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arXiv 2023年

作者： Wang, Yubin Li, Yulin Peng, Zengqi Ghazzai, Hakim Ma, Jun Guangzhou China The Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia The Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong The HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Skyrmion-Magnetic Tunnel Junction Synapse with Mixed Synaptic Plasticity for Neuromorphic Computing

arXiv

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arXiv 2022年

作者： Lone, Aijaz H. Ganguly, Arnab Amara, Selma Das, Gobind Fariborzi, H. Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Physics Khalifa University Abu Dhabi12788 United Arab Emirates

Magnetic skyrmion-based data storage and unconventional computing devices have gained increasing attention due to their topological protection, small size, and low driving current. However, skyrmion creation, deletion, and motion are still being studied. In this study, we propose a skyrmion-based neuromorphic magnetic tunnel junction (MTJ) device with both long- and short-term plasticity (LTP and STP) (mixed synaptic plasticity). We showed that plasticity could be controlled by magnetic field, spin-orbit torque (SOT), and the voltage-controlled magnetic anisotropy (VCMA) switching mechanism. LTP depends on the skyrmion density and is manipulated by the SOT and magnetic field while STP is controlled by the VCMA. The LTP property of the device was utilized for static image recognition. By incorporating the STP feature, the device gained additional temporal filtering ability and could adapt to a dynamic environment. The skyrmions were conserved and confined to a nanotrack to minimize the skyrmion nucleation energy. The synapse device was trained and tested for emulating a deep neural network. We observed that when the skyrmion density was increased, the inference accuracy improved: 90% accuracy was achieved by the system at the highest density. We further demonstrated the dynamic environment learning and inference capabilities of the proposed device. © 2022, CC BY.

关键词： Magnetic anisotropy

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Design and Implementation of a Security Analysis Tool that Detects and Eliminates Code Caves in Windows Applications

Design and Implementation of a Security Analysis Tool that D...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Noman, Haitham Ameen Al-Maatouk, Qusay Noman, Sinan Ameen Princess Sumaya University for Technology King Abdullah Ii School of Engineering Amman Jordan School of Technology Kuala Lumpur Malaysia The University of Alabama Graduate School of Computer Science Alabama United States

ISBN: (纸本)9781665416566

Process injection techniques on Windows appli-cations are considered a serious threat to software security specialists. The attackers use these techniques to exploit the targeted program or process and take advantage of it by injecting a malicious process within the address space of the hosted process. Such attacks could be carried out using the so-called reverse engineering realm' the code caves'. For that reason, detecting these code caves in a particular application/program is deemed crucial to prevent the adversary from exploiting the programs through them. Code caves are simply a sequence of null bytes inside the executable program. They form due to the unuse of uninitialized variables. This paper presents a tool that can detect code caves in Windows programs by disassembling the program and looking for the code caves inside it;additionally, the tool will also eliminate those code caves without affecting the program's functionality. The tool has proven reliable and accurate when tested on various types of programs under the Windows operating system. © 2021 IEEE.

关键词： Caves

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Modern extreme value statistics for Utopian extremes

arXiv

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arXiv 2023年

作者： Richards, Jordan Alotaibi, Noura Cisneros, Daniela Gong, Yan Guerrero, Matheus B. Redondo, Paolo Shao, Xuanjie School of Mathematics University of Edinburgh United Kingdom Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Harvard School of Public Health BostonMA United States Department of Mathematics California State University FullertonCA United States

Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapolation into the upper tails of the data-generating distribution. We present a toolbox of four methodological frameworks, motivated by modern extreme value theory, that can be used to accurately estimate extreme exceedance probabilities or the corresponding level in either a univariate or multivariate setting. Our frameworks were used to facilitate the winning contribution of Team Yalla to the EVA (2023) Conference Data Challenge, which was organised for the 13th International Conference on Extreme Value Analysis. This competition comprised seven teams competing across four separate sub-challenges, with each requiring the modelling of data simulated from known, yet highly complex, statistical distributions, and extrapolation far beyond the range of the available samples in order to predict probabilities of extreme events. Data were constructed to be representative of real environmental data, sampled from the fantasy country of "Utopia". © 2023, CC BY.

