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arXiv 2022年

作者： Canoy, Rocsildes Bucarey, Victor Molenbruch, Yves Mulamba, Maxime Mandi, Jayanta Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Vrije Universiteit Brussel Belgium Institute of Engineering Sciences Universidad de o'Higgins Rancagua Chile Dept. Computer Science KU Leuven Belgium

We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the historical data: one is through a probability estimation method that learns transition probabilities between stops (or zones). This is a fast and accurate method, recently studied in a VRP setting. Furthermore, we explore the use of machine learning to infer how to best balance multiple objectives such as distance, probability and penalties. Specifically, we cast the learning problem as a structured output prediction problem, where training is done by repeatedly calling the TSP solver. Another important aspect we consider is that for last-mile delivery, every address is a potential client and hence the data is very sparse. Hence, we propose a two-stage approach that first learns preferences at the zone level in order to compute a zone routing;after which a penalty-based TSP computes the stop routing. Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further. We hence showcase a successful combination of both probability estimation and machine learning, all the while using standard TSP solvers, both during learning and to compute the final solution;this means the methodology is applicable to other, real-life, TSP variants, or proprietary solvers. © 2022, CC BY.

关键词： Decision making

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A Review of the Weaponization of IoT: Security Threats and Countermeasures

A Review of the Weaponization of IoT: Security Threats and C...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Marek Kulbacki Zenon Chaczko Sophie Barton Peter Wajs-Chaczko Jan Nikodem Jerzy W. Rozenblit Ryszard Klempous Atsushi Ito Michał Kulbacki Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wrocław Poland School of Elect. & Data Engineering University of Technology Sydney Australia School of Elect. and Data Engineering University of Technology Sydney Australia Macquarie University NSW Australia Wrocław University of Science and Technology Wrocław Poland Dept. of Elect. and Computer Engineeering The University of Arizona Tucson USA Chuo University Hachioji Tokyo Japan Mathematical Institute University of Wrocław Wroclaw Poland

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

This research paper discusses the current state of Internet of Things (IoT) security in relation to smart bot technology and suggests actions to mitigate IoT weaponization threats. One of the most dangerous cyberattack activities is infecting IoT devices with malware to gain control of their operations. This allows attackers to combine the computational power of infected devices to form a botnet, which can then be used to carry out Distributed Denial of Service (DDoS) attacks against specified IP addresses. The aim is to overwhelm the target IP address servers with requests and prevent them from servicing legitimate requests from other users, thereby degrading the availability of the target's services and resources. This paper aims to analyze IoT DDoS attacks or weaponization of IoT to determine the most effective methods of eliminating such threats and preventing serious damage to IoT infrastructure and business-related data. We will describe what IoT weaponization malware is, outline its features, and discuss the most common IoT malware currently present on the Internet. We will then analyze the Mirai source code to fully understand its qualities, including attack types and operations, that allowed it to remain effective.

关键词： Weapons Source coding Botnet Denial-of-service attack Malware Internet of Things Security

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Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning

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Measurement: Sensors 2022年 24卷

作者： Naidu, K Bapayya Ravi Prasad, B. Hassen, Samar Mansour Kaur, Chamandeep Al Ansari, Mohammed Saleh Vinod, R. Nivetha, M. Kiran Bala, B. Surampalem Jawaharlal Nehru Technological University Kakinada India CSE Dept Marri Laxman Reddy Institute of Technology and Management Hyderabad500043 India Computer Science JazanUnivertsity Jazan Saudi Arabia Jazan University Saudi Arabia Jazan Saudi Arabia College of Engineering Department of Chemical Engineering University of Bahrain Bahrain School of Contemporary Knowledge Systems Chinmaya Vishwavidyapeeth Cochin Kerala India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy India Head of the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Log files are a significant piece of records that once properly analyzed, can yield helpful information. The usage of cloud services for log analysis is almost inevitable due to the greater rate of production and quantity of hardware and sensors that produce logs. The idea is to adopt machine learning mechanisms to "understand" what such a test suite's "anticipated" behavior is. This implies the system needs to be able to learn the difference among a Hadoop log file that contains a threat that does not really, based on past patterns. Many programs that really can effectively monitor the Hadoop cluster have been developed. The bulk of these tools collect relevant information from every cluster node and send it to be processed. The majority of these diagnostic tools were post-execution techniques commonly. This paper gives an experimental evaluation of the various log analysts used in Hadoop for error monitoring and detection. MapReduce is configured using these files. The algorithms were tested on a parser set of labelled log files, and their efficiency was assessed by looking for abnormal events in the Hadoop log files and executing an experimentation using the methods. The algorithms used were Term Frequency Inverse Document Frequency, Random Forest, and Local Outlier Factor. Later, the clustering with K-Means and Principal component analysis is utilized to extract certain relevant details from the data, monitor groups of bits of data to find aberrant events. The Term Frequency Inverse Document Frequency strategy surpassed the other two techniques in predicting threat occurrences in data, according to the findings. The findings would help developers in locating abnormal occurrences without having to go through the Hadoop log file row by row individually. The framework generates events that behave in a different way than the rest of such events in the log and leads the mechanism to fail to work. © 2022 The Authors

关键词： Principal component analysis

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Enhancing engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks 1

Enhancing Engineering Pedagogy through Deep Learning: Applic...

