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Artificial intelligence and computer vision education: Codifying student learning gains and attitudes

computers and Education: Artificial Intelligence 2023年 5卷

作者： Abichandani, Pramod Iaboni, Craig Lobo, Deepan Kelly, Thomas School of Applied Engineering and Technology (SAET) New Jersey Institute of Technology 323 Martin Luther King Jr. Blvd Newark NJ United States Department of Computer Science New Jersey Institute of Technology Newark NJ United States Electrical and Computer Engineering Department New Jersey Institute of Technology Newark NJ United States

Artificial Intelligence (AI) and computer Vision (CV) have rapidly permeated various industries, increasing demand for professionals well-versed in these disciplines. In response to this need, this study evaluates the effectiveness of a novel undergraduate course that aims to equip students with foundational AI and CV knowledge and skills. Unlike existing software-centric courses, this class capitalizes on drones as a practical application area for AI and CV, fostering an active learning environment. The course was conducted over five semesters at a U.S. research institution, with 153 participating students. Data were collected using diverse methods, including formative and summative assessments, surveys of learning gains, focus group interviews, and analysis of capstone project process videos. The course design included Intended Learning Outcomes (ILOs) tailored to meet the requirements of contemporary AI/CV professionals, weekly AI-based curricular modules, unrestricted access to computational resources, and diverse assessment strategies. This study also delves into students’ perceptions of this unique learning approach and challenges, with students from varied academic majors. Initial findings suggest that employing active learning techniques significantly enhances student engagement and understanding of AI/CV concepts, leading to substantial skill gains and positive attitudes toward the subject. © 2023 The Authors

关键词： Active Learning Artificial intelligence education computer Vision computer Vision Education Drones in education

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Strain-induced formation of self-assembled InGaN/GaN superlattices in nominal InGaN films grown by plasma-assisted molecular beam epitaxy

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Physical Review Materials 2021年第12期5卷 124606-124606页

作者： Kamruzzaman Khan Kai Sun Christian Wurm Kanak Datta Parag B. Deotare Elaheh Ahmadi Department of Materials Science and Engineering University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA

In this work, we report the spontaneous formation of superlattice structures in nominal InGaN films grown by plasma-assisted molecular beam epitaxy. A 700-nm-thick self-assembled In0.2Ga0.8N/GaN superlattice with excellent structural quality was achieved. Strain was studied as a possible driving force for the formation of self-assembled superlattice (SASL) structure by growth of InGaN on ZnO substrate using similar growth conditions. The SASL structures were optically characterized using photoluminescence spectroscopy. Structural characterization was conducted via transmission electron microscopy and atom probe tomography. High-resolution x-ray diffraction (XRD) and XRD reciprocal space map were utilized to determine the average composition and the degree of relaxation of InGaN films. We propose that the vertical phase separation observed in the SASL structure is caused by high-temperature growth and intensified by strain. This work provides a method for engineering strain and growth of thick InGaN films for a variety of applications including solar cells and photodetectors.

关键词： Nitrides Superlattices Molecular beam epitaxy Photoluminescence Time-resolved photoluminescence X-ray diffraction

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AI4FIDS: Multimodal Federated Intrusion Detection

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Radoglou-Grammatikis, Panagiotis Bouzinis, Pavlos S. Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th Fouliras, Panagiotis Seritan, George Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Campus ZEP Kozani Kozani50100 Greece K3Y Ltd William Gladstone 31 Sofia1000 Bulgaria MetaMind Innovation P.C. Kila Kozani Kozani50100 Greece Democritus University of Thrace Department of Computer Science Kavala Campus Kavala65404 Greece Kingston University London Department of Networks and Digital Media Penrhyn Road Kingston upon Thames Surrey KT1 2EE London United Kingdom Harokopio University of Athens Department of Informatics and Telematics Omirou 9 Tavros AthensGR17778 Greece University of Macedonia Department of Applied Informatics 156 Egnatia Street ThessalonikiGR54-636 Greece Politehnica University of Bucharest Electrical Engineering Faculty Bucharest Romania

The rapid progression of smart technologies creates several advantages like enhanced connectivity, personalisation solutions and environmental sustainability. However, this revolution creates also several cyber risks. In particular, the attackers have the ability to synthesise and automate advanced attack scenarios over time, while it is evident that Artificial Intelligence (AI) allows the composition of intelligent attack vectors that can adapt in real-time to conventional countermeasures. Despite the fact that AI can also benefit defensive mechanisms, there are still functional and privacy issues that need to be resolved. First, AI requires appropriate datasets that can differ from environment to environment. In addition, these datasets usually are not available due to privacy issues. Finally, adversarial attacks have the ability to target and affect the AI-based decision-making process. Therefore, in light of the previous remarks, we provide AI4FIDS, a multimodal Intrusion Detection System (IDS) for critical infrastructures. AI4FIDS leverages Federated learning (FL) and combines multiple data sources, thus allowing cooperative intelligence across multiple domains in a private manner and minimising the impact of potential adversarial attacks. In this paper, we present in detail the architectural design and specifications of AI4FIDS, while the evaluation results demonstrate their detection performance, taking into account several datasets and aggregation strategies. Finally, based on the evaluation results, we discuss how the overall reliability and detection capabilities (in terms of detecting multi-step attack scenarios) of AI4FIDS can be improved by combining the detection outcomes of the components behind AI4FIDS. © 2013 IEEE.

