Article
Engineering, Multidisciplinary
Wei Wang, Xiaohui Hu, Yao Du
Summary: In this paper, the authors proposed the LD-EJP anomaly detection method based on extended jarvis-patrick clustering and outlier detection, which significantly improved the false alarm rate. LD-EJP showed better detection rate and false alarm rate compared to k-means and LGCCB methods, with potential for further improvement in the detection rate and false alarm rate.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Computer Science, Information Systems
Yuehua Huang, Wenfen Liu, Song Li, Ying Guo, Wen Chen
Summary: This paper introduces the importance of outlier detection in data mining and proposes an unsupervised outlier detection algorithm MISC-OD based on mutual information and reduced spectral clustering. Experimental results demonstrate the superior performance of the MISC-OD algorithm compared to eight state-of-the-art baselines.
Article
Computer Science, Artificial Intelligence
Junli Li, Zhanfeng Liu
Summary: Outlier detection plays a crucial role in data mining. However, most existing algorithms focus on either numerical or categorical attributes and neglect the mixture of attributes commonly found in real-world data. In this study, we propose a high-dimensional and massive mixed data outlier detection algorithm called PMIOD, which incorporates attribute weighting using mutual information. We also parallelize the mutual information computation on the Spark platform to improve efficiency. Experimental results on various datasets demonstrate the superior performance of the proposed algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Biochemical Research Methods
Yuchen Yang, Gang Li, Huijun Qian, Kirk C. Wilhelmsen, Yin Shen, Yun Li
Summary: A new method called SMNN is proposed for batch effect correction of single-cell RNA sequencing data, which outperforms other state-of-the-art methods in terms of improved merging within cell types and reduced differentiation across batches. The precision of differentially expressed genes identified between cell types is significantly improved after SMNN correction, making it a promising tool for scRNA-seq data integration.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Engineering, Chemical
Jian Wang, Zhe Zhou, Zuxin Li, Shuxin Du
Summary: This paper proposes a new fault detection scheme using the mutual k-nearest neighbor (MkNN) method, which improves the performance of fault detection by eliminating the influence of outliers and directly detecting fault samples without MkNNs.
Article
Computer Science, Artificial Intelligence
Qiang Gao, Qin-Qin Gao, Zhong-Yang Xiong, Yu-Fang Zhang, Yu-Qin Wang, Min Zhang
Summary: This paper conducts in-depth research on the problems of low-density pattern and local outliers detection in outlier detection algorithms and proposes a double-weighted algorithm considering the dense direction. The algorithm explores the relationship between data points and their neighbor distribution by considering distance and orientation, designs new point weighting and edge weighting strategies, and achieves better representation of the potential structural information inside the data.
APPLIED INTELLIGENCE
(2023)
Article
Geochemistry & Geophysics
Wei Zheng, Ziwei Shi, Guobao Xiao, Jiayi Ma
Summary: Finding reliable correspondences between two images is crucial in remote-sensing image registration. We propose a simple yet effective method to collect abundant local information by establishing a local neighborhood structure, extracting and aggregating neighborhood context, and thereby improving the registration performance.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Yan Shao-hong, Niu Jia-yang, Chen Tai-long, Liu Qiu-tong, Yang Cen, Cheng Jia-qing, Fu Zhi-zhen, Li Jie
Summary: This paper proposes a new algorithm for the selection of transfer station locations in rural areas, which effectively addresses the difficulty in choosing suitable locations. Experimental results demonstrate the effectiveness and advantages of this algorithm.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Ali Mousavi, Richard G. Baraniuk
Summary: This article introduces a method called the uniform information coefficient (UIC), which is able to infer relationships among variables from large datasets. Compared to traditional methods, the UIC calculation is more efficient and robust to the type of association between variables.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Astronomy & Astrophysics
Sian G. Phillips, Ricardo P. Schiavon, J. Ted Mackereth, Carlos Allende Prieto, Borja Anguiano, Rachael L. Beaton, Roger E. Cohen, D. A. Garcia-Hernandez, Douglas Geisler, Danny Horta, Henrik Jonsson, Shobhit Kisku, Richard R. Lane, Steven R. Majewski, Andrew Mason, Dante Minniti, Mathias Schultheis, Dominic Taylor
Summary: Recent chemical tagging studies have found a connection between the chemical abundance patterns of stars in globular clusters and chemically peculiar populations in the Galactic halo field. In this paper, the authors analyze the chemical compositions of stars in Palomar 5, a globular cluster that is being disrupted by the Galactic gravitational potential. They identify nitrogen-rich stars both within the cluster and in the tidal streams, confirming that nitrogen-rich stars are lost from globular clusters and supporting the hypothesis that certain nitrogen-rich stars in the Galactic field have a globular cluster origin.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Mahboobeh Riahi-Madvar, Ahmad Akbari Azirani, Babak Nasersharif, Bijan Raahemi
