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The Tele-Rehabilitaion as a Service (TRaaS) Project: Rationale, Study Design, and Methodology 19

The Tele-Rehabilitaion as a Service (TRaaS) Project: Rationa...

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19th IEEE international conference on e-Science, e-Science 2023

作者： Celesti, Antonio Sannino, Giovanna Bochicchio, Mario Fazio, Maria Villari, Massimo Celesti, Fabrizio Bonanno, Mirjam Calabrò, Rocco Salvatore University of Messina Department Mift Italy Naples Italy Department of Computer Science Università degli Studi di Bari Aldo Moro Bari Italy University of Siena Department of Medicine Surgery and Neuroscience Italy Ircss Centro Neurolesi 'Bonino Pulejo' Messina Italy

ISBN: (纸本)9798350322231

Tele-rehabilitation has recently emerged as an effective solution for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering their reintegration into society, also pushing down clinical costs. Cloud computing in combination with Edge computing, the Internet of Things (IoT), big data storage and analytics, and Artificial intelligence (AI) are the main enablers for tele-rehabilitation. In this paper, we present the Italian founded PRIN 2022 project entitled 'Tele-rehabilitation as a Service (TRaaS)'. It aims at creating a piece of reference intelligent Cloud/Edge framework architecture and a standard data model for the development of different kinds of new de-hospitalized tele-rehabilitation services. In particular, the rationale, study design, and methodology are discussed, also highlighting future research directions. © 2023 IEEE.

关键词： big data

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AIC-Bench: Workload Selection Methodology for Benchmarking AI Chips 24

AIC-Bench: Workload Selection Methodology for Benchmarking A...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Quan, Zhenyu Chen, Xiaoming Han, Yinhe Research Center for Intelligent Computing System Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350319934

Artificial intelligence (AI) chips are gaining tremendous attention due to their successes in accelerating deep learning algorithms. AI chips need standard benchmarks to drive the advancements in their software and hardware systems. However, the deep learning workloads in most existing AI benchmarks are fixed and have similar computation characteristics. Therefore, we introduce AIC-Bench, a deep learning workload selection methodology for comparing the performance of AI chips. The core of the proposed method is to select a number of representative deep learning workloads in terms of computational and memory access characteristics to represent other workloads fully. As a case study, we select eight workloads from a workload candidate pool with the proposed method. Using the eight selected workloads, we evaluate the performance of CPU, GPU, and MLU. We also compare the energy efficiency between GPU and MLU. Based on the evaluation result, suggestions for performance improvements of these AI chips are put forward. © 2022 IEEE.

关键词： Deep learning

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Analysis Of Cloud computing Security Threats and Countermeasures 10

Analysis Of Cloud Computing Security Threats and Countermeas...

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10th international conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Kumari, Sushila Solanki, Kamna Dalal, Sandeep Dhankhar, Amita Rohtak India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

The demand for Cloud computing (CC) is increasing at a higher growth rate because of the emerging technologies like machine learning, artificial intelligence, big data analytics, IoT, mobile supercomputing, serverless computing, and the industrial need for reducing operational costs. Cloud computing features such as customized offerings, pay-as-you-go or pay-per-use model, availability, seamless scalability, and flexibility have accelerated the demand for cloud services. Despite these many benefits, security is the top challenge in the adoption of cloud services. However, the recent research studies lack the gap between analyzing potential security threats and an exhaustive list of countermeasures proposed for mitigating the threats. The main objective of this paper is to provide a comprehensive view of cloud security threats and to identify potential (more vulnerable) threats. Further, the taxonomy of proposed solutions is provided for data security and privacy on the basis of analysis of research papers in the last decade. © 2022 IEEE.

关键词： data Analytics

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Design and Application of Intelligent Internet of Things Architecture Based on Digital Twin

Design and Application of Intelligent Internet of Things Arc...

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2022 international conference on Artificial intelligence, Information Processing and Cloud computing, AIIPCC 2022

作者： Liu, Han Li, Zhenghao Li, Xiuqiang Xu, Xia State Grid Shandong Electric Power Company Jinan250000 China State Grid Jinan Power Supply Company Jinan250000 China

ISBN: (纸本)9781665462877

Aiming at the problem of low similarity between virtual nodes and physical objects due to the weak design of virtual nodes in the current smart Internet of Things architecture design method, a smart Internet of Things architecture design method based on digital twins is proposed. The Internet of Things system constructs a five-dimensional model of digital twins, processes physical entities with digital twins, and obtains virtual node data. Using wireless communication technology or remote communication technology, the entity data of the Internet of Things is transmitted to the digital twin to optimize the architecture of the Internet of Things. The ISO/1RC18000 is selected as the communication protocol of the intelligent Internet of Things system to realize the data transmission of the virtual node. An application test link is constructed to test the method. The test results show that this method can effectively improve the similarity between virtual nodes and physical objects and the construction speed of virtual nodes, and can improve the operation quality of the intelligent Internet of Things system. © 2022 IEEE.

