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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling Software

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Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness

Improving E-commerce Fraud Detection via Machine Learning: C...

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Innovative Computing, Intelligent Communication and Smart Electrical systems.(ICSES), International Conference on

作者： Gangalakshmi T V. Sathya S. Vinodhini E. Chandralekha V. Usha Ravikumar S Dept.of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept.of Computer Science and Business Systems Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Dept. of Information Technology Velammal Engineering College Chennai India

In the fast-growing E-commerce world, fraud has become a major concern, causing economic losses and eroding consumer trust. This work aimed to detect E-commerce fraud using machine learning on a customer dataset. The dataset contained transactional and customer data, requiring preprocessing to handle missing values and categorical features. Four models—Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Classifier—were trained and evaluated for fraud detection accuracy using metrics like Accuracy, Precision, Recall, and F1-Score. Results revealed distinct model strengths and weaknesses. Logistic Regression achieved balanced performance, while Random Forest and Gradient Boosting excelled in certain aspects. Support Vector Classifier also demonstrated competitive results. This work highlights the potential of machine learning in E-commerce fraud prevention, enhancing transaction security and trust. Further research could refine and advance fraud detection techniques for online platforms.

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Czech medical coding assistant based on transformer networks

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computers in Biology and Medicine 2024年 178卷 108672-108672页

作者： Lenc, Ladislav Martínek, Jiří Baloun, Josef Přibáň, Pavel Prantl, Martin Taylor, Stephen Eugene Král, Pavel Kyliš, Jiří Dept. of Computer Science & Engineering Faculty of Applied Sciences University of West Bohemia Univerzitni 8 Plzeň30100 Czech Republic NTIS - New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Univerzitni 8 Plzeň30100 Czech Republic ICZ Group Na Hřebenech II 1718/10 Praha14000 Czech Republic

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes. The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis;and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability. The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems.within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based "Four-headed" model incorporating four distinct classification heads. This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller. © 2024 Elsevier Ltd

关键词： Network coding

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Energy-Aware Packet Schedulers for Battery-Less LoRaWAN Nodes

arXiv

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arXiv 2022年

作者： Capuzzo, Martina Delgado, Carmen Famaey, Jeroen Zanella, Andrea Human Inspired Technologies Research Centre University of Padova Italy i2CAT Foundation Barcelona Spain IDLab Dept. of Computer Science University of Antwerp - imec Belgium Dept. of Information Engineering University of Padova Italy

The Internet of Things (IoT) enables a wide variety of applications where large sensor networks are deployed in remote areas without power grid access. Thus, the sensor nodes often run on batteries, whose replacement and disposal represent important economical and environmental costs. To realize more sustainable IoT solutions, it is therefore desirable to adopt battery-less energy-neutral devices that can harvest energy from renewable sources and store it in super-capacitors, whose environmental impact is much lower than that of batteries. To achieve the energetic self-sustainability of such nodes, however, communication and computational processes must be optimized to make the best use of the scarce and volatile energy available. In this work, we propose different energy-aware packet scheduling algorithms for battery-less LoRaWAN nodes, and compare them in various simulated scenarios, using actual energy-harvesting measurements taken from a testbed. We show that an energy-aware design can significantly increase the number of transmitted packets, also lowering the mean time between packet transmissions, though (as predictable) the gain strongly depends on the harvesting capabilities of the nodes. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy harvesting

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e-Inu: Simulating A Quadruped Robot With Emotional Sentience

arXiv

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arXiv 2023年

作者： Chakravarty, Abhiruph Tripathy, Jatin Karthik Sibi Chakkaravarthy, S. Cherukuri, Aswani Kumar Anitha, S. Kamalov, Firuz Jonnalagadda, Annapurna Cognitive Systems University of Potsdam Germany School of Electronics Engineering VIT-AP University Andhra Pradesh India School of Information Technology Vellore Institute of Technology Vellore India Dept. of Electrical Engineering Canadian University Dubai United Arab Emirates School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work. © 2023, CC BY-SA.

关键词： Reinforcement learning

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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI

Analysis of Defect Associated with Powder Bed Fusion with De...

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International Conference on Knowledge and Smart Technology (KST)

作者： Ayush Pratap Neha Sardana Sapdo Utomo A John P. Karthikeyan Pao-Ann Hsiung Dept. of Metallurgical and Materials Engineering Indian Institute of Technology Ropar Rupnagar India Grad. Ins. Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan R.O.C Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan R.O.C

Research into the detection, classification, and prediction of internal defects using surface morphology data of parts created via powder bed fusion-type additive manufacturing has become a hot topic in the previous decade thanks to the development of deep learning. However, there is no other evidence to evaluate the model other than accuracy and metrics. In this paper, a novel data set is compiled from various literature and other sources to evaluate the black box model using explainable artificial intelligence (XAI). The data set contains three major powder bed fusion defects: gas porosity, lack of fusion, and balling. The anomaly was initially found using convolutional neural networks (CNN) and transfer learning. Based on test data, a model comparison was performed to determine the best accuracy and an F1 score. VGG16 has outperformed all other models in terms of accuracy, with an F1 score of 98.6 percent. Further, the model has been compared with the existing state-of-the-art model for classification in the domain of powder bed fusion defects. Finally, VGG16 was employed to interpret and explain the test data set. The LIME explanations revealed that the feature predicted by the model was present in conjunction with the fault. As a result, we are confident that the proposed model with XAI would considerably improve the fairness and trustworthiness of the output result in the powder bed fusion field. This can also aid in the automation of additive manufacturing in the realm of Industry 4.0.

