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IEEE International Conference on Communications (ICC)

作者： Saurabh Agrawal Anusha Vangala Ashok Kumar Das Neeraj Kumar Sachin Shetty Sajal K. Das Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science Missouri University of Science and Technology Rolla Missouri USA Department of Computer Science and Engineering Thapar University Patiala India Department of Computational Modeling and Simulation Engineering Old Dominion University Suffolk Virginia USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low, and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther tool demonstrates that location-based keys as proposed in our protocol are secure against location inference and spoofing attacks among others.

关键词： Resistance Protocols Authentication Seaports Data aggregation Security Servers

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Automatic classification of activities in classroom videos

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computers and Education: Artificial Intelligence 2024年 6卷

作者： Foster, Jonathan K. Korban, Matthew Youngs, Peter Watson, Ginger S. Acton, Scott T. Department of Educational Theory and Practice University at Albany United States C.L. Brown Department of Electrical and Computer Engineering University of Virginia United States Department of Curriculum Instruction and Special Education University of Virginia United States Virginia Modeling Analysis and Simulation Center Old Dominion University United States

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning. © 2024 The Authors

关键词： computer vision

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Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid

arXiv

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arXiv 2023年

作者： Munir, Shirajum Shetty, Sachin Rawat, Danda B. Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States Department of Electrical and Computer Science Howard University 2400 Sixth Street NW WashingtonDC20059 United States

The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker’s DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward’s minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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An Anomaly Detection Method Based on Adaptive Log and Dual Feature Fusion Analysis for Distributed Systems

An Anomaly Detection Method Based on Adaptive Log and Dual F...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Yijiang Jia Kefei Li Ping Lu Baodi Xie Huayang Wang Jincai Chen School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

In this study, we present a middleware-based approach for detecting anomalies in distributed systems. Our method facilitates the dynamic collection of logs at various levels of detail and incorporates an a priori dictionary-based compression strategy to process and transmit logs, minimizing the performance impact on distributed systems. Additionally, we utilize a dual feature fusion technique to analyze the logs. We evaluate the effectiveness of our approach by performing anomaly detection in a publish/subscribe distributed system and comparing it with existing methods. The results illustrate that our method outperforms other approaches, demonstrating superior performance.

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AUGMENTING DEEP NEURAL NETWORKS WITH SYMBOLIC KNOWLEDGE: TOWARDS TRUSTWORTHY AND INTERPRETABLE AI FOR EDUCATION

arXiv

引用

arXiv 2023年

作者： Hooshyar, Danial Azevedo, Roger Yang, Yeongwook School of Digital Technologies Tallinn University Tallinn10120 Estonia School of Modeling Simulation and Training University of Central Florida OrlandoFL32826 United States Division of Computer Engineering Hanshin University Osan18101 Korea Republic of

Artificial neural networks (ANNs) have shown to be amongst the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to three major challenges: i) difficulty in incorporating symbolic educational knowledge (e.g., causal relationships) in their development, ii) learning and reflecting biases, and iii) lack of interpretability. Given the high-risk nature of education, the integration of educational knowledge into ANNs becomes crucial for developing AI applications that adhere to essential educational restrictions, and provide interpretability over the predictions. This research argues that the neural-symbolic family of AI has the potential to address the named challenges. To this end, it adapts a neural-symbolic AI framework and accordingly develops an approach called NSAI, which injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking. Our findings reveal that the NSAI approach has better generalizability compared to deep neural networks trained merely on training data, as well as training data augmented by SMOTE and autoencoder methods. More importantly, unlike the other models, the NSAI approach prioritizes robust representations that capture causal relationships between input features and output labels, ensuring safety in learning to avoid spurious correlations and control biases in training data. Furthermore, the NSAI approach enables the extraction of rules from the learned network, facilitating interpretation and reasoning about the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI can overcome the limitations of ANNs in education, enabling trustworthy and interpretable *** Codes I.2.0, I.2.1, I.2.6 Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Leveraging Intel SGX to enable trusted and privacy preserving membership service in distributed ledgers

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International Journal of Information and computer Security 2021年第1-2期16卷 63-83页

作者： Liang, Xueping Shetty, Sachin Tosh, Deepak K. Foytik, Peter Zhang, Lingchen Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Department of Computer Science University of Texas at EL Paso El PasoTX United States

Distributed ledger technology (DLT) provides decentralised services by removing the need of trust among distributed nodes in the distributed system. Transactions across the network are visible to all participants. However, some transactions may contain sensitive information such as business contracts or even personal health records. To protect user privacy, the architecture of distributed multi-channel ledger with membership service as a critical component can be adopted. The proposed multi-channel membership service architecture combines two promising technologies, distributed ledger and Intel Software Guard Extensions (SGX). With SGX remote attestation and isolated execution, each node is enrolled as a trusted entity to the channels, which separate different applications and provide better flexibility. We propose security properties for membership service in distributed ledger and illustrate how SGX help to achieve these properties in each phase. Security analysis and evaluation show that the proposed architecture could enhance the privacy preservation and capabilities against attacks. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Blockchain

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modeling and simulation to Improve Real Electric Vehicles Charging Processes by Integration of Renewable Energies and Buffer Storage 22

Modeling and Simulation to Improve Real Electric Vehicles Ch...

