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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Graph convolutional network for visual question answering based on fine-grained question representation 5

Graph convolutional network for visual question answering ba...

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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Hu, Ze Wei, Jielong Huang, Qingbao Liang, Hanyu Zhang, Xingmao Liu, Qingguang Guangxi University School of Electrical Engineering Nanning Guangxi China School of Software Engineering South China University of Technology Guangzhou Guangdong China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Guangxi Arts University General Education Department Nanning Guangxi China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Recent research works mainly focus on the bilinear fusion methods and attention mechanism methods to explore the global features and local features, respectively. When an image exists multiple objects, the fine-grained question features can effectively identify the relationship between objects in an image. Therefore we propose a graph convolutional network based on fine-grained question representation (FQ-GCN). The object relation graph is firstly constructed. The fine-grained question features are used to explore the relations between objects in an image and prune unrelated edges between object nodes. Graph convolutional network is used to aggregate all neighborhood information of each object in an object graph. Experiments on the VQA 2.0 dataset show that the performance of our FQ-GCN model improves 2% ~ 3% than the classical methods. © 2020 IEEE.

关键词： Convolution

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Synthesis of Ca 2+ substituted cobalt zinc ferrite nanoparticles for magnetic induction hyperthermia

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Materials Today Communications 2025年 47卷

作者： Suwanan Thammarong Xiaogang Yu Peinan Yin Renpeng Yang Chengwei Wu Wei Zhang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian 116024 China Xinyu Key Laboratory of Materials Technology and Application for Intelligent Manufacturing School of Mechanical and Electrical Engineering Xinyu University Xinyu 338004 China

Magnetic induction hyperthermia has great potential in cancer treatment. However, the low heating efficiency of the magnetic nanoparticles (MNPs) with self-regulated temperature capability limits its clinical application. Here, the self-regulated temperature MNPs of Ca-doped Co-Zn ferrite MNPs (Ca x Co 0.5-x Zn 0.5 Fe 2 O 4 , 0 ≤ x ≤ 0.20), with high heating efficiency, were successfully synthesized by co-precipitation method followed by heat treatment. Noticeably, the x = 0.1 suspension sample can self-stabilize the temperature for high-temperature hyperthermia, whereas x = 0.2 suspension sample can self-stabilize the temperature suitable for conventional hyperthermia under an alternating magnetic field of 32 kA/m and 100 kHz. In vitro cell experiments indicate that the MNPs have good biocompatibility. The results demonstrate that optimized saturation magnetization ( σ S ) and tunable Curie temperature (Tc) enhance both the heating efficiency and thermal safety of the nanoparticles during magnetic induction hyperthermia treatment.

关键词： Saturation magnetization

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A Privacy-Preserving Aggregation and Authentication Scheme Towards Mobile Users in Smart Grid

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Journal of Shanghai Jiaotong university(Science) 2020年第1期25卷 37-43页

作者： GUO Fei CAO Zhenfu LIU Zhusen CAO Nanyuan Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Software Engineering Institute East China Normal UniversityShanghai 200062China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen and Shanghai Institute of Intelligent Science and TechnologyTongji UniversityShanghai 200092China School of Information Management Shanghai Lixin University of Accounting and FinanceShanghai 201209China

With the enormous developmen t of imformation and communicat ion technology,more concerns are focused to achieve secure and reliable smart grids as the social infrastrueture,especially in the explosion era of mobile *** this paper,we propose an efficient scheme to satis勿the outdoor electrical demand of mobile *** scheme protects the privacy and integrity of users'electricity consumption ***,we encrypt users'electricity consumption data by a chosen-plai nt ext-at tack(CPA)secure public key encryption(PKE)scheme and aggregate ciphertexts by the aggregator(an untrusted third party).In the scheme,internal and external adversaries cannot obtain the electricity consumption ***,we require users to provide authentication and commitment of consumption data that can track who modifies the data,which protects the integrity of users1 electricity consumption data.

关键词： smart grid authentication commitment

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

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Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

arXiv

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arXiv 2021年

作者： Khan, Kaleem Nawaz Ullah, Najeeb Ali, Sikandar Khan, Muhammad Salman Nauman, Mohammad Ghani, Anwar Department of Computer Science University of Engineering and Technology Mardan Pakistan Department of Computer Science and Technology University of Swat Pakistan AI in Healthcare Intelligent Information Processing Lab National Center of Artificial Intelligence UET Peshawar Pakistan University of Engineering and Technology Peshawar Pakistan Department of Computer Science National University of Computer and Emerging Sciences Peshawar Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan

Android is one of the leading operating systems for smart phones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts' intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce;(1) a novel technique for opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we have developed a dataset for end-to-end detection of android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted features extraction, and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979. Copyright © 2021, The Authors. All rights reserved.

