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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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Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 315-329页

作者： Yifang Ma Zhenyu Wang Hong Yang Lin Yang Department of Information Technology and Decision Sciences Old Dominion UniversityNorfolkVA 23529 USA Department of Computational Modeling and Simulation Engineering Old Dominion UniversityNorfolkVA 23529 USA Department of Mechanical and Electrical Engineering Huazhong Agricultural UniversityWuhan 430070China

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation *** by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in *** achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV *** research have made great efforts in investigating different aspects of applying AI in AV ***,few studies have offered the research community a thorough examination of current practices in implementing AI in ***,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research ***,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision *** investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their *** on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected *** paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

关键词： Artificial intelligence(AI) autonomous vehicles(AVs) deep learning(DL) motion planning perception self-driving

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Selective Targeted Transfer Learning for Malware Classification

Selective Targeted Transfer Learning for Malware Classificat...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Priyanka Aggarwal Sayyed F. Ahamed Sachin Shetty Laura J. Freeman Computational Modeling and Simulation Engineering Old Dominion University Norfolk Virginia USA Virginia Tech Department of StatisticsVirginiaUSA

ISBN: (纸本)9781665416245

Data driven machine learning and deep neural models demand accessibility to high-quality, clean, and enormous datasets. Obtaining a labeled dataset is an expensive, challenging and time-consuming process, leading to the development of transfer learning (TL) methodology. TL leverages gained knowledge from a previously trained source model into specific yet similar task models with limited data domain coverage. For effective transfer, the training (source) and test (target) data may share similar or different marginal and conditional distributions. Negative transfer may occur, due to noise accumulated during learning iterations decreasing system performance metrics [1]. In this paper, we propose a selective targeted transfer learning (STTL) method to resolve the transferability between source and target with minimal data requirements to statically detect malicious Windows portable executable files. Kernel layers from deep models map the marginal distributions of the source and target domain into a common latent space while selective source feature and hard target samples selection draws the conditional distributions closer. Here we utilize benchmark Endgame Malware Benchmark for Research (EMBER) dataset which contains more than one million malware and benign samples, to validate our work. We automatically identify source, target pairs for three different malware families (banking trojan, adware, spyware) based on statistical measures and perform feature extraction/reduction (2381 features to 29, 28, 18 features) to formulate the new training dataset for effective targeted TL. For three different families (trojan, adware, spyware malwares) with varying dimensions, we prove that acquiring a small number of intentionally well-chosen samples from the target environment (2441, 2404, 3083 which is less than 14% of target sample count), we can drastically improve model accuracy to 90.8%, 94.4%, 93.5 % in comparison to the baseline transfer learning accuracy of 80.5%, 76.1 %

关键词： Training System performance Transfer learning Benchmark testing Feature extraction Data models Trojan horses

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Predicting Fracture in the Proximal Humerus using Phase Field Models

arXiv

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arXiv 2022年

作者： Hug, Lisa Dahan, G. Kollmannsberger, S. Rank, E. Yosibash, Z. Chair for Computational Modeling and Simulation Technical University of Munich Arcisstr. 21 Munich80333 Germany School of Mechanical Engineering The Iby and Aladar Fleischman Faculty of Engineering Tel-Aviv University Ramat-Aviv69978 Israel

Proximal humerus impacted fractures are of clinical concern in the elderly population. Prediction of such fractures by CT-based finite element methods encounters several major obstacles such as heterogeneous mechanical properties and fracture due to compressive strains. We herein propose to investigate a variation of the phase field method (PFM) embedded into the finite cell method (FCM) to simulate impacted humeral fractures in fresh frozen human humeri. The force-strain response, failure loads and the fracture path are compared to experimental observations for validation purposes. The PFM (by means of the regularization parameter `0) is first calibrated by one experiment and thereafter used for the prediction of the mechanical response of two other human fresh frozen humeri. All humeri are fractured at the surgical neck and strains are monitored by Digital Image Correlation (DIC). Experimental strains in the elastic regime are reproduced with good agreement (R2 = 0.726), similarly to the validated finite element method [9]. The failure pattern and fracture evolution at the surgical neck predicted by the PFM mimic extremely well the experimental observations for all three humeri. The maximum relative error in the computed failure loads is 3.8%. To the best of our knowledge this is the first method that can predict well the experimental compressive failure pattern as well as the force-strain relationship in proximal humerus fractures. © 2022, CC BY-NC-ND.

关键词： Finite element method

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Distributed Strategy for Power Re-Allocation in High Performance Applications

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Journal of Computer and Communications 2020年第12期8卷 142-158页

作者： Vaibhav Sundriyal Masha Sosonkina Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk Virginia USA

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in a performance degradation of as low as 1% under the proposed strategy compared with the execution in the unlimited power case.

关键词： Multinode Power Allocation RAPL UFS DVFS Maximizing Performance Component Power

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ADC@

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Applied Intelligence 2021年第2期52卷 2134-2153页

作者： Jain, Ankush Nagar, Surendra Singh, Pramod Kumar Dhar, Joydip Computational Intelligence and Data Mining Laboratory ABV- Indian Institute of Information Technology and Management Gwalior India Modeling and Simulation Laboratory ABV- Indian Institute of Information Technology and Management Gwalior India

In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the fixed categorization of accessible ratings. However, due to the excessive variation in similarly co-rated item pairs, they produce ambiguous contextual meanings that yield inconsistent results for the user pairs similarities. Therefore, to deal with this problem, this paper proposes an adaptive divisional categorization (ADC)-based RS, namely ADC@

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Attack Graph Embedded Machine Learning Platform For Cyber Situational Awareness

Attack Graph Embedded Machine Learning Platform For Cyber Si...

