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38th International Cosmic Ray Conference, ICRC 2023

作者： Eller, Philipp Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Classen, L. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, D.F. Dave, P. De Clercq, C. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Fox, D. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.D. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Technical University of Munich Munich Data Science Institute Walther-von-Dyck-Strasse 10 Garching85748 Germany III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen Copenh

The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube – Neutrinos in Deep ice" was a public machine learning challenge designed to encourage the development of innovative solutions to improve the accuracy and efficiency of neutrino event reconstruction. Participants worked with a dataset of simulated neutrino events and were tasked with creating a suitable model to predict the direction vector of incoming neutrinos. From January to April 2023, hundreds of teams competed for a total of $50k prize money, which was awarded to the best performing few out of the many thousand submissions. In this contribution I will present some insights into the organization of this large outreach project, and summarize some of the main findings, results and takeaways. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Neutrons

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A Few-Shot Learning Approach for Sound Source Distance Estimation Using Relation Networks

arXiv

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arXiv 2021年

作者： Sobhdel, Amirreza Razavi-Far, Roozbeh Palade, Vasile Dept. of Electrical and Computer Engineering Urmia University Iran Faculty of Computer Science University of New Brunswick Canada Centre for Computational Science Coventry University United Kingdom

In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimation (SSDE). In previous research on deep supervised SSDE, low accuracies have often resulted from the mismatch between the training data (from known environments) and the test data (from unknown environments). By performing comparative experiments on a sufficient amount of data, we show that the few-shot relation network outperforms other competitors including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and MultiLayer Perceptron (MLP). Hence it is possible to calibrate a microphone-equipped system, with a few labeled samples of audio recorded in a particular unknown environment to adjust and generalize our classifier to the possible input data and gain higher accuracies. Copyright © 2021, The Authors. All rights reserved.

关键词： Zero-shot learning

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Verification of Personal Location data Consistency for COVID-19 Dissemination Prevention Cancellation

Research Square

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Research Square 2021年

作者： Kim, Seong-Kyu Jun, Dongsan Huh, Jun-Ho Kim, Byung-Gyu Dept. of Information Security Joongbu University Korea Republic of Dept. of Information and Communication Engineering Kyungnam University Korea Republic of Dept. of Data Science National Korea Maritime and Ocean University Korea Republic of Dept. of IT Engineering Sookmyung Women’s University Korea Republic of

Recently, several disasters have occurred. In particular, COVID-19 has had the strongest impact of many disasters in recent years. However, the world is also overcoming COVID-19 in line with the era of the Fourth Industrial Revolution. In preparation for these COVID-19 events, this paper aims to use the Moving Query Object to increase CPU performance for these tracking systems by tracking smartphones worldwide, using LBS systems, such as Wifi, 5G, GPS, etc. So we have created an artificial intelligence-based COVID-19 Location-Based Research Model and have presented the Artificial Neural Network Positioning Research Model Design. Based on the model presented, the Architectural Neural Network Positioning COVID-19 Location Forecast Architecture was also designed. We also want to see real-time information from people infected with COVID-19 for smart cities. Finally, these tracing systems were simulated and tested 10 times and 1,000 times for comparative analysis. Therefore, active tracking and performance are studied when COVID-19 viruses and other disasters occur in smart cities in the future. © 2021, CC BY.

关键词： COVID-19

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data Analytics in Structural engineering

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Journal of Structural engineering (United States) 2021年第8期147卷

作者： El-Dakhakhni, Wael INTERFACE Institute and Professor School of Computational Science and Engineering Dept. of Civil Engineering McMaster Univ HamiltonONL8S4L7 Canada

The special collection on data Analytics in Structural engineering is available in the ASCE Library (https://***/jsendh/data_analytics_structural).The term analytics refers to the process of developing actionable decisions or recommendations for actions based on insights mainly generated from historical data. Supported by the fast developments in sensor technologies and data collection, storage, processing, and visualization capabilities, the field of data analytics has been experiencing

