Article
Computer Science, Artificial Intelligence
Xuanzhao Wang, Zhengping Che, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang, Qi Qi
Summary: This article proposes a novel video anomaly detection method based on frame prediction, with better performance and noise tolerance loss, which outperforms existing state-of-the-art methods as confirmed by experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Umaporn Yokkampon, Abbe Mowshowitz, Sakmongkon Chumkamon, Eiji Hayashi
Summary: This article discusses the importance of accurately detecting anomalies in multivariate time series data and proposes a new unsupervised anomaly detection algorithm. The algorithm uses a Multi Scale Convolutional Variational Autoencoder (MSCVAE) to capture the inter-correlations between time series and an attention-based ConvLSTM network to capture temporal patterns. Experimental results show that the proposed framework outperforms competing algorithms in terms of model performance and robustness.
Article
Computer Science, Artificial Intelligence
Markus Thill, Wolfgang Konen, Hao Wang, Thomas Back
Summary: Learning temporal patterns in time series, especially for anomaly detection, remains challenging. The TCN-AE, an unsupervised temporal convolutional network autoencoder based on dilated convolutions, significantly outperforms other state-of-the-art anomaly detection algorithms on a real-world benchmark. Each new enhancement contributes to improving the overall performance of the algorithm.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Longyuan Li, Junchi Yan, Qingsong Wen, Yaohui Jin, Xiaokang Yang
Summary: In this paper, an unsupervised density reconstruction model is proposed for multi-dimensional time-series anomaly detection. The model can handle raw time-series contaminated with noise and shows superior performance in both synthetic and real-world datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yuxin Zhang, Yiqiang Chen, Jindong Wang, Zhiwen Pan
Summary: Nowadays, multi-sensor technologies are widely used in various fields, generating a large amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor data is critical but challenging due to the need to capture spatial-temporal correlation and distinguish between normal, abnormal, and noisy data. This paper proposes a novel deep learning-based algorithm called CAE-M, which characterizes spatial dependence using a deep convolutional autoencoder and temporal dependence with a memory network. Experimental results on HAR and HC datasets show that the proposed approach outperforms existing methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Hongbo Li, Wenli Zheng, Feilong Tang, Yanmin Zhu, Jielong Huang
Summary: Anomaly detection for time-series data is crucial in the management of streaming applications, computational services, and cloud platforms. This paper proposes a Few-Shot time-series Anomaly Detection framework with unsupervised domAin adaPTation (FS-ADAPT) to address the challenges of few-shot learning and unsupervised domain adaptation in the context of time-series anomaly detection. The framework consists of a dueling triplet network and an incremental adaptation module, which are designed to learn a classifier with limited labeled data and address the limitations of few anomaly samples in an online scenario. Experimental results on five real-world time-series datasets demonstrate that FS-ADAPT outperforms state-of-the-art models and their naive combinations in time-series classification.
INFORMATION SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Kai Yang, Shaoyu Dou, Pan Luo, Xin Wang, H. Vincent Poor
Summary: This paper introduces a sequence to Gaussian Mixture Model (seq2GMM) framework, aiming to identify anomalous and interesting time series within a network time series database. By developing a surrogate-based optimization algorithm, the model exhibits strong performance on multiple public benchmark datasets, outperforming state-of-the-art anomaly detection techniques.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Wei Zhu, Weijian Li, E. Ray Dorsey, Jiebo Luo
Summary: In this paper, a novel anomaly detection method for time series data is proposed. By contrasting the whole time series with its sub-sequences at different timestamps in a latent space, the model effectively captures local and global features using convolutional neural networks and attention mechanism. The proposed approach shows promising potential in tackling real-world anomaly detection tasks.
Article
Computer Science, Artificial Intelligence
Haoran Liang, Lei Song, Jianxing Wang, Lili Guo, Xuzhi Li, Ji Liang
Summary: The study proposes a novel framework named multi-time scale deep convolutional generative adversarial network (MTS-DCGAN) to deal with anomaly detection of industrial time series. The framework transforms multivariate time series into multi-channel signature matrices and introduces a forgetting mechanism to avoid false alarms. Additionally, a new threshold setting strategy is proposed to optimize anomaly detection performance under data imbalance.
Article
Computer Science, Artificial Intelligence
Wentai Wu, Ligang He, Weiwei Lin, Yi Su, Yuhua Cui, Carsten Maple, Stephen Jarvis
Summary: This paper presents a prediction-driven, unsupervised anomaly detection scheme using a backbone model that combines decomposition and inference of time series data. It proposes a novel metric called Local Trend Inconsistency (LTI) and an efficient detection algorithm. The experimental results show that the scheme outperforms existing algorithms in terms of the commonly used metric, Area Under Curve (AUC), while maintaining high efficiency.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, Christian S. Jensen
Summary: This paper proposes a diversity-driven, convolutional ensemble method to improve the accuracy and efficiency of outlier detection in time series data. The method utilizes multiple basic models and a novel training approach to enhance accuracy, while enabling high parallelism and parameter transfer during training to improve efficiency. Extensive experiments with real-world multivariate time series demonstrate the capability of the approach to achieve improved accuracy and efficiency.
PROCEEDINGS OF THE VLDB ENDOWMENT
(2021)
Article
Computer Science, Artificial Intelligence
Tianyang Lei, Chang Gong, Gang Chen, Mengxin Ou, Kewei Yang, Jichao Li
Summary: This paper proposes an unsupervised deep framework for anomaly detection in time series data based on spectrum analysis and time series decomposition. It decomposes the time series into trend, seasonal, and residual series, and detects anomalies through prediction models and reconstruction.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Ya Liu, Yingjie Zhou, Kai Yang, Xin Wang
Summary: Internet of Things (IoT) time-series analysis has been widely used in various fields, but the complexity and high dimensionality of IoT time series make the analysis challenging. Deep learning has provided an effective method for IoT time-series analysis with its powerful feature extraction and representation learning capabilities. However, there are few existing surveys on unsupervised DL-based methods. In this study, we investigate unsupervised DL for IoT time series under a unified framework, including unsupervised anomaly detection and clustering, as well as discussing application scenarios, public data sets, existing challenges, and future research directions.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Jin Fan, Zehao Wang, Huifeng Wu, Danfeng Sun, Jia Wu, Xin Lu
Summary: This study focuses on detecting anomalies in massive volumes of multivariate time series data and proposes a new unsupervised anomaly detection model called ATF-UAD. The model reconstructs abnormal values using a time reconstructor and a frequency reconstructor, and maximizes the identification of normal and abnormal values through dual-view adversarial learning mechanism. Experimental results show an average improvement of 6.94% in terms of F1 score compared to the state-of-the-art method.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.