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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Ha, Oai Berrier, Seth Oss, Keif University of Wisconsin-Stout Dept. of Engineering and Technology MenomonieWI United States Dept. of Math Stats and Computer Science University of Wisconsin-Stout MenomonieWI United States University of Wisconsin-Stout Dept. of Design MenomonieWI United States

ISBN: (纸本)9798350336429

This work in progress introduces a framework for developing a virtual tour of the central heating and chiller plant (the Plant) at a university in the US Midwest for teaching and learning Thermodynamics at the campus. Traditionally, this theory-heavy and abstract course is taught using textbooks, lectures, problem solving, and some laboratory experiments. The majority of course content lacks the tangible connections between engineering devices and systems in a close real-world application, making the course challenging and uninteresting for many students. In this project, 360-degree panoramic photos and proper authoring software are employed to render an immersive virtual environment of the Plant that students can visit at any time and from anywhere. The virtual field trip would provide students with tangible insights into thermodynamic and heat transfer processes from over 25 main engineering equipment, devices, and systems at the Plant. Among them are boilers, chillers, a steam turbine, heat exchangers, cooling towers, blowers, and economizers. The follow-up quizzes and assessments are designed to contribute to the course learning outcomes, such as an understanding of phase transitions, thermodynamic properties of steam, and thermal efficiencies of engineering devices. The virtual immersive experiences can help students enhance their learning experiences and engagement in the course's materials without the costs and dangers inherent in a physical tour of the Plant facility. A preliminary result of the virtual tour's evaluation with a group of students and faculty is also included to provide inputs to the next phase of the project. © 2023 IEEE.

关键词： 360-degree panoramic images immersive education mechanical engineering education Thermodynamics education virtual field trip

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An efficient algorithm for st-Connected Vertex Separator problem 2

An efficient algorithm for st-Connected Vertex Separator pro...

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2nd International Virtual Conference on Pure science, 2IVCPS 2021

作者： Younis, Althoby Haeder Dept. of Math. Collage of Science University of AL-Qadisiyah AL-Diwaniyah Iraq

In this paper, we give some basic notions concerning the st-connected vertex separator problem(st-CVS problem), then we give math.matical formula for st-CVS problem, then some special cases on some types of graphs, after then introduce an integer programming formulations for this problem. Also, we introduce a heuristic to solve this problem. © Published under licence by IOP Publishing Ltd.

关键词： Heuristic algorithms

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Complexity of Conformant Election Manipulation

arXiv

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arXiv 2023年

作者： Fitzsimmons, Zack Hemaspaandra, Edith Dept. of Math. and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Computer Science Rochester Institute of Technology RochesterNY14623 United States

It is important to study how strategic agents can affect the outcome of an election. There has been a long line of research in the computational study of elections on the complexity of manipulative actions such as manipulation and bribery. These problems model scenarios such as voters casting strategic votes and agents campaigning for voters to change their votes to make a desired candidate win. A common assumption is that the preferences of the voters follow the structure of a domain restriction such as single peakedness, and so manipulators only consider votes that also satisfy this restriction. We introduce the model where the preferences of the voters define their own restriction and strategic actions must "conform" by using only these votes. In this model, the election after manipulation will retain common domain restrictions. We explore the computational complexity of conformant manipulative actions and we discuss how conformant manipulative actions relate to other manipulative actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Using Weighted Matching to Solve 2-Approval/Veto Control and Bribery

arXiv

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arXiv 2023年

Determining the complexity of election attack problems is a major research direction in the computational study of voting problems. The paper "Towards completing the puzzle: complexity of control by replacing, adding, and deleting candidates or voters" by Erdélyi et al. (JAAMAS 2021) provides a comprehensive study of the complexity of control problems. The sole open problem is constructive control by replacing voters for 2-Approval. We show that this case is in P, strengthening the recent RP (randomized polynomial-time) upper bound due to Fitzsimmons and Hemaspaandra (IJCAI 2022). We show this by transforming 2-Approval CCRV to weighted matching. We also use this approach to show that priced bribery for 2-Veto elections is in P. With this result, and the accompanying (unsurprising) result that priced bribery for 3-Veto elections is NP-complete, this settles the complexity for k-Approval and k-Veto standard control and bribery cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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Benchmarking Deep Learning Algorithms for Intrusion Detection IoT Networks

Benchmarking Deep Learning Algorithms for Intrusion Detectio...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Hoummady Enzo Fehmi Jaafar Dept. of Math and Computer Science Université du Quebec àChicoutimi Saguenay Canada

ISBN: (数字)9798331522728

ISBN: (纸本)9798331522735

With the increasing popularity of Internet of Things (IoT) and its connected devices, security has become a major concern. In this paper, we conducted a benchmark to evaluate performance of different deep learning algorithm device based on its network traffic. We developed our own dataset for our pilot study that included three different types of cyberattacks: reverse shell, keylogger, and *** diversity and scope of our research has been enhanced by the incorporation of the CIC IoT dataset, which has been added to our initial work. We conducted a systematic evaluation of the performance of various deep learning models, which included CNNs and LSTM networks. Our benchmarking efforts on the CIC IoT dataset resulted in a significant improvement, with all models achieving an accuracy of over 99% and more than 93% on our custom dataset.

