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Secure Medical Data Management Based on Homomorphic Encryption and Secret Sharing

Secure Medical Data Management Based on Homomorphic Encrypti...

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ieee international conference on Smart Cloud (SmartCloud)

作者： Po Ying Law Chia-Cheng Tsai Tsz Wun Fok Ching-Ting Wang Chi-Hsien Chang Tsung-Yu Chin Yi-Chen Liao Jen-Kuang Lee Chung-Wei Lin Department of Computer Science and Information Engineering National Taiwan University Department of Electrical Engineering National Taiwan University Department of Engineering Science and Ocean Engineering National Taiwan University School of Medicine National Taiwan University

Medical data are highly sensitive and require secure storage systems to protect patients' privacy. Centralized databases pose significant risks in the context of a breach, since all data are concentrated in a single location. Additionally, privacy concerns often prevent medical institutions from sharing data, hindering potential research breakthroughs. To address these challenges, we propose a novel cloud storage system that utilizes homomorphic encryption and secret sharing. Our approach enables secure cloud storage of medical data and allows for secure data sharing without compromising patients' privacy. In addition to enhancing confidentiality, our proposed approach also improves data integrity and availability. We implement a proof-of-concept that demonstrates the feasibility of our storage system.

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Generating Robot Framework Test Scripts from User Stories and Scenarios for Web Application Testing

Generating Robot Framework Test Scripts from User Stories an...

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international conference on computers, Communications, and systems (ICCCS)

作者： Kritchawat Waitchasarn Arthit thongtak Taratip Suwannasart Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

the Scrum Framework combined with Behavior-Driven Development (BDD) has gained popularity as an agile development process. In this approach, user requirements are captured and expressed through user stories and scenarios, which serve as examples and acceptance criteria. Employing small iterations, this process reduces both time and cost of software development. Nonetheless, frequent small iterations pose a significant challenge for testing, especially regression testing. To mitigate the problem, automation testing has been adopted; however, manually composing test scripts can be error-prone and inefficient. this can result in the essential regression tests being deferred to later iterations, rendering it too late to identify errors. this paper proposes an approach to address this issue by generating Robot test scripts automatically from user stories and scenarios. Using XML to lay out the UI structures of the web page, test scripts can be created during the design phase. In addition to the examples provided in scenarios, the XML Schema Definition (XSD) is utilized to produce augmented test datasets automatically. Parameterizing test scripts also enables multiple test scripts with shared test steps but different test datasets, to be consolidated into a single script, thus drastically reducing maintenance effort.

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Early Prediction of Student Performance with LSTM-Based Deep Neural Network

Early Prediction of Student Performance with LSTM-Based Deep...

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ieee Annual international computer software and Applications conference (COMPSAC)

作者： Han Wan Mengying Li Zihao Zhong Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

the shift to hybrid teaching during the COVID-19 pandemic brought about a real challenge to predict student performance and conduct timely interventions on at-risk students. this study proposed a deep neural network supporting the early prediction of student performance. Bidirectional LSTM, Global Average Pooling, and TIME MASK structure were utilized in the improved GritNet model. Subsequently, this study optimized the hyperparameters with the aid of the hill-climbing algorithm. Finally, on-campus data sets were used in experiments to evaluate the model's performance. Data were collected from a course that carried out multiple iterations from Fall 2021 to Fall 2022. In Fall 2022, the proposed model achieved a ROC-AUC value of 95.47% in the 8th week, while the baseline model only achieved 91.44% in the same week. Besides, the proposed model achieved a ROC-AUC value of 89.67% in the 4th week, which meant it had acceptable prediction performance in the early stage. the experimental findings demonstrated that the model was capable of predicting the academic performance of students in hybrid courses early on.

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Identifying Image Modifications using DCT and JPEG Quantization Technique

Identifying Image Modifications using DCT and JPEG Quantizat...

