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3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

作者： Sun, Wei Ran, Xiangying Luo, Xiangyang Xu, Yunlai Wang, Chongjun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783030260743

Multi-label classification is a challenging task in natural language processing. Most of existing methods tend to ignore the semantic information of the text. Besides, different parts of the text contribute differently to each label, which is not considered by most of existing methods. In this paper, we propose a novel model for multi-label text classification. This model generates high-level semantic understanding representations with a multi-level dilated convolution. The multi-level dilated convolution effectively reduces dimension and expands the receptive fields without loss of information. Moreover, a hybrid attention mechanism is designed to capture most relevant information of the text based on trainable label embeddings and semantic understanding. Experimental results on the dataset AAPD and RCV1-V2 show that our model has significant advantages over baseline methods. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

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computers, Materials & Continua 2020年第10期65卷 723-741页

作者： Jun Li Jieren Cheng Naixue Xiong Lougao Zhan Yuan Zhang Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China School of Compute Science and Cyberspace Security Hainan UniversityHaikou570228China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA Key Laboratory of Internet Information Retrieval of Hainan Province Hainan UniversityHaikou570228China

Security and privacy issues have become a rapidly growing problem with the fast development of big data in public ***,big data faces many ongoing serious challenges in the process of collection,storage,and *** them,data security and privacy problems have attracted extensive *** an effort to overcome this challenge,this article aims to present a distributed privacy preservation approach based on smart contracts and Intel software Guard Extensions(SGX).First of all,we define SGX as a trusted edge computing node,design data access module,data protection module,and data integrity check module,to achieve hardware-enhanced data privacy ***,we design a smart contract framework to realize distributed data access control management in a big data *** crucial role of the smart contract was revealed by designing multiple access control contracts,register contracts,and history *** control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history *** contract contains user property information,edge computing node information,the access control and history smart contract information,and provides functions such as registration,update,and *** contract records the historical behavior information of malicious users,receives the report information of malicious requestors from the access control contract,implements a misbehavior check method to determines whether the requestor has misbehavior,and returns the corresponding ***,we design decentralized system architecture,prove the security properties,and analysis to verify the feasibility of the *** demonstrate that our method can effectively improve the timeliness of data,reduce network latency,and ensure the security,reliability,and traceability of data.

关键词： SGX big data privacy protection smart contract access control

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Fea Study on the Tsv Copper Filling Influenced by the Additives and Electroplating Process

SSRN

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SSRN 2023年

作者： Zhao, Zifeng Liu, Ziyu Chen, Lin Sun, Qingqing Liu, Hongye Sun, Yabin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China Jiashan Fudan Institute Zhejiang Province Jiaxing314100 China Department of Electrical Engineering East China Normal University Shanghai200241 China

Abstract Through-Silicon Via (TSV) is a key technology of three-dimensional (3D) integration. However, it is easy to have voids in the Cu filling process of TSV. Additives and optimized filling process are used to solve this problem. However, the comprehensive analysis of these affecting parameter needs many experiments and cost much time. Thus, the finite element analysis (FEA) for the mass transferring of electroplating is carried out by the COMSOL software in the work. The filling processes with different inhibitor concentrations are first studied and the most suitable density of 64mol/m2 is obtained. However, high inhibitor concentration will lead to much lower plating speed. Thus, the effect of filling process such as current density and plating bath flow velocity on the voids are investigated under the optimized concentrations above. The best filling quality is obtained at the current density of 1.4 ASD and the bath stirring velocity of 0.4 m/s. The underlying growth mechanism of these influencing factors are also analyzed and revealed. © 2023, The Authors. All rights reserved.

关键词： Copper

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Maintainable software Solution Development Using Collaboration Between Architecture and Requirements in Heterogeneous IoT Paradigm (Short Paper) 15th

Maintainable Software Solution Development Using Collaborati...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Rafique, Wajid Khan, Maqbool Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China The Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783030301453

Internet of Things (IoT) has been tremendously involved in the development of smart infrastructure. software solutions in IoT have to consider lack of abstractions, heterogeneity, multiple stakeholders, scalability, and interoperability among the devices. The developers need to implement application logic on multiple hardware platforms to satisfy the fundamental business goals. Moreover, long-term maintenance issues due to the frequent introduction of new requirements and hardware platforms pose a vital challenge in IoT solution development. Numerous techniques have been devised to satisfy the issues mentioned above for ubiquitous and smart infrastructure. However, these techniques lack in providing a comprehensive approach in dealing with the above challenges. In this paper, we argue that fundamentally, there is no difference between the architecturally significant requirements and the architectural design decisions in IoT solution development. The architecture revolves around the requirements gathered by the analyst at the requirements gathering phase. We stress that the requirements elicitation process must consider the software architectural assessment for maintainable software development. By adopting this perspective, we identify areas where both requirements and architecture communities collaborate to effectively increase the user acceptability, maintainability, and fulfill the heterogeneous needs of IoT solutions. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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CFADefense: A security solution to detect and mitigate crossfire attacks in software-defined iot-edge infrastructure 21

CFADefense: A security solution to detect and mitigate cross...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Rafique, Wajid He, Xin Liu, Zifan Sun, Yuhu Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9781728120584

Managing the Internet of Things (IoT) infrastructure has become a critical challenge due to an enormous increase in the connected devices and the lack of available security solutions. software-Defined Networking (SDN) has been extensively involved in network infrastructure management. Moreover, numerous recent studies demonstrate the use of SDN for managing IoT networks. In SDN, policy consistency and security of the data plane is maintained through Waypoint Enforcement (WPE) which ensures that the traffic traverses policy nodes/switches to implement high-level network requirements. Previous studies on SDN primarily secure SDN infrastructure against traditional Distributed Denial of Service (DDoS) attacks. However, we investigate Crossfire Attack (CFA), which is a novel DDoS attack capable of interrupting communication of data plane switches using low-rate legitimate traffic. CFA has the potential to isolate policy switch from the rest of the data plane devices which introduces many security anomalies and routing inconsistencies. We first demonstrate how CFA is lethal on policy switch attacks and then present the design and implementation of a novel CFA countermeasure called CFADefense, which employs link selection, attack detection, and malicious flows interception modules. CFADefense has been developed as an application at the application layer of the open-source Floodlight controller. Our evaluation demonstrates that CFADefense accurately detects and efficiently mitigates CFA and poses minimal overhead on the controller in dealing with this attack. © 2019 IEEE.

