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SSRN 2024年

作者： Chen, Long Sun, Rui Yu, Yiheng Du, Yun Zhang, Xudong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China The School of Computer and Information Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China

Visible-thermal Person Re-identification (VT Re-ID) is a challenging task in all-weather surveillance system. Existing methods focus on extracting the modality-shared features, ignoring the discriminative inter-modality complementary features. To tackle this issue, we propose the multi-branch modality residual complementary learning method consisting of modality residual complementary learning (MRCL) module and multi-branch feature learning (MBFL) module. The MRCL module can be easily integrated into existing CNN and Transformer baselines and drive the network to focus on both intra-modality and inter-modality information simultaneously. On one hand, we adopt the basic two-stream network to obtain the intra-modality features, on the other hand, we feed the residual image acquired by cross-modality correlation saliency erasing operation to another two-stream network to capture the inter-modality complementary features. Moreover, we employ the MBFL module to capture local spatial features and local channel features and integrate them with global features to increase the discriminability and robustness of the ultimate representations for handling intra-modality variations. The extensive experiments on RegDB and SYSU-MM01 datasets demonstrate the superiority of our proposed method compared with state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Sunday Oladayo Oladejo Stephen Obono Ekwe Mbuyu Sumbwanyanbe Ernest Mnkandla Oluwagbemiga Omotayo Shoewu Departments of Computer Science and Elect. Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Department of Electrical Engineering CSET University of South Africa Johannesburg South Africa Department for Computer Science CSET University of South Africa Johannesburg South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages.

关键词： Support vector machines Machine learning algorithms Accuracy data analysis Smart cities Network slicing Prediction algorithms

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CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Retrieval

CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Ret...

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International Conference on computer Supported Cooperative Work in Design

作者： Mengying Xu Hanjiang Lai Jian Yin School of Systems Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P. R. China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Cross-modal hashing has attracted great interest in the past decades. Due to the traditional hashing retrieval model requiring hand-crafted feature extraction, much research on the end-to-end deep hashing models has been explored. However, this training process of deep models is complex and needs a large number of samples. To address the above issues, we propose a lightweight hashing retrieval network with the pre-trained CLIP model, which is called CLIP-Hash. It can obtain better hash features via the pre-training CLIP model and provide a lightweight fine-tune model which is easily trained and only few training samples are required. First, we obtain the powerful feature representations by the visual and textual encodes. Then, we propose a simple hash encoding module, which is a lightweight network and only few parameters are needed to be trained. With the proposed hash encoding module, we can obtain efficient binary hash codes that can align the images and texts. We conduct experiments on MIRFlickr and NUS-WIDE two benchmark datasets. The results show that the proposed simple method can outperform the SOTA hashing methods.

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Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection

arXiv

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arXiv 2024年

作者： Phai, Vu Dinh Uy, Quang Nguyen Hoang, Dinh Thai Nguyen, Diep N. Son, Pham Bao Dutkiewicz, Eryk The School of Electrical and Data Engineering The University of Technology Sydney SydneyNSW2007 Australia The Computer Science Department Faculty of Information Technology Le Quy Don Technical University Hanoi Viet Nam The University of Engineering and Technology Vietnam National University Hanoi Viet Nam

Representation Learning (RL) plays a pivotal role in the success of many problems including cyberattack detection. Most of the RL methods for cyberattack detection are based on the latent vector of Auto-Encoder (AE) models. An AE transforms raw data into a new latent representation that better exposes the underlying characteristics of the input data. Thus, it is very useful for identifying cyberattacks. However, due to the heterogeneity and sophistication of cyberattacks, the representation of AEs is often entangled/mixed resulting in the difficulty for downstream attack detection models. To tackle this problem, we propose a novel model called Twin Auto-Encoder (TAE). TAE deterministically transforms the latent representation into a more distinguishable representation namely the separable representation and then reconstruct the separable representation at the output. The output of TAE called the reconstruction representation is input to downstream models to detect cyberattacks. We extensively evaluate the effectiveness of TAE using a wide range of benchmarking datasets. Experiment results show the superior accuracy of TAE over state-of-the-art RL models and well-known machine learning algorithms. Moreover, TAE also outperforms state-of-the-art models on some sophisticated and challenging attacks. We then investigate various characteristics of TAE to further demonstrate its superiority. © 2024, CC BY.