关键词： Extrapolation

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On the Particle Approximation of Lagged Feynman-Kac Formulae

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Caffarel, Michel Moral, Pierre Del Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University Hong Kong Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman-Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman-Kac marginals associated with a positive operator iterated l ∈ N times;a lagged Feynman-Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) have been extensively employed in the physics literature. In short, DMC is an iterative algorithm involving N ∈ N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman-Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for_ κ > 0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/√N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman-Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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Improved lithium-ion battery health prediction with data-based approach

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e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 7卷

作者： Merrouche, Walid Harrou, Fouzi Taghezouit, Bilal Sun, Ying Center for Renewable Energies Development (CDER) Photovoltaic Solar Energy Division Algiers Bouzareah Algeria Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

The accurate modeling and forecasting of Battery State of Health (SOH) is crucial for ensuring reliable performance and longevity of lithium-ion batteries. This article introduces a data-driven approach for SOH prediction using Gaussian Process Regression (GPR), selected for its ability to model complex data relationships and capture prediction uncertainty without relying on future load information. Recognizing the effect of reversible capacity recoveries on prediction accuracy, this work employs the ISEA-RWTH 48 Li-ion Cells dataset, deliberately devoid of such recoveries prior to training the GPR model. The GPR model was evaluated and compared with Support Vector Regression (SVR) using the publicly available dataset. First and second End of Life (EOL) scenarios were considered, relevant to primary and secondary battery applications. The results demonstrated the GPR model's superior performance. Particularly, mid-life and late-life predictions displayed better accuracy with GPR, showcasing higher R2 values and lower MAPE values (e.g., mid-life prediction: GPR's average R2 = 0.99, SVR's = 0.9789;GPR's average MAPE = 0.1916, SVR's = 1.3028). Moreover, GPR exhibited the ability to quantify uncertainty in capacity degradation and forecast first and second EOL instances effectively (e.g., mid-life predictions had 1.7 cycle error at 1st EOL and 8.9 cycle error at 2nd EOL). The research also offers valuable insights into the application of machine learning methods for predicting the health degradation of lithium-ion batteries. © 2024 The Author(s)

关键词： Battery dataset First life Gaussian process regression Lithium-ion battery Second life State of health Support vector regression

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Decarbonizing Maritime Shipping and Aviation: Disruption, Regime Resistance and Breaking Through Carbon Lock-In for Hard-to-Abate Transport Sectors

SSRN

引用

SSRN 2023年

作者： Urban, Frauke Nurdiawati, Anissa Harahap, Fumi Morozovska, Kateryna Department of Industrial Economics and Management Royal Institute of Development KTH Sweden King Abdullah University of Science and Technology KAUST Saudi Arabia Department of Energy Technology Royal Institute of Development KTH Sweden School of Electrical Engineering and Computer Science Royal Institute of Development KTH Sweden

Aviation and maritime shipping are hard-to-abate transport sectors that are heavily dependent on fossil fuels. They jointly account for nearly 10% of global greenhouse gas emissions, while infrastructure and investments are locked into high-carbon pathways for decades. Fuels and technologies to decarbonize include advanced biofuels, electrofuels, hydrogen and electric propulsion. This research aims to analyse the decarbonization strategies for maritime shipping and aviation from a comparative perspective, and analyzing the role of different actors for disruption to break through carbon lock-in and path dependency. The research uses Sweden as a case study and applies qualitative methods, including expert interviews, focus group discussions, site visits. Our research finds that aviation and maritime shipping are slowly changing, albeit with different dynamics. Both sectors show that incumbent regime actors play a major role in shaping transition pathways and disrupting the (quasi)equilibrium, while niche innovation is often developed together by incumbents and niche players. © 2023, The Authors. All rights reserved.

关键词： Carbon

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(10$\bar{1}$3)-oriented semipolar GaN growth on (10$\bar{1}$0) m-plane ScAlMgO4 substrate

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Japanese Journal of Applied Physics 2024年第10期63卷 101001-101001页

作者： Mohammed A. Najmi Daisuke Iida Kazuhiro Ohkawa Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

We report the growth of a GaN layer on a (10\bar{1}0)m-plane ScAlMgO4 (SAM) substrate. The GaN layer demonstrated (10\bar{1}3) preference growth orientation. The anisotropy of the crystalline quality was distinctly ob...

