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arXiv 2024年

作者： Zervakis, Georgios Frustaci, Fabio Spantidi, Ourania Anagnostopoulos, Iraklis Amrouch, Hussam Henkel, Jörg The Computer Engineering & Informatics Dept. University of Patras Greece The DIMES University of Calabria Italy The Department of Computer Science Eastern Michigan University United States The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States Germany Karlsruhe Institute of Technology Germany

In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our approach does not require retraining and significantly decreases the induced error due to approximate multiplications, improving the overall inference accuracy. As a result, our approach enables satisfying tight accuracy loss constraints while boosting the power savings. Our experimental evaluation, across six different DNNs and several approximate multipliers, demonstrates the versatility of our approach and shows that compared to the accurate design, our control variate approximation achieves the same performance, 45% power reduction, and less than 1% average accuracy loss. Compared to the corresponding approximate designs without using our technique, our approach improves the accuracy by 1.9x on average. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Modeling of Digital Controllers in Electric Power System Dynamic Simulations

Modeling of Digital Controllers in Electric Power System Dyn...

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Power Tech Conference

作者： Mehran Jafari Gautier Bureau Marco Chiaramello Adrien Guironnet Patrick Panciatici Petros Aristidou Dept. of Electrical Eng. Computer Eng. & Informatics Cyprus University of Technology Limassol Cyprus Réseau de Transport d’ Électricité (RTE) France

In modern electric power systems there are thousands of control devices, spanning from low-voltage distribution networks to high-voltage transmission networks, that manage the energy flows, ensure the secure operation and stabilize the system. To analyze the performance and assess the impact of controllers on the electricity grid, time-domain simulations are most frequently used. In recent decades, all the new controllers introduced in electric power systems are digital controllers and their analog counterparts are gradually being replaced. Nevertheless, many of the most frequently employed controllers are still modeled in time-domain simulations as analog (continuous) systems, employing transfer functions or differential-algebraic equations. This approach introduces a discrepancy between the real response of digital controllers and the simulated one. In this paper, we investigate the impact that correctly modeling digital controllers has on simulation accuracy and performance.

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A Framework for Digital Twin Collaboration

A Framework for Digital Twin Collaboration

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Simulation Winter Conference

作者： Zhengchang Hua Karim Djemame Nikos Tziritas Georgios Theodoropoulos Dept. of Computer Science and Eng. Southern University of Science and Technology (SUSTech) Shenzhen China School of Computing University of Leeds Leeds UK Dept. of Informatics and Telecommunications University of Thessaly Lamia GREECE Research Institute of Trustworthy Autonomous Systems (RITAS) SUSTech Shenzhen China

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Digital Twins (DTs) have emerged as a powerful tool for modeling Large Complex Systems (LCSs). Their strength lies in the detailed virtual models that enable accurate predictions, presenting challenges in traditionally centralized approaches due to the immense scale and decentralized ownership of LCSs. This paper proposes a framework that leverages the prevalence of individual DTs within LCSs. By facilitating the exchange of decisions and predictions, this framework fosters collaboration among autonomous DTs, enhancing performance. Additionally, a trust-based mechanism is introduced to improve system robustness against poor decision-making within the collaborative network. The framework's effectiveness is demonstrated in a virtual power plant (VPP) scenario. The evaluation results confirm the system's objectives across various test cases and show scalability for large deployments.

关键词： Accuracy Federated learning Scalability Decision making Collaboration Virtual power plants Predictive models Robustness Digital twins Complex systems

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Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection

arXiv

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arXiv 2023年

作者： Molina-Coronado, Borja Ruggia, Antonio Mori, Usue Merlo, Alessio Mendiburu, Alexander Miguel-Alonso, Jose The Dept. of Computer Architecture and Technology University of the Basque Country UPV/EHU Donostia Spain The Dept. of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Donostia Spain The Dept. of Informatics Bioengineering Robotics and Sytems Engineering University of Genoa Genoa Italy The CASD - Centre for Advanced Defense Studies Rome Italy

Malware authors have seen obfuscation as the mean to bypass malware detectors based on static analysis features. For Android, several studies have confirmed that many anti-malware products are easily evaded with simple program transformations. As opposed to these works, ML detection proposals for Android leveraging static analysis features have also been proposed as obfuscation-resilient. Therefore, it needs to be determined to what extent the use of a specific obfuscation strategy or tool poses a risk for the validity of ML malware detectors for Android based on static analysis features. To shed some light in this regard, in this article we assess the impact of specific obfuscation techniques on common features extracted using static analysis and determine whether the changes are significant enough to undermine the effectiveness of ML malware detectors that rely on these features. The experimental results suggest that obfuscation techniques affect all static analysis features to varying degrees across different tools. However, certain features retain their validity for ML malware detection even in the presence of obfuscation. Based on these findings, we propose a ML malware detector for Android that is robust against obfuscation and outperforms current state-of-the-art detectors. © 2023, CC BY-NC-SA.