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Gamification and Serious Games for Cybersecurity Awareness and First Responders Training: An overview

TechRxiv

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TechRxiv 2023年

作者： Batzos, Zisis Saoulidis, Theocharis Margounakis, Dimitrios Fountoukidis, Eleftherios Grigoriou, Elisavet Moukoulis, Achilleas Sarigiannidis, Antonios Liatifis, Athanasios Karypidis, Paris-Alexandros Bibi, Stamatia Filippidis, Adam Kazanidis, Ioannis Nifakos, Sokratis Kasig, Timo Heydari, Mohammad Mouratidis, Haralambos Sidroco Holdings Ltd. Nicosia Cyprus Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Computer Science International Hellenic University Kavala Campus Greece Massive Dynamic Sweden Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Institute for Analytics and Data Science University of Essex United Kingdom

Gamification has been characterized as the usage of video game elements in non-game environments. Gamification in cybersecurity awareness training can lead to more engaging interactions, greater pleasure, and increased security. Serious games are becoming increasingly popular since they allow learning in a natural setting. Although many of the principles covered in cybersecurity awareness training are universal, many forms of training fail because they rely on rote learning and do not require users to think about security concepts. The primary goal of this research is to conduct a review of the literature on cybersecurity awareness training methodologies and frameworks, as well as serious games that may be used to raise awareness among people, organizations, and first responders. © 2023, CC BY.

关键词： Human computer interaction

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EEG-Based Discrimination Between Patients with MCI and Alzheimer

EEG-Based Discrimination Between Patients with MCI and Alzhe...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Borhan Kazimipour Reza Boostani Afshin Borhani-Haghighi Sattam Almatarneh Mohammad Aljaidi CSE Dept. Monash University Melbourne Australia CSE and IT Dept. Electrical and Computer Engineering Faculty Shiraz University Shiraz Iran Neurology Department Namazi hospital Shiraz University of Medical Sciences Shiraz Iran Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Department of Computer Science Zarqa University Zarqa Jordan

There is a high similarity between the signs and symptoms of patients with Alzheimer and those with mild cognitive impairment (MCI). Although several attempts have been made to differentiate these two groups of patients by decoding the fluctuation of their electroencephalogram (EEG), the achieved results are not yet promising. To increase the differentiation rate, in this study, 14 patients with Alzheimer from 13 patients with MCI have been voluntarily enrolled while their EEG signals are recorded in presence of visual stimuli. To suppress the disrupting artifacts and noises (e.g., eye-blink and movement artefact) from the recorded EEGs, independent component analysis is applied. Next, the visual evoke potential (VEP) patterns are extracted by synchronous averaging and then multi-linear principal component analysis (MPCA) is applied to elicit discriminative features from VEPs of the patients. After feature extraction by MPCA, the reduced feature vectors of both groups are applied to a nearest neighbor classifier, leading to 77.35% differentiation accuracy.

关键词： Visualization Patient monitoring Fluctuations Independent component analysis Feature extraction Electroencephalography Real-time systems

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An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

SSRN

引用

SSRN 2022年

作者： Sun, Qi Wu, Jian Chiclana, Francisco Wang, Sha Herrera-Viedma, Enrique School of Economics and Management Shanghai Maritime University Shanghai201306 China Center for Artificial Intelligence and Decision Sciences Shanghai Maritime University Shanghai201306 China Institute of Artificial Intelligence Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

This paper proposes a theoretical framework to prevent weight manipulation in large-scale group decision making (LSGDM). Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the rights index (RI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’manipulation behavior. The minimum adjustment rule aims for ‘effificiency’ while the maximum entropy rule aims for ‘justice’. Consequently, by keeping a balance between ‘effificiency’ and ‘justice’ in the mechanism of assigning weights, the rationality of weight allocation to reach consensus effificiently can be ensured. Detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Anomaly detection on lot network intrusion using machine learning

Anomaly detection on lot network intrusion using machine lea...

