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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Janghyun Shin, Ukcheol Heo, Seokyong Park, Jinsun Department of Information Convergence Engineering Pusan National University Busan Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States School of Computer Science and Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9789819609680

Single-modal depth estimation has shown steady improvement over the years. However, relying solely on a single imaging sensor such as RGB and near-infrared (NIR) cameras can result in unreliable and erroneous depth estimation, particularly in challenging lighting conditions such as low-light or sudden lighting change scenarios. Thereby, several approaches have leveraged multiple sensors for robust depth estimation. However, the effective fusion method that maximally utilizes multi-modal sensor information still requires further investigation. With this in mind, we propose a multi-modal cost volume fusion strategy with cross-modal attention, incorporating information from both cross-spectral and single-modality pairs. Our method initially constructs low-level cost volumes that consist of modality-specific (ı.e., single modality) and modality-invariant (ı.e., cross-spectral) volumes from multi-modal sensors. These cost volumes are then gradually fused using bidirectional cross-modal fusion and unidirectional LiDAR fusion to generate a multi-sensory cost volume. Furthermore, we introduce a straightforward domain gap reduction approach to learn modality-invariant features and depth refinement techniques through cost volume-guided propagation. Experimental results demonstrate that our method achieves SOTA (State-of-the-Art) performance under diverse environmental changes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Depth perception

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2024年第4期9卷 330-339页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui UniversityHefei 230000China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of TechnologyMa’anshan 243032China School of Computer science School of Cyber Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210000China

In the realm of the Internet of things(IoT),radio frequency identification(RFID)technology is essential for linking physical objects to digital *** introduction of harmonic technology has expanded RFID’s applications by improving sensitivity and enabling communication with wireless fidelity(Wi-Fi)***,this trend faces challenges,notably interference from strong Wi-Fi signals,which impacts RFID-based sensing *** paper proposes the WiFID(Wi-Fi and RFID)algorithm,which enables Wi-Fi devices to sense weak RFID harmonic *** subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic *** validation on the universal software radio peripheral(USRP)platform demonstrates that WiFID successfully detects 90% of RFID harmonics at−30 dBm,with only a minimal 4%decrease in Wi-Fi throughput.

关键词： RFID harmonic wireless sensing Wi-Fi interference avoidence

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

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computers, Materials & Continua 2022年第6期71卷 5117-5133页

作者： S.Sreenivasa Chakravarthi R.Jagadeesh Kannan V.Anantha Natarajan Xiao-Zhi Gao CSE Center for Intelligent ComputingSree Vidyanikethan Engineering College&School of Computer Science and EngineeringVITChennaiTamilnadu600127India School of Computer Science and Engineering VITChennaiTamilnadu600127India CSE Center for Intelligent ComputingSree Vidyanikethan Engineering CollegeTirupati517102India School of Computing University of Eastern FinlandKuopio02130EspooFinland

In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology,and invest in building *** the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering *** this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing *** part of the innovations happening in the industrial sector,a new concept termed as‘smart manufacturing’has emerged,which employs the benefits of emerging technologies like internet of things and cloud *** services deliver an on-demand access to computing,storage,and infrastructural platforms for the industrial users through *** the recent era of information technology the number of business and individual users of cloud services have been increased and larger volumes of data is being processed and stored in *** a consequence,the data breaches in the cloud services are also increasing day by *** to various security vulnerabilities in the cloud architecture;as a result the cloud environment has become *** restore the normal behavior of the cloud,detect the deviations,and achieve higher resilience,anomaly detection becomes *** deep learning architectures-based anomaly detection mechanisms uses various monitoring metrics characterize the normal behavior of cloud services and identify the abnormal *** paper focuses on designing an intelligent deep learning based approach for detecting cloud anomalies in real time to make it more *** deep learning models are trained using features extracted from the system level and network level performance metrics observed in the Transfer Control Protocol(TCP)traces of the *** experimental results of the proposed approach demonstrate a superior performance in terms

关键词： Anomaly detection network flow data deep learning migration auto-encoder support vector machine

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A multiscale differential-algebraic neural network-based method for learning dynamical systems

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International Journal of Mechanical System Dynamics 2024年第1期4卷 77-87页

作者： Yin Huang Jieyu Ding College of Computer Science&Technology Qingdao UniversityQingdaoChina School of Mathematics and Statistics Qingdao UniversityQingdaoChina Center for Computational Mechanics and Engineering Simulation Qingdao UniversityQingdaoChina

The objective of dynamical system learning tasks is to forecast the future behavior of a system by leveraging observed ***,such systems can sometimes exhibit rigidity due to significant variations in component parameters or the presence of slow and fast variables,leading to challenges in *** overcome this limitation,we propose a multiscale differential-algebraic neural network(MDANN)method that utilizes Lagrangian mechanics and incorporates multiscale information for dynamical system *** MDANN method consists of two main components:the Lagrangian mechanics module and the multiscale *** Lagrangian mechanics module embeds the system in Cartesian coordinates,adopts a differential-algebraic equation format,and uses Lagrange multipliers to impose constraints explicitly,simplifying the learning *** multiscale module converts high-frequency components into low-frequency components using radial scaling to learn subprocesses with large differences in *** results demonstrate that the proposed MDANN method effectively improves the learning of dynamical systems under rigid conditions.