We report the growth of a GaN layer on a $$(10\bar<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=" data-src="https://***/journals/1347-4065/63/10/101001/revision2/***" style="max-width: 100%;" alt="$(10\bar{1}3)$" align="top">0)$ m-plane ScAlMgO₄ (SAM) substrate. The GaN layer demonstrated $(10\bar<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=" data-src="https://***/journals/1347-4065/63/10/101001/revision2/***" style="max-width: 100%;" alt="$(10\bar{1}3)$" align="top">3)$ preference growth orientation. The anisotropy of the crystalline quality was distinctly observed through an X-ray rocking curve (XRC) taken across the sample surface over various azimuths across the orthogonal directions [0001] and $[1\bar<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=" data-src="https://***/journals/1347-4065/63/10/101001/revision2/***" style="max-width: 100%;" alt="$[1\bar{2}10]$" align="top">10]$ of the SAM substrate. Notably, the crystalline quality exhibited gradual degradation as the substrate was rotated around its surface normal away from the c-direction

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A Static Analysis Tool for Malware Detection

A Static Analysis Tool for Malware Detection

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Noman, Haitham Ameen Al-Maatouk, Qusay Noman, Sinan Ameen Princess Sumaya University for Technology King Abdullah Ii School of Engineering Amman Jordan School of Technology Kuala Lumpur Malaysia The University of Alabama Gradute School of Computer Science Alabama United States

ISBN: (纸本)9781665416566

Malware detection refers to the process of detecting the presence of Malware on a host system or of distinguishing whether a specific program is malicious. The different types of Malware created new challenges for the researchers to develop a concrete detection solution that can tackle the Malware effectively. Malware analysis can be classified into two methods: The first is done by analyzing the Malware statically without executing it. The second method is conducted by analyzing the Malware dynamically, which is conducted by monitoring it during its execution in an isolated, safe environment. This paper developed a tool that performs static analysis on the Malware to detect its behaviour. The tool works by extracting the suspected program's APIs and checking if those APIs are malicious or not. The tool showed promising results and high accuracy to tell whether the analyzed program is Keylogger, Ransomware, Backdoor or benign. Moreover, some false-positive results appeared during the tests when trying to identify software like Zoom and Team Viewer. © 2021 IEEE.

关键词： Static analysis

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Proactive Insider Threat Detection Using Facial and Behavioral Biometrics

Proactive Insider Threat Detection Using Facial and Behavior...

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Information technology (ACIT)

作者： Hanay Almomani Ayoub Alsarhan Mahmoud AlJamal Mohammad Aljaidi Tamam Alsarhan Bashar Khassawneh Ghassan Samara Manish Kumar Singla Ahmed BaniMustafa Department of Information Technology The Hashemite University Zarqa Jordan Department of Cybersecurity Irbid National University Irbid Jordan Department of Computer Science Zarqa University Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia King Abdullah II School of IT University of Jordan Amman Jordan Department of Computer Science Amman Arab University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Punjab India Department of Computer Information Science The Hashemite University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Insider threats have long posed significant risks to organizational security due to their internal origins and the ethical complexities involved. This paper addresses the critical problem of detecting insider threats by proposing a novel method based on analyzing physiological indicators exhibited by users while interacting with the system. Our approach involves monitoring and recording video footage of users, extracting frames, and analyzing physiological characteristics such as eye movements, facial expressions, and other stress-related indicators. These data points are quantified and assessed to determine the risk level associated with each user. The results demonstrate the effectiveness of our method in identifying potential insider threats with high accuracy. This work’s novelty lies in integrating advanced biometric analysis into insider threat detection, providing a proactive and reliable solution to a pervasive security challenge.

关键词： Biometrics Accuracy Psychology Threat assessment Real-time systems Recording Security Reliability Biomedical monitoring Monitoring

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