Article
Computer Science, Artificial Intelligence
Lihe Guan
Summary: The dominance-based rough set approach has been successfully used for analyzing multicriteria decision problems, and various extensions have been proposed for incomplete ordered decision systems. This paper introduces a data-driven valued dominance relation method to calculate dominance degree between objects objectively and automatically determine the threshold, addressing issues of dependence on prior knowledge and difficulty in threshold selection. Experimental results show the superiority of this method in handling incomplete information compared to other generalized dominance relations.
KNOWLEDGE AND INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Haoxiang Zhou, Wentao Li, Chao Zhang, Tao Zhan
Summary: In the new era of information society, studying incremental methods of calculating approximations and constructing incremental algorithms is wise to save computing time in rough methods. This study focuses on maintaining approximations dynamically in interval-valued ordered decision systems when the feature set and sample set increase or decrease. Incremental updating rules for four circumstances are obtained based on the matrix expression of approximations and dominated sets, and incremental algorithms are derived accordingly. Comparative experiments on calculation time verify the effectiveness and superiority of the proposed dynamic algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Ahmed Hamed, Ahmed Sobhy, Hamed Nassar
Summary: This approach tackles the challenges of processing a big incomplete information system by developing an efficient RST algorithm and distributing computational chores using the MapReduce framework. Experimental results validate the validity, accuracy, and efficiency of the approach, showing superior performance compared to similar approaches.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: Inconsistency refers to the situation where instances that share a certain relationship on condition attributes do not exhibit the same relationship on the decision attribute. Various methods, including rough sets and statistical/machine learning approaches, can be used to handle this inconsistency. The Kotlowski-Slowinski (KS) approach addresses the issue by relabeling objects to remove inconsistencies. In this paper, the KS approach is extended to handle inconsistency determined by a fuzzy preorder relation, leading to a consistent fuzzy relabeling that can be used in binary classification and regression algorithms. The method is supported by statistical foundations, includes optimization procedures, and is illustrated through examples.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Xingchen Hu, Yinghua Shen, Witold Pedrycz, Yan Li, Guohua Wu
Summary: This study introduces a novel approach using information granules to represent missing data and build granular fuzzy models directly. Evaluation and optimization are guided by the principle of justifiable granularity and carried out using particle swarm optimization. Experimental studies demonstrate the feasibility and main features of this method using synthetic and real-world datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Wentao Li, Weihua Xu, Xiaoyan Zhang, Jia Zhang
Summary: This paper discusses the application of a local rough set model based on dominance relation in ordered information systems, as well as the construction of multigranulation rough set models and the updating process of dynamic objects. Experimental evaluation demonstrates the superiority and effectiveness of the proposed dynamic updating approaches in ordered information systems.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Wenjie Wang, Jianming Zhan, Enrique Herrera-Viedma
Summary: This paper explores a three-way decision approach based on prospect theory for incomplete information systems. It defines a probability dominance relation to handle the default information of incomplete information systems and proposes an objective weight function through prospect theory. The stability, effectiveness, and superiority of the approach are validated through experimental studies and comparative analysis.
INFORMATION SCIENCES
(2022)
Article
Materials Science, Multidisciplinary
M. Just, A. Medina Peschiutta, F. Hippe, R. Useldinger, J. Baller
Summary: Uniaxial die pressing is a commonly used shaping technique in powder metallurgy. The flowability of granular materials is crucial for achieving consistent quality in the filling process. This study evaluates the flow behavior using two experimental methods and proposes an empirical modification of the granular Bond number.
INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS
(2023)
Article
Computer Science, Artificial Intelligence
Jinbo Wang, Wei-Zhi Wu, Anhui Tan
Summary: This study investigates knowledge discovery in incomplete generalized multi-scale decision systems based on multi-granulation rough sets. It discusses the multi-granulation structures, defines pessimistic and optimistic optimal scale combinations, and explores reducts of scale combinations. Numerical algorithms are designed for finding optimal scale combinations.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Wenjie Wang, Jianming Zhan, Chao Zhang, Enrique Herrera-Viedma, Gang Kou
Summary: This research proposes a new decision-making method that combines regret theory with three-way decision in fuzzy incomplete information systems. The method considers the influence of decision-makers' psychological states on decision outcomes by obtaining integrated utility perception values.