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1st IEEE International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024

作者： Umamaheswari, U. Mamidi, Pushpa Latha Nimma, Divya Venkateswarlu, D. Al Ansari, Mohammed Saleh Valavan, Ponni Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha School of Law Chennai India S.R.K.R Engineering College Department of Engineering Mathematics & Humanities Andhra Pradesh Bhimavaram India University of Southern Mississippi Data Analyst in UMMC Computational Science United States Permanently Affiliated JNTUK Dept. of MBA AP Mylavaram India University of Bahrain College of Engineering Department of Chemical Engineering Bahrain K. Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798331540142

Conventional engineering pedagogy frequently fails to deliver individualized learning experiences and places a strong emphasis on one-size-fits-all methods of instruction. Meeting the requirements of every individual student and improving learning outcomes become more challenging as a result. Modern instructional tools, however state-of-the-art, typically cannot offer personalized, data-driven insights. Since many systems are clear, it might be challenging for educators and learners to comprehend the recommendations made. The concept-based transformer network presented in this work is intended to improve the personalized learning experience in engineering education. Using focus, this new neural system focuses on the most relevant elements of a student's education, providing personalized recommendations and assessments. Transparency is achieved through the inclusion of semantic AI, so that teachers and students can understand and validate the recommendations made by the system The proposed approach begins with the collection of large amounts of data, which including demographic information, contact records, and measures of student performance. Pre-processing assures data accuracy and quality. Personalized learning strategies and recommendations are generated through feedforward neural networks and integrated into the instructional platform. Performance measurement and user feedback for incremental change and continuous monitoring. Key metrics such as student engagement, outcomes for learning, and satisfaction levels are used to assess the effectiveness of the model. A comparison of the proposed model with current methods demonstrates its ability to provide scalable, transparent and customizable learning solutions. © 2024 IEEE.

关键词： engineering education

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An improved machine learning model for data analysis in Big-data environment

An improved machine learning model for data analysis in Big-...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta T. Kiruthiga 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 Payments &Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy Tamil Nadu INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract offers an outline of the ability of artificial Intelligence (AI) and device studying (ML) answers for managing leverage threats in banking. Leverage is a key element in banking danger management, as it could expose the organization to disproportionate liabilities if left unchecked. AI and ML may be used to automate the tracking system of leverage hazards, allowing extra correct and welltimed identification and management of exposures. The abstract examines how AI/ML-driven solutions can leverage to be had facts to detect subtle modifications in leverage through the years, combining techniques from supervised and unsupervised ML to generate actionable insights. It then opinions viable implementation fashions and capacity challenges and gives first-rate practices for a hit implementation. The summary concludes that AI/ML-driven hazard management structures offer a promising new approach to leverage threat management inside the banking sector. In addition, research is warranted on this location.

关键词： Analytical models data analysis Banking Organizations Machine learning Hazards data models Pattern recognition Artificial intelligence

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Learning superconductivity from ordered and disordered material structures 24

Learning superconductivity from ordered and disordered mater...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pin Chen Luoxuan Peng Rui Jiao Qing Mo Zhen Wang Wenbing Huang Yang Liu Yutong Lu National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University and Institute for AIR Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9798331314385

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates.

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Public Kaggle Competition "IceCube – Neutrinos in Deep Ice" 38

Public Kaggle Competition "IceCube – Neutrinos in Deep Ice"

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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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Enhancing engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks

Enhancing Engineering Pedagogy through Deep Learning: Applic...

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Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International Conference on

作者： Umamaheswari. U Pushpa Latha Mamidi Divya Nimma D. Venkateswarlu Mohammed Saleh Al Ansari Ponni Valavan M Saveetha School of law Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Department of Engineering Mathematics & Humanities S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Computational Science University of Southern Mississippi Data Analyst in UMMC USA Dept. of MBA Lakireddy Balireddy College of Engineering (Autonomous) Permanently Affiliated to JNTUK Mylavaram AP India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Conventional engineering pedagogy frequently fails to deliver individualized learning experiences and places a strong emphasis on one-size-fits-all methods of instruction. Meeting the requirements of every individual student and improving learning outcomes become more challenging as a result. Modern instructional tools, however state-of-the-art, typically cannot offer personalized, data-driven insights. Since many systems are clear, it might be challenging for educators and learners to comprehend the recommendations made. The concept-based transformer network presented in this work is intended to improve the personalized learning experience in engineering education. Using focus, this new neural system focuses on the most relevant elements of a student’s education, providing personalized recommendations and assessments. Transparency is achieved through the inclusion of semantic AI, so that teachers and students can understand and validate the recommendations made by the system The proposed approach begins with the collection of large amounts of data, which including demographic information, contact records, and measures of student performance. Pre-processing assures data accuracy and quality. Personalized learning strategies and recommendations are generated through feedforward neural networks and integrated into the instructional platform. Performance measurement and user feedback for incremental change and continuous monitoring. Key metrics such as student engagement, outcomes for learning, and satisfaction levels are used to assess the effectiveness of the model. A comparison of the proposed model with current methods demonstrates its ability to provide scalable, transparent and customizable learning solutions.

关键词： Deep learning Performance evaluation Industrial electronics Accuracy Explainable AI Semantics Transformers Feedforward neural networks engineering education Monitoring

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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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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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