Summary: A useful strategy for outlier detection in high-dimensional data is to decompose the problem into relevant subspace selection. This paper proposes a linear programming-based method that maximizes local sparsity to select relevant subspaces and simultaneously solves the subspace nearest neighbor search problem. Experimental results demonstrate the effectiveness of the proposed method in terms of detection accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Statistics & Probability
Priyanga Dilini Talagala, Rob J. Hyndman, Kate Smith-Miles
Summary: The article introduces an algorithm for detecting anomalies in high-dimensional data, addressing limitations of the HDoutliers algorithm to improve performance, and demonstrates its wide applicability on various datasets.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Computer Science, Information Systems
Akanksha Mukhriya, Rajeev Kumar
Summary: We discuss the issue of score combinations in outlier detection ensembles (ODEs). Despite normalization, ODE score combinations may still be biased. Determining suitable normalization and avoiding dominance of specific detectors is challenging. We propose a framework called FairComb to address this issue and promote fairer combinations in ODEs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Manmohan Singh, Rajendra Pamula
Summary: This paper discusses the issue of outlier detection in stream data and proposes a new Self-Adaptive Density Summarizing incremental Natural Outlier Detection in Data Stream (ADINOF) algorithm, which successfully addresses some of the challenges faced by traditional algorithms.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Yu Wang, Xuejing Cao, Yupeng Li
Summary: An unsupervised outlier detection method for datasets with mixed-valued attributes based on an adaptive k-NN global network is proposed in this study. By introducing an adaptive search algorithm and a Heterogeneous Euclidean-Overlap Metric for distance measurement, as well as using transition probabilities to limit behaviors of random walkers, the method effectively detects outliers in the dataset.
Article
Computer Science, Information Systems
Quanwang Wu, Fuyuki Ishikawa, Qingsheng Zhu, Yunni Xia
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2019)
Article
Computer Science, Artificial Intelligence
Junnan Li, Qingsheng Zhu, Quanwang Wu
APPLIED INTELLIGENCE
(2020)
Article
Automation & Control Systems
Quanwang Wu, MengChu Zhou, Qingsheng Zhu, Yunni Xia, Junhao Wen
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Junnan Li, Qingsheng Zhu
APPLIED INTELLIGENCE
(2020)
Article
Computer Science, Artificial Intelligence
Junnan Li, Qingsheng Zhu, Quanwang Wu, Dongdong Cheng
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Dongdong Cheng, Qingsheng Zhu, Jinlong Huang, Quanwang Wu, Lijun Yang
Summary: The paper introduces a novel MST-based clustering algorithm LDP-MST, which utilizes local density peaks and a new distance measurement method to effectively discover clusters with complex structures. The experimental results demonstrate that the proposed algorithm is competitive with state-of-the-art methods in cluster discovery.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Junnan Li, Qingsheng Zhu, Quanwang Wu, Zhu Fan
Summary: Class imbalance is a significant factor leading to performance deterioration in classifiers. Techniques such as SMOTE and its extension, NaNSMOTE, have been successful in addressing this issue and have been proven effective on real data sets.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jinxin Shi, Qingsheng Zhu, Junnan Li
Summary: Hierarchical clustering is a common unsupervised learning technique used to discover relationships in data sets. A novel Hierarchical Clustering algorithm with a Merging strategy based on Shared Subordinates (HCMSS) is proposed to overcome challenges like inaccuracy and time-consuming. Experiments show that HCMSS can effectively improve clustering accuracy and save time compared to state-of-the-art benchmarks.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jinghui Zhang, Lijun Yang, Yong Zhang, Dongming Tang, Tao Liu
Summary: This paper introduces a non-parameter clustering algorithm based on saturated neighborhood graph (NPCSNG), which preprocesses the data set using mathematical methods and clusters the data using the characteristics of SNG adaptive clustering to achieve non-parameter clustering. The NPCSNG algorithm has the advantages of not requiring manual parameter setting, significantly improving clustering performance and model robustness, and adapting easily to data sets with complex manifold structure.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Tao Liu, Xiaomei Qu, Wenrong Tan, Ruihan Wen, Lijun Yang
Summary: This study proposes an energy-efficient joint collaborative and passive beamforming design for an Intelligent Reflecting Surface (IRS)-assisted Wireless Sensor Network (WSN), aiming to maximize the network lifetime. Through the development of a penalty dual-decomposition (PDD)-based algorithm, the joint optimization problem is efficiently solved, and a low computational complexity approximate iteration algorithm is proposed for the IRS's phase-shift optimization subproblem.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Hong Xie, Qiao Tang, Qingsheng Zhu
Summary: In this study, we propose an estimator based on the multiplier bootstrap technique to improve the application of Upper Confidence Bound (UCB) algorithms in Contextual Bandit (CB) problems. The estimator adaptsively converges to the ground truth and has theoretical guarantees on the convergence. Extensive experiments on synthetic and real-world datasets validate the superior performance of the proposed estimator.
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)
