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Deep Bimodal Fusion Approach for Apparent Personality Analysis

computers, Materials & Continua 2023年第4期75卷 2301-2312页

作者： Saman Riaz Ali Arshad Shahab S.Band Amir Mosavi Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Computer Science Institute of Space TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliu64002YunlinTaiwan Faculty of Civil Engineering Technische Universitat DresdenDresden01069Germany

Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.

关键词： Apparent personality analysis deep bimodal fusion convolutional neural network long short-term memory bimodal information fusion approach

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DetSpace:Distributed Congestion Free Routing for Global Deterministic Network in Mega-Constellations

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China Communications 2024年第12期21卷 203-216页

作者： Li Jihao Li Hewu Lai Zeqi Wang Xiaomo Beijing National Research Center for Information Science and Technology(BNRist) Beijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute of Network Science and Cyberspace Tsinghua UniversityBeijing 100084China Zhongguancun Laboratory Beijing 100095China China Academy of Electronics and Information Technology Beijing 100040China

Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic *** many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation,they cannot be applied on a global *** emerging mega-constellations enable new opportunities for realizing deterministic delay *** one constellation(e.g.,Starlink)might be managed by a single operator(e.g.,SpaceX),packets can be routed within deterministic number of ***,the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by *** paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output,and to determine the routing hops,and thus realizing a global deterministic network(DETSPACE).The model based on the 2D Markov chain theoretically verifies the correctness of *** effectiveness of DETSPACE in different traffic load con-ditions is also verified by extensive simulations.

关键词： congestion free deterministic network mega-constellations path diversity

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Downscaling Taiwan precipitation with a residual deep learning approach

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Geoscience Letters 2024年第1期11卷 1-13页

作者： Hsu, Li-Huan Chiang, Chou-Chun Lin, Kuan-Ling Lin, Hsin-Hung Chu, Jung-Lien Yu, Yi-Chiang Fahn, Chin-Shyurng National Science and Technology Center for Disaster Reduction New Taipei City 231007 Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei 106335 Taiwan

In response to the growing demand for high-resolution rainfall data to support disaster prevention in Taiwan, this study presents an innovative approach for downscaling precipitation data. We employed a hierarchical architecture of Multi-Scale Residual Networks (MSRN) to downscale rainfall from a coarse 0.25-degree resolution to a fine 0.0125-degree resolution, representing a substantial challenge due to a resolution increase of over 20 times. Our results demonstrate that the hierarchical MSRN outperforms both the one-step MSRN and linear interpolation methods when reconstructing high-resolution daily rainfall. It surpasses the linear interpolation method by 15.1 and 9.1% in terms of mean absolute error and root mean square error, respectively. Furthermore, the hierarchical MSRN excels in accurately reproducing high-resolution rainfall for various rainfall thresholds, displaying minimal biases. The threat score (TS) highlights the hierarchical MSRN's capability to replicate extreme rainfall events, achieving TS scores exceeding 0.54 and 0.46 at rainfall thresholds of 350 and 500 mm per day, outperforming alternative methods. This method is also applied to an operational global model, the ECMWF’s daily rainfall forecasts over Taiwan. The evaluation results indicate that our approach is effective at improving rainfall forecasts for thresholds greater than 100 mm per day, with more significant improvement for the 1- to 3-day lead forecast. This approach also offers a realistic visual representation of fine-grained rainfall distribution, showing promise for making significant contributions to disaster preparedness and weather forecasting in Taiwan. © The Author(s) 2024.

关键词： Deep learning Downscaling Precipitation forecasting Residual network Super-resolution

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RON-based cross-chain routing optimization strategy in metaverse

IET Blockchain

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IET Blockchain 2024年第4期4卷 379-392页

作者： Huang, Junjie Tan, Liang Xiao, Jianmei College of Computer Science Sichuan Normal University Sichuan Chengdu China Information Technology Center Sichuan Rural Credit Sichuan Chengdu China Institute of Computing Technology Chinese Academy of Sciences Beijing China Science and Innovation Center Deyang Jinshajianglu School Sichuan Deyang China

Metaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross-chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross-chain communication mode is dominated by direct-connect routing, leading to network congestion and high propagation delay once the direct-connect link fails and cannot be recovered quickly. To optimize direct-connect routing, this paper proposed a cross-chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross-Chain_RON, which firstly applies RON to reconstruct the direct-connect routing model, and then selects the optimal link through the shortest-path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross-Chain_RON outperforms the direct-connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Blockchain

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Event-Triggered Cooperative Tracking Control of Multiagent Systems with a Dynamic Leader via Approximate Dynamic Programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2752-2765页

作者： Fu, Hao He, Haodong Chen, Yang Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System The School of Computer Science and Technology Wuhan430081 China Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

This article investigates the optimal cooperative tracking control problem for unknown nonlinear multiagent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multiplayer game for the distributed auxiliary systems employing observer-based feedforward compensation, which contributes to reducing conservatism;via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weight estimation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify the effectiveness and superiority of our algorithm. © 2020 IEEE.

关键词： Dynamic programming

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MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning 24

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-s...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zhenyu Chen, Guangyao Zou, Yixiong Huang, Zhimeng Li, Yuhua Li, Ruixuan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

ISBN: (纸本)9798400706868

Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Unsupervised Few-Shot Learning (U-FSL) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases. In this work, we first quantitatively assess the impacts of Masked Image Modeling (MIM) and Contrastive Learning (CL) on few-shot learning tasks. Our findings highlight the respective limitations of MIM and CL in terms of discriminative and generalization abilities, which contribute to their underperformance in U-FSL contexts. To address these trade-offs between generalization and discriminability in unsupervised pretraining, we introduce a novel paradigm named Masked Image Contrastive Modeling (MICM). MICM creatively combines the targeted object learning strength of CL with the generalized visual feature learning capability of MIM, significantly enhancing its efficacy in downstream few-shot learning inference. Extensive experimental analyses confirm the advantages of MICM, demonstrating significant improvements in both generalization and discrimination capabilities for few-shot learning. Our comprehensive quantitative evaluations further substantiate the superiority of MICM, showing that our two-stage U-FSL framework based on MICM markedly outperforms existing leading baselines. The repository of this project is available at https://***/iCGY96/MICM. © 2024 ACM.

