Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Rongjun Chen, Xuanhui Yan, Shiping Wang, Guobao Xiao
Summary: In this paper, a novel network called DA-Net is proposed for mining local-global features in multivariate time series classification. DA-Net consists of two layers, the Squeeze-Excitation Window Attention (SEWA) layer and the Sparse Self-Attention within Windows (SSAW) layer, which capture local and global features and mine critical local sequence fragments through establishing global long-range dependencies.
INFORMATION SCIENCES
(2022)
Article
Agricultural Engineering
Chunying Wang, Mengli Sun, Lipeng Liu, Wenjing Zhu, Ping Liu, Xiang Li
Summary: This paper proposes a high-accuracy approach for plant genotype classification using a combination of Densenet201 and bi-directional LSTM models. The proposed approach achieves an accuracy of 98.31% in classifying different genotypes of panicoid grain crops, and it can also be useful for the classification of progeny accessions based on their similarity to reference accessions.
BIOSYSTEMS ENGINEERING
(2022)
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
Automation & Control Systems
Samuel Harford, Fazle Karim, Houshang Darabi
Summary: This study proposes a method for generating adversarial samples on multivariate time series classification models, combining adversarial autoencoders and gradient adversarial transformation networks. By utilizing adversarial attacks, the adversarial samples are improved by replacing the adversarial generator function with variational autoencoders.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Artificial Intelligence
Shilei Hao, Zhihai Wang, Afanasiev D. Alexander, Jidong Yuan, Wei Zhang
Summary: Multivariate time series (MTS) classification is a growing field with increasing demand. Existing representation learning methods for MTS classification are limited in utilizing labels due to their reliance on self-supervised learning. To address this, a new Mixed Supervised Contrastive Loss (MSCL) is introduced for MTS representation learning, which combines self-supervised, intra-class, and inter-class supervised contrastive learning approaches. Based on MSCL, a novel Mixed supervised Contrastive learning framework for MTS classification (MICOS) is proposed, utilizing spatial and temporal channels to extract complex spatio-temporal features and applying MSCL at the timestamp level to capture multiscale contextual information. Experimental results on 30 public datasets from the UEA MTS archives demonstrate the reliability and efficiency of MICOS compared to 13 competitive baselines.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Omer David Harel, Robert Moskovitch
Summary: In this paper, a novel deep learning-based framework called INSTINCT is proposed for Symbolic Time Intervals (STIs) series classification (STIC). INSTINCT transforms raw STIs series into real matrices while preserving almost all information and uses a ensemble of deep inception based convolutional neural networks for classification. Experimental results show that INSTINCT significantly improves accuracy compared to state-of-the-art methods and deep learning-based baselines on six real-world STIC benchmark datasets. Additionally, a comprehensive architecture study and scalability analysis of INSTINCT are conducted, revealing an overall linear time complexity in each main property of the input STIs series.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Fabio Giampaolo, Federico Gatta, Edoardo Prezioso, Salvatore Cuomo, Mengchu Zhou, Giancarlo Fortino, Francesco Piccialli
Summary: This study proposes a novel ensemble approach for generating predictions in a multivariate framework. It reduces data dimensionality through an encoding technique, extracts useful information via single predictive procedures, and combines the processed data to produce the final forecast. Extensive experiments demonstrate the higher accuracy and robustness of the proposed ensemble compared to conventional methods and state-of-the-art strategies.
