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IEEE International Conference on Advanced Trends in Information Theory (ATIT)

作者： Valeriy Lakhno Yurii Matus Volodymyr Malyukov Alyona Desyatko Tetyana Hnatchenko Department of Computer systems and networks National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Engineering and Cyber Security National University of Trade and Economics Kyiv Ukraine

ISBN: (数字)9781728161440

ISBN: (纸本)9781728161457

The paper describes the model of choosing mutual investment strategies for Smart City cybersecurity projects with incomplete information on the financial resources of the second investor. The case when the financial resources of the parties belong to fuzzy sets is considered. The model is designed for the developing decision support system in the tasks of choosing rational options for investing in Smart City cybersecurity projects. The solution was obtained through the use of tools of the theory of multi-step games with several terminal surfaces. Distinctive feature of the model is the assumption that the first investor does not have complete information about both the financial strategies of the second investor and of his financial resources state. Verifying the model was performed using computational experiment in MatLab.

关键词： Smart cities computer security Investment Computational modeling Mathematical model Fuzzy sets Games

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Toward Privacy-Preserving Personalized Recommendation Services

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engineering 2018年第1期4卷 21-28页

作者： CongWang Yifeng Zheng Jinghua Jiang Kui Ren Department of Computer Science City University of Hong Kong Hong Kong China City University of Hong Kong Shenzhen Research Institute Shenzhen Guangdong 518057 China Department of Computer Science and Technology Xi'an fiaotong University Xi'an 710049 China Institute of Cyber Security Research Zhejiang University Hangzhou Zhejiang 310058 China

Recommendation systems are crucially important for the delivery of personalized services to users. With personalized recommendation services, users can enjoy a variety of targeted recommendations such as movies, hooks, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying per- sonalized recommendation services typically requires the collection of users＇ personal data for processing and anafytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions.

关键词： Privacy protectionPersonalized recommendation servicesTargeted deliveryCollaborative filteringMachine learning

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NTIRE 2021 challenge on video super-resolution

arXiv

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arXiv 2021年

作者： Son, Sanghyun Lee, Suyoung Nah, Seungjun Timofte, Radu Lee, Kyoung Mu Chan, Kelvin C.K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Jiang, Boyuan Lin, Chuming Dong, Yuchun Luo, Donghao Chu, Wenqing Ji, Xiaozhong Yang, Siqian Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Chengpeng Chu, Xiaojie Zhang, Jie Lu, Xin Chen, Liangyu Lin, Jing Du, Guodong Hao, Jia Zou, Xueyi Zhang, Qi Jiang, Lielin Li, Xin Zheng, He Liu, Fanglong He, Dongliang Li, Fu Dang, Qingqing Yi, Peng Wang, Zhongyuan Jiang, Kui Jiang, Junjun Ma, Jiayi Chen, Yuxiang Wang, Yutong Liu, Ting Sun, Qichao Liang, Huanwei Li, Yiming Li, Zekun Ruan, Zhubo Shang, Fanjie Guo, Chen Li, Haining Luo, Renjun Shen, Longjie Zafirouli, Kassiani Karageorgos, Konstantinos Konstantoudakis, Konstantinos Dimou, Anastasios Daras, Petros Song, Xiaowei Zhuo, Xu Liu, Hanxi Guo, Mengxi Li, Junlin Li, Yu Zhu, Ye Wang, Qing Zhao, Shijie Sun, Xiaopeng Zhan, Gen Xie, Tangxin Jia, Yu Lu, Yunhua Zhang, Wenhao Sun, Mengdi Michelini, Pablo Navarrete Zhang, Xueheng Jiang, Hao Chen, Zhiyu Chen, Li Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Cheng, Zhen Fu, Xueyang Song, Fenglong Luo, Zhipeng Yao, Yuehan Dutta, Saikat Shah, Nisarg A. Das, Sourya Dipta Zhao, Peng Shi, Yukai Liu, Hongying Shang, Fanhua Liu, Yuanyuan Chen, Fei Yu, Fangxu Gao, Ruisheng Bai, Yixin Heo, Jeonghwan Yue, Shijie Li, Chenghua Li, Jinjing Zheng, Qian Gang, Ruipeng Song, Ruixia Wee, Seungwoo Jeong, Jechang Li, Chen Wen, Geyingjie Chai, Xinning Song, Li Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland S-Lab Nanyang Technological University Singapore YouTu Lab Tencent East China Normal University China Megvii Peking University China Fudan University China Noah's Ark Lab Huawei Canada Technologies Co. Ltd Baidu Inc National Engineering Research Center for Multimedia Software Wuhan University China School of Computer Science and Technology Harbin Institute of Technology China Electronic Information School Wuhan University China School of Computer Science and Engineering University of Electronic Science and Technology of China China MTlab Meitu Inc Xidian university China ITI CERTH Greece Southeast University China Tencent PCG South China University of Technology China ByteDance Sun Yat-Sen University China Electronic Technology Cyber Security Co. Ltd. AIoT R&D Centre BOE Technology Group Co. Ltd. Shanghai Jiao Tong University China University of Science and Technology of China China Huawei Noah's Ark Lab DeepBlue Technology Shanghai Co. Ltd Indian Institute of Technology Madras India Indian Institute of Technology Jodhpur India Jadavpur University India Xidian University China Hanyang University Korea Republic of NCUT China CASIA China CUC China NRTA China

