference graph. Our method captures the relationships between local preferences and produces more distinguishable local preference nodes using graph convolutional networks. Experimental results demonstrate its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux
Summary: In this paper, the authors propose LBSN2Vec++, a heterogeneous hypergraph embedding approach designed specifically for LBSN data. By automatically learning features, LBSN2Vec++ outperforms other methods and handcrafted features in friendship and location prediction tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Maoguo Gong, Shunfei Ji, Yu Xie, Yuan Gao, A. K. Qin
Summary: Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks. This paper proposes an unsupervised deep learning model called DTINE, which explores temporal information to enhance the robustness of node representations in dynamic networks. Experimental results demonstrate the superiority of this method.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Shilong Liu, Yang Liu, Xiaotong Zhang, Cheng Xu, Jie He, Yue Qi
Summary: This paper proposes a new model for cold-start recommendation, which combines the attention mechanism and meta learning. By learning the weights between users and items based on the attention mechanism, the model enhances the ability of modeling personalized user interests and improves the performance of cold-start recommendation.
Article
Computer Science, Information Systems
Baiyang Chen, Xiaoliang Chen
Summary: User identity linkage across social networks has practical value and research challenges. Existing methods have limitations in covering all attribute features and capturing higher-level semantic features. This paper proposes a novel semi-supervised model, MAUIL, to address these challenges using multi-level attribute embedding and regularized canonical correlation analysis.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Lin Yue, Zhenkun Shi, Jiayu Han, Sen Wang, Weitong Chen, Wanli Zuo
Article
Computer Science, Artificial Intelligence
Yuwen Li, Yaojin Lin, Jinghua Liu, Wei Weng, Zhenkun Shi, Shunxiang Wu
Article
Computer Science, Information Systems
Zhenkun Shi, Sen Wang, Lin Yue, Lixin Pang, Xianglin Zuo, Wanli Zuo, Xue Li
Summary: This study proposes a 2-step integrated imputation-prediction model based on gated recurrent units for addressing missing values in clinical time-series data. The model performs well in terms of imputation quality and prediction accuracy.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Shining Liang, Wanli Zuo, Zhenkun Shi, Sen Wang, Junhu Wang, Xianglin Zuo
Summary: Mining causality from text is a crucial task, and existing studies can be categorized into feature engineering-based and neural model-based methods. However, the former is incomplete and prone to errors, while the latter lacks sufficient causal inference. To address these limitations, this paper proposes a novel causality detection model named MCDN, which combines the advantages of both methods and demonstrates its superiority and robustness through experiments.
Article
Biochemistry & Molecular Biology
Yi Yang, Yufeng Mao, Ruoyu Wang, Haoran Li, Ye Liu, Haijiao Cheng, Zhenkun Shi, Yu Wang, Meng Wang, Ping Zheng, Xiaoping Liao, Hongwu Ma
Summary: The researchers introduced AutoESD, a cloud-based platform for designing editing sequences, which automates and enables high-throughput ESD for genetic manipulation. It offers high reliability and scalability.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Microbiology
Lei Xu, Mengyuan Li, Yadong Yang, Chen Zhang, Zhen Xie, Jingjing Tang, Zhenkun Shi, Shukun Chen, Guangzhe Li, Yanchao Gu, Xiao Wang, Fuhua Zhang, Yao Wang, Xihui Shen
Summary: This study reveals the important role of the cytosolic surveillance pathway cGAS-STING in the activation of the type I interferon response during S. Typhimurium infection, in addition to the TLR4-dependent response. The study also highlights the significance of mtDNA release triggered by S. Typhimurium infection in the induction of type I interferon.
Article
Computer Science, Artificial Intelligence
Yijia Zhang, Wanli Zuo, Zhenkun Shi, Binod Kumar Adhikari
Summary: In this paper, a unified graph convolutional network (GCN) is proposed to address the issues in rating prediction task of recommendation systems. By effectively utilizing the user-item interaction information, review features, and rating scores, the proposed model achieves improved prediction performance.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2022)
Article
Biochemistry & Molecular Biology
Jinhui Niu, Zhitao Mao, Yufeng Mao, Ke Wu, Zhenkun Shi, Qianqian Yuan, Jingyi Cai, Hongwu Ma
Summary: The genome-scale enzyme-constrained model (ecCGL1) improves the prediction of phenotypes and simulates overflow metabolism in Corynebacterium glutamicum, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency.
Proceedings Paper
Computer Science, Artificial Intelligence
Suresh Pokharel, Zhenkun Shi, Guido Zuccon, Yu Li
ADVANCED DATA MINING AND APPLICATIONS
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhenkun Shi, Weitong Chen, Shining Liang, Wanli Zuo, Lin Yue, Sen Wang
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2019
(2019)
Proceedings Paper
Computer Science, Information Systems
Zhenkun Shi, Wanli Zuo, Weitong Chen, Lin Yue, Yuwei Hao, Shining Liang
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2019), PT II
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Tian-Wen Yuan, Yi-Nan Lu, Zhen-Kun Shi, Zhe Zhang
FUZZY SYSTEMS AND DATA MINING II
(2016)
Article
Computer Science, Information Systems
Zhenkun Shi, Wanli Zuo, Shining Liang, Xianglin Zuo, Lin Yue, Xue Li
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.