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Synthesis Lectures on Artificial Intelligence and Machine Learning 2018年第3期12卷 1-169页

作者： Vorobeychik, Yevgeniy Kantarcioglu, Murat Vanderbilt University Department of Computer Science Computer Engineering and Biomedical Informatics United States UTD Data Security and Privacy Lab. University of Texas Dallas United States

ISBN: (纸本)9781681733951

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for

关键词： Game theory

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Agent morality via counterfactuals in logic programming

Agent morality via counterfactuals in logic programming

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2017 Workshop on Bridging the Gap between Human and Automated Reasoning - Is Logic and Automated Reasoning a Foundation for Human Reasoning?, Bridging 2017

作者： Pereira, Lúis Moniz Saptawijaya, Ari Lab. for Computer Science and Informatics NOVA-LINCS Universidade Nova de Lisboa Portugal Faculty of Computer Science Universitas Indonesia Indonesia

This paper presents a computational model, via Logic Programming (LP), of counterfactual reasoning with applications to agent morality. Counterfactuals are conjectures about what would have happened, had an alternative event occurred. In the first part, we show how counterfactual reasoning, inspired by Pearl's structural causal model of counterfactuals, is modeled using LP, by benefiting from LP abduction and updating. In the second part, counterfactuals are applied to agent morality, resorting to this LP-based approach. We demonstrate its potential for specifying and querying moral issues, by examining viewpoints on moral permissibility via classic moral principles and examples taken from the literature. Finally, we discuss some potential extensions of our LP approach to cover other aspects of counterfactual reasoning and show how these aspects are relevant in modeling agent morality.

关键词： Logic programming

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Improved strategies to counter the COVID-19 pandemic: Lockdowns vs. primary and community healthcare

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Toxicology Reports 2021年 8卷 1-9页

作者： Farsalinos, Konstantinos Poulas, Konstantinos Kouretas, Dimitrios Vantarakis, Apostolos Leotsinidis, Michalis Kouvelas, Dimitrios Docea, Anca Oana Kostoff, Ronald Gerotziafas, Grigorios T. Antoniou, Michael N. Polosa, Riccardo Barbouni, Anastastia Yiakoumaki, Vassiliki Giannouchos, Theodoros V. Bagos, Pantelis G. Lazopoulos, George Izotov, Boris N. Tutelyan, Victor A. Aschner, Michael Hartung, Thomas Wallace, Heather M. Carvalho, Félix Domingo, Jose L. Tsatsakis, Aristides Laboratory of Molecular Biology and Immunology Department of Pharmacy University of Patras Panepistimiopolis 26500 Greece School of Public Health University of West Attica L Alexandras 196A Athens 11521 Greece Department of Biochemistry and Biotechnology University of Thessaly Larisa 41500 Greece School of Medicine University of Patras Panepistimiopolis 26500 Greece Lab. of Public Health Medical School University of Patras University Campus 26504 Greece Laboratory of Clinical Pharmacology School of Medicine Aristotle University of Thessaloniki Thessaloniki 54124 Greece Department of Toxicology University of Medicine and Pharmacy of Craiova Craiova 200349 Romania School of Public Policy Georgia Institute of Technology Gainesville 20155 VA United States Sorbonne Université INSERM UMR_S 938 Group de recherche « Cancer-Hemostasis-Angiogenesis » Centre de recherche Saint-Antoine CRSA Centre de Thrombose Tenon-Saint Antoine University Hospitals Assistance publique Hôpitaux de Paris France Gene Expression and Therapy Group King's College London Department of Medical and Molecular Genetics School of Basic & Medical Biosciences 8th Floor Tower Wing Guy's Hospital Great Maze Pond London SE1 9RT United Kingdom Department of Clinical and Experimental Medicine University of Catania Via S. Sofia Catania 97 95131 Italy Centro Prevenzione Cura Tabagismo Center of Excellence for the Acceleration of Harm Reduction University of Catania Catania 95123 Italy Department of History Archaeology and Social Anthropology University of Thessaly Volos 38221 Greece Pharmacotherapy Outcomes Research Center College of Pharmacy University of Utah Salt Lake City UT United States Department of Computer Science and Biomedical Informatics University of Thessaly Lamia 35100 Greece Department of Cardiac Surgery University Hospital of Heraklion Crete Greece Department of Analytical Toxicology Pharmaceutical Chemistry and Pharmacognosy Sechenov University Moscow 1199

COVID-19 pandemic mitigation strategies are mainly based on social distancing measures and healthcare system reinforcement. However, many countries in Europe and elsewhere implemented strict, horizontal lockdowns because of extensive viral spread in the community which challenges the capacity of the healthcare systems. However, strict lockdowns have various untintended adverse social, economic and health effects, which have yet to be fully elucidated, and have not been considered in models examining the effects of various mitigation measures. Unlike commonly suggested, the dilemma is not about health vs wealth because the economic devastation of long-lasting lockdowns will definitely have adverse health effects in the population. Furthermore, they cannot provide a lasting solution in pandemic containment, potentially resulting in a vicious cycle of consecutive lockdowns with in-between breaks. Hospital preparedness has been the main strategy used by governments. However, a major characteristic of the COVID-19 pandemic is the rapid viral transmission in populations with no immunity. Thus, even the best hospital system could not cope with the demand. Primary, community and home care are the only viable strategies that could achieve the goal of pandemic mitigation. We present the case example of Greece, a country which followed a strategy focused on hospital preparedness but failed to reinforce primary and community care. This, along with strategic mistakes in epidemiological surveillance, resulted in Greece implementing a second strict, horizontal lockdown and having one of the highest COVID-19 death rates in Europe during the second wave. We provide recommendations for measures that will reinstate primary and community care at the forefront in managing the current public health crisis by protecting hospitals from unnecessary admissions, providing primary and secondary prevention services in relation to COVID-19 and maintaining population health through treatment of n

关键词： COVID-19 pandemic Horizontal lockdowns Hospital preparedness Primary care

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Introducing the HOBBIT platform into the Ontology Alignment Evaluation campaign 13

Introducing the HOBBIT platform into the Ontology Alignment ...

引用

13th International Workshop on Ontology Matching, OM 2018

作者： Jiménez-Ruiz, Ernesto Saveta, Tzanina Zamazal, Ondřej Hertling, Sven Röder, Michael Fundulaki, Irini Ngomo, Axel-Cyrille Ngonga Sherif, Mohamed Ahmed Annane, Amina Bellahsene, Zohra Ben Yahia, Sadok Diallo, Gayo Faria, Daniel Kachroudi, Marouen Khiat, Abderrahmane Lambrix, Patrick Li, Huanyu Mackeprang, Maximilian Mohammadi, Majid Rybinski, Maciej Balasubramani, Booma Sowkarthiga Trojahn, Cassia Alan Turing Institute London United Kingdom Department of Informatics University of Oslo Norway Institute of Computer Science - FORTH Greece University of Economics Prague Czech Republic Data and Web Science Group University of Mannheim Germany Paderborn University Data Science Group Pohlweg 51 PaderbornD-33098 Germany Ecole Nationale Superieure d'Informatique Alger Algeria LIRMM Universit de Montpellier CNRS Montpellier France Université de Tunis El Manar LIPAH-LR 1ES14 Tunis2092 Tunisia BPH Center INSERM U1219 Team ERIAS LaBRI UMR5800 University of Bordeaux France Instituto Gulbenkian de Ciência Portugal Human-Centered Computing Freie Universitat Berlin Berlin4195 Germany Linköping University Swedish e-Science Research Centre Linköping Sweden Delft University of Technology Netherlands Dpto. Lenguajes y Ciencias de la Computación University of Malaga Spain ADVIS Lab. Department of Computer Science University of Illinois Chicago United States Institut de Recherche en Informatique de Toulouse France

This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this pre-campaign and discuss the future steps for the OAEI. © 2018 CEUR-WS.

关键词： Ontology

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Feature selection parallel technique for remotely sensed imagery classification

arXiv

引用

arXiv 2017年

作者： LeKhac, N-A. Kechadi, M-T. Wu, B. Chen, C. School of Computer Science & Informatics University College Dublin Dublin Ireland Key Lab. of Spatial Data Mining and Information Sharing Fuzhou University Fuzhou China

Remote sensing research focusing on feature selection has long attracted the attention of the remote sensing community because feature selection is a prerequisite for image processing and various applications. Different feature selection methods have been proposed to improve the classification accuracy. They vary from basic search techniques to clonal selections, and various optimal criteria have been investigated. Recently, methods using dependence-based measures have attracted much attention due to their ability to deal with very high dimensional datasets. However, these methods are based on Cramer's V test, which has performance issues with large datasets. In this paper, we propose a parallel approach to improve their performance. We evaluate our approach on hyperspectral and high spatial resolution images and compare it to the proposed methods with a centralized version as preliminary results. The results are very promising. Copyright © 2017, The Authors. All rights reserved.

关键词： Feature extraction

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A text alignment corpus for Persian plagiarism detection

A text alignment corpus for Persian plagiarism detection

引用

2016 Forum for Information Retrieval Evaluation, FIRE 2016

作者： Mashhadirajab, Fatemeh Shamsfard, Mehrnoush Adelkhah, Razieh Shafiee, Fatemeh Saedi, Chakaveh NLP Research Lab. Faculty of Computer Science and Engineering Shahid Beheshti University Iran NLX Lab. University of Lisbon Department of Informatics Portugal

This paper describes how a Persian text alignment corpus was constructed to evaluate plagiarism detection systems. This corpus is in PAN format and contains 11,089 documents and more than 11,603 plagiarism cases. Efforts were made to simulate various types of plagiarism manually, semi-automatically, or automatically in this large-scale corpus.

关键词： Intellectual property

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving lab.

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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RESeED: A secure regular-expression search tool for storage clouds

RESeED: A secure regular-expression search tool for storage ...

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作者： Salehi, Mohsen Amini Caldwell, Thomas Fernandez, Alejandro Mickiewicz, Emmanuel Rozier, Eric W. D. Zonouz, Saman Redberg, David HPCC lab. School of Computing and Informatics University of Louisiana at Lafayette LA70503 United States Microsoft Corp. BellevueWA98004 United States Citrix Systems Inc. Fort LauderdaleFL33309 United States SIGFOX Inc. Toulouse France Department of Computer Science Iowa State University AmesIA50011 United States Electrical and Computer Engineering Department Rutgers University New BrunswickNJ08854 United States Fortinet Inc. New York CityNY10119 United States

Lack of trust has become one of the main concerns of users who tend to utilize one or multiple Cloud providers. Trustworthy Cloud-based computing and data storage require secure and efficient solutions which allow clients to remotely store and process their data in the Cloud. User-side encryption is an established method to secure the user data on the Cloud. However, using encryption, we lose processing capabilities, such as searching, over the Cloud data. In this paper, we present RESeED, a tool that provides user-transparent and Cloud-agnostic regular-expression search functionality over encrypted data across multiple Clouds. Upon a client's intent to upload a new document to the Cloud, RESeED analyzes the document's content and updates its data structures accordingly. Then, it encrypts and transfers the document to the Cloud. RESeED provides the regular-expression search functionality over encrypted data by translating the search queries on-the-fly to finite automata and analyzing concise and secure representations of the data before asking the Cloud to download the encrypted documents. RESeED's parallel architecture enables efficient search over large-scale (and potentially big data scale) data-sets. We evaluate the performance of RESeED experimentally and demonstrate its scalab.lity and correctness using real-world data-sets from *** and Internet Engineering Task Force (IETF). Our results show that RESeED produces accurate query responses with a reasonable (6%) storage overhead. The results also demonstrate that for many search queries, RESeED performs faster in compare with the grep utility that functions on unencrypted data. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Cloud storage

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DevsServer: Ambient intelligence and DEVS modeling based simulation server

DevsServer: Ambient intelligence and DEVS modeling based sim...

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Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems 2016, MSCIAAS 2016 and Space Simulation for Planetary Space Exploration, SPACE 2016, Part of the 2016 Spring Simulation Multi-Conference, SpringSim 2016

作者： Mokadem, Mostefa Atmani, Baghdad Boularas, Abdelmalek Computer Science Dept. Lab. of Informatics of Oran Univ. of Oran 1 Ahmed BenBella Oran Algeria Computer Information System Dept. Ahmed Bin Mohamed Milit. College Doha Qatar

ISBN: (纸本)9781510823198

To improve disease surveillance systems (DSS) with faster and accurate outbreak detection and epidemics propagation capabilities, the availab.lity of fine-tuned models is required along with the design of server based solution that simulate the effects of public health authorities' measures, and integrate Ambient Intelligence (AmI) capabilities to semantize epidemic models. Hosting Discrete Event System Specifications (DEVS) models, these AmI servers and their communication protocols are different, miscellaneous and require interoperability. The Triple Space Computing (TSC) paradigm addresses interoperability by sharing information represented in a semantic format through a common virtual space. In this this paper we present DevsServer, a fully distributed TSC simulation server solution (middleware) designed to meet the needs of parallel and distributed discrete event simulation. DevsServer defines an SOA (service oriented architecture) interface for the TSC operations. This interface convinces with DEVS formalism and focuses on simplicity, conviviality and modularity, so that a single or many simulations that support different models can still interact. © 2016 Society for Modeling & Simulation International (SCS).

关键词： Interoperability

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