关键词： Static analysis

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A New Construction Method for Keystream Generators

TechRxiv

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TechRxiv 2023年

作者： Gül, Çagdaş Kara, Orhun TUBITAK BILGEM Informatics And Information Security Research Center Kocaeli41470 Turkey Computer Eng. Dept. Marmara University Istanbul34722 Turkey Math dept. IZTECH Izmir Institute of TechnologyUrla Izmir Turkey

We introduce a new construction method of diffusion layers for Substitution Permutation Network (SPN) structures along with its security proofs. The new method can be used in block ciphers, stream ciphers, hash functions, and sponge constructions. Moreover, we define a new stream cipher mode of operation through a fixed pseudorandom permutation and provide its security proofs in the indistinguishability model. We refer to a stream cipher as a Small Internal State Stream (SISS) cipher if its internal state size is less than twice its key size. There are not many studies about how to design and analyze SISS ciphers due to the criterion on the internal state sizes, resulting from the classical tradeoff attacks. We utilize our new mode and diffusion layer construction to design an SISS cipher having two versions, which we call DIZY. We further provide security analyses and hardware implementations of DIZY. In terms of area cost, power, and energy consumption, the hardware performance is among the best when compared to some prominent stream ciphers, especially for frame-based encryptions that need frequent initialization. Unlike recent SISS ciphers such as Sprout, Plantlet, LILLE, and Fruit;DIZY does not have a keyed update function, enabling efficient key changing. © 2023, CC BY.

关键词： Hash functions

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Automatic Parkinson's tremor assessment with data analysis from daily activities 6

Automatic Parkinson's tremor assessment with data analysis f...

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6th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Fiska, Vasiliki Gakilazou, Vasiliki Katertsidis, Nikolaos Tzallas, Alexandros T. Giannakeas, Nikolaos Tsipouras, Markos G. Dept. of Electrical and Computer Engineering Univ. of Western Macedonia Kozani Greece Dept. of Informatics and Telecommunications Univ. of Ioannina Arta Greece Science and Technology Park of Epirus UNIVEYE IKE Ioannina Greece

ISBN: (纸本)9781665427425

A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based on data recorded using a smart mobile phone, is presented in this manuscript. Data were obtained from the online platform ***, and were analyzed based on machine learning techniques to produce a comprehensive physician report, presenting motor symptoms in everyday life. The idea of this study is to equip PD patients with a smartphone, which will monitor signals in real time for a period of time in order to enhance the medical diagnosis routine of a physician, providing information about the overall picture of the patient's motor condition, resulting in the provision of individualized treatment to the patient. © 2021 IEEE

关键词： Patient treatment

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Mental Health Professionals' Insights on Digital Health Solutions for Anxiety in the UAE

Mental Health Professionals' Insights on Digital Health Solu...

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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Mariam Al Meqbaali Sofia Ouhbi Reem K Jan Leena Amiri Fadwa Al Mugaddam Mohamed Adel Serhani Nazar Zaki Dept. Computer Science & Software Engineering CIT UAEU Al Ain UAE Dept. Information Technology Uppsala University Uppsala Sweden Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Dept. Psychiatry and Behavioral Science CMHS UAEU Al Ain UAE College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

