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arXiv 2024年

作者： Jang, Kathy Lichtlé, Nathan Vinitsky, Eugene Shah, Adit Bunting, Matthew Nice, Matthew Piccoli, Benedetto Seibold, Benjamin Work, Daniel B. Delle Monache, Maria Laura Sprinkle, Jonathan Lee, Jonathan W. Bayen, Alexandre M. University of California Berkeley Department of Electrical Engineering and Computer Sciences United States University of California Berkeley Department of Mechanical Engineering United States University of California Berkeley Department of Civil and Environmental Engineering United States University of California Berkeley Institute for Transportation Studies United States École des Ponts Paristech Marne la Vallée France Vanderbilt University Institute for Software Integrated Systems United States Temple University Department of Mathematics United States Rutgers University-Camden Department of Mathematical Sciences United States

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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Qemtrace: A multi-platform memory tracer based on system emulation

Qemtrace: A multi-platform memory tracer based on system emu...

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2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： Aurora Dugo, Alexy Torres Lefoul, Jean-Baptiste de Magalhães, Felipe Göhring Nicolescu, Gabriela Assal, Dahman Department of computer engineering Polytechnique Montréal MontréalQC Canada MANNARINO Systems and Software MontréalQC Canada

ISBN: (纸本)9781713819462

Memory simulation is now widely used in system design processes. This process allows debugging, profiling and sometimes validating all memory-related tasks. The step before memory profiling is memory tracing, which consists in collecting memory events. State-of-the-art solutions rely on well-known environments provided by operating systems. Such environments might not be available when designing custom operating systems or bare-metal applications (e.g., applications directly running on hardware). In this context, aside from hardware-assisted approaches, no memory tracing mean is available. In this paper, we propose QEMTrace, a QEMU based open-source tool for memory tracing. QEMTrace provides a flexible way to trace memory accesses on operating systems, bare-metal applications and regular applications. After traces collection, we inject them in a cache simulator to show its usage. A shared memory interface to allow co-simulation of trace driven simulators is also presented. © 2020 Society for Modeling & Simulation International (SCS)

关键词： Virtualization

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PMU-Spill: Performance Monitor Unit Counters Leak Secrets in Transient Executions

PMU-Spill: Performance Monitor Unit Counters Leak Secrets in...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Pengfei Qiu Qiang Gao Dongsheng Wang Yongqiang Lyu Chang Liu Xiaoyong Li Chunlu Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Tsinghua University Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing China Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland Maryland USA

ISBN: (纸本)9781665461153

The processor's Performance Monitor Unit (PMU) allows the recording of architectural and microarchitectural events for profiling purposes. In this study, we reveal a security issue caused by the fact that current PMU implementations are capable of recording some events that happened during transient executions. We propose the PMU -Spill attack, a new kind of attack that enables attackers to maliciously leak the secret data in transient executions. We demonstrate on real hardware that PMU -Spill attack can leak the secret data stored in Intel software Guard Extensions (SGX). In addition, we perform a thorough study to reveal all the vulnerable PMU counters and find that 20 of them can be used to achieve PMU -Spill attack. Our experiments suggest that the throughput of PMU -Spill attack is up to 575.3 bytes per second (Bps) with an average error rate of 1.89% when leaking the SGX-protected secret data.

关键词： Side-channel attacks Throughput Phasor measurement units Hardware software Recording Security

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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DeepSmartFuzzer: Reward guided test generation for deep learning

DeepSmartFuzzer: Reward guided test generation for deep lear...

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2020 Workshop on Artificial Intelligence Safety, AISafety 2020

作者： Demir, Samet Eniser, Hasan Ferit Sen, Alper Department of Computer Engineering Bogaziçi University Turkey Max Planck Institute for Software Systems Germany Bogaziçi Uni Turkey

