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IEEE International Conference on Automation Science and engineering (CASE)

作者： Shenghao Xia Yinwei Zhang Biao Zhang Jian Liu Department of Mathematics Statistics & Data Science GIDP program University of Arizona USA Department of System & Industrial Engineering University of Arizona USA ABB Inc. USA

Foreground detection is a critical step for separating the moving object from the background in video processing. Tensor factorization has been used in foreground detection due to its ability to process complex high-dimensional data, such as color images and videos. However, traditional tensor factorization often lacks the ability for uncertainty quantification. Bayesian tensor factorization can measure the uncertainty by considering the distributions of the tensor factorization model parameters. Besides, domain knowledge is commonly available and could improve the accuracy of foreground detection of the Bayesian tensor factorization model if it can be appropriately incorporated. In this work, a new Bayesian tensor factorization model, named Posterior Regularized Bayesian Robust Tensor Factorization (PR-BRTF), is proposed with incorporating characteristics of dynamic foreground, as a sparsity posterior regularization term. Furthermore, the variational Bayesian inference and $L_{1}$ norm is combined for inducing sparsity with an efficient inference. The experiments in real-world case studies have shown the performance improvement of the proposed model over state-of-art methods.

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Improving Routing Decision Algorithm for RPL Networks 11

Improving Routing Decision Algorithm for RPL Networks

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11th International Conference on Information Technology, ICIT 2023

作者： Zaqebah, Abdullah Al Almomani, Omar Almomani, Malek Alsaaidah, Adeeb Abu-Shareha, Ahmad Adel Althunibat, Ahmad The World IslamicSciences and Education University Department of Computer Science Amman Jordan The World Islamic Sciences and Education University Department of Information System and Networks Amman Jordan Software Engineering The World Islamic Sciences and Education University Amman Jordan Al-Ahliyya Amman University Department of Networks and Information Security Amman Jordan Al-Ahliyya Amman University Department of Data Science and Artificial Intelligence Amman Jordan Al-Zaytoonah University of Jordan Department of Software Engineering Amman11733 Jordan

ISBN: (纸本)9798350320060

Low Power Networks are spreading worldwide, seeking to enable small devices to join wireless networks. This requires a routing mechanism that makes it possible and seamless. This research aims to create a Low Power and Lossy Networks (RPL) routing protocol that balances power usage and node dependability. The main emphasis of the algorithm presented here is the choice made by network nodes (Zigbees) regarding which path to deliver the message along. Results for this routing technique seem promising. Utilizing it increased packet transfer reliability while enforcing power conservation. Furthermore, a 10% reduction in the network's overall power consumption was made. © 2023 IEEE.

关键词： Routing protocols

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Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Advancing Healthcare IoT: Blockchain and Federated Learning ...

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Communication, Computer Sciences and engineering (IC3SE), International Conference on

作者： Rida Malik Atta ur-Rehaman Hamza Razzaq Chandradeep Bhatt Keshav Kaushik Inam Ullah Khan Department of Information Security Riphah Institute of System Engineering Islamabad Pakistan Department of Data Science Riphah Institute of System Engineering Islamabad Pakistan Department of CSE Graphic Era Hill University Uttarakhand India Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Electronic Engineering SEAS Isra University Islamabad Campus Pakistan

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored.

关键词： Training data privacy Technological innovation Federated learning data security Collaboration Training data

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer Science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation

Interpretable Skill Learning for Dynamic Treatment Regimes t...

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Annual Conference on Information Sciences and systems (CISS)

作者： Yushan Jiang Wenchao Yu Dongjin Song Wei Cheng Haifeng Chen Department of Computer Science and Engineering University of Connecticut Storrs CT USA NEC Laboratories American Data Science & System Security Princeton NJ USA

Imitation learning that mimics experts' skills from their demonstrations has shown great success in discovering dynamic treatment regimes, i.e., the optimal decision rules to treat an individual patient based on related evolving treatment and covariate history. Existing imitation learning methods, however, still lack the capability to interpret the underlying rationales of the learned policy in a faithful way. Moreover, since dynamic treatment regimes for patients often exhibit varying patterns, i.e., symptoms that transit from one to another, the flat policy learned by a vanilla imitation learning method is typically undesired. To this end, we propose an Interpretable Skill Learning (ISL) framework to resolve the aforementioned challenges for dynamic treatment regimes through imitation. The key idea is to model each segment of experts' demonstrations with a prototype layer and integrate it with the imitation learning layer to enhance the interpretation capability. On one hand, the ISL framework is able to provide interpretable explanations by matching the prototype to exemplar segments during the inference stage, which enables doctors to perform reasoning of the learned demonstrations based on human-understandable patient symptoms and lab results. On the other hand, the obtained skill embedding consisting of prototypes serves as conditional information to the imitation learning layer, which implicitly guides the policy network to provide a more accurate demonstration when the patients' state switches from one stage to another. Thoroughly empirical studies demonstrate that our proposed ISL technique can achieve better performance than state-of-the-art methods. Moreover, the proposed ISL framework also exhibits good interpretability which cannot be observed in existing methods.