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Proceedings of the Winter simulation Conference

作者： Konstantin Sing Pierre Mertiny Marco Pruckner Department of Mechanical Engineering University of Erlangen-Nürnberg Erlangen GERMANY Department of Mechanical Engineering University of Alberta Edmonton Alberta CANADA Modeling and Simulation Institute of Computer Science University of Würzburg Am Hubland Würzburg GERMANY

The present study explores a simulation model combining system dynamics and discrete-event simulation for an electric vehicle charging system. For the representation of the charging demand the model employs data from an actual facility for vehicle charging. While being connected to the electrical grid, the system is augmented by a solar photovoltaic installation and stationary battery energy storage. Multiple simulation runs were performed to analyze the considered energy system over a 1-year period and compare relevant output parameters for different system configurations and system locations. Results show that a solar photovoltaic installation can be effectively integrated. For the degree of self-sufficiency, high values of 87% can be achieved with combined solar photovoltaic and battery energy storage systems.

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Discovering API Directives from API Specifications with Text Classification

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Journal of computer Science & Technology 2021年第4期36卷 922-943页

作者： Jing-Xuan Zhang Chuan-Qi Tao Zhi-Qiu Huang Xin Chen College of Compute Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Safety-Critical Software(Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information TechnologyNanjing 210016China Key Laboratory of Complex Systems Modeling and Simulation(Hangzhou Dianzi University) Ministry of Education Hangzhou 310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China

Application programming interface(API)libraries are extensively used by *** correctly program with APIs and avoid bugs,developers shall pay attention to API directives,which illustrate the constraints of ***,API directives usually have diverse morphologies,making it time-consuming and error-prone for developers to discover all the relevant API *** this paper,we propose an approach leveraging text classification to discover API directives from API ***,given a set of training sentences in API specifications,our approach first characterizes each sentence by three groups of ***,to deal with the unequal distribution between API directives and non-directives,our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several *** a new sentence in an API specification,our approach synthesizes the trained classifiers to predict whether it is an API *** have evaluated our approach over a publicly available annotated API directive *** experimental results reveal that our approach achieves an F-measure value of up to 82.08%.In addition,our approach statistically outperforms the state-of-the-art approach by up to 29.67%in terms of F-measure.

关键词： Application programming interface(API)directive API specification imbalanced learning text classification

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Well-posedness analysis of the Cahn-Hilliard-Biot model

arXiv

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arXiv 2023年

作者： Riethmüller, Cedric Storvik, Erlend Both, Jakub Wiktor Radu, Florin Adrian Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Center for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway

We investigate the well-posedness of the recently proposed Cahn-Hilliard-Biot model. The model is a three-way coupled PDE of elliptic-parabolic nature, with several nonlinearities and the fourth order term known to the Cahn-Hilliard system. We show existence of weak solutions to the variational form of the equations and uniqueness under certain conditions of the material parameters and secondary consolidation, adding regularizing effects. Existence is shown by discretizing in space and applying ODE-theory (the Peano-Cauchy theorem) to prove existence of the discrete system, followed by compactness arguments to retain solutions of the continuous system. In addition, the continuous dependence of solutions on the data is established, in particular implying uniqueness. Both results build strongly on the inherent gradient flow structure of the model. Copyright © 2023, The Authors. All rights reserved.

关键词： Variational techniques

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Electronic band energy of a bent ZnO piezoelectric semiconductor nanowire

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Applied Mathematics and Mechanics(English Edition) 2020年第6期41卷 833-844页

作者： Wanli YANG Yuantai HU E.N.PAN Department of Mechanics Hubei Key Laboratory of Engineering Structural Analysis and Safety AssessmentHuazhong University of Science and TechnologyWuhan 430074China Computer Modeling and Simulation Group College of EngineeringUniversity of AkronAkronOhio 44325U.S.A.

The electric band energy variation in a bent piezoelectric semiconductor(PSC) nanowire of circular cross-section induced by the mechanical force is analyzed based on a six-band k · p method. The electric-mechanical fields are first obtained analytically in a cantilever bent PSC nanowire by solving the fully-coupled electro-mechanical equations. Then, the band energy is acquired numerically via the six-band *** considering further the nonlinear coupling between the piezoelectric and semiconducting quantities, the contribution of the redistribution carriers to the electric field is analyzed from the Gauss’ s law. Numerical examples are carried out for an n-type Zn O nanowire in different locations induced by an applied concentrated end force. They include the electric potential, heavy hole(HH), light hole(LH), spin-orbit split-off(SO),and conduction band(CB) edges along the axial and thickness directions. Our results show that the applied force has a significant effect on the band energies. For instance, on the bottom surface along the axial direction, the bandgaps near the fixed end are greater than those near the loading end, and this trend is reversed on the top surface. Moreover,at a fixed axial location, the energy level of the lower side can be enhanced by applying a bending force at the end. The present results could be of significant guidance to the electronic devices and piezotronics.

关键词： piezoelectric semiconductor(PSC) mechanical bending energy band ZnO

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