关键词： big data

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Towards data-Driven Pediatrics in Zimbabwe 5

Towards Data-Driven Pediatrics in Zimbabwe

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Batani, John Maharaj, Manoj Sewak Univ KwaZulu Natal Sch Management IT & Governance Durban South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Efficient and effective healthcare systems utilize the available data at every level to provide evidence-based care and improve procedures and practice in order to meet the three goals of healthcare institutions - access, quality and efficiency. Regardless of the changing child health needs and often failure by traditional healthcare models to cope, most of the public health institutions in Sub-Saharan Africa are not providing data-driven pediatrics to meet the ever-changing child health needs. There is a lack of utilization of the data collected by the routine health information systems to provide evidence-based and personalized pediatrics. This study employed an exploratory research design to explore the opportunities, and potential challenges of data-driven pediatrics based on the lessons learnt from the introduction of the electronic health record and Neotree (a digital health system deployed in Zimbabwe and Malawi to help health workers manage neonates' health) in Makonde District. Twenty public health workers participated in interviews and focus groups and reports from the district health information system provided further insights. The study revealed that data-driven pediatrics could improve access, efficiency and quality of pediatric care, regardless of such potential challenges as fear of medico-legal hazards, centralization of decision-making, resistance by healthcare workers, network challenges and computer illiteracy. To increase the chances of success, the following lessons learnt from electronic health record and neotree introduction could help: start small, sensitize communities first, involve line healthcare workers from the beginning, do not train in a haste and demystify technology's purpose. The study revealed that there are pediatricians and nurses willing to shift to data-driven pediatrics if the technologies are available.

关键词： data-driven healthcare evidence-based healthcare data-driven pediatrics neotree child health

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Massive data Exploration using Estimated Cardinalities

Massive Data Exploration using Estimated Cardinalities

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2022 IEEE international conference on Fuzzy Systems, FUZZ 2022

作者： Nerzic, Pierre Smits, Gregory Pivert, Olivier Lesot, Marie-Jeanne Université de Rennes 1 Irisa Lannion France Sorbonne Université Cnrs ParisLIP6 F-75005 France

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

Linguistic summaries are used in this work to provide personalized exploration functionalities on massive relational data. To ensure a fluid exploration of the data, cardinalities of the data properties described in the summaries are estimated from statistics about the data distribution. The proposed workflow also involves a vocabulary inference mechanism from these statistics and a sampling-based approach to consolidate the estimated cardinalities. The paper shows that soft computing techniques are particularly relevant to build concrete and functional business intelligence solutions. © 2022 IEEE.

关键词： big data

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MODLSTM: A Method to Recognize DoS Attacks on Modbus/TCP

MODLSTM: A Method to Recognize DoS Attacks on Modbus/TCP

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IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Zhang, Hao Min, Yuandong Liu, Sanya Tong, Hang Li, Yaopeng Cent China Normal Univ Fac Artificial Intelligence Educ Natl Engn Res Ctr Learning Wuhan 430079 Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Natl Engn Lab Educ Big Data Wuhan 430079 Peoples R China

ISBN: (数字)9781665480185

ISBN: (纸本)9781665480185

With the rapid development of technology, the scale of traffics in industrial control networks is increasing day by day. More malicious traffics brought terrible impacts on industrial areas. Modbus plays a momentous role in the communications of Industrial Control Systems (ICS), but it's vulnerable to Denial of Service attacks(DoS). Traditional methods cannot perform well on fine-grained detection tasks which could contribute to locating targets of DoS and preventing the destruction. Considering the temporal locality and high dimension of malicious traffic, we proposed a Neural Network architecture named MODLSTM, which consists of three parts: input preprocessing, feature recoding, and traffic classification. By virtue of the design, MODLSTM can perform high-precision identification and fine-grained classification of DOS attacks in the Modbus/TCP-based system. To test our model's performances, we conducted experiments on our traffic dataset collected from the industrial control network, and the models achieved excellent performances in comparison with previous work(accuracy increased by 0.74%). The results show that our proposed method has satisfactory abilities to detect DoS attacks related to Modbus, it could help to build a reliable firewall to address the DoS traffic in industrial environments.