关键词： Learning systems Visualization MIMICs Decision making Prototypes Medical services Cognition

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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Priority Based Cyber Incident Handling: Work in Progress

Priority Based Cyber Incident Handling: Work in Progress

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International Conference on Electrical engineering, Computer Science and Informatics (EECSI)

作者： Mulyati Deris Stiawan Wijang Widhiarso Rahmat Budiarto Department of Information System Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Sriwijaya University Palembang Indonesia Department of Informatics Multi Data Palembang University Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate.

关键词： Support vector machines Error analysis Prototypes Focusing Organizations Machine learning NIST Classification algorithms Security Informatics

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Delayed Antarctic melt season reduces albedo feedback

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National Science Review 2023年第9期10卷 236-246页

作者： Lei Liang Huadong Guo Shuang Liang Xichen Li John C.Moore Xinwu Li Xiao Cheng Wenjin Wu Yan Liu Annette Rinke Gensuo Jia Feifei Pan Chen Gong Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences International Research Center of Big Data for Sustainable Development Goals Institute of Atmospheric Physics Chinese Academy of Sciences College of Global Change and Earth System Science Beijing Normal University Arctic Centre University of Lapland CAS Center for Excellence in Tibetan Plateau Earth Sciences School of Geospatial Engineering and Science Sun Yat-sen University Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research Department of Geography University of North Texas

Antarctica' s response to climate change varies greatly both spatially and *** melting impacts mass balance and also lowers surface *** use a 43-year record(from 1978 to 2020) of Antarctic snow melt seasons from space-borne microwave radiometers with a machine-learning algorithm to show that both the onset and the end of the melt season are being ***-causality analysis shows that melt end is delayed due to increased heat flux from the ocean to the atmosphere at minimum sea-ice extent from warming *** onset is Granger-caused primarily by the turbulent heat flux from ocean to atmosphere that is in turn driven by sea-ice *** snowmelt season leads to a net decrease in the absorption of solar irradiance,as a delayed summer means that higher albedo occurs after the period of maximum solar radiation,which changes Antarctica's radiation balance more than sea-ice cover.

关键词： Antarctic ice sheet snowmelt season delay sea ice solar irradiance

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Towards a Partial Computation offloading in In-networking Computing-Assisted MEC: A Digital Twin Approach

Towards a Partial Computation offloading in In-networking Co...

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IEEE Symposium on Network Operations and Management

作者： Ibrahim Aliyu Awwal Arigi Seungmin Oh Tai-Won Um Jinsul Kim Department of ICT Convergence System Engineering Chonnam National University Gwangju Korea Department of Humans and Automation Institute for Energy Technology Halden Norway Graduate School of Data Science Chonnam National University Gwangju Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

This paper addresses the problem of minimizing latency with partial computation offloading within Industrial Internet-of-Things (IoT) systems in in-network computing (COIN)-assisted Multiaccess Edge Computing (C-MEC) via ultra-reliable and low latency communications (URLLC) links. We propose a digital twin (DT) scheme for a multiuser scenario, allowing collaborative partial task offloading from user equipment (UE) to COIN-aided nodes or MEC. Specifically, we formulate the problem as joint task offloading decision, ratio and resource allocation. We employ game theory to create a low-complexity distributed offloading scheme in which the task offloading decision problem is modelled as an exact potential game. Double Deep Q-Network (DDQN) is utilized within the game to proactively predict optimal offloading ratio and resource allocation. This approach optimizes resource allocation across the whole system and enhances the robustness of the computing framework, ensuring efficient execution of computation-intensive services. Additionally, it addresses centralized approaches and UE resource contention issues, thus ensuring faster and more reliable communication.

关键词： Collaboration Games Ultra reliable low latency communication Probabilistic logic Robustness Digital twins Resource management

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