Article
Computer Science, Hardware & Architecture
Fan Liu, Zhiyu Chen, Yuhua Ding, Sai Yang, Tao Zhang
Summary: This study proposes a recommendation system based on face recognition technology, using hash coding and autoencoder to extract user features, and utilizes collaborative filtering for recommendation.The experimental results on the MovieLens database demonstrate that the method is effective and robust.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
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
Remote Sensing
Minkai Hou, Tao Wang, Yanzhao Su, Yanping Cai, Jiping Cao
Summary: This study proposes an adaptive weighted hyperspectral anomaly detection algorithm that combines an autoencoder and convolutional neural network (CNN). By performing morphological attribute filtering and reconstruction error calculation on hyperspectral images, the algorithm eliminates the blurring boundary between background and anomalies, improving detection accuracy and discrimination.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Multidisciplinary Sciences
He-xuan Hu, Yicheng Cai, Qiang Hu, Ye Zhang
Summary: The article introduces the importance of bearing fault diagnosis and the shortcomings of existing models. It proposes a bearing fault diagnosis model based on deep generative models with multiscale features (DGMMFs) to solve these problems. The effectiveness of the DGMMF model is verified through experiments.
Article
Multidisciplinary Sciences
He-xuan Hu, Yicheng Cai, Qiang Hu, Ye Zhang
Summary: Effective condition monitoring and fault diagnosis of bearings can extend the bearing's lifespan, prevent unexpected shutdowns, and reduce excessive maintenance costs. Existing deep-learning-based models for bearing fault diagnosis have limitations in terms of data requirements and the effectiveness of single-scale features. To address these issues, we designed a data collection platform based on the Industrial Internet of Things and proposed a bearing fault diagnosis model using deep generative models with multiscale features. Experimental results on real bearing fault datasets demonstrated the effectiveness of the proposed model, achieving the highest values for evaluation metrics.
Article
Computer Science, Artificial Intelligence
Yadong Zhou, Zhihao Ding, Xiaoming Liu, Chao Shen, Lingling Tong, Xiaohong Guan
Summary: This paper proposes an attribute inference model based on Adversarial VAE (Infer-AVAE) to address the issues of overfitting and oversmoothing in attribute inference. The model combines multi-layer perceptron (MLP) and graph neural networks (GNNs) in the encoder to learn positive and negative latent representations, and reduces noise through adversarial training. Additionally, a mutual information constraint is introduced as a regularizer for the decoder to improve output quality. Experimental results demonstrate that the model outperforms baselines in terms of accuracy.
Article
Computer Science, Hardware & Architecture
Mulyanto Mulyanto, Jenq-Shiou Leu, Muhamad Faisal, Wawan Yunanto
Summary: In this study, a method based on autoencoder is proposed to improve the accuracy of network intrusion detection systems by sharing feature representations through weight embedding. The experiments demonstrate the effectiveness and superiority of this method in terms of accuracy on two benchmark datasets.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jianli Zhao, Tianheng Zhang, Qiuxia Sun, Huan Huo, Maoguo Gong
Summary: In this paper, a new initialization method based on FPC is proposed for rating prediction in recommendation systems, by analyzing the shrinkage properties of matrix elements and combining them with known rating information. Experimental results show that this method greatly improves algorithm efficiency and prediction accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang
Summary: This paper investigates the problem of social recommendation and proposes DiffNet++, an algorithm that models neural influence diffusion and interest diffusion simultaneously, in order to address the issue of existing methods only focusing on social network influence diffusion neglecting user-item interests.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yihao Zhang, Chu Zhao, Weiwen Liao, Wei Zhou, Meng Yuan
Summary: Popularity bias is a significant challenge for autoencoder-based models in recommendations, and incorporating user reviews can help mitigate this bias. However, most existing approaches treat user and item reviews as a homogeneous document, ignoring their heterogeneity. In this article, a novel asymmetric attention network fused with autoencoders is proposed to jointly learn representations from user and item reviews, addressing data sparsity and explainability issues. Additionally, a noise-contrastive estimation objective is applied to learn high-quality de-popularity embedding, further reducing popularity bias. Extensive experiments on benchmark datasets demonstrate that leveraging user review information can eliminate popularity bias and outperform state-of-the-art recommendation techniques.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Mashael Aldayel, Abeer Al-Nafjan, Waleed M. Al-Nuwaiser, Ghadeer Alrehaili, Ghadi Alyahya
Summary: This study investigates the impact of tourism categories on travel agents' destination recommendations and compares different algorithms to enhance the recommendation process. The results suggest that the BiVAE algorithm outperforms others in terms of ranking and prediction metrics, highlighting the importance of considering multiple measurements and exploring diverse techniques. This research provides valuable insights for tourism marketers and researchers, and holds potential for applications in travel recommendation systems.