关键词： Internet of things

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Mining Core Contributors in Open-Source Projects 16th

Mining Core Contributors in Open-Source Projects

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Liu, Xiaojin Bai, Jiayang Liu, Lanfeng Ouyang, Hongrong Zhou, Hang Xu, Lei Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030309510

Open-source projects are becoming more and more prevalent, and the number of contributors involved in open-source project development is increasing. The evolution of a project is driven by all contributors. However, the role of core contributors is more important, especially in evaluating the progress and discovering bottlenecks of the project, etc. Visualization technology can show distribution and intrinsic relationship of data visually and reduce the complexity of information analysis. And it’s appropriate to use different visualization methods in different research topics. The paper is based on common contribution behavioral indicators, such as adding code lines, submitting new files and solving a bug by committing, etc. We analyze the relationship between multiple indicators, measure the cooperation activities of the contributor, mine and analyze open-source projects on GitHub combining with a variety of visualization techniques, which reveals the core contributors in projects. © 2019, Springer Nature Switzerland AG.

关键词： Visualization

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A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography

arXiv

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arXiv 2024年

作者： Peng, Bo Li, Xiaofeng Li, Xinyu Wang, Zhenghan Deng, Hui Luo, Xiaoxian Yin, Lixue Zhang, Hongmei School of Computer Science and Software Engineering Southwest Petroleum University No. 8 Xin Du Avenue Chengdu610500 China Department of Cardiovascular Ultrasound Noninvasive Cardiology Sichuan Provincial People’s Hospital University of Electronic Science and Technology of China No. 32 West Second Ring Road Chengdu610072 China Ultrasound in Cardiac Electrophysiology Biomechanics Key Laboratory of Sichuan Province Sichuan Provincial People’s Hospital University of Electronic Science and Technology of China 32 West Second Ring Road Chengdu610072 China School of Medical and Life Sciences Chengdu University of Traditional Chinese Medicine No. 37 Twelve Bridge Road Chengdu610075 China

Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA. The method begins by classifying 2D echocardiography data into five distinct echocardiographic views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. It then extracts features of each view separately and combines five features for disease classification. A total of 212 patients diagnosed with HCM, and 30 patients diagnosed with CA, along with 200 individuals with normal cardiac function(Normal), were enrolled in this study from 2018 to 2022. This approach achieved a precision, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA using a multi-view analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Echocardiography

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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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Large language model-driven probabilistic trajectory prediction in the Internet of Things using spatio-temporal encoding and normalizing flows

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Digital Communications and Networks 2025年

作者： Xiaoliang Wang Liming Xu Lian Zhou Yuzhen Liu Neal Xiong Kuan-Ching Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Sanya Institute of Hunan University of Science and Technology Sanya 572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China Department of Computer Mathematical and Physical Sciences Sul Ross State University Alpine TX 79830 USA Dept of Computer Science and Information Engineering Providence University Taichung 43301 Taiwan Republic of China

The convergence of the Internet of Things (IoT) and foundation models, particularly Large Language Models (LLMs), heralds a new era of intelligent and adaptive systems. This investigation explores the integration of LLMs and IoT for probabilistic vehicle trajectory prediction, a critical component in autonomous driving. Traditional deterministic models often fail to capture the complexity and uncertainty of real-world driving environments. To address these limitations, we propose an innovative approach that leverages LLM-driven spatio-temporal encoding and normalizing flows. The proposed method extracts latent motion patterns from historical trajectory data using LLMs, providing a nuanced understanding of vehicle dynamics. Advanced spatio-temporal encoders are employed for comprehensive feature extraction, capturing temporal dependencies and spatial relationships. Additionally, scaled dot-product attention is utilized for effective multimodal feature fusion, enhancing the integration of diverse data sources. The normalizing flows framework enhances the model's ability to capture complex, multimodal probability distributions of future trajectories by constructing an invertible transformation network. The network systematically maps simple base distributions to intricate target distributions through reversible transformations, significantly improving prediction accuracy. Evaluation of the nuScenes dataset demonstrates substantial improvements over existing methods. The results underscore the model's capability to generate diverse and realistic trajectory distributions, paving the way for safer and more efficient autonomous driving systems.

关键词： Internet of things Large language models Normalizing flows Spatio-temporal encoding Trajectory prediction

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DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences

arXiv

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arXiv 2023年

作者： Liu, Wentao Tian, Tong Wang, Lemeng Xu, Weijin Li, Lei Li, Haoyuan Zhao, Wenyi Tian, Siyu Pan, Xipeng Deng, Yiming Gao, Feng Yang, Huihua Wang, Xin Su, Ruisheng School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Aeronautics and Astronautics Dalian University of Technology Dalian China Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing China Ultrasonic Department The Fourth Hospital of Hebei Medical University Hebei Tumor Hospital Shijiazhuang China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Radiology The Netherlands Cancer Institute Amsterdam Netherlands Department of Radiology & Nuclear Medicine Erasmus MC University Medical Center Rotterdam Netherlands Medical Image Analysis group Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://***/10.5281/zenodo.11396520 and https://***/lseventeen/DIAS. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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