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2020 International Conference on Artificial Intelligence, Big data, Computing and data Communication Systems, icABCD 2020

作者： Liu, Zhipeng Thapa, Niraj Shaver, Addison Roy, Kaushik Yuan, Xiaohong Khorsandroo, Sajad Department of Computer Science North Carolina Agricultural and Technology State University Greensboro United States Dept. of Computational Science and Engineering North Carolina Agricultural and Technology State University Greensboro United States

ISBN: (纸本)9781728167701

Enhancing the security of loT networks is trending as one of the most crucial issues the information technology community faces. With large scales of loT devices being developed and deployed, the ability for these devices to communicate securely without compromising performance is chaUenging. The challenges exist because most of loT devices are limited by power hence constrained to less computational ability. Subsequently, encryption and authentication are difficult to be applied to fence of f malicious cyber-attacks. Intrusion Detection System (IDS) logically becomes the forefront security solution. Anomaly-based network intrusion detection plays a major role in safeguarding networks against different malicious activities. In this paper, we apply different machine learning algorithms to efficiently detect anomalies on the loT Network Intrusion dataset. The results show promise as we were able to achieve 99%-100% accuracy while having high efficiency. ©2020 IEEE.

关键词： Machine learning

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Uncovering the Benefits of Machine Learning for Automating Financial Regulatory Tasks

Uncovering the Benefits of Machine Learning for Automating F...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sagar Varma Samanthapudi Piyush Rohella Sheetal Temara Ketan Gupta S. Dhanasekaran Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Machine Learning (ML) gives the ability to automate economic regulatory responsibilities in terms of price savings and more green process control. Current advances in ML have enabled the improvement of models that may interpret complicated sets of facts and automate the technique of extracting the preferred facts from these records. The development of such models gives a price-effective and efficient opportunity to greater luxurious and time-consuming guide labor. Moreover, these fashions are capable of perceiving and correctly categorizing data to ensure information accuracy and compliance with regulatory policies. It allows the company to adhere to policies without human oversight, lowering each value and time constraints. It will be further prolonged to other factors of the enterprise by making use of unsupervised fashions and fashions advanced on switch learning, together with deep learning. Those models allow the automation of responsibilities inclusive of assessing the threat associated with investments or transactions, facilitating the development of quicker and more knowledgeable techniques. Ordinary, the usage of ML fashions for financial law automatization processes is precious for decreasing price and time constraints and making sure of compliance. Through leveraging the ability of ML, financial institutions can take advantage of extra internal manner efficiency and statistics accuracy with fewer risks.

关键词： Accuracy Regulators Biological system modeling Machine learning Switches Time factors Stakeholders

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Investigating the Role of AI in Personalized Consumer Banking Experiences

Investigating the Role of AI in Personalized Consumer Bankin...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Piyush Rohella Sheetal Temara Sagar Varma Samanthapudi Ketan Gupta K. Suganyadevi Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

This research examines the function of synthetic Intelligence in developing customized purchaser banking reviews. Artificial Intelligent -based total gear is increasingly being followed to offer consumers customized banking reports. Studies into the use of AI in this context will tell the layout of future banking products and services. The primary intention of this research is to understand the modern technological and personal adoption developments related to Artificial Intelligent and customized banking reviews. Particularly, this study will identify the modern use cases of Artificial Intelligent in personalized banking, look into clients' attitudes and motivations towards Artificial Intelligent -enabled banking experiences, and discover the ability possibilities and challenges related to utilizing Artificial Intelligent in growing individualized banking studies for purchasers. Additionally, the role of ethical issues in implementing and the use of Artificial Intelligent in personalized consumer banking experiences may be evaluated. This study is crucial for presenting a better understanding of the function of Artificial Intelligent in developing personalized and moral consumer banking experiences.

关键词： Ethics Reviews Shape Power control Layout Banking Fraud

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