关键词： Android (operating system)

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A proof-of-majority consensus protocol for blockchain-enabled collaboration infrastructure of 5G network slice brokers 2

A proof-of-majority consensus protocol for blockchain-enable...

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2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2020, Co-located with AsiaCCS 2020

作者： Lin, Wenmin Xu, Xiaolong Qi, Lianyong Zhang, Xuyun Dou, Wanchun Khosravi, Mohammad R. Institute of VR and Intelligent Systems Alibaba Business School Hangzhou Normal University Hangzhou China Nanjing University of Information and Technology Nanjing China Qufu Normal University Rizhao China Department of Computing Macquarie University Sydney Australia State Key Laboratory of Novel Software Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Department of Computer Engineering Persian Gulf University Bushehr Iran

ISBN: (纸本)9781450376105

With network slicing technology, 5G changes the classical mobile network business model from a network-operator-oriented business to a more open system with multiple stakeholders. In this context, a network slice broker plays the role of agency between network resource providers and vertical service providers, to lease and provide on-demand network slice services. In practice, a single network slice broker normally owns limited types of network slices. And a collaboration infrastructure could be built up to enable network slice sharing among multiple network slice brokers, so as to improve user experience, and enhance the resource utilization rate. However, a major challenge with such an infrastructure is how to support fair and secure slice trading process among multiple un-trusted network slice providers. Blockchain technology, on the other side, brings in high opportunities to implement a fair and secure trading system without a trusted third-party. In view of this observation, this paper proposes a design of blockchain-enabled collaboration infrastructure for network slice brokers, where a group of registered brokers can trade their network slices in a fair and secure manner. To enhance the accountability of slice trading records, transactions are verified and sorted by each broker node, which are further packed into blocks and appended to a shared ledger maintained by each member in the collaboration network. More specifically, we design a Proof-of-Majority (PoM) consensus protocol to sort slice trading transactions before packing transaction blocks of the shared ledger. Furthermore, an optimization for the basic PoM protocol is discussed, with dependency resolving strategy to improve system performance. Experiments are conducted to verify the feasibility of our proposal. © 2020 ACM.

关键词： 5G mobile communication systems

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TPFIANet: Three path feature progressive interactive attention learning network for medical image segmentation

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Knowledge-Based Systems 2025年 323卷

作者： Yawu Zhao Shudong Wang Yande Ren Jiehuan Wang Shaoqiang Wang Sibo Qiao Tiyao Liu Shanchen Pang Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum Qingdao Shandong China Department of Medical Imaging Center Affiliated Hospital of Qingdao University Qingdao Shandong China Department of Medical Imaging Affiliated Hospital of Jining Medical University Jining Shandong China School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong China School of Software Tiangong University Tianjin China

In the field of medical image segmentation, medical image segmentation methods with high accuracy and generalization capabilities are crucial for driving personalized healthcare solutions and enhancing patient experience. To address the inherent challenges of difficulty in acquiring complex structural features and weak generalization ability in medical image segmentation, we propose a three-path spatial learning (TPSL) module, which takes advantage of convolutional neural networks (CNNs) to skillfully learn specific details at different scales to acquire better feature representations, and then, we propose a progressive feature cross-fusion (PFCF) module, which aims to dynamically learn feature information under different levels in the encoder, which enhances the feature representation. Finally, we propose the feature interactive attention fusion (FIAF) module, which mixes features from different scales and channels, adaptively learns feature weights and enhances the connection between channel and local spatial information. Based on the above modules, we design a three path feature progressive interactive attention learning network (TPFIANet), which captures image features at different levels and scales. We demonstrate the segmentation performance of TPFIANet on three public datasets and three self-organized datasets. Notably, our TPFIANet also demonstrates amiable segmentation performance on datasets of different modalities while maintaining a lighter architecture.

关键词： Feature interactive attention fusion Medical image segmentation Progressive feature cross-fusion Three-path spatial learning

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A novel Skin lesion prediction and classification technique: ViT-GradCAM

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Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

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