关键词： Deep learning Performance evaluation Systematics Intrusion detection Telecommunication traffic Benchmark testing Internet of Things Security computer crime Long short term memory

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Centralized and Decentralized Federated Learning in Autonomous Swarm Robots: Approaches, Algorithms, Optimization Criteria and Challenges : The Sixth Edition of International Conference on Pattern Analysis and Intelligent Systems (PAIS’24)

Centralized and Decentralized Federated Learning in Autonomo...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Aicha Hafid Riadh Hocine Lahcene Guezouli Mohamed. R Abdessemed Dept. Math and Computer Science University of Batna2 Batna Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Fault tolerance and energy consumption optimization are critical issues in swarm robotics. This study examines recent approaches to address these challenges, focusing on a comparative analysis between Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL). CFL requires a centralized access point for model aggregation, while DFL eliminates the need for a central server, enabling aggregation at each node according to a specific architecture. The analysis of the results reveals that other approaches, such as Hybrid Federated Learning (HFL), more effectively meet the needs of intelligent agents (swarm robots). This effectiveness is particularly enhanced when HFL is combined with Deep Reinforcement Learning (DRL), resulting in Deep Hybrid Federated Reinforcement Learning (DHFRL). The results demonstrate that, although DFL eliminates the necessity of a central server, hybrid approaches are more efficient, especially when combined with Deep Reinforcement Learning (DRL), thus forming Deep Hybrid Federated Reinforcement Learning (DHFRL).

关键词： Federated learning Swarm robotics Focusing Deep reinforcement learning Servers Intelligent agents Pattern analysis

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SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions 3

SynthBA: Reliable Brain Age Estimation Across Multiple MRI S...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Puglisi, Lemuel Rondinella, Alessia De Meo, Linda Guarnera, Francesco Battiato, Sebastiano Ravi, Daniele University of Catania Dept. of Math and Computer Science Catania Italy University College London Department of Neuroinflammation London United Kingdom School of Physics Engineering and Cs University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9798350378009

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://***/LemuelPuglisi/SynthBA. © 2024 IEEE.

关键词： Deep learning

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Automated classification technique for edge-on galaxies based on math.matical treatment of brightness data

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Research in Astronomy and Astrophysics 2021年第10期21卷 231-238页

作者： Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour Passent Elkafrawy Computer Science Department Integrated Thebes InstituteCairoEgypt Faculty of Computer&Artificial Intelligence Sadat City UniversitySadat CityEgypt Math and Computer Science Dept. Faculty of ScienceMenoufia UniversityEgypt Computer Science Dept. Taibah UniversityAlula BranchKingdom of Saudi Arabia School of Information Technology and Computer Science(ITCS) Nile UniversityEgypt

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on ***-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of *** the current work,a novel technique for the classification of edge-on galaxies has been *** technique is based on the math.matical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their *** technique has the capacity to be optimized for different catalogs with different brightness *** the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this *** classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.

关键词： techniques:image processing methods:data analysis galaxies:formation

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New Version of Fast Automatic Determination of Cluster Numbers for High Dimensional Big Data

New Version of Fast Automatic Determination of Cluster Numbe...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Safari, Zohreh Elizabeth City State University Dept. Computer Science Math Engineerign Technology Elizabeth City United States

ISBN: (纸本)9798350320282

The main issue in clustering algorithms is how to efficiently define number of clusters automatically. Considering both the quality of clustering and efficiency of clustering algorithm during determination of number of clusters can be a trade off that was our main purpose to overcome with. Successfully, In our approach the best number of clusters for a large data set of high dimensional data automatically would be determined with respect to clustering quality and efficiency. We carried out experimental studies on our five previous data sets [43]. and 4 new larger ones by which we found that our procedure has the flexibility of choosing different criteria to determine the optimal K under each of them. The procedure takes the advantages of Bisecting K-Means algorithm, and indicated higher efficiency compared with Ray & Turi method when it comes to find the best number of clusters. © 2022 IEEE.

关键词： Big data

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Explainable RNN Classification of Right Ventricular Dysfunction from Echocardiogram Reports

Explainable RNN Classification of Right Ventricular Dysfunct...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rajaratanam, Arun Corizzo, Roberto Boukouvalas, Zois Mid-Atlantic Permanente Group AlexandriaVA United States American University Department of Computer Science WashingtonDC United States American University Dept. of Math and Statistics WashingtonDC United States

ISBN: (纸本)9798350362480

Accurately identifying patients with Right Ventricular Dysfunction (RVD) is critical for timely diagnosis and treatment, yet it remains a significant challenge in clinical practice due to the complexity and variability of echocardiography text reports. In this study, we address this crucial problem by evaluating the effectiveness of recurrent neural networks (RNNs) in classifying patients with and without RVD from echocardiography text reports. We demonstrate that our model exhibits strong performance, with high recall and precision in identifying RVD, underscoring its potential utility in clinical settings. Furthermore, we enhance the interpretability of the framework by leveraging the Local Interpretable Model-Agnostic Explanations (LIME) framework. This approach allows us to extract and validate meaningful semantic information related to RVD, providing deeper insights into the model's local predictive capabilities and improving transparency in decision-making. © 2024 IEEE.

关键词： Patient treatment

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