Article
Computer Science, Information Systems
Xinglong Wu, Hui He, Hongwei Yang, Yu Tai, Zejun Wang, Weizhe Zhang
Summary: This paper proposes a novel framework, PDA-GNN, for distinguishing user and item attributes in recommender systems and allocating different propagation depths on the graph.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yishu Liu, Wenjian Zhang
Summary: This article discusses the design and improvement of precision marketing push algorithm under the deep learning target detection system. By simulating training on user rating data and using deep learning algorithms, users can be clustered and data filtering can be performed. The NSSVD++ algorithm, which uses neighborhood sampling, is found to be the most effective and has the best marketing recommendation effect.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Lei Chen, Jie Cao, Youquan Wang, Weichao Liang, Guixiang Zhu
Summary: The recommender system plays a significant role in e-commerce and e-tourism, with travel product recommendation requiring consideration of multiple factors and differing from traditional recommendations. Researchers have proposed the MV-GAN model to enrich user and product semantics through multi-view fusion and neighbor aggregation, and experiments have shown its effectiveness and interpretability.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Dengcheng Yan, Tianyi Tang, Wenxin Xie, Yiwen Zhang, Qiang He
Summary: With the increase in complexity of modern software, social collaborative coding and reuse of open source software packages have become popular. This paper proposes a Session-based Social and Dependency-aware software Recommendation (SSDRec) model that considers social influence and dependency constraints. Extensive experiments demonstrate the superiority of the model.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Wenmin Wu, Jianli Zhao, Chunsheng Zhang, Fang Meng, Zeli Zhang, Yang Zhang, Qiuxia Sun
KNOWLEDGE-BASED SYSTEMS
(2017)
Article
Engineering, Marine
Jianli Zhao, Xiang Gao, Xin Wang, Chunxiu Li, Min Song, Qiuxia Sun
JOURNAL OF NAVIGATION
(2018)
Article
Engineering, Aerospace
Jianli Zhao, Qiuxia Sun
INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING
(2012)
Article
Computer Science, Artificial Intelligence
Jianli Zhao, Shangcheng Yang, Huan Huo, Qiuxia Sun, Xijiao Geng
Summary: This study focuses on the issue of user and item bias changing over time, proposing a time-varying bias tensor factorization recommendation algorithm based on the bias tensor factorization model. The experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of accuracy on two real datasets.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Tianheng Zhang, Jianli Zhao, Qiuxia Sun, Bin Zhang, Jianjian Chen, Maoguo Gong
Summary: Low-rank tensor completion, particularly using Tensor Train (TT) method, has shown promising results in color image recovery by capturing hidden information effectively. However, TT may destroy the original tensor structure, leading to inadequate access to global information, especially when dealing with images with significant missing data. To address this issue, a new tensor completion model that combines Tucker rank and Tensor Train rank has been proposed, showing effectiveness in numerical experiments using various tensor data.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Jianli Zhao, Liang Li, Mengdie Zhang, Hao Li, ChunXiu Li
Summary: The research on WiFi-based fingerprint positioning is an important topic in indoor positioning due to its low-cost deployment and wide applications. However, the missing of received signal strength (RSS) from access points (APs) can occur due to factors like Green WiFi or AP malfunctions. A fingerprint recovery framework called TR-ReFloc is proposed, which utilizes the correlation of RSS to recover missed fingerprints in both offline and online phases. Experimental results demonstrate that the proposed framework is robust in recovering missed RSS and can maintain localization accuracy within 2 m even with a high missing rate, showing significant improvement over traditional methods.
PERVASIVE AND MOBILE COMPUTING
(2023)
Article
Business
Jianli Zhao, Lutong Yao, Tingting Li, Lijun Qu, Sheng Fang, Shidong Zheng, Maoguo Gong, Liang Li, Hao Li, Tianheng Zhang
Summary: This paper proposes a cross-domain recommendation method for e-commerce using the User-level Preferences Transfer Network (CDR-ULPT). It personalizes the alignment of users' domain-sharing preferences by adding an attention mechanism on adversarial domain adaptation and captures users' indirect interactions using multiple graph convolution layers. Experimental results demonstrate that this method outperforms other cross-domain recommendation methods on five public datasets.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jianli Zhao, Zhengbin Fu, Qiuxia Sung, Sheng Fang, Wenmin Wu, Yang Zhang, Wei Wang
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
(2019)
Article
Computer Science, Information Systems
Xuejian Zhang, Zhongying Zhao, Chao Li, Yong Zhang, Jianli Zhao
Article
Telecommunications
Jianli Zhao, Bin Zhang, Xin Wang, Shuo Sun
INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING
(2017)
Article
Computer Science, Information Systems
Shanchen Pang, Sibo Qiao, Tao Song, Jianli Zhao, Pan Zheng
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)