Article
Computer Science, Information Systems
Duksan Ryu, Kwangkyu Lee, Jongmoon Baik
Summary: The article introduces a new method LMF-PP for addressing the cold start problem in predicting the QoS values of web services, and experimental results show that LMF-PP outperforms existing methods in both cold start and warm start environments.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jiangyuan Zhu, Bing Li, Jian Wang, Duantengchuan Li, Yongqiang Liu, Zhen Zhang
Summary: With the increase in the number of services published on the cloud due to Web service technologies, the importance of quality of service (QoS) as a criterion for selection becomes crucial. Collaborative filtering (CF) is a popular approach for personalized QoS prediction, but it faces challenges like data sparsity and cold-start difficulties. To address these issues, the proposed BGCL model leverages graph contrastive learning and attention aggregation mechanisms to generate user and service embeddings and predict QoS values. Experimental results demonstrate that the BGCL model outperforms existing models in terms of prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Seyyed Hamid Ghafouri, Seyyed Mohsen Hashemi, Patrick C. K. Hung
Summary: This article discusses methods for predicting QoS values of web services and recommending the best services based on these values. The methods are categorized into memory-based, model-based, and collaborative filtering methods combined with other techniques. The article introduces famous studies in each category, reviews the problems and benefits of each category, and proposes suggestions for future work.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Zibin Zheng, Xiaoli Li, Mingdong Tang, Fenfang Xie, Michael R. Lyu
Summary: With the increase in competing web services, quality-of-service (QoS) prediction becomes important for QoS-aware approaches. Collaborative filtering (CF) is a successful personalized prediction technique that has been widely used in web service QoS prediction. In addition to conventional CF techniques, studies have extended CF by incorporating additional information about services and users. This survey summarizes and analyzes the state-of-the-art CF QoS prediction approaches, discusses their features and differences, and presents benchmark datasets for evaluating prediction accuracy and future research directions.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Software Engineering
Seyyed Mohsen Hashemi, Seyyed Hamid Ghafouri, Patrick C. K. Hung, Chen Ding
Summary: With the rapid increase in the number of web services, selecting the best one has become crucial, with Quality of Service being a common criterion. A new model has been proposed in this study to reduce the impact of unreliable user data and produce trustworthy predictions. Experiments showed that this model can eliminate the influence of unreliable users when applied to existing prediction methods.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Information Systems
Yatao Yang, Zibin Zheng, Xiangdong Niu, Mingdong Tang, Yutong Lu, Xiangke Liao
Summary: With the prevalence of web services, selecting the optimal service among similar candidates relies on the Quality of Service (QoS). To enhance prediction accuracy of QoS values, a novel method based on factorization machine has been proposed, leveraging both user and service information for prediction.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Guobing Zou, Tengfei Li, Ming Jiang, Shengxiang Hu, Chenhong Cao, Bofeng Zhang, Yanglan Gan, Yixin Chen
Summary: This paper proposes a deep learning based approach called DeepTSQP to perform temporal-aware service QoS prediction by feature integration. Extensive experiments show that DeepTSQP significantly outperforms state-of-the-art approaches in terms of multiple evaluation metrics for temporal-aware service QoS prediction.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Software Engineering
Seyyed Hamid Ghafouri, Seyyed Mohsen Hashemi, Mohammad Reza Razzazi, Ali Movaghar
Summary: The combination of regional reputation concept and matrix factorization proposed a prediction method that can improve accuracy and better handle data contributed by unreliable users.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Information Systems
Abdessalam Messiaid, Farid Mokhati, Rohallah Benaboud, Hajer Salem
Summary: The research proposed a method for dynamic reconfiguration based on HMM to predict QoS degradation and prevent the invocation of degraded web services. PSO and SFLA were used to enhance prediction efficiency, leading to better prediction accuracy demonstrated in experiments. Additionally, a case study showed that the proposed approach outperformed other methods in terms of execution time.
Article
Computer Science, Artificial Intelligence
Lei-lei Shi, Lu Liu, Liang Jiang, Rongbo Zhu, John Panneerselvam
Summary: The Quality of Service (QoS) directly impacts the satisfaction of users' nonfunctional requirements by service providers. Recent research has focused on sparse data prediction and user personalized recommendations in service recommendation and management. The proposed hybrid mobile service recommendation and management model utilizes semantic recommendation and location-based quality preference analysis to predict QoS requirements and offer the most suitable services to users.
Article
Computer Science, Information Systems
Xiaoke Zhu, Xiao-Yuan Jing, Di Wu, Zhenyu He, Jicheng Cao, Dong Yue, Lina Wang
Summary: The study introduces a novel approach for QoS prediction in Web service recommendation, combining the SPP strategy and LLMF algorithm to protect user privacy and improve prediction accuracy.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Cybernetics
Zhaohong Jia, Li Jin, Yiwen Zhang, Chuang Liu, Kai Li, Yun Yang
Summary: Currently, there are numerous web services providing similar functions and users choose the best one based on the quality of service (QoS). Thus, predicting QoS accurately is a primary challenge in service recommendation. Most existing approaches focus on modeling the interaction relationship between users and services, but they rarely consider both the low-dimensional linear and high-dimensional nonlinear relationships simultaneously. Moreover, although many approaches incorporate location information to overcome data sparsity, they seldom consider the impact of global location information. To address these shortcomings, this paper proposes a new QoS prediction model that combines both local and global location information in the interaction layer. The model utilizes a multilayer perceptron (MLP) to capture the high-dimensional nonlinear relationships between users and services, complemented by the dot product to learn the low-dimensional linear relationships. Experimental results on the real-world dataset WS-Dream validate the prediction performance of the proposed model.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhenhua Chang, Ding Ding, Youhao Xia
Summary: With the development of the Internet, the recommendation based on Quality of Service (QoS) has become increasingly important in dealing with web services. The research proposes a Graph-based Matrix Factorization approach (GMF) for QoS prediction, which consolidates multi-source information and uses a Gaussian Mixture Model (GMM) to combine local and global information for accurate predictions. Extensive experimental analysis on a publicly available dataset demonstrates the accuracy and practicality of the proposed method.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Hardware & Architecture
Hongxia Zhang, Mengde Wu, Qiqi Feng, Hao Li
Summary: This study proposes an adaptive embedding representation-based QoS prediction method for Web services recommendation. By analyzing the problems of inaccurate semantic representation and inadequate service invocation modeling in QoS prediction within the cloud, this method improves the accuracy of QoS prediction.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Di Wu, Qiang He, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang
Summary: This paper proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction, which decomposes the LF analysis process into three phases. Experimental results show that PLF outperforms existing models in terms of prediction accuracy and efficiency on large scale QoS datasets.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
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)