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A Complex Fuzzy LSTM Network for Temporal-Related Forecasting Problems

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Computers, Materials & Continua 2024年第9期80卷 4173-4196页

作者： Nguyen Tho Thong Nguyen Van Quyet Cu Nguyen Giap Nguyen Long Giang Luong Thi Hong Lan institute of information technology Vietnam Academy of Science and TechnologyHoang Quoc VietCau GiayHanoi100000Vietnam Academic Affairs Department Thai Nguyen University of EducationThai Nguyen250000Vietnam Center of science and technology Research and Development Thuongmai UniversityHo Tung MauCau GiayHanoi100000Vietnam Faculty of information technology Hanoi University of IndustryBac Tu LiemHanoi100000Vietnam

Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development ofmanagement platforms and systems based on the Internet and cutting-edge information communication *** the time series data including time series prediction has many practical *** new techniques were developed for use with various types of time series data in the prediction *** those,this work suggests a unique strategy to enhance predicting quality on time-series datasets that the timecycle matters by fusing deep learning methods with fuzzy *** order to increase forecasting accuracy on such type of time-series data,this study proposes integrating deep learning approaches with fuzzy ***,it combines the long short-termmemory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model(CFLSTM).The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory(LSTM)technique to have greater power for processing time series *** on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposedmodel’s utility compared to other well-known forecasting *** results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.

关键词： Complex fuzzy set long short-term memory(LSTM) CFLSTM T-CFLSTM

Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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science China(information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG institute of information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer science and technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

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Computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer science and technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of information technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

The Exchange Attack and the Mixture Differential Attack Revisited: From the Perspective of Automatic Evaluation

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Chinese Journal of Electronics 2024年第1期33卷 19-29页

作者： Kexin QIAO Zhiyu ZHANG Zhongfeng NIU Liehuang ZHU School of Cyberspace science and technology Beijing Institute of Technology State Key Laboratory of Cryptology institute of information Engineering Chinese Academy of Sciences School of Cryptography University of Chinese Academy of Sciences

Recent results show that the differential properties within quadruples boom as a new inspiration in cryptanalysis of Advanced Encryption Standard(AES)-like constructions. These methods include the exchange attack proposed in Asiacrypt’19, the mixture differential attack proposed in To SC’18, etc., where the essential properties are obtained by manually scrutinizing the structures of the AES-like constructions. This paper presents a novel framework and an automatic tool based on mixed integer linear programming to search for mixture differential distinguishers for general constructions. This framework considers what equality patterns among quadruples can make a distinguisher and traces how the patterns propagate through cipher components with accurate probability *** this tool, a 5-round AES distinguishing attack with lower complexity and more 6-round distinguishing attacks in the chosen plaintext scenarios are deduced. We prove that no exchange-type or mixture differential distinguisher exists for 7 and above rounds AES if the details of the Sbox and Mix Columns matrix are not taken into account.

关键词： Ciphers Estimation Encryption Mixed integer linear programming Complexity theory Standards

A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

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Computers, Materials & Continua 2024年第4期79卷 1335-1351页

作者： Xue Zhao Shaojun Tao Hongying Tang Jiang Wang Baoqing Li science and technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai200050China

In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.

关键词： Node scheduling pre-selection target tracking WSN

Robust video question answering via contrastive cross-modality representation learning

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science China(information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of information science and technology University of Science and Technology of China institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer science and information Engineering Hefei University of Technology institutes of Physical science and information technology Anhui University School of Computer science and technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition

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Defence technology（防务技术） 2024年第2期32卷 619-628页

作者： Yi Liu Weiqing Huang Shang Jiang Bobai Zhao Shuai Wang Siye Wang Yanfang Zhang institute of information Engineering Chinese academy of sciences School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina School of information Management Beijing Information Science and Technology UniversityBeijingChina

RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy *** RFID-based HAR methods use modeling,CNN,or LSTM to extract features ***,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF *** solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called *** model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without ***,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human *** with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and ***,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification *** results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.

关键词： Human activity recognition RFID Transformer

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Clinical validation of the early embryo viability assessment system: Analysis for the blastocyst morphology and pregnancy outcomes

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Asian pacific Journal of Reproduction 2024年第5期13卷 219-227页

作者： Vu D Hop An M Cuong Phi T T Anh Nguyen T L Huong Le Hoang Nguyen V Hanh Assisted Reproductive technology Centre Tam Anh General HospitalHanoiVietnam institute of Biotechnology Vietnam Academy of Science and TechnologyHanoiVietnam

Objective:To determine the relationship between the early embryo viability assessment(EEVA)and blastocyst morphological parameters and pregnancy ***:This retrospective cohort study was conducted on 291 intracytoplasmic sperm injection cycles including 2522 embryos with indications of prolonging embryo culture to the blastocyst stage in the Genea embryo review incubator,and 511 single vitrified-warmed blastocyst transfer cycles from January 2020 to June *** EEVA system produced an EEVA score from E1(best)to E5(worse)for the potential of blastocyst *** morphology was *** association between the EEVA score and each type of blastocyst morphology,implantation rate,clinical pregnancy,and ongoing pregnancy were assessed using generalized estimating ***:The inner cell mass A(ICM A),trophectoderm A(TE A),blastocoele expansion degree of 3,4,5,6,7 rates were higher with lower the EEVA *** adjusted odd ratio(aOR)(E5 vs E1)was 0.3 for ICM A,0.174 for TE A and 0.210 for BL3,4,5,6,7(all P<0.001),suggesting a significant association between lower EEVA scores and improved embryo *** implantation,clinical pregnancy,and ongoing pregnancy rate were also higher with lower the EEVA *** aOR of E5 vs E1 was 0.245 for implantation,0.185 for clinical pregnancy and 0.200 for ongoing pregnancy rate(P<0.001).Conclusions:There were associations between blastocyst morphology,pregnancy outcome and EEVA *** good blastocyst morphology and pregnancy outcomes are higher with lower the EEVA score.

关键词： Automated embryo assessment Blastocyst Early embryo viability assessment Genea embryo review incubator

A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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science China(information sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU institute of information science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced information science and Network technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last ***, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM