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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Scaling Optimizations for Large-Scale Distributed Data with Lightweight Coresets

Scaling Optimizations for Large-Scale Distributed Data with ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Daniel N. Pinheiro Samuel Xavier-de-Souza Daniel Aloise Grad. Program of Electrical and Computer Engineering Univ. Federal do Rio Grande do Norte Natal Brazil Dept. of Computer Engineering and Automation Univ. Federal do Rio Grande do Norte Natal Brazil Dept. of Computer and Software Engineering Polytechnique Montreal Québec Canada

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Lightweight coresets are compact representations of data sets such that clustering methods present competitive results in relation to the complete data set. They are constructed by sampling important points from the complete set. We propose a fast method to approximate the sampling of lightweight coresets from very large data sets which are distributed among multiple machines. We show that the proposed method is much faster and scalable, reaching results 48 times faster than the original lightweight coresets, while holding similar properties.

关键词： Clustering algorithms Approximation algorithms Distributed databases Optimization Big Data Random access memory Linear programming

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Serverless architecture efficiency: An exploratory study

arXiv

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arXiv 2019年

作者： Lavoie, Samuel Garant, Anthony Petrillo, Fabio Department of Computer and Software Engineering Ecole Polytechnique de Montreal Quebec Canada Universit du Qubec de Chicoutimi Department of Mathematics and Computer science 555 boulevard de l’Universit Chicoutimi Canada

Cloud service provider propose services to insensitive customers to use their platform. Different services can achieve the same result at different cost. In this paper, we study the efficiency of a serverless architecture for running highly parallelizable tasks to compare theses services in order to find the most efficient in term of performance and cost. More precisely, we look at the compute time and at the cost per task for a given task. The tasks studied is the count of the occurrence of a given word in a corpus. We compare the serverless architecture to the Apache Spark map reduce technique commonly used for this type of task. Using AWS Lambda for the serverless architecture and Amazon EMR for the Apache Spark map reduce, with similar compute power, we show that the serverless technique achieve comparable performance in term of compute time and cost. We observed that the lambda function is a great approach for real time computing, while EMR is preferable for task that require long compute time. Copyright © 2019, The Authors. All rights reserved.

关键词： Efficiency

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Antenna characterization for the HIRAX experiment

arXiv

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arXiv 2022年

作者： Kuhn, Emily R. Saliwanchik, Benjamin R.B. Bandura, Kevin Bianco, Michele Chiang, H. Cynthia Crichton, devin deng, Meiling Gaddam, Sindhu Gerodias, Kit Gumba, Austin Harris, Maile Moodley, Kavilan Mugundhan, V. Newburgh, Laura Peterson, Jeffrey Pieters, Elizabeth Polish, Anna R. Refregier, Alexandre Sampath, Ajith Santos, Mario G. Sengate, Onkabetse Sievers, Jonathan Smith, Ema Tyndall, Will Walters, Anthony Weltman, Amanda Wulf, Dallas Department of Physics Yale University New HavenCT United States Instrumentation Division Brookhaven National Laboratory UptonNY United States Department of Computer Science and Electrical Engineering Center for GravitationalWaves and Cosmology West Virginia University MorgantownWV United States Laboratoire d'Astrophysique Ecole Polytechnique Federale de Lausanne EPFL Observatoire de Sauverny Versoix Switzerland Department of Physics McGill University MontrealQC Canada Institute for Particle Physics and Astrophysics ETH Zurich Zurich Switzerland Perimeter Institute for Theoretical Physics WaterlooON Canada Dominion Radio Astrophysical Observatory KaledenBC Canada School of Mathematics Statistics and Computer Science UKZN Saudi Arabia Astrophysics Research Centre UKZN Saudi Arabia Dept of Physics Carnegie Mellon University PittsburghPA United States Department of Physics and Astronomy University of Western Cape Cape Town7535 South Africa School of Chemistry and Physics UKZN Saudi Arabia Department of Mathematics and Applied Mathematics University of Cape Town Saudi Arabia

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) aims to improve constraints on the dark energy equation of state through measurements of large-scale structure at high redshift (0.8 700MHz. Noise temperature measurements of the HIRAX feeds were performed in a custom apparatus built at Yale. In this system, identical loads, one cryogenic and the other at room temperature, are used to take a differential (Y-factor) measurement from which the noise of the system is inferred. Several measurement sets have been conducted using the system, involving CHIME feeds as well as four of the HIRAX active feeds. These measurements give the first noise temperature measurements of the HIRAX feed, revealing a ∼60K noise temperature (relative to 30K target) with 40K peak- to-peak frequency-dependent features, and provide the first demonstration of feed repeatability. Both findings inform current and future feed designs. © 2022, CC BY.

关键词： Equations of state

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An analysis of random cache effects on real-time multi-core scheduling algorithms 28

An analysis of random cache effects on real-time multi-core ...

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2017 28th International Symposium on Rapid System Prototyping: Shortening the Path from Specification to Prototype, RSP 2017

作者： Hafnaoui, Imane Chen, Chao Ayari, Rabeh Nicolescu, Gabriela Beltrame, Giovanni Department of Computer and Software Engineering Ecole Polytechnique de Montreal MontréalQC Canada

ISBN: (纸本)9781450354189

The effect of sharing the last-level cache (LLC) among cores in a multi-core system has not been thoroughly investigated especially in the design of efficient scheduling algorithms. And with the growing interest in random caches, which allow for an easier estimation of the worst-case execution time of tasks in critical real-time embedded systems, tools that analyse the sensitivity of workloads to sharing the LLC become necessary. In this paper, we extend a realtime multiprocessor scheduling simulator, SimSo, with a framework that incorporates a random cache model for multi-level caches to evaluate emerging scheduling algorithms under the influence of shared caches. A set of experiments were performed to study the behavior of workloads with respect to worst-case response time, average slack time, and maximum utilization, with varying cache designs under different scheduling algorithms. © 2017 ACM.

关键词： Scheduling algorithms

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An Analysis of Random Cache Effects on Real-Time Multi-Core Scheduling Algorithms

An Analysis of Random Cache Effects on Real-Time Multi-Core ...

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International Workshop on Rapid System Prototyping (RSP)

作者： Imane Hafnaoui Chao Chen Rabeh Ayari Gabriela Nicolescu Giovanni Beltrame Department of Computer and Software Engineering Ecole Polytechnique de Montreal Montréal QC Canada

The following topics are dealt with: embedded systems; program compilers; microprocessor chips; multiprocessing systems; Internet of Things; power aware computing; field programmable gate arrays; reduced instruction s... 详细信息

ISBN: (纸本)9781450354189;9781450354189

关键词： Task analysis Real-time systems Scheduling algorithms Timing Computational modeling Scheduling Multicore processing

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Enhanced Userspace and In-Kernel Trace Filtering for Production Systems

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Journal of computer Science & Technology 2016年第6期31卷 1161-1178页

作者： Suchakrapani Datt Sharma Michel Dagenais Department of Computer and Software Engineering Ecole Polytechnique de Montrdal Montreal H3T 1J4 Canada

Trace tools like LTTng have a very low impact on the traced software as compared with traditional debuggers. However, for long runs, in resource constrained and high throughput environments, such as embedded network switching nodes and production servers, the collective tracing impact on the target software adds up considerably. The overhead is not just in terms of execution time but also in terms of the huge amount of data to be stored, processed and analyzed offiine. This paper presents a novel way of dealing with such huge trace data generation by introducing a Just-In-Time （JIT） filter based tracing system, for sieving through the flood of high frequency events, and recording only those that are relevant, when a specific condition is met. With a tiny filtering cost, the user can filter out most events and focus only on the events of interest. We show that in certain scenarios, the JIT compiled filters prove to be three times more effective than similar interpreted filters. We also show that with the increasing number of filter predicates and context variables, the benefits of JIT compilation increase with some JIT compiled filters being even three times faster than their interpreted counterparts. We further present a new architecture, using our filtering system, which can enable co-operative tracing between kernel and process tracing VMs （virtual machines） that share data efficiently. We demonstrate its use through a tracing scenario where the user can dynamically specify syscall latency through the userspace tracing VM whose effect is reflected in tracing decisions made by the kernel tracing VM. We compare the data access performance on our shared memory system and show an almost 100 times improvement over traditional data sharing for co-operative tracing.

关键词： tracing debugging filtering operating system interpreter

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Entity typing and linking using SPARQL patterns and DBpedia 3rd

Entity typing and linking using SPARQL patterns and DBpedia

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3rd Semantic Web Challenges Conference, ESWC 2016

作者： Haidar-Ahmad, Lara Font, Ludovic Zouaq, Amal Gagnon, Michel Department of Computer and Software Engineering Ecole Polytechnique de Montreal Montreal Canada School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9783319465647

The automatic extraction of entities and their types from text, coupled with entity linking to LOD datasets, are fundamental challenges for the evolution of the Semantic Web. In this paper, we describe an approach to automatically process natural language definitions to (a) extract entity types and (b) align those types to the DOLCE+DUL ontology. We propose SPARQL patterns based on recurring dependency representations between entities and their candidate types. For the alignment subtask, we essentially rely on a pipeline of strategies that exploit the DBpedia knowledge base and we discuss some limitations of DBpedia in this context. © Springer International Publishing Switzerland 2016.

关键词： Knowledge based systems

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin de Leener Maxime descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van de Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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