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks. © 2021, CC BY-NC-ND.

关键词： Optical resolving power

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On the Design of Blockchain-Based Access Control Protocol for IoT-Enabled Healthcare Applications

On the Design of Blockchain-Based Access Control Protocol fo...

引用

IEEE International Conference on Communications (ICC)

作者： Sourav Saha Anil Kumar Sutrala Ashok Kumar Das Neeraj Kumar Joel J. P. C. Rodrigues Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India CA Technologies – A Broadcom Company Hyderabad India Department of Computer Science and Engineering Thapar University Patiala India Federal University of Piauí Teresina - PI Brazil

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Access control is one of the important security services that is essential for an Internet of Things (IoT)-enabled authorized user using his/her smart mobile device to authenticate with the trusted Hospital Authority (HA) in a hospital. After mutual authentication, a secret key is established among the user and HA for secure data transmission. The secure data (transactions) gathered by the HA from the users in the hospital is encrypted using a shared key among various trusted hospital authorities involved in the private blockchain network of hospitals. The HA of each hospital is responsible for constructing the blocks in the blockchain using the encrypted transactions because the data in healthcare application is treated as confidential and private. To deal with this important problem, we design a novel access control scheme using private blockchain technology. The proposed scheme is shown to be secure against various well-known attacks. Moreover, the proposed scheme provides better security and functionality features, and also requires low communication and computational costs as compared to relevant approaches.

关键词： Access control Hospitals Cryptography Biometrics (access control)

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Towards Fine-grained Fingerprinting of Firmware in Online Embedded Devices

Towards Fine-grained Fingerprinting of Firmware in Online Em...

引用

2018 IEEE Conference on computer Communications, INFOCOM 2018

作者： Li, Qiang Feng, Xuan Wang, Raining Li, Zhi Sun, Limin School of Computer and Information Technology Beijing Jiaotong University China Beijing Key Laboratory of IOT Information Security Technology Institute of Information Enzineering CAS China Department of Electrical and Computer Engineering University of Delaware United States School of Cyber Security University of Chinese Academy of Sciences China

ISBN: (纸本)9781538641286

An increasing number of embedded devices are connecting to the Internet at a surprising rate. Those devices usually run firmware and are exposed to the public by device search engines. Firmware in embedded devices comes from different manufacturers and product versions. More importantly, many embedded devices are still using outdated versions of firmware due to compatibility and release-time issues, raising serious security concerns. In this paper, we propose generating fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. We leverage the natural language processing technique to process the file content and the document object model to obtain the firmware fingerprint. To validate the fingerprints, we have crawled 9,716 firmware images from official websites of device vendors and conducted real-world experiments for performance evaluation. The results show that the recall and precision of the firmware fingerprints exceed 90%. Furthermore, we have deployed the prototype system on Amazon EC2 and collected firmware in online embedded devices across the IPv4 space. Our findings indicate that thousands of devices are still using vulnerable firmware on the Internet. © 2018 IEEE.

关键词： Firmware

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Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wu, Shangrui Li, Yupeng Wang, Wenhua Guo, Jianxiong Fan, Wentao Liu, Qin Jia, Weijia Yu, Shui Cao, Jiannong Wang, Tian Beijing Normal-Hong Kong Baptist University Guangdong Provincial/Zhuhai Key Laboratory of IRADS Department of Computer Science Zhuhai China Hong Kong Baptist University Hong Kong Hong Kong Hong Kong Baptist University Department of Interactive Media Hong Kong Hong Kong Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an China Hunan University College of Computer Science and Electronic Engineering Changsha China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency. © 1983-2012 IEEE.