Testing Deep Neural Network (DNN) models has become more important than ever with the increasing usage of DNN models in safety-critical domains such as autonomous cars. Traditionally, DNN testing relies on the performance on a dedicated subset of the available data, namely test set. However, DNNs require more thorough testing approaches to exercise corner-case behaviors. Coverage-guided fuzzing (CGF) which is a common practice in software testing aims to produce new test inputs by mutating existing ones to achieve high coverage on a test adequacy criterion. CGF has been an effective method for finding error inducing inputs by satisfying a well-established criterion. In this paper, we propose a novel CGF algorithm for structural testing of DNNs. The proposed algorithm employs Monte Carlo Tree Search to drive the coverage-guided search. In our evaluation, we show that the inputs generated by our method result in higher coverage than the inputs produced by the previously introduced CGF techniques on various criteria in a fixed amount of time. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey

TechRxiv

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TechRxiv 2022年

作者： Ghourab, Esraa M. Jaafar, Wael Bariah, Lina Naser, Shimaa Muhaidat, Sami Yanikomeroglu, Halim The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates The Department of Software and Information Technology Engineering Ecole de Technologie Supérieure MontrealQCH3C 1K3 Canada Abu Dhabi9639 United Arab Emirates The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada The Department of Systems and Computer Engineering Carleton University OttawaON Canada

Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of vertical applications like high traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, their deployment poses several challenges, including a lack of network transparency, management decentralization, and reliability. Moreover, the heterogeneity of future networks raises security concerns, e.g., confidentiality, privacy, and trustworthiness. Indeed, due to the emergence of novel paradigms, e.g., quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Hence, 5G/xG networks must consider smart security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of communications, while blockchain may enable networks’ decentralization, integrity, and trustworthiness. Motivated by these advancements, we provide an in-depth review of the existing PLS and blockchain literature. Then, for the first time in the literature, we present a framework to integrate PLS with blockchain in 5G/xG systems. We first provide a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Then, we present our vision of a cross-layer architecture that leverages PLS and blockchain. Through a case study, we demonstrate the high potential of cross-layer design to improve the security of vehicular networks. Finally, we identify related challenges and shed light on future research directions. © 2022, CC BY.

关键词： MIMO systems

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Assessing Team-Based Capstone Projects: Challenges and Recommendations 2025

Assessing Team-Based Capstone Projects: Challenges and Recom...

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Proceedings of the 30th ACM Conference on Innovation and Technology in computer Science Education V. 2

作者： Sara Hooshangi Asma Shakil Steve Riddle Ilknur Aydin Nayla Nasir Tejasvi Parupudi Attiqa Rehman Michael James Scott Jan Vahrenhold Amali Weerasinghe Xi Wu Department of Computer Science Virginia Tech Blacksburg VA USA School of Computer Science University of Auckland Auckland New Zealand School of Computing Newcastle University Newcastle upon Tyne United Kingdom Computer Systems Farmingdale State College Farmingdale NY USA Software Engineering Research Lab Blekinge Institute of Technology Karlskrona Blekinge Sweden School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Faculty of Screen Technology and Performance Falmouth University Penryn Cornwall United Kingdom Department of Computer Science University of Munster Munster Germany School of Computer and Mathematical Sciences University of Adelaide Adelaide Australia School of Computer Science University of Sydney Sydney NSW Australia

ISBN: (纸本)9798400715693

Team-based capstone projects are vital in preparing computer science students for real-world challenges by fostering teamwork, communication, and industry-relevant technical skills. However, their assessment presents challenges, such as aligning academic criteria with other stakeholders' expectations, evaluating individual contributions within teams, fairly addressing the diverse skills required, and determining the appropriate level of external partners' involvement in the evaluation process. Moreover, the high stakes of these projects necessitate transparent and equitable assessment methods that all stakeholders perceive as fair. Our working group (WG) aims to address the challenges of assessing capstone projects by examining the perspectives of instructors, students, and other stakeholders to ensure fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we will employ a mixed-methods approach to explore the issues faced by various stakeholders in assessing capstone projects and to capture both common challenges experienced (quantitative), and delve into nuanced individual experiences (qualitative). By conducting this research in a multi-national, multi-institutional context, we aim to capture a diverse range of global perspectives while accounting for the variation in capstone courses. Our goal is to provide actionable recommendations that enhance assessment practices, improve learning outcomes, and foster effective team collaboration in team-based capstone courses, ultimately preparing students for real-world challenges.