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international conference for Convergence of Technology (I2CT)

作者： Prajakta Kubal Namita Pulgam Vanita Mane Department of Computer Engineering Ramrao Adik Institution of Technology D Y Patil Deemed to be University Navi Mumbai India

the volume of images and the videos are being shared on various social media platform are huge and there are cybercrimes happening in these areas. Hence, identification of the main source of social network, based on the images uploaded or downloaded from social network has become an important activity in the multimedia forensic analysis. When media shared on social network there are possibilities of exploiting different pattern embedding in image content by social network. To make it easier system is proposed with discrete cosine transform (DCT) method and JPEG Quantization to identify the changes in shared images on social networks applications. the quantization used in JPEG compression is used to help in separating the images that have been processed by software. DCT finds the pixel value of the blur images and it is easier in implementation. the combination of DCT and JPEG quantization can give the better accuracy rate which helps in finding the images shared or the source of the images.

关键词： Visualization Quantization (signal) Social networking (online) Transform coding Streaming media Metadata software

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EPCP: An Efficient Point Cloud Classification Network with Position Fusion 19

EPCP: An Efficient Point Cloud Classification Network with P...

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2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and Computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable Computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy Computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yunming Wang, Jianming Ma, Haoran Sun, Yukuan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin China Tiangong University School of Software Tianjin China Tiangong University Center for Engineering Intership and Training Tianjin China

ISBN: (纸本)9798350346558

Recently, deep learning on 3D point cloud data has driven rapid development in the field of autonomous driving. However, processing 3D point clouds is a challenging task. Transformers are widely used in various fields of deep learning. In the community of 3D point clouds, Point Cloud Transformer(PCT, a transformer-based framework) also achieves excellent performance on 3D point cloud tasks. However, the well-known attention mechanism benefits performance improvement while it also leads to expensive computational costs. To alleviate the problem, we propose a novel structure named efficient Point Cloud Classification Network with Position Fusion(EPCP), which uses the strategy of fusing absolute coordinates-based features and relative position information instead of the attention mechanism. By fusing relative position information, our method ensures that the output features maintain different sensitivity to specific areas, which plays a similar role as the attention mechanism. To verify the effectiveness of our method, we conduct experiments on ModelNet40 and real-world ScanObjectNN datasets. Compared with PCT, our method maintains the same accuracy on ModelNet40 and improves by 5.5% on ScanObjectNN. At the same time, our method reduces the number of parameters by 91% and FLOPs by 50% compared to PCT on the ModelNet40 dataset. © 2022 ieee.

关键词： Classification (of information)

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Privacy-aware Data Fusion and Prediction for Smart City Services in Edge Computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 ieee Congress on Cybermatics: 15th ieee international conferences on Internet of things, ithings 2022, 18th ieee international conferences on Green Computing and Communications, GreenCom 2022, 2022 ieee international conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th ieee international conference on Smart Data, SmartData 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 ieee.

关键词： Smart city

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Overview of Autonomous Driving Simulation Environment

Overview of Autonomous Driving Simulation Environment

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international conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Yunxiang Liu Yujie Pan Xinxin Yuan Yuntao Zhao School of Computer Information and Engineering Shanghai Institute Of Technology Shanghai China R&D Department Shanghai Waylancer Automotive Technology Co. Ltd Shanghai China

the goal of many companies is to provide auto drive system that meet SAE Level 5. Due to the much more complex software running on these systems than typical high-end cars today, a thorough testing strategy is required. Simulation test is an essential part of the development process of autonomous vehicle. At present, there are many automatic driving simulation platforms. this papper summarizes the development status and existing problems of autonomous driving simulation platforms by organizing them from aspects such as simulation hierarchy, simulation content, and usage, and proposes the development trend of autonomous driving simulation platforms.

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Machine Learning Based Framework for Attack Detection on IoT Devices

Machine Learning Based Framework for Attack Detection on IoT...

