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High accuracy base calls in nanopore sequencing 17

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High accuracy base calls in nanopore sequencing

6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017

作者： Faucon, Philippe Trevino, Robert Balachandran, Parithi Standage-Beier, Kylie Wang, Xiao School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85282 United States School of Biological and Health Systems Engineering Arizona State University TempeAZ85287-9709 United States

ISBN: (纸本)9781450352222

Nanopore sequencing has introduced the ability to sequence long stretches of DNA, enabling the resolution of repeating segments, or paired SNPs across long stretches of DNA. Unfortunately, significant error rates >15%, introduced through systematic and random noise inhibit downstream analysis. We propose a novel method, using unsupervised learning, to correct biologically amplified reads before downstream analysis proceeds. We also demonstrate that our method has performance comparable to existing techniques without limiting the detection of repeats, or the length of the input sequence. © 2017 Association for computing Machinery.

关键词： DNA

Packaging, containerization, and virtualization of computational omics methods: Advances, challenges, and opportunities

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arXiv 2022年

作者： Alser, Mohammed Waymost, Sharon Ayyala, Ram Lawlor, Brendan Abdill, Richard J. Rajkumar, Neha LaPierre, Nathan Brito, Jaqueline Ribeiro-Dos-Santos, André M. Firtina, Can Almadhoun, Nour Sarwal, Varuni Eskin, Eleazar Hu, Qiyang Strong, Derek Kim, Byoung-Do Abedalthagafi, Malak S. Mutlu, Onur Mangul, Serghei Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Department of Computer Science UCLA Los AngelesCA90095 United States Department of Translational Biomedical Informatics University of Southern California Los AngelesCA90089 United States Department of Clinical Pharmacy University of Southern California Los AngelesCA90089 United States Department of Computer Science Munster Technological University CorkT12 P928 Ireland Department of Genetics Cell Biology and Development University of Minnesota MinneapolisMN55417 United States Department of Bioengineering UCLA Los AngelesCA90095 United States Institute for Systems Genetics NYU Grossman School of Medicine New YorkNY10016 United States Department of Computational Medicine University of California Los AngelesCA90095 United States Department of Human Genetics University of California Los AngelesCA90095 United States Office of Advanced Research Computing UCLA Los AngelesCA90095 United States Center for Advanced Research Computing University of Southern California Los AngelesCA90089 United States Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia King Salman Center for Disability Research Riyadh12512 Saudi Arabia Riyadh Saudi Arabia

Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging, virtualization, and containerization are different approaches to satisfy this need by wrapping omics tools in additional software that makes the omics tools easier to install and use. Here, we systematically review practices across prominent packaging, virtualization, and containerization platforms. We outline the challenges, advantages, and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers, and system administrators. We also propose principles to make packaging, virtualization, and containerization of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research. © 2022, CC BY.

关键词： Containers

Beware of "Explanations" of AI

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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Illumination estimation challenge: Experience of past two years

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arXiv 2020年

作者： Ershov, Egor I. Savchik, Alex Semenkov, Ilya Banić, Nikola Koščević, Karlo Subašić, Marko Belokopytov, Alexander Li, Zhihao Terekhin, Arseniy Senshina, Daria Nikonorov, Artem Qian, Yanlin Buzzelli, Marco Riva, Riccardo Bianco, Simone Schettini, Raimondo Lončarić, Sven Nikolaev, Dmitry Institute for Information Transmission Problems RAS Bol’shoi Karetnyi per. 19 Moscow Russia National Research University Higher School of Economics Myasnitskaya Ulitsa 20 Moscow101000 Russia Image Processing Systems Institute of RAS Molodogvardeyskaya st. 151 Samara Russia Gideon Brothers Radnička 177 Zagreb10000 Croatia Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia University of Milano – Bicocca Department of Informatics Systems and Communication Viale Sarca Milan336 Italy Nanjing University Xianlin Road 163 Qixia District NanjingJiangsu Province China Huawei MultiMedia Team

Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, the 2nd Illumination estimation challenge (IEC#2) was conducted. The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The challenge had several tracks: general, indoor, and two-illuminant with each of them focusing on different parameters of the scenes. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest-like markup for the images from the Cube+ dataset that was used in IEC#1. This paper focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the 1st and 2nd challenge that can be useful for similar future developments. © 2020, CC BY.

关键词： Large dataset

Experimental Evaluation of a Novel Personal Assistant in Greek Language for Ambient Assisted Living Environments employing home robots

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Experimental Evaluation of a Novel Personal Assistant in Gre...

2018 South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA_CECNSM 2018

作者： Spournias, Alexandros Christopoulos, Konstantinos Antonopoulos, Konstantinos Panagiotou, Christos Antonopoulos, Christos Orfanoudakis, Theofanis Voros, Nikolaos Computer and Informatics Engineering Department Embedded System Design and Application Laboratory TEI of Western Greece Antirio Greece Embedded System Design and Application Laboratory School of Science and Technology Digital Systems and Media Computing Laboratory TEI of Western Greece Hellenic Open University Patras Greece Embedded System Design and Application Laboratory TEI of Western Greece Antirio Greece School of Science and Technology Digital Systems and Media Computing Laboratory Hellenic Open University Patras Greece

ISBN: (纸本)9786188331419

A personal assistance, is a software component offering advanced Human Machine Interface (HMI) features by initiating and making a "conversations" between humans and machines. Its main objective is to identify the sentences and the context from human's question and respectively give an adequate response to the question posed. The proper response is given after analyzing the words of the question with those registered in its database. This type of application is called a personal assistant - chatbot. In this work, such an application is presented where it can be installed in any smart home as well as assisted living environment. Through the application, all automations, as well as the home robot also, are controlled by human voice in Greek language. Also, a number of auxiliary applications can be performed in the direction of better human service. © 2018 Tei of Western Macedonia.

关键词： Assisted living

Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

On Hardening Problems in Critical Infrastructure systems

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arXiv 2017年

作者： Banerjee, Joydeep Basu, Kaustav Sen, Arunabha School of Computing Informatics Decision Systems Engineering Arizona State University

The power and communication networks are highly interdependent and form a part of the critical infrastructure of a country. Similarly, dependencies exist within the networks itself. It is essential to have a model which captures these dependencies precisely. Previous research has proposed certain models but these models have certain limitations. The limitations of the aforementioned models have been overcome by the Implicative Interdependency Model, which uses Boolean Logic to denote the dependencies. This paper formulates the Entity Hardening problem and the Targeted Entity Hardening problem using the Implicative Interdependency Model. The Entity Hardening problem describes a situation where an operator, with a limited budget, must decide which entities to harden, which in turn would minimize the damage, provided a set of entities fail initially. The Targeted Entity Hardening problem is a restricted version of the Entity Hardening problem. This problem presents a scenario where, the protection of certain entities is of higher priority. If these entities were to be nonfunctional, the economic and societal damage would be higher when compared to other entities being nonfunctional. It has been shown that both problems are NP-Complete. An Integer Linear Program (ILP) has been devised to find the optimal solution. A heuristic has been described whose accuracy is found by comparing its performance with the optimal solution using real-world and simulated data. Copyright © 2017, The Authors. All rights reserved.

关键词： Integer programming

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Preface

CEUR Workshop Proceedings 2021年 2899卷

作者： Kovalev, Igor Semenkin, Eugene Gulyaev, Yuri Barakaev, Nusratilla Shaidurov, Vladimir Škraba, Andrej Tsarev, Roman Legalov, Alexander Saramud, Mikhail Grosspietsch, Karl-Erwin Stupina, Alena Department of Computer Science Siberian Federal University Krasnoyarsk Russia Department of system analysis and operations research of Reshetnev University Krasnoyarsk Russia International and Russian Union of Scientific and Engineering Associations Moscow Russia Rector of Bukhara Engineering Technological Institute Bukhara Uzbekistan Institute of Computational Modeling SB RAS Krasnoyarsk Russia University of Maribor UM Faculty of Organizational Sciences - Cybernetics and Decision Support Systems Laboratory Slovenia Department of Informatics Siberian Federal University Krasnoyarsk Russia Faculty of Computer Science School of Software Engineering HSE University Moscow Russia Department of Informatics Siberian Federal University Krasnoyarsk Russia European Organization for Information and Microelectronics Germany The Siberian Federal University Krasnoyarsk Russia

Student motivation towards learning to program

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Student motivation towards learning to program

Frontiers in Education (FIE) Conference

作者： Anabela Gomes Wei Ke Chan-Tong Lam Maria José Marcelino António Mendes Informatics Engineering Department Coimbra Polytechnic - ISEC Centre for Informatics and Systems University of Coimbra Coimbra Portugal Computing Programme School of Public Administration Macao Polytechnic Institute Macao S. A. R China Department of Informatics Engineering Centre for Informatics and Systems University of Coimbra Coimbra Portugal

ISBN: (纸本)9781538611753;9781538611746

This Research to Practice Full Paper presents a study on student's motivation towards learning to program. Motivation is a key factor in learning. Hence, stimulating student motivation strategies should be present in any pedagogical approach. This is particularly true in courses where a very active student attitude is fundamental. Introductory programming courses in higher education are a good example, which are known to be difficult for many students. To be successful students need to be motivated, as effort and commitment are necessary to overcome the difficulties many of them experience. In our study we analyzed several motivational aspects separately and then we correlated that information with the marks students obtained in introductory programming courses. We used two questionnaires. The Course Interest Survey (CIS) and the Instructional Materials Motivation Survey (IMMS). We could find some interesting correlations that confirm the importance of different motivational aspects to learning. We found other issues that demand more investigation, in order to create the best context to promote student motivation and learning.

关键词： Instruments Programming profession Education Task analysis Collaborative work informatics