关键词： Deep learning Powders Transfer learning Surface morphology Predictive models Three-dimensional printing Data models

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AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding

AIX Implementation in Image-Based PM2.5 Estimation: Toward a...

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International Conference on Knowledge and Smart Technology (KST)

作者： Sapdo Utomo A John Ayush Pratap Zhi-Sheng Jiang P. Karthikeyan Pao-Ann Hsiung Grad. Ins. Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan R.O.C Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan R.O.C Dept. of Metallurgical and Materials Engineering Indian Institute of Technology Ropar Rupnagar India

In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model’s output, LIME has been implemented. According to LIME’s explanation, the proposed model’s output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model’s decision. We hope that with this discovery, we can contribute to the theme of “AI for social good,” notably in the domains of the environment and human welfare.

关键词： Deep learning Pollution Atmospheric modeling Buildings Estimation Air quality Artificial intelligence

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Deep Bayesian Blind Color Deconvolution of Histological Images

Deep Bayesian Blind Color Deconvolution of Histological Imag...

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IEEE International Conference on Image Processing

作者： Shuowen Yang Fernando Pérez-Bueno Francisco M. Castro-Macías Rafael Molina Aggelos K. Katsaggelos School of Optoelectronic Engineering Xidian University Xi’an China Dpto. Ciencias de la Computación e Inteligencia Artificial University of Granada Spain Research Center for Information and Communication Technologies (CITIC-UGR) Dept. of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

Histological images are often tainted with two or more stains to reveal their underlying structures and conditions. Blind Color Deconvolution (BCD) techniques separate colors (stains) and structural information (concentrations), which is useful for the processing, data augmentation, and classification of such images. Classical BCD methods rely on a complicated optimization procedure that has to be carried out on each image independently, i.e., they are not amortized methods. In contrast, once they have been trained, deep neural networks can be used in a fast, amortized manner on unseen inputs. Unfortunately, the lack of large databases of ground truth color and concentrations has limited the development of deep models for BCD. In this work, we propose a deep variational Bayesian BCD neural network (BCD-Net) for stain separation and concentration estimation. BCD-Net is trained by maximizing the evidence lower bound of the observed images, which does not require the use of ground truth examples of stains and concentrations. Results obtained using two multicenter databases (Camelyon-17 and a stain separation benchmark) demonstrate the effectiveness of BCD-Net in the stain separation tasks, while drastically reducing the computation time compared to classical non-amortized methods.

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A Review Study on ML-based Methods for Defect-Pattern Recognition in Wafer Maps

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Procedia computer science 2023年 217卷 570-583页

作者： T. Theodosiou A. Rapti K. Papageorgiou T. Tziolas E. Papageorgiou N. Dimitriou G. Margetis D. Tzovaras University of Thessalyirst affiliation Dept. of Energy Systems Gaiopolis 41500 Larissa Greece Centre for Research and Technology Hellas Information Technologies Institute 6th Km Charilaou-Thermi Road 57001 Thermi-Thessaloniki Greece Foundation for Research and Technology Hellas Institute of Computer Science N. Plastira 100 Vassilika Vouton GR-700 13 Heraklion Greece

The identification of defects plays a key role in the semiconductor industry as it can reduce production risks, minimize the effects of unexpected downtimes and optimize the production process. A literature review protocol is implemented and latest advances are reported in defect detection considering wafer maps towards quality control. In particular, the most recent works are outlined to demonstrate the use of AI-technologies in wafer maps defect detection. The popularity of these technologies is then presented in the form of visualizing graphs. This enables the identification of the most popular and most prominent ML-methods that can be exploited for the purposes of Industry 4.0.

关键词： Artificial Intelligence Machine Learning Deep Learning Defect Identification Smart Manufacturing Industry 4.0

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Competitive ratios for online multi-capacity ridesharing 19

Competitive ratios for online multi-capacity ridesharing

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19th International Conference on Autonomous Agents and Multiagent systems. AAMAS 2020

作者： Lowalekar, Meghna Varakantham, Pradeep Jaillet, Patrick School of Information Systems Singapore Management University Singapore Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

ISBN: (纸本)9781450375184

In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand shuttles have become hugely popular in transportation, food delivery, logistics and other domains. This is because multi-capacity ridesharing services benefit all parties involved - the customers (due to lower costs), the drivers (due to higher revenues) and the matching platforms (due to higher revenues per vehicle/resource). Most importantly these services can also help reduce carbon emissions (due to fewer vehicles on roads). Online multi-capacity ridesharing is extremely challenging as the underlying matching graph is no longer bipartite (as in the unit-capacity case) but a tripartite graph with resources (e.g., taxis, cars), requests and request groups (combinations of requests that can travel together). The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i.e., a request can be part of at most one request group in the final assignment). While there have been myopic heuristic approaches employed for solving the online multi-capacity ridesharing problem, they do not provide any guarantees on the solution quality. To that end, this paper presents the first approach with bounds on the competitive ratio for online multi-capacity ridesharing (when resources rejoin the system at their initial location/depot after serving a group of requests). The competitive ratio is: (i) 0.31767 for capacity 2;and (ii) γ for any general capacity κ, where γ is a solution to the equation γ = (1 − γ )κ+1 © 2020 International Foundation for Autonomous.

关键词： Heuristic methods

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