关键词： dynamical systems learning multibody system dynamics differential-algebraic equation neural networks multiscale structures

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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A Method for Detecting Depression in Adolescence Based on an Affective Brain‑computer Interface and Resting‑State Electroencephalogram Signals

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Neuroscience Bulletin 2025年第3期41卷 434-448页

作者： Zijing Guan Xiaofei Zhang Weichen Huang Kendi Li Di Chen Weiming Li Jiaqi Sun Lei Chen Yimiao Mao Huijun Sun Xiongzi Tang Liping Cao Yuanqing Li School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China The Affiliated Brain Hospital Guangzhou Medical UniversityGuangzhou 510370China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China

Depression is increasingly prevalent among adolescents and can profoundly impact their ***,the early detection of depression is often hindered by the timeconsuming diagnostic process and the absence of objective *** this study,we propose a novel approach for depression detection based on an affective brain-computer interface(aBCI)and the resting-state electroencephalogram(EEG).By fusing EEG features associated with both emotional and resting states,our method captures comprehensive depression-related *** final depression detection model,derived through decision fusion with multiple independent models,further enhances detection *** experiments involved 40 adolescents with depression and 40 matched *** proposed model achieved an accuracy of 86.54%on cross-validation and 88.20%on the independent test set,demonstrating the efficiency of multi-modal *** addition,further analysis revealed distinct brain activity patterns between the two groups across different *** findings hold promise for new directions in depression detection and intervention.

关键词： Depression detection Brain-computer interface EEG Multimodal

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Learning group interaction for sports video understanding from a perspective of athlete

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Frontiers of computer science 2024年第4期18卷 175-188页

作者： Rui HE Zehua FU Qingjie LIU Yunhong WANG Xunxun CHEN Intelligent Recognition and Image Processing(IRIP)Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Hangzhou Innovation Institute Behang UniversityHangzhou 310051China National Computer Network Emergency Response Technical Team/Coordination Center of China(CNCERT or CNCERT/CC) Beijing 100029China

Learning activities interactions between small groups is a key step in understanding team sports *** research focusing on team sports videos can be strictly regarded from the perspective of the audience rather than the *** team sports videos such as volleyball and basketball videos,there are plenty of intra-team and inter-team *** this paper,a new task named Group Scene Graph Generation is introduced to better understand intra-team relations and inter-team relations in sports *** tackle this problem,a novel Hierarchical Relation Network is *** all players in a video are finely divided into two teams,the feature of the two teams’activities and interactions will be enhanced by Graph Convolutional Networks,which are finally recognized to generate Group Scene *** evaluation,built on Volleyball dataset with additional 9660 team activity labels,a Volleyball+dataset is proposed.A baseline is set for better comparison and our experimental results demonstrate the effectiveness of our ***,the idea of our method can be directly utilized in another video-based task,Group Activity *** show the priority of our method and display the link between the two ***,from the athlete’s view,we elaborately present an interpretation that shows how to utilize Group Scene Graph to analyze teams’activities and provide professional gaming suggestions.

关键词： group scene graph group activity recognition scene graph generation graph convolutional network sports video understanding

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Identification of Important FPGA Modules Based on Complex Network

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computers, Materials & Continua 2024年第1期78卷 1027-1047页

作者： Senjie Zhang Jinbo Wang Shan Zhou Jingpei Wang Zhenyong Zhang Ruixue Wang School of Aeronautics and Astronautics University of Chinese Academy of SciencesBeijing101408China Technology and Engineering Center for Space Utilization Chinese Academy of SciencesBeijing100094China College of Control Science and Engineering Zhejiang UniversityHangzhou310063China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing ***,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource *** the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)*** studies have pointed out that the failure of key modules tends to cause greater damage to the ***,under limited conditions,priority protection designs need to be made on key modules to improve protection *** have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA *** the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)***,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important ***,we conduct empirical research using actual FPGA designs as *** results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.

关键词： Hardware security FPGA circuits node importance interdependent network

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