关键词： Modbus DoS Deep Learning Fine-grained Classification

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Detection of DDoS Attack on Smart Home Infrastructure Using Artificial intelligence Models 12

Detection of DDoS Attack on Smart Home Infrastructure Using ...

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12th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery (CyberC)

作者： Raja, Thejavathy Vengappa Ezziane, Zoheir He, Jun Ma, Xiaoqi Kazaure, Asmau Wali-Zubai Nottingham Trent Univ Dept Comp Sci Nottingham NG1 18NS England

ISBN: (纸本)9798350331547

The whole web world is concerned and constantly threatened by security intrusion. From the topmost corporate companies to the recently established start-ups, every company focuses on their network, system, and information security as it is the core of any company. Even a simple small security breach can cause a considerable loss to the company and compromises the CIA Triad (Confidentiality, Integrity, and Availability). Security concerns and hacking activities such as Distributed Denial of Service (DDoS) attacks are also experienced within home networks which could be saturated reaching a crashing point. This work focuses on using Artificial intelligence (AI) and identifying suitable models to train, identify, and detect DDoS attacks. In addition, it aims to implement on smart home datasets and find the best model from those which performs with a high accuracy rate on the smart home dataset. The novelty of this project is identifying one best AI model among many of the existing models that works best on smart home datasets and in identifying and detecting DDoS attacks.

关键词： smart homes DDoS machine learning deep learning AI cybersecurity

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Amalgamation of Cloud computing with big data 7

Amalgamation of Cloud computing with big data

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7th international conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Gupta, Vanshit Verma, Vansh Kaur, Supreet Kaur, Inderpreet Chitkara University Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665454018

As technology advances, massive amounts of data in many different formats are produced everywhere. The industries that have fueled the growth of data, including retail, media, finance, healthcare, and education, have produced a tremendously large and complicated collection of data that is known as "big data."On the other hand, a virtual service called cloud computing is utilised for processing, data storage, and data mining in order to maximise flexibility and cut costs. It is assisting society in addressing upcoming issues, including large data management, cyber security, and quality control. Additionally, cutting-edge technologies like distributed ledger technology, artificial intelligence, and many other capabilities are becoming accessible as services thanks to cloud computing. The two most important techniques in the field of information technology. These two IT efforts have the potential to transform every aspect of the company and have an impact on how we analyse data. It's become popular in research to use the cloud environment for large data analysis. This work attempts to comprehend the Cloud computing technology in big data so as to obtain efficient and quick results. Furthermore, the paper sheds light on the challenges faced in the domain and concludes with the future scope of these technologies. © 2022 IEEE.

关键词： big data

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A novel real-time trajectory compression method for privacy protection 9

A novel real-time trajectory compression method for privacy ...

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9th IEEE international conference on data Science and Advanced Analytics (DSAA)

作者： Tao, Jiachun Chen, Liang Fang, Junhua Soochow Univ Dept CS & Tech Suzhou Peoples R China

ISBN: (纸本)9781665473309

As an essential branch of web service applications, the location-based service (LBS) plays an irreplaceable role in our daily lives. Usually, the LBS is time-sensitive, which requires the system to process trajectory data in a real-time manner. Due to the sensitivity of trajectory data, LBS services may violate users' privacy. Furthermore, trajectory compression plays a crucial auxiliary role in analyzing and mining massive trajectory raw data such as trajectory clustering and trajectory similarity calculation and can help keep users' privacy. In other words, trajectory compression serves as the prerequisite for privacypreserved trajectory data mining, which retains points with highinformation content and removes redundant approximate points with low information value under the premise of protecting users' privacy. We can speed up the applications' response speed and save computing resources if we take advantage of trajectory compression and provide lightweight data support for big-datadriven web page extraction, convenient for fast and accurate response. Unfortunately, trajectory compression's current real-time processing capacity is still not big enough and not cost-effective. In terms of the implementation principle, most of the existing works are micro-batch processing. Consequently, the system will overly consume resources and respond with a high latency with the trajectory data inputting. In addition, it is difficult for users to understand and set compression parameters correctly. In this context, we propose an algorithm to incrementally compress the trajectory in real-time based on the azimuth change, and two kinds of user-perceivable parameters are proposed to facilitate real-time specific compression. For verification, our study uses real-world data sets, such as GeoLife Trajectory data. We also found that compared with the current OPW-TR algorithm with better all-around performance, our algorithm dramatically improves the processing speed with a minimal l

关键词： Trajectory data Trajectory Compression Realtime computing Spatiotemporal data

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