INFORMATION FUSION
(2023)
Article
Operations Research & Management Science
Xiaoxuan Hu, Yanjun Wang, Haiquan Sun, Peng Jin
Summary: An original method using granular computing is proposed to evaluate remote sensing satellite observation schemes with intuitionistic linguistic preference relation, solving optimization problem through particle swarm optimization. Optimization criterion of consensus and consistency is maximized, and evaluation solution is constructed based on dominance levels and prioritization relationship. Experimental example is reported to support feasibility, with comparative analysis to demonstrate performance.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Mathematics, Applied
Takashi Arima
Summary: This study explores the characteristic velocities of molecular rotational and vibrational relaxation processes in a van der Waals gas, estimating lower and upper bounds based on rarefied gases and the spinodal curve. Additionally, the dispersion relation of linear waves, including phase velocity, attenuation factor, and attenuation per wavelength in a low-frequency range, is investigated in detail.
RICERCHE DI MATEMATICA
(2021)
Article
Computer Science, Artificial Intelligence
Hui Cui, Ansheng Deng, Chunmei Chang, Hongyue Diao, Li Zou
Summary: Concept lattice is an effective tool for knowledge acquisition and data analysis, and this paper focuses on linguistic concept lattice for dealing with uncertain information in the linguistic environment. The research mainly discusses object-oriented and attribute-oriented linguistic concept lattice to describe and handle linguistic information effectively with different importance levels of objects.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Nana Liu, Zeshui Xu, Yue He, Xiao-Jun Zeng
Summary: Selecting financial products is a crucial investment activity, and this paper proposes an algorithm based on incomplete additive probabilistic linguistic preference relation to address the challenge of complicated decision-making environment and decision makers' expression habits. By extending concepts, the algorithm's practicality and robustness are ensured, and an inverse prospect theory-based algorithm is introduced to choose proper financial products.
FUZZY OPTIMIZATION AND DECISION MAKING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Patrick G. Clark, Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe, Teresa Mroczek
Summary: This paper discusses data mining on incomplete data sets with missing attribute values interpreted as do not care conditions. Experimental results show significant differences between different data mining approaches on incomplete data sets.
ROUGH SETS (IJCRS 2021)
(2021)
Article
Computer Science, Artificial Intelligence
Yunfei Liu, Hongmei Chen, Tianrui Li, Weiyi Li
Summary: Feature selection for multilabel data is challenging yet meaningful. This study proposes a robust approach based on sparse learning framework, which considers manifold information, compresses label matrix, constrains the least squares regression term, and designs an improved weight matrix. Extensive experiments demonstrate the effectiveness of the proposed approach.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jun He, Hongmei Chen, Tianrui Li, Jihong Wan
Summary: This study proposes a new multi-view clustering method (MLSL) that improves clustering performance through low-rank representation and embedding space learning, using orthogonal constraints to ensure that the final embedding space has the same rank as the low-rank representations of each view.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zaid Al-Huda, Bo Peng, Riyadh Nazar Ali Algburi, Saghir Alfasly, Tianrui Li
Summary: This article presents a novel weakly supervised framework for pavement crack segmentation based on multi-scale object localization and incremental annotation refinement. It utilizes a trained pavement crack classification network to generate initial annotations and trains a new segmentation network with triplet attention module and multiple loss functions. Incremental annotation refinement is used to iteratively optimize the segmentation network and refine the segmentation masks.
APPLIED INTELLIGENCE
(2023)
Article
Polymer Science
Sheng-Li Wu, Junfei Qiao, Jintao Guan, Hong-Mei Chen, Tielin Wang, Cunwen Wang, Yi Wang
Summary: The advent of nascent d-UHMWPE has overcome the intrinsic defects of commercial UHMWPE products and provides access to high value-added materials with improved qualities. The synthesis of d-UHMWPE can be achieved using either single site catalysts or heterogeneous solid catalysts, resulting in a much easier processability. The processed d-UHMWPE demonstrates superior drawability, flexibility, toughness, and thermal conductivity, making it suitable for various applications.
EUROPEAN POLYMER JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Chuan Luo, Qian Cao, Tianrui Li, Hongmei Chen, Sizhao Wang
Summary: Attribute reduction is an important data preprocessing technique in data mining, and this paper proposes a parallel attribute reduction algorithm based on neighborhood rough sets using Apache Spark. The algorithm achieves parallel computation in a distributed computing environment, and experimental results show its superior parallel performance and scalability.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Jihong Wan, Hongmei Chen, Tianrui Li, Zhong Yuan, Jia Liu, Wei Huang
Summary: This paper proposes a novel interactive and complementary feature selection approach based on a fuzzy multineighborhood rough set model. The approach effectively improves the classification performance of feature subsets while reducing the dimension of feature space.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Polymer Science
Junhao Liu, Wanting Wei, Feng Cao, Zhiyong Zeng, Kun Qian, Hongmei Chen, Fenghua Zhang, Wenbing Li
Summary: Inspired by nature, scientists have developed surfaces with controllable wettability through alterations in surface micro/nanostructures or changes in chemical compositions. This article reports the control of surface wettability using three types of shape-memory micropatterns that can be reversibly transformed between a stretched and an original/recovered state due to the shape-memory properties of poly (ethylene-co-vinyl acetate) (EVA). The study also demonstrates that surface wettability can be controlled by the reversible transformation of surface micropatterns.