关键词： Federated learning

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

SSRN

引用

SSRN 2023年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Czech Republic Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India Departament of Industrial Engineering University of Chile Santiago Chile Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India

Selecting outstanding tech stocks for investment is challenging. Specifically, studying the investment for academic purposes is not mature enough due to the disarray of the publications and the `over' informative experience from profit-making websites. Often some authors entitle stock price prediction subject as a stock selection problem;both prediction and selection are just sub-section of portfolio management. Moreover, stock websites provide tons of potential criteria showing their perspective evaluation to simulate and monitor the stock market professionally, which causes an extra problem for academic studies screening out the less informative criteria. The paper generalizes a novel framework with a user-interactive interface for stock selection problems based on multi-source data fusion and decision-level fusion to enhance reliability that is limited by the narrow criteria performance and to enhance the strength of a model overcoming the weakness of a single-performed model. This framework benefits the time-series prediction and decision-making study. Besides, we propose adopting dynamic time warping to assist a task-learning process by customizing a loss function that improves the accuracy of data prediction. The experiment shows that the proposed method reduces the prediction log error by 28\% in a best case of real-situation data and decreases the warping cost by 20\%. Finally, we illustrate the proposed framework by implementing it in a real-world stock data selection. The results are practical and effective, further justified through a detailed ablation study. © 2023, The Authors. All rights reserved.

关键词： Investments

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Generation of Multiple Obstruction-Free Channels for Free Space Optical Communication

Generation of Multiple Obstruction-Free Channels for Free Sp...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Saad Bin Ali Reza Milos Burger Pascal Bassène Tanner Nutting Igor Jovanovic Moussa N’Gom Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy New York USA Department of Nuclear Engineering and Radiological Sciences University of Michigan Ann Arbor Michigan USA Gérard Mourou Center for Ultrafast Optical Science University of Michigan Ann Arbor Michigan USA Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York USA

Plasma filaments generated by ultrafast beams with orbital angular momentum can simultaneously clear two cylindrical transmission channels, within which an annular beam can propagate free of obstructions.

ISBN: (纸本)9781957171258

关键词： Atmospheric turbulence Free space optics Laser beams Plasma filaments Quantum key distribution Spatial light modulators

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Machine Learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

引用

Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Pragya Bhatt Mohammad S. Obaidat Gagan Dangwal Ashok Kumar Das Mohammad Wazid Balqies Sadoun Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India KASIT University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University Kattankulathur India Amity University Noida India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy.

关键词： Uniform resource locators Training Accuracy Machine learning algorithms Phishing Electronic mail Classification algorithms Telecommunication computing computer crime Random forests

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A quantum algorithm to estimate the closeness to the Strict Avalanche criterion in Boolean functions

arXiv

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arXiv 2022年

作者： Jothishwaran, C.A. Chakraborty, Abhishek Poonia, Vishvendra Singh Stanica, Pantelimon Gangopadhyay, Sugata Department of Electronics and Communication Engineering Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India Department of Physics and Astronomy University of Rochester RochesterNY14627–0171 United States Department of Applied Mathematics Naval Postgraduate School MontereyCA93943–5216 United States

We propose a quantum algorithm (in the form of a quantum oracle) that estimates the closeness of a given Boolean function to one that satisfies the "strict avalanche criterion" (SAC). This algorithm requires n queries of the Boolean function oracle, where n is the number fo input variables, this is fewer than the queries required by the classical algorithm to perform the same task. We compare our approach with other quantum algorithms that may be used for estimating the closeness to SAC and it is shown our algorithm verifies SAC with the fewest possible calls to quantum oracle and requires the fewest samples to for a given confidence bound. © 2022, CC BY.

关键词： Boolean functions

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RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

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The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

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The Impact of Selfish Behavior in Load Balancing Games

arXiv

引用

arXiv 2022年

作者： Bilò, Vittorio Vinci, Cosimo Department of Mathematics and Physics "Ennio De Giorgi" University of Salento Lecce73100 Italy Department of Computer Engineering Electrical Engineering and Applied Mathematics University of Salerno Fisciano84084 Italy Gran Sasso Science Institute L’Aquila67100 Italy

To what extent does the structure of the players’ strategy space influence the efficiency of decentralized solutions in congestion games? In this work, we investigate whether better performance are possible when restricting to load balancing games in which players can only choose among single resources. We consider three different solutions concepts, namely, approximate pure Nash equilibria, approximate one-round walks generated by selfish players aiming at minimizing their personal cost and approximate one-round walks generated by cooperative players aiming at minimizing the marginal increase in the sum of the players’ personal costs. The last two concepts can be interpreted as solutions of greedy online algorithms for the related resource selection problem. We show that, under fairly general latency functions on the resources, better bounds cannot be achieved if players are either weighted or asymmetric. On the positive side, we prove that, under mild assumptions on the latency functions, improvements on the performance of approximate pure Nash equilibria are possible for load balancing games with weighted and symmetric players in the case of identical resources. We also design lower bounds on the performance of one-round walks in load balancing games with unweighted players and identical *** Codes 91A99 (Primary) 68W27, 90C05 (Secondary) © 2022, CC BY.

关键词： Resource allocation

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