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Abdul Khader Jilani Faraz Ahmad Mohd Abdul Rahim Khan Almas Jabeen Department of Computer Science College of Computer Studies University of Technology Bahrain Salmabad Kingdom of Bahrain Department of Computer Science and Engineering Lingaya's Vidyapeeth Faridabad India Deprtment of Computer Science College of Computer and Information Sciences Majmaah University Almajmaah Saudi Arabia Department of Bioinformatic Jamia Millia Islamia New Delhi India

the IoT devices are targeted by increased hacks due to their rapid development and widespread use. According to a report, botnet attacks dominate IoT. Due to memory and processing limitations, IoT devices face security vulnerabilities. Several rule-based detection approaches are vulnerable. Many rule-based detection systems are also attackable. this research proposes a machine learning (ML)-based botnet detection system using network traffic data from five smart home IoT devices under attack. the “Gafgyt” and “Mirai” botnet attacks against IoT devices were categorized into five. the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) ML algorithms were used to identify attack from benign traffic data in over a million records from individual devices utilizing 115 factors. the best feature analysis was done utilizing the RF method to improve model correctness and processing speed. With 99.99% accuracy, 99.87% precision, 99.79% recall, and 99.89% AUROC rating, RF is superior. Experimental results show that our approach can efficiently identify botnet attacks in network traffic.

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Integrating RC6 Stream Cipher to A Chaotic Synchronization System

Integrating RC6 Stream Cipher to A Chaotic Synchronization S...

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ieee international conference on Smart Cloud (SmartCloud)

作者： Feng-Hsiag Hsiao Po-Wei Chiu Shou- Wen Chang Dept. of Electrical Engineering National University of Tainan Tainan Taiwan

this study proposes a new fuzzy control method based on Improved Genetic Algorithm (IGA) that employs the Parallel Distributed Compensation technology to achieve the exponential synchronization of two chaotic systems with multiple time delays. In addition, combining the chaotic synchronization and cryptography concepts will result in a more secured communication system. To ensure the exponential stability of the error system, the initial message (plaintext) is first encrypted using the Rivest cipher 6 (RC6) algorithm and the key, and the encrypted message (ciphertext) is re-encrypted through chaotic synchronization, and exponential stability related to time delay is derived by using the Lyapunov function criterion. Afterward, the exponential stability criterion is transformed into linear matrix inequality (LMI). A set of fuzzy controller parameters is obtained by using the LMI Toolbox of the numerical simulation software MATLAB. this parameter is by the IGA primitive populations to evolve. Given IGA's ability to randomly search for the best solution in the entire domain, it is possible to find a better fuzzy controller feedback gain to accelerate the error system's convergence, allowing the master and slave systems to achieve synchronization quickly.

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Overview of Embedded Soft PLC Based on Codesys and Communication System Design

Overview of Embedded Soft PLC Based on Codesys and Communica...

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Multimedia Communication Technologies (ICMCT), international conference on

作者： Hao Song Yuanshuang Fu Mengmeng Liu School of Electrical and Control Engineering Beijing China North China University of Technology

CoDeSys is a complete development environment for programmable logic control PLCs with the advantages of openness, flexibility, power and portability. this paper introduces the application features of the CoDeSys software and the embedded soft PLC technology respectively. the CoDeSys hierarchical architecture and the application of visualization features, the advantages of embedded systems and soft PLC technology are described in detail. then a complete embedded soft PLC system is designed by combining CoDeSys as the upper computer development system with the real-time operation system. the system is modified in real time by adding CoDeSys RTE extension software to the controller kernel, CoDeSys as the development software, based on TCP/IP protocol to communicate and control the real-time operation system, the raspberry pi development board into an industrial controller, and finally the rationality of the system is confirmed by the test of red and green light on/off control, reflecting the the final test confirmed the system's reasonableness through the red and green light control test, reflecting the practicality of combining CoDeSys and soft PLC.

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