关键词： Contrastive Learning

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Multivariate long sequence time-series forecasting using dynamic graph learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7679-7693页

作者： Wang, Xiaohu Wang, Yong Peng, Jianjian Zhang, Zhicheng School of Computer Science and Engineering Center for CyberSecurity University of Electronic Science and Technology of China Xi Yuan Ave. Sichuan Chengdu611731 China

Time series prediction is a subset of temporal data mining, which seeks to forecast its values in the future by using the accessible historical observations within the specified time periods. Deep neural networks have shown their superiority in predicting time series according to recent studies. Unfortunately, most models overlook differences and interdependencies between variables when tackling the multivariate long sequence time-series forecasting problem. In order to explicitly learn them and take them into account at a fine-grained level to resolve the dynamics of variable dependencies, we introduce Graph Convolutional Networks into Transformer in this article. To counteract the local insensitivity of the Transformer, we further integrate the Temporal Convolutional Network as a component of the self-attention layer. The experimental evaluation results on four open datasets show that our model performs noticeably better than a diverse range of state-of-the-art benchmarks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Time series

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An Adaptable Algorithm for Optimizing Bus Line Distribution Using the Clustering Method

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Wireless Personal Communications 2024年第4期134卷 2347-2370页

作者： Sheikhi, Fatemeh Rahmani, Amir Masoud Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The current fleet of cars requires reevaluation, upgrades, and replacement in response to the expanding human population, the increase in urban traffic volume, and the elevated levels of air pollution attributed to vehicular emissions. In densely populated cities, bus routes effectively compete with private transportation, optimize user accessibility, and provide access to all urban residents. This study proposes a novel method for optimizing bus routes, encompassing four primary processes: preprocessing, site clustering, ridership prediction, and optimization. The clustering process employs the K-means technique to classify available bus stops based on their geographical information. The suggested method utilizes an artificial neural network model to forecast the number of passengers at different locations. Subsequently, the bee colony optimization algorithm is implemented to determine bus frequencies and achieve an optimal distribution of buses across various traffic lines. Results obtained using a real traffic line dataset indicate a 32% increase in traffic fleet capacity and a 22.33% reduction in passenger waiting time upon application of the proposed method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

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Counteracting New Attacks in CPS: A Few-shot Class-incremental Adaptation Strategy for Intrusion Detection System

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2473-2488页

作者： Dong, Xinrui Lai, Yingxu Zhang, Xiao Xu, Xinyu Beijing University of Technology College of Computer Science Beijing100124 China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing100124 China

The deep integration of physical devices and communication networks has increased the security risks of cyber-physical systems (CPSs) compared to traditional control systems. Deep learning-based intrusion detection systems (IDSs) play a crucial role in ensuring CPSs security. However, the existing IDSs often rely on known attack features, rendering them unable to withstand emerging new attacks arising from the dynamic evolution of intrusion behaviors. This paper aims to develop an IDSs with high adaptability and strong generalization capabilities, which is capable of rapidly adapting to new attack classes with only a few new samples. To achieve this objective, we propose CAT-IDS, a few-shot class-incremental adaptation strategy for an IDS to counteract new attacks on CPSs. We design a highly symmetric classifier structure for CAT-IDS that can flexibly adjust the classification space to adapt to new attacks. Furthermore, we calibrate the biased distribution formed by a few training samples through statistical feature transfer. In order to prevent the model from forgetting old attack information during the adaptation process, we devise hybrid features for attack detection. These features contain essential information for both old and new class classifications. We demonstrate the effectiveness of CAT-IDS through multiple experiments on three CPSs datasets. The results show that CAT-IDS achieves an average accuracy improvement of approximately 4. 5% compared to the state-of-the-art methods, demonstrating its superior ability to adapt to new attacks while maintaining high performance in classifying existing attacks. © 2004-2012 IEEE.

关键词： Network intrusion

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Study on Method of Bearing Fault Detection Based on Vibration Signal Analysis

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Engineering Letters 2023年第3期31卷 1009-1015页

作者： Yang, Guang Liu, Xinrong Engineering Training Center Yantai University Yantai264005 China School of Computer Science and Technology Shandong Technology and Business University Yantai264005 China

This paper introduces a complete method for bearing fault detection. By analyzing the vibration signal when a fault occurs, the type of fault can be detected. The number of high-frequency intrinsic mode functions (IMFs) is determined by calculating the cumulative mean of each IMF. In the modified ensemble empirical mode decomposition (EEMD), only the high-frequency IMFs need to be decomposed out. The scientific calculation process for the parameters of the algorithm is explained in detail. The function of the kurtosis value and the correlation coefficient is analyzed, and the IMFs with the most abundant fault information are selected to restructure the signal. The reconstructed signal is then processed by wavelet algorithm. The fault vibration signal from the bearing inner ring is used as an example for the experiment. The fault characteristic frequency of bearing inner race is calculated by formula. By analyzing the envelope spectrum of the final restructured signal, the fault information is effectively detected. The results demonstrate that the method given by this paper can detect bearing fault information more quickly and efficiently. © 2023, International Association of Engineers. All rights reserved.

关键词： Intrinsic mode functions

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