EUROPEAN POLYMER JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Lei Ma, Chuan Luo, Tianrui Li, Hongmei Chen, Dun Liu
Summary: With the accumulation of interesting data in various application fields, incremental datasets are becoming more common. However, selecting informative attributes from dynamically changing datasets poses challenges. Therefore, an incremental processing mechanism is desired to update the attribute reducts efficiently. In this paper, a novel dynamic graph-based fuzzy rough attribute reduction approach is proposed to handle the maintenance of fuzzy rough attribute reduction in dynamic data, which outperforms existing methods in terms of speed and quality preservation.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Horticulture
Butian Wang, Hongmei Chen, Peng Qu, Rong Lin, Suming He, Weifeng Li, Chuanli Zhang, Xuedong Shi, Yi Liu, Huabo Du, Yu Ge
Summary: This study investigated the influence of rubber plantations and natural secondary forests on the growth and quality of Amomum villosum. The findings suggest that natural secondary forests are more suitable for the cultivation of A. villosum, resulting in higher yields and better soil quality. These findings provide practical value for increasing the production of high-quality A. villosum through the exploitation of natural environments.
Article
Computer Science, Artificial Intelligence
Zhong Yuan, Hongmei Chen, Chuan Luo, Dezhong Peng
Summary: Unsupervised anomaly detection is vital for unsupervised knowledge acquisition and has been successfully applied in various fields. Multi-granularity thinking allows analysis from multiple perspectives, but there are limited studies on anomaly detection using multi-fuzzy granules. This paper proposes a multi-fuzzy granules anomaly detection method using a fuzzy rough computing model, which demonstrates effectiveness through experimental results.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Dexian Wang, Tianrui Li, Wei Huang, Zhipeng Luo, Ping Deng, Pengfei Zhang, Minbo Ma
Summary: This paper proposes a multi-view clustering algorithm based on deep semi-nonnegative matrix factorization (MCDS) to address the issues in existing algorithms. The algorithm achieves excellent clustering results and outperforms other methods according to experiments on multiple multi-view datasets.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Yanyong Huang, Kejun Guo, Xiuwen Yi, Zhong Li, Tianrui Li
Summary: Multi-view unsupervised feature selection has been proven efficient but assumes complete views, which fails in real applications where multi-view data are often incomplete. To address this, we propose I2MUFS, which embeds unsupervised feature selection into an extended weighted non-negative matrix factorization model, considering consistent and complementary information across different views. It also introduces incremental learning mechanisms to update the feature selection matrix iteratively on the updated data.
INFORMATION FUSION
(2023)
Article
Operations Research & Management Science
Dawei Zhan, Jintao Wu, Huanlai Xing, Tianrui Li
Summary: This study proposes a simple and efficient cooperative framework to solve high-dimensional optimization problems. The original problem is randomly decomposed into sub-problems, and the Kriging model and optimization problem are solved for each sub-problem. Context vectors are used to link the sub-problems to facilitate cooperative training and optimization. Experimental results demonstrate that this approach achieves nearly linear speedup in solving high-dimensional problems.
JOURNAL OF GLOBAL OPTIMIZATION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Yongsheng Dai, Li Yang, Ji Huang, Minbo Ma, Tianrui Li, Shengdong Du
Summary: Accurate transmission line tension prediction is crucial for avoiding power grid suffering from serious lines ice coating. To address this, we propose a Globally Attentive Gated Convolutional Network (GAGCN) to integrate multiple sources of Spatial-Temporal information for transmission line icing tension prediction. Experiments on two real-world datasets demonstrate the superior performance of our GAGCN beyond state-of-the-art methods.
2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA
(2023)
Article
Computer Science, Artificial Intelligence
Chuan Luo, Sizhao Wang, Tianrui Li, Hongmei Chen, Jiancheng Lv, Zhang Yi
Summary: This article proposes a novel global search method for numerical feature selection, RH-BPSO, based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm. Parallelization approaches for large-scale datasets are also presented by decomposing and recombining hypercuboid equivalence partition matrix. The experimental results indicate that RH-BPSO outperforms other feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, the distributed meta-heuristic optimized rough hypercuboid feature selection algorithm, DiRH-BPSO, is significantly faster than its sequential counterpart and can handle large-scale feature selection tasks on distributed-memory multicore clusters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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)