INFORMATION FUSION
(2023)
Article
Automation & Control Systems
Azza Abidi, Dino Ienco, Ali Ben Abbes, Imed Riadh Farah
Summary: The use of high spatial resolution Satellite Image Time Series (SITS) provides an opportunity for a wide spectrum of Earth surface monitoring applications such as Land Use/Land Cover (LULC) mapping. This study proposes a framework for LULC mapping based on 2D encoded multivariate SITS data to enhance their classification performances. The multivariate SITS data are transformed into 2D representations using various encoding techniques and then input into a convolutional neural network (CNN) classification model.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xingtai Gui, Jiyang Zhang, Jianxiong Tang, Hongbing Xu, Jianxiao Zou, Shicai Fan
Summary: This paper proposes a deep learning framework for fault diagnosis that addresses the issue of imbalanced samples. It introduces a novel data pair and loss function to improve classification performance and encourage mining of imbalanced data.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wonkeun Jo, Dongil Kim
Summary: Deep neural networks are important in machine learning for their excellent prediction performance and versatility. However, they lack explanatory power due to being black-box models. This study proposes a new neural network architecture that includes interpretability for multivariate time-series data. Experimental results show that the interpretable neural architecture performs well in predicting MTS data and provides reasonable importance for each input value.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Zhi Chen, Yongguo Liu, Jiajing Zhu, Yun Zhang, Qiaoqin Li, Rongjiang Jin, Xia He
Summary: The study proposes a novel deep multiple metric learning (DMML) method for time series classification. It utilizes a convolutional network to extract nonlinear features of time series and builds multiple metric learners to obtain multiple metrics for exploiting locality information. An adversarial negative generator and an auxiliary loss are introduced to increase the robustness of the method for the magnitude of distance.
Article
Computer Science, Artificial Intelligence
Huanlai Xing, Zhiwen Xiao, Dawei Zhan, Shouxi Luo, Penglin Dai, Ke Li
Summary: This study introduces a powerful semisupervised deep learning model SelfMatch, which combines supervised learning, unsupervised learning, and self-distillation techniques. Experimental results demonstrate that SelfMatch performs exceptionally well on 35 widely used UCR2018 datasets compared to various semisupervised and supervised algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Le Fang, Wei Xiang, Yuan Zhou, Juan Fang, Lianhua Chi, Zongyuan Ge
Summary: This paper proposes a novel dual-branch cross-dimensional self-attention-based imputation model for multivariate time series. Through global and auxiliary cross-dimensional analyses, the model is capable of learning and utilizing correlations across the temporal and cross-variable dimensions more effectively.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Chemistry, Analytical
Xue-Bo Jin, Aiqiang Yang, Tingli Su, Jian-Lei Kong, Yuting Bai
Summary: This study focuses on the classification of time-series data using deep learning and broad learning systems, with a particular emphasis on univariate time-series data. Various networks and methods like LSTM and bidirectional LSTM are used to learn and test the data, with images generated from the data used for classification. The Dempster-Shafer evidence theory is applied to fuse probability outputs for final classification results.
Article
Chemistry, Inorganic & Nuclear
Jin Xie, Zhong-Min Huang, Ji-Min Liu, Hang Xu, Fang-Fang Yang, Li-Dian Chen
Summary: In this study, coordination polymers based on Cu(II) and Co(II) ions as nodes were synthesized using the mixed ligand synthesis approach. The application values and specific mechanism of these polymers in cerebral ischemia were evaluated and studied.
JOURNAL OF CLUSTER SCIENCE
(2023)
Article
Hospitality, Leisure, Sport & Tourism
Huawei Lin, Jiayong Zhang, Yaling Dai, Huanhuan Liu, Xiaojun He, Lewen Chen, Jing Tao, Chaohui Li, Weilin Liu
Summary: This study aims to investigate the role of neurogranin (Ng) in swimming training to improve cognitive impairment caused by chronic cerebral hypoperfusion (CCH). The results showed that swimming training can enhance hippocampal synaptic plasticity by regulating the expression of Ng, thereby ameliorating the spatial memory impairment of vascular cognitive impairment.
JOURNAL OF SPORT AND HEALTH SCIENCE
(2023)
Article
Neuroimaging
Ruibin Zhang, Sammi-Kenzie T. S. Tam, Nichol M. L. Wong, Jingsong Wu, Jing Tao, Lidian Chen, Kangguang Lin, Tatia M. C. Lee
Summary: This study investigated the relationship between white matter connectivity and the temporal dynamics of functional connectivity in rumination. Results showed that lower global metastability and higher global synchrony of dynamic functional connectivity were associated with higher levels of rumination, and these variables mediated the connection between white matter integrity and rumination.