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MILCOM, Military Communications Conference

作者： Md Ariful Haque Sachin Shetty Charles A. Kamhoua Kimberly Gold Computational Modeling and Simulation Engineering Old Dominion University Norfolk VA USA The U.S. Army Research Laboratory Network Security Research Adelphi MD USA Crane Division Naval Surface Warfare Center Crane IN USA

An attack graph is a crucial tool for network security analysis. Traditionally, network administrators utilize attack graphs to generate possible attack paths and estimate the attack probability (risk) in a networked environment. However, the attack probability does not alone provide enough directions to take security measures. Furthermore, there is a lack of combining the attack probability with the attack node's topological influence factors, which also contributes to propagate cyberattacks. This work proposes a cyber risk assessment platform to enhance cyber situational awareness by addressing those gaps. We first present methods to compute cyber risk using the attack graph and then extract network topological influence factors (i.e., features) using graph centrality measures. Next, we apply unsupervised learning to the extracted features to find the network's highly exploitable attack points. Finally, we use graph embedding techniques to identify the objective similarity among the attack privilege nodes. We illustrate the applications of our machine learning-based cyber risk assessment platform using a SCADA (supervisory control and data acquisition) case study for the cyber-physical power systems. The simulation results demonstrate that the platform provides a better understanding of the cyber risk assessment and situational awareness by applying machine learning techniques on the attack graph.

关键词： Military communication simulation SCADA systems Machine learning Network security Feature extraction Power systems

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Approximate Deconvolution Discretisation

SSRN

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SSRN 2023年

作者： Boguslawski, Andrzej Tyliszczak, Artur Geurts, B.J. Czestochowa University of Technology Poland Mathematics of Multiscale Modeling and Simulation Faculty EEMCS University of Twente PO Box 217 Enschede7500 AE Netherlands Multiscale Physics of Energy Systems Center for Computational Energy Research Faculty of Applied Physics Eindhoven University of Technology PO Box 513 Eindhoven5600 MB Netherlands

A new strategy is presented for the construction of high-order spatial discretisations extracted from a lower-order basic discretisation. The key consideration is that any discretisation of a derivative can be associated with exact differentiation of a `filtered' solution. By approximately inverting the numerically implied filter of the basic discretisation an augmented high-order method can be obtained. This construction is illustrated for finite difference (FD) discretisation schemes, solving partial differential equations in fluid mechanics. Knowing the implied filter of the basic discretisation, one can arrive at a corresponding much higher order method by (approximately) eliminating the implied spatial filter. We use deconvolution to compensate for the implied filter. This corresponds to a `sharpening' of numerical solution features before the application of the basic FD method. The combination will be referred to as Approximate Deconvolution Discretisation (ADD). The accuracy of the deconvolved FD scheme depends on the order of approximation of the inverse filter. We present the `sharpening' of several well-known FD operators for first- and second-order derivatives and quantify the achieved accuracy in terms of the modified wavenumber spectrum. Examples include high-order extensions up to new schemes with spectral accuracy. The practicality of the deconvolved FD schemes is illustrated by showing that ADD yields spectral accuracy for the Burgers equation and for double-jet flow of an incompressible fluid. © 2023, The Authors. All rights reserved.

关键词： computational fluid dynamics

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Spatial information theory and construction informatics - A fruitful symbiosis 14

Spatial information theory and construction informatics - A ...

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14th International Conference on Spatial Information Theory, COSIT 2019

作者： Borrmann, André Chair of Computational Modeling and Simulation Technical University of Munich Germany

ISBN: (纸本)9783959771153

Traditionally, Spatial Information Theory and Construction Engineering have been recognized as widely separated fields with only very little connections. However, in recent years the construction industry has undergone a substantial change: It is evolving from rather historic practices based on 2D drawings into modern digital processes based on information-rich 3D models that can be generated, analyzed and processed by means of computer technology. This progression, driven and fostered by the Construction Informatics Community, opens the possibility for innovative research in the fascinating area where Spatial Information Theory and Construction Engineering overlap. The paper gives an overview on ongoing activities in this area. © 2019 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Information theory

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Parallel solution of saddle point systems with nested iterative solvers based on the Golub-Kahan Bidiagonalization

Parallel solution of saddle point systems with nested iterat...

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作者： Kruse, Carola Sosonkina, Masha Arioli, Mario Tardieu, Nicolas Rüde, Ulrich Cerfacs Toulouse France Department of Computational Modeling and Simulation Engineering Old Dominion University NorfolkVA United States Libera Universita Mediterranea Casamassima Italy IMSIA UMR 9219 EDF-CNRS-CEA-ENSTA Université Paris Saclay Paris France Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

The Golub-Kahan bidiagonalization is widely used in the singular value decomposition of rectangular matrices and has been generalized to an iterative solver for symmetric indefinite linear systems with a two-by-two block structure. In this work, we present a scalability study of this generalized solver as implemented in a recent release of the parallel numerical library PETSc (Portable, Extensible Toolkit for Scientific Computation). We present an improved solver performance for the two-dimensional (2D) Stokes equations as compared to previous work. Furthermore, we investigate the performance of different parallel inner solvers in the outer Golub-Kahan iteration for a three-dimensional Stokes problem. The study includes parallel sparse direct solvers and multigrid methods. When increasing the number of cores for a fixed total problem size, the solver exhibits good speedups of up to 50% at the 1024 core count. For the tests in which the total problem size grows while the workload in each core stays constant, the parallel performance of the solver scales almost linearly with the increase in the core counts. In particular, the computation time increases only by about 15% when the number of cores increases from 80 to 1024 for a 2D test case. © 2020 John Wiley & Sons, Ltd.

关键词： Singular value decomposition

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