关键词： Deep neural networks

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A power flow tracing based load curtailment technique

A power flow tracing based load curtailment technique

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Wu, Tao Hu, Bo Xie, Kaigui Tai, Heng-Ming Wang, Shuocheng Li, Bo State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing China Department of Electrical and Computer Engineering University of Tulsa Tulsa United States State Grid Chongqing Economic Research Institute Chongqing China

ISBN: (纸本)9781728135205

A reasonable and efficient load curtailment method enables to restore the system quickly as well as reduce the economic losses both for the power providers and customers when the electrical power system is under contingency. This paper proposes a power flow tracing based load curtailment technique. When the outage occurs, the proposed method determines the load curtailment of each bus according to the power flow tracing results. The technical requirements can be satisfied by just curtailing these loads. It takes the distribution in power transfers between units, transmission lines and loads into account. An extended incidence matrix based power flow tracing scheme is developed for efficient load curtailment. Results in the load curtailment and reliability analysis of the RBTS system demonstrate the correctness of the proposed method. © 2019 IEEE.

关键词： Reliability analysis

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An Acute Kidney Injury Prediction Model Based on Ensemble Learning Algorithm

An Acute Kidney Injury Prediction Model Based on Ensemble Le...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yuan Wang Yake Wei Qin Wu Hao Yang Jingwei Li College of Computer Science and Information Engineering Tianjin University of Science and Technology Population and Precision Health Care (Tianjin) Ltd Center for Cyber Security University of Electronic Science and Technology of China Department of critical care medicine Sichuan University State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences

Acute Kidney Injury (AKI), a common disease in Intensive Care Unit (ICU) patients, is related to high cost, morbidity and mortality. The early prediction of AKI is critical for improving patients' outcome. However, sparse clinical data and highly imbalanced dataset bring great challenges to AKI prediction. Among existed machine learning algorithms, ensemble learning often stands out with its good performance on complex classification problems. Boosting algorithm is one of the best ensemble learning algorithms. Therefore, we develop a prediction model based on it aiming to forecast AKI ahead 24 hours and 48 hours. We also adopt the way that is analogous to text modeling transforming heterogeneous time series reflecting patients' medication information into multidimensional vector to overcome problem brought by sparse data. Additionally, since the imbalanced dataset would affect predictive performance, we artificially construct a more balanced dataset based on the original dataset to initialize the model. According to the experimental results, our model works well on the ICU patients dataset (AUC 24h ahead: 0.80 48h ahead 0.77). We also verify that medication information improves model performance (24h ahead: AUC 0.75 to 0.80 48h ahead: AUC 0.75 to 0.77) and find the optimal ratio of number between classes when initializing model for AKI prediction.

关键词： Predictive models Sensitivity Prediction algorithms Drugs Feature extraction Machine learning algorithms Kidney

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Blockchain-enabled authentication handover with efficient privacy protection in SDN-based 5G networks

arXiv

引用

arXiv 2019年

作者： Yazdinejad, Abbas Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond Faculty of Computer Engineering University of Isfahan Iran Department of Software Engineering and Game Development Kennesaw State University GA30060 United States Cyber Science Lab School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

5G mobile networks provide additional benefits in terms of lower latency, higher data rates, and more coverage, in comparison to 4G networks, and they are also coming close to standardization. For example, 5G has a new level of data transfer and processing speed that assures users are not disconnected when they move from one cell to another;thus, supporting faster connection. However, it comes with its own technical challenges relating to resource management, authentication handover and user privacy protection. In 5G, the frequent displacement of the users among the cells as a result of repeated authentication handovers often lead to a delay, contradicting the 5G objectives. In this paper, we propose a new authentication approach that utilizes blockchain and software defined networking (SDN) techniques to remove the re-authentication in repeated handover among heterogeneous cells. The proposed approach is designed to assure the low delay, appropriate for the 5G network in which users can be replaced with the least delay among heterogeneous cells using their public and private keys provided by the devised blockchain component while protecting their privacy. In our comparison between Proof-of-Work (POW)-based and network-based models, the delay of our authentication handover was shown to be less than 1ms. Also, our approach demonstrated less signaling overhead and energy consumption compared to peer models. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding

arXiv

引用

arXiv 2019年

作者： Najafipour, Saeed Hosseini, Saeid Hua, Wen Kangavari, Mohammad Reza Zhou, Xiaofang Computational Cognitive Model Research Lab School of Computer Engineering Iran University of Science and Technology Iran Cyber Security Lab. Singapore University of Technology and Design Singapore School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional text mining methods fail to effectively extract concepts through words and phrases. Thirdly, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using the complementary knowledge-bases makes the results biased to the content of the external database and deviates the understanding and interpretation away from the real nature of the given short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the tightly connected author subgraphs from microblog short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. Additionally, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be. Copyright © 2019, The Authors. All rights reserved.

关键词： Vector spaces

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