In recent years, anxiety disorders have become more prevalent worldwide, leading to an increased demand for mental healthcare services. Barriers such as stigma and cost, limit access to these services, prompting the exploration of alternative solutions. Digital technology shows significant promise in improving access to mental healthcare services. This study aims to explore the preferences, and concerns of Mental Health Professionals (MHPs) regarding digital health solutions for self-management of anxiety disorders in the UAE via an online survey. Participation was voluntary and anonymous, with 18 MHPs taking part. The survey included 12 questions covering demographics and key design elements. Most respondents displayed positive attitudes toward integrating digital anxiety solutions in the UAE, providing feedback and highlighting considerations. 61% endorsed the use of digital technology in anxiety disorder treatment, while 94% favored mindfulness exercises for anxiety management. 72% expressed interest in utilizing technology for therapy. Notable concerns raised included privacy and dependency. Guided by MHPs insights, we propose a conceptual framework for the design of digital anxiety solutions in the UAE. This framework prioritizes cultural appropriateness and language accessibility, offering solutions in Arabic and English. It encompasses diverse features, including a blended approach, treatment approaches, and relaxation techniques, to address anxiety disorders and ensures user safety through risk assessment and management. This framework can assist developers of digital anxiety solutions by highlighting the features and elements that MHPs deem most important.

关键词： Surveys Privacy Anxiety disorders Medical treatment Mental health Safety Electronic healthcare

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Lane Departure Warning based on Road Marking Detection using Mask Region-based Convolutional Neural Networks

Lane Departure Warning based on Road Marking Detection using...

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International Conference on Electrical Engineering, computer and Information technology (ICEECIT)

作者： Ilham Ramadani Arianto Eko Mulyanto Yuniarno Reza Fuad Rachmadi Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781665464277

Lane departure warning system (LDW) is a system where when the condition of the car being driven on the road and maneuvering out of the lane without the driver realizing it, there is a visual or audible warning that will allow the driver to correct the maneuver. In implementing this system, tracking is needed on road lane line markings. One way to detect road markings is to use a camera that is placed on the windshield in the middle of the car with various methods. In previous research, the method used was using image filters, namely Hough Transform and Canny Edge. This method is quite effective on straight road conditions and sunny conditions without any obstacles. The weakness is when the road conditions start to turn quite sharply and also in low light conditions. In this study, the proposed method for tracking road markings is Mask Regional-based Convolutional Neural Networks (Mask R-CNN). This method is the latest development of the existing image segmentation method. The tracking process is with input from the video camera and then pre-processing is carried out to reduce distortion and calibrate the incoming frame. After that, the detection process uses a Mask R-CNN and the output will be line fitting to get the middle point and the right and left lines. After that the last process is Lane Departure Warning where the midpoint when approaching the line will give a warning to the driver.

关键词： Visualization Roads Transforms Cameras Surface texture Convolutional neural networks Automobiles

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Generating Automated Assistance Mechanism in Android Programming Self-learning System Using Automatic Testing Tools

Generating Automated Assistance Mechanism in Android Program...

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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Yan Watequlis Syaifudin Dewi Yanti Liliana Andi Baso Kaswar Triana Fatmawati Nobuo Funabiki Indrazno Siradjuddin Dept. of Information Technology State Polytechnic of Malang Malang Indonesia Dept. of Informatics Engineering State Polytechnic of Jakarta Jakarta Indonesia Dept. of Computer Engineering State University of Makassar Makassar Indonesia Dept. of Elec. and Comm. Engineering Okayama University Okayama Japan Dept. of Electrical Engineering State Polytechnic of Malang Malang Indonesia

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide instant feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment’s specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components.

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Scheduling Real Tim Security Aware Tasks in Fog Networks

Scheduling Real Tim Security Aware Tasks in Fog Networks

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2021 IEEE World Congress on Services, SERVICES 2021

作者： Singh, Anil Auluck, Nitin Rana, Omer Nepal, Surya Indian Institute Of Technology Ropar Dept. Of Computer Science And Engineering Rupnagar India Cardiff University School Of Computer Science And Informatics Cardiff United Kingdom Commonwealth Scientific And Research Organization Sydney Australia

ISBN: (纸本)9781665427197

Fog computing extends the capability of cloud services to support latency sensitive applications. Adding fog computing nodes in proximity to a data generation/ actuation source can support data analysis tasks that have stringent deadline constraints. We introduce a real time, security-aware scheduling algorithm that can execute over a fog environment [1 , 2]. The applications we consider comprise of: (i) interactive applications which are less compute intensive, but require faster response time;(ii) computationally intensive batch applications which can tolerate some delay in execution. From a security perspective, applications are divided into three categories: public, private and semi-private which must be hosted over trusted, semi-trusted and untrusted resources. We propose the architecture and implementation of a distributed orchestrator for fog computing, able to combine task requirements (both performance and security) and resource properties. © 2021 IEEE.

关键词： Fog computing

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