NEUROIMAGE-CLINICAL
(2022)
Article
Clinical Neurology
Weilin Liu, Jianhong Li, Minguang Yang, Xiaohua Ke, Yaling Dai, Huawei Lin, Sinuo Wang, Lidian Chen, Jing Tao
Summary: The degeneration of the cholinergic circuit from the basal forebrain to the hippocampus contributes to memory loss in Alzheimer's disease patients. Activation of the cholinergic circuit using chemical genetics improves learning and memory function in APP/PS1 mice models. Levels of choline in the basal forebrain and N-acetyl aspartate in the hippocampus could serve as biomarkers for early diagnosis of AD.
ALZHEIMERS RESEARCH & THERAPY
(2022)
Article
Public, Environmental & Occupational Health
Jun Wang, Jue Liu, Xueyao Wang, Jingmin Zhu, Yang Bai, Yue Che, Jing Tao
Summary: This study aimed to examine the association between changes in social participation and long-term improved cognitive function among older adults. The results showed that stable or increased social participation was positively associated with improved cognitive function, particularly in participation in organized social activities and group leisure-time activities. This study highlights the importance of promoting social participation from multiple perspectives in improving cognitive function among older adults.
HEALTH & SOCIAL CARE IN THE COMMUNITY
(2022)
Article
Biology
Zhi Chen, Yongguo Liu, Yun Zhang, Rongjiang Jin, Jing Tao, Lidian Chen
Summary: The study introduces a new model LSFSIL for predicting cognitive performance and identifying neuroimaging markers with incomplete labeled data using MRI. Experimental results demonstrate that LSFSIL outperforms existing methods and selects consistent neuroimaging markers with previous studies.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Neurosciences
Weilin Liu, Xiaojun He, Huawei Lin, Minguang Yang, Yaling Dai, Lewen Chen, Chaohui Li, Shengxiang Liang, Jing Tao, Lidian Chen
Summary: Optogenetic stimulation can improve the excitation/inhibition balance between bilateral motor cortices, leading to reduced neurological deficit and improved motor dysfunction in ischemic stroke rats.
EXPERIMENTAL NEUROLOGY
(2023)
Article
Health Care Sciences & Services
Jingsong Wu, Jingnan Tu, Zhizhen Liu, Lei Cao, Youze He, Jia Huang, Jing Tao, Mabel N. K. Wong, Lidian Chen, Tatia M. C. Lee, Chetwyn C. H. Chan
Summary: The Efficient Online MCI Screening System (EOmciSS) is a self-paced cognitive test developed for identification of community-dwelling older adults with mild cognitive impairment(MCI) risks. This study validated the EOmciSS and found that it has higher sensitivity and specificity compared to other screening systems. Depressive symptoms were also found to be influential in the test performance and MCI risk.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Integrative & Complementary Medicine
Zhizhen Liu, Lei Cao, Jingsong Wu, Youze He, Jingnan Tu, Jia Huang, Jing Tao, Lidian Chen
Summary: This study investigated the depression and sleep disturbance in patients with mild cognitive impairment (MCI) among Chinese community-dwelling elderly. It found that Qi-deficiency and Qi-stagnation constitution may be risk factors for MCI. Sleep latency, daytime dysfunction, and Qi-stagnation were identified as risk factors for MCI. MCI patients have a higher prevalence of sleep disorders and Qi-stagnation, and may exhibit specific sleep characteristics such as difficulty falling asleep, easily waking up at night/early morning, and daytime dysfunction.
EUROPEAN JOURNAL OF INTEGRATIVE MEDICINE
(2023)
Article
Geriatrics & Gerontology
Clive H. Y. Wong, Jiao Liu, Jing Tao, Li-dian Chen, Huan-ling Yuan, Mabel N. K. Wong, Yan-wen Xu, Tatia M. C. Lee, Chetwyn C. H. Chan
Summary: Age-related cognitive slowing is a precursor of cognitive decline. This study examined how inter- and intra-brain network influences mediate age-related cognitive slowing. The results suggest that inter-network connectivity from the cerebellar network (CN) and fronto-insular salience network (SN) to the frontoparietal dorsal attention network (DAN) play significant roles in age-related cognitive slowing.
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.