关键词： capstone

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Achieving Enhanced Security in Biometric Authentication: A Rigorous Analysis of Code-Based Fuzzy Extractor 10

Achieving Enhanced Security in Biometric Authentication: A R...

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10th International Scientific Conference "Information Technology and Implementation", IT and I 2023

作者： Kuznetsov, Oleksandr Frontoni, Emanuele Kuznetsova, Yelyzaveta Smirnov, Oleksii Chevardin, Vladyslav Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security School of Computer Sciences V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine Department of Information Engineering Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy Department of cyber security and software Central Ukrainian National Technical University 8 University Ave Kropyvnytskyi25006 Ukraine Military Institute of Telecommunications and Information Technologies Moskovska str. 45/1 Kiev01015 Ukraine

In the contemporary digital era, the intersectionality between biometric authentication and cryptographic security has emerged as a pivotal research domain, particularly in the context of facial recognition. This study embarks on a meticulous exploration of code-based fuzzy extractors, delving into their theoretical underpinnings and practical applications within biometric authentication systems. Through a comprehensive examination of False Rejection Rate (FRR) and False Acceptance Rate (FAR) metrics, the research illuminates the delicate balance and trade-offs inherent in optimizing security while ensuring user-friendly interactions. The study juxtaposes theoretical predictions with empirical findings, revealing notable disparities and highlighting the complexities and unpredictabilities embedded within real-world biometric data. Furthermore, the research navigates through the Receiver Operating Characteristic (ROC) curves, providing a nuanced understanding of the interplay between FRR and FAR, and its implications on system performance and reliability. While the findings offer a foundational framework and insights into the potentialities and challenges of implementing fuzzy extractors in biometric authentication, they also underscore the necessity for continuous exploration and development, especially in the context of post-quantum cryptographic resilience and real-world applicability. The study, while providing a stepping stone, invites further research and development to navigate the evolving challenges and potentials that permeate the dynamic landscape of biometric authentication and cryptographic systems. © 2023 Copyright for this paper by its authors.

关键词： Economic and social effects

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Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection

arXiv

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arXiv 2021年

作者： Gunter, George Li, Huichen Hojjati, Avesta Nice, Matthew Bunting, Matthew Gunter, Carl A. Li, Bo Sprinkle, Jonathan Work, Daniel The Department of Civil and Environmental Engineering The Institute for Software Integrated Systems Vanderbilt University TN United States The Department of Electrical and Computer Engineering The Institute for Software Integrated Systems Vanderbilt University TN United States The Department of Computer Science University of Illinois at Urbana-Champaign IL United States The Department of Computer and Electrical Engineering University of Arizona AZ United States

We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performance of current day mixed-autonomy freeway networks. Via a simple threat model which causes random deceleration attacks (RDAs), compromised vehicles create congestion waves in the traffic that decrease average speed and network throughput. We use a detailed and realistic traffic simulation environment to quantify the impacts of the attack on a model of a real high-volume freeway in the United States. We find that the effect of the attack depends both on the level of underlying traffic congestion, and what percentage of ACC vehicles can be compromised. In moderate congestion regimes the attack can degrade mean commuter speed by over 7%. In high density regimes overall network throughput can be reduced by up to 3%. And, in moderate to high congestion regimes, it can cost commuters on the network over 300 kmhrUSD. All of these results motivate that the proposed attack is able to significantly degrade performance of the traffic network. We also develop an anomaly detection technique that uses GPS traces on vehicles to identify malicious/compromised vehicles. We employ this technique on data from the simulation experiments and find that it is unable to identify compromised ACCs compared to benign/normal drivers. That is, these attacks are stealthy to detection. Stronger attacks can be accurately labeled as malicious, motivating that there is a limit to how impactful attacks can be before they are no longer stealthy. Finally, we experimentally execute the attack on a real and commercially available ACC vehicle, demonstrating the possible real world feasibility of an RDA. In particular, we test two well-known CAN bus detection techniques on the ensuing data and find that they fail to identify the malicious messages, suggesting that the attacks may be stealthy to detection at the vehicular level as

关键词： Anomaly detection

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