关键词： Learning algorithms

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Dts-Adapstnet: An Adaptive Spatiotemporal Neural Networks for Traffic Prediction with Mulit-Graph Fusion

SSRN

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SSRN 2023年

作者： Zhang, Jing Shi, Wenlong Ye, Xiucai Tsai, Peiwei School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe

关键词： Travel time

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Multi-scale Variable Window Attention Network for Continuous Sign Language Recognition

Multi-scale Variable Window Attention Network for Continuous...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Nan Zhang Chao Wei Leming Guo Zeyu Cheng Zhao Zhao Yangcan Wu Wanli Xue School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Emergency Centre Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China Shaanxi Big Data Group Co. Ltd Shaanxi China Shaanxi Logistics Group Industry Research Institute Co Shaanxi China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Sign language is primarily conveyed through hands and face movements. Therefore, a pivotal challenge in continuous sign language recognition (CSLR) is the meticulous modeling and interpreting of visual cues emanating from hands and facial movements. However, contemporary CSLR methods have exhibited limitations in adequately capturing information from both the hands and facial regions, often overlooking the continuity, and correlations across multiple consecutive frames. To mitigate these shortcomings, we propose the Multi-scale Variable Window Attention Network (MVAN). This framework fuses the strengths of an improved Efficient Multi-scale Attention (iEMA) with Variable Window Adaptation (VWA) attention. Specifically, iEMA first scans information spatially at multiple scales to simultaneously capture information from areas such as hands and faces, and models spatial continuity interactions between them, enhancing spatial features. Secondly, VWA dynamically changes its window size at the temporal level to adaptively capture complex temporal changes in hands and face movements in successive frames. Thus, VWA can effectively model the continuity and correlation between hands and face movements to have the ability to capture sign language movements of different lengths. Comprehensive experimental results demonstrate that our proposed MVAN strengthens the visual feature extraction of hands and face, improving the recognition performance.

关键词： Hands Sign language Adaptation models Visualization Correlation Face recognition Neural networks Feature extraction Robustness Social intelligence

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Multi-variate hybrid modeling for pacific ocean acidification: predicting future pH trends and analyzing key biogeochemical drivers

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CSI Transactions on ICT 2024年第1期13卷 99-116页

作者： Vasanth, K. Kishore, R. K. Sugumaran, Vijayan Krishnamoorthy, Raja Ramdas, Rithvik Tadepalli, Satya Kiranmai Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thavapalayam Karur India Department of Decision and Information Sciences School of Business Administration Oakland University Rochester USA Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India

Ocean acidification, driven by rising atmospheric carbon dioxide levels, poses a significant threat to the health of marine ecosystems, particularly in the Pacific Ocean. This study employs a multi-variate hybrid machine learning approach to predict future pH trends within the Pacific and to analyze the influence of key biogeochemical drivers on these trends. Hybrid models, strategically combining the strengths of individual algorithms, were developed for predicting several ocean acidification parameters. A performance analysis demonstrated the superior accuracy of hybrid models compared to their counterparts. The predicted pH trends reveal a concerning shift towards increased acidity within the Pacific Ocean, highlighting the urgency of understanding and mitigating its impacts. In-depth analysis was conducted to identify the relative influence of key biogeochemical factors on the changing pH dynamics. This research aims to provide crucial insights for developing targeted mitigation strategies and protecting the vulnerable ecosystems of the Pacific Ocean from the escalating consequences of ocean acidification.

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video datasets

Temporal Transformation Network Based On Scale Sequences for...

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International Conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles

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Journal of Shanghai Jiaotong university(Science) 2020年第2期25卷 201-207页

作者： WU Xin PU Juan XIE Shaorong School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Institute for Advanced Communication and Data Science Shanghai 200444China

Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information *** attack strategy plays a crucial role in unmanned combat system,which has to ensure the attack by unmanned surface vehicles(USVs)from *** meet the challenge,we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization(DAGWO).The traditional grey wolf optimization(GWO)algorithm is improved with a distributed auction mechanism(DAM)to constrain the initialization of wolves,which improves the optimization process according to the actual *** addition,one unmanned aerial vehicle(UAV)is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack *** proposed DAGWO algorithm can not only ensure the diversity of wolves,but also avoid the local optimum *** results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.

关键词： unmanned surface vehicle(USV) attack strategy grey wolf optimization(GWO) task allocation unmanned aerial vehicle(UAV)

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