Article
Computer Science, Artificial Intelligence
Jose L. Salazar Gonzalez, Juan A. Alvarez-Garcia, Fernando J. Rendon-Segador, Fabio Carrara
Summary: This study presents a semi-supervised learning approach based on conditioned cooperative student-teacher training, which utilizes Closed Circuit Television (CCTV) and weapon detection models to reduce violent assaults and homicides. The effectiveness of the approach is demonstrated by collecting a new firearms image dataset and comparing it with various learning techniques.
Article
Computer Science, Artificial Intelligence
Xiao Wang, Daisuke Kihara, Jiebo Luo, Guo-Jun Qi
Summary: The study introduces a new EnAET framework to enhance semi-supervised learning methods with self-supervised information. Experimental results demonstrate that the EnAET framework significantly improves the performance of semi-supervised algorithms, even in scenarios with a limited number of images, and can greatly enhance supervised learning as well.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Automation & Control Systems
Chuanxia Jian, Kaijun Yang, Yinhui Ao
Summary: The study proposes a fault diagnosis method based on active and semi-supervised learning, which improves model performance by selecting uncertain unlabelled samples and using heterogeneous classifiers, suitable for fault diagnosis with a small training set.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Wei Feng, Yinghui Quan, Gabriel Dauphin, Qiang Li, Lianru Gao, Wenjiang Huang, Junshi Xia, Wentao Zhu, Mengdao Xing
Summary: An adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training data, which increases the number of training instances by mining high-quality unlabeled samples and utilizes SMOTE to overcome class imbalance. The effectiveness of the proposed method is demonstrated on three real hyperspectral remote sensing datasets through comparisons with ensemble methods and semi-supervised methods.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Esra Adiyeke, Mustafa Gokce Baydogan
Summary: This paper introduces alternative semi-supervised tree-based strategies that are robust to scale differences both in terms of feature and target variables. Proposing the use of a scale-invariant proximity measure by means of tree-based ensembles to preserve the original characteristics of the data, the paper updates the classical tree derivation procedure to a multi-criteria form to resolve scale inconsistencies.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Jovan Chavoshinejad, Seyed Amjad Seyedi, Fardin Akhlaghian Tab, Navid Salahian
Summary: Semi-supervised nonnegative matrix factorization combines the strengths of matrix factorization in learning part-based representation and can achieve high learning performance with limited labeled data and a large amount of unlabeled data. Recent research focuses on utilizing self-supervised learning to enhance semi-supervised learning. This paper proposes an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S4NMF) model that directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. Experimental results on standard benchmark datasets demonstrate the effectiveness of the proposed model in semi-supervised clustering.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Xufeng Niu, Wenping Ma
Summary: To tackle the challenging task of high-dimensional data classification with limited labeled samples, we propose two semi-supervised learning models, SSRS and its adaptive version, ASSRS. These models address the unique characteristics of high-dimensional data by selecting subspaces of sample and feature dimensions and reducing dimensions. By incorporating sample-labeling auxiliary algorithm, adaptive sample subspace algorithm, and adaptive weight voting rule, ASSRS outperforms SSRS in terms of performance. Experiments demonstrate that SSRS and ASSRS perform better than other competitive algorithms and accurately label samples in datasets with limited labeled samples.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Shufei Zhang, Kaizhu Huang, Jianke Zhu, Yang Liu
Summary: The study introduces a new regularization method for deep learning, based on the manifold adversarial training (MAT), which takes into account the local manifold of latent representations. Experimental results show that MAT performs remarkably well in both supervised and semi-supervised learning.
Article
Computer Science, Artificial Intelligence
Chuanxia Jian, Yinhui Ao
Summary: This study proposes a semi-supervised ensemble learning method for imbalanced fault diagnosis. It evaluates sample information and presents a novel synthetic minority oversampling technique to balance the labeled dataset. It also utilizes co-training technique to exploit information from the unlabeled dataset, improving the performance of fault diagnosis.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Computer Science, Artificial Intelligence
Yiming Li, Baoyuan Wu, Yan Feng, Yanbo Fan, Yong Jiang, Zhifeng Li, Shu-Tao Xia
Summary: This paper introduces a novel defense method, robust training (RT), which enhances accuracy and adversarial robustness by jointly minimizing separated risks of benign examples and their neighborhoods. Extensive experiments show the effectiveness of the proposed SRT method in defending against pixel-wise or spatial perturbations separately, as well as its robustness to both perturbations simultaneously.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
Summary: Supervised deep learning methods require large labeled datasets for accurate medical image segmentation. This paper proposes a local contrastive loss-based approach that utilizes pseudo-labels of unlabeled images and limited annotated images to learn pixel-level features for segmentation. Experimental results on three public medical datasets demonstrate the substantial improvement achieved by the proposed method.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Tianshu Yang, Nicolas Pasquier, Frederic Precioso
Summary: A novel semi-supervised consensus clustering algorithm is proposed in this article, which utilizes closed pattern mining technique to generate a recommended consensus solution without the need for inputting the number of generated clusters k, and can improve the quality of clustering results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Karliane Medeiros Ovidio Vale, Arthur Costa Gorgonio, Flavius Da Luz E. Gorgonio, Anne Magaly De Paula Canuto
Summary: Semi-supervised learning is a machine learning approach that combines supervised and unsupervised learning mechanisms, aiming to enhance performance using labeled and unlabeled data. This paper investigates and improves two well-known semi-supervised learning algorithms, self-training and co-training. Three methods are proposed to automate the labeling process of unlabeled instances, with different confidence rate calculations and label selection strategies. An empirical analysis on 30 datasets with diverse characteristics demonstrates that all three proposed methods outperform the original self-training and co-training methods in most cases.
Article
Computer Science, Artificial Intelligence
Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz
Summary: Interpolation Consistency Training (ICT) is a simple and efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm, which moves the decision boundary to low-density regions of the data distribution in classification problems. Experimental results show that ICT achieves state-of-the-art performance when applied to CIFAR-10 and SVHN benchmark datasets.
Article
Computer Science, Artificial Intelligence
Zijia Zhang, Yaoming Cai, Wenyin Gong
Summary: This paper presents a novel semi-supervised learning framework, Graph Convolutional Extreme Learning Machines (GCELM), for handling graph data in non-Euclidean domains. The proposed methods achieve significantly better results than previous methods on 36 benchmark datasets, thanks to the use of random graph convolution and a voting ensemble strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biology
Sjoerd de Vries, Thijs ten Doesschate, Joan E. E. Tott, Judith W. Heutz, Yvette G. T. Loeffen, Jan Jelrik Oosterheert, Dirk Thierens, Edwin Boel
Summary: Urinalysis has low specificity and may lead to unnecessary antibiotic treatment and antibiotic resistance. By combining urinalysis results with other parameters, UTI can be effectively predicted. The developed CDSS system is more accurate in predicting UTI than urinalysis or urine culture.
COMPUTERS IN BIOLOGY AND MEDICINE
(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)