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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zmuda, Michael A. Miami University Dept. of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= 'remove', points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing

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Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection

arXiv

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arXiv 2024年

作者： Qin, Qiaolin Li, Heng Merlo, Ettore Lamothe, Maxime Dept. of Computer and Software Engineering Polytechnique Montreal Montreal Canada

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, these studies usually require data-specific configurations (i.e., parameterized) or use carefully curated data as learning examples (i.e., supervised), relying on domain knowledge and deep understanding of the data, or demanding significant manual effort. In this paper, we introduce RIOLU: Regex Inferencer autO-parameterized Learning with Uncleaned data. RIOLU is fully automated, automatically parameterized, and does not need labeled samples. RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column’s error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline, even outperforming ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). A variant of RIOLU, with user guidance, can further boost its precision, with up to 37.4% improvement in terms of F1. Our evaluation in an industrial setting further demonstrates the practical benefits of *** Codes 68P01 © 2024, CC BY.

关键词： Self-supervised learning

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion Recognition

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Impacts of Earthing Variations on Overvoltage in Meshed Multi-terminal DC Grid 50

Impacts of Earthing Variations on Overvoltage in Meshed Mult...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bader, Amir Ar-Rahman Ukil, Abhisek Dept. of Engineering Science The University of Auckland Auckland New Zealand Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

This paper investigates the impact of different earthing methods on overvoltage within modular multilevel Voltage Source Converter (MMC VSC) based Direct Current (DC) transmission and distribution grids, particularly in meshed multi-terminal (MTDC) configurations. A bipolar MTDC grid is modelled and simulated in PSCAD/EMTDC to investigate the influence of earthing on midpoint overvoltages. The transient response of the grid under DC fault scenarios is analysed through various case-studies, including solid earthing placement, variations in earthing impedance, and the application of surge arresters. The findings show the critical role of earthing in mitigating overvoltage, with low-resistive earthing options proving to be the most effective trade-off. © 2024 IEEE.

关键词： bipolar dedicated metallic return earthing meshed MTDC overvoltage

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Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods

Systematic Literature Review: Evaluating Effects of Adversar...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Akram, Saira Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Hamza, Muhammad Shah, Syed Ali Asghar Dept. of Computer Engineering Quetta Pakistan Pakistan Television Corporation Dept. of Engineering Lahore Pakistan Dept. of Software Engineering Quetta Pakistan

ISBN: (纸本)9781665476010

Advancement in Artificial Intelligence (AI) aims to train the Machine Learning (ML) Models in such a way that they would be able to take decisions spontaneously, however on the other side attackers attempt to manipulate the results generated by these models which makes the application of these models difficult in security-critical areas including classification of medical images, autonomous system installed in vehicles, street lights, malware detection and identification of a person as criminal or innocent. Advancement in research proved the vulnerability of these classifiers due to adversarial attacks that can alter their result in training as well as in testing phase of the model. Causative attacks are the training phase attacks whereas the attacks done at testing phase are the exploratory attacks. This systematic literature Review (SLR) is conducted to gain in-dept. knowledge of adversarial attacks, which is the most effective type of exploratory attack, parameters on which these attacks are based, along with the most conventional methods of generating adversarial attacks. © 2023 IEEE.

关键词： Well testing

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Feature Selection-driven Bias Deduction in Histopathology Images: Tackling Site-Specific Influences 13

Feature Selection-driven Bias Deduction in Histopathology Im...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kheiri, Farnaz Bidgoli, Azam Asilian Makrehchi, Masoud Rahnamayan, Shahryar Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada Wilfrid Laurier University Dept. of Computer Science Waterloo Canada Brock University Dept. of Engineering St. Catharines Canada

ISBN: (纸本)9798350308365

The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on unseen datasets. Recent studies conducted on The Cancer Genome Atlas (TCGA), which is a publicly available repository of histopathology images, reveal that the TCGA cancerous features extracted by deep neural networks surprisingly are able to discriminate slides based on their origin sites. This finding undoubtedly indicates the existence of site-specific patterns embedded in the extracted features learned by deep networks rather than focusing on histomorphologic patterns. Consequently, this biased behavior raises concerns about the reliability of these networks. This observation motivates us to conduct a series of experiments in which we present two distinct evolutionary feature selection strategies, each differentiated by its objective function evaluation. The primary goal is to select the features with a minimized foot-print of data source signatures, thereby ensuring a more accurate and site-independent cancer classification. We have conducted nine comprehensive independent experiments across nine cancer types, employing each evolutionary strategy. The comparison between results obtained through evolutionary strategies and the original feature sets highlights the substantial impact of feature selection methods on bias reduction while maintaining accuracy in cancer-type discrimination. Furthermore, the comparison of the two strategies with each other demonstrates the intricate nature of bias and its integration with cancerous features during the training process. © 2024 IEEE.

关键词： Lung cancer

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Enhanced Classification of Alzheimer's Disease Using Deep Learning and Machine Learning Models with MRI Scans

Enhanced Classification of Alzheimer's Disease Using Deep Le...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Salahudin, Amna Qadeer, Eeman Ahmed, Ayesha Fahad, Labiba Gillani Dept. of Software Engineering FAST National University of Computer and Emerging Sciences Islamabad Pakistan Dept. of Computer Science FAST National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9798331510503

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease, which necessitates early and accurate diagnosis for effective management. This research presents a comprehensive approach to the classification of Alzheimer's disease using Magnetic Resonance Imaging (MRI) scans, focusing on four distinct classes: nondemented, mild, very mild, and moderate demented. Our main goals are to combine diverse datasets to improve the robustness and adaptability of the model and to fully investigate Alzheimer's disease in all four stages. To achieve these goals, we employed a combination of machine learning and deep learning models. The models utilized include Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM), Gabor filters with SVM, Gabor-Gray Level Co-occurrence Matrix (GLCM) with SVM, ResNet50, AlexNet, and InceptionV3. Among these, the InceptionV3 model achieved the highest accuracy of 96.20% using 5-fold cross-validation followed the Resnet50 model achieving accuracy of 95.43%. Our approach addresses key challenges such as class imbalance, high intra-class variation, inter-class similarities, and dataset variability, demonstrating significant potential in improving Alzheimer's disease classification and detection. © 2024 IEEE.

关键词： Gabor filters

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification 16

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

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16th International Symposium on Electronics and Telecommunications, ISETC 2024

作者： Hazim, Layth Rafea Ata, Oguz Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350390865

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models' robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications. © 2024 IEEE.

关键词： Logistic regression

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Secure IoT Entrance Using Mobile Application 7

Secure IoT Entrance Using Mobile Application

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7th International Electromagnetic Compatibility Conference, EMC Turkiye 2023

作者： Abdulkareem, Marwa Issam Ashour, Osama Ibraheem Salman, Yucel Batu Bahcesehir University Dept. Software Engineering Istanbul Turkey Cankaya University Dept. Computer Engineering Tikrit Iraq

ISBN: (纸本)9798350342130

The impact of cyber security attacks increased globally, implementing proper security standards to protect data in the cyber environment has been prioritized, while assets are also at risk of being exploited through physical security breaches. Considering the potential risk of Radio Frequency Identification (RFID) cards being lost or stolen, third parties can effortlessly gain the privileges of an authorized person. In this paper, we propose a design for a secure access control system that operates on an Android phone with multi-factor authentication to enable secure access control systems. For this purpose, the authorized users are registered in a database that can be accessed by the access control system when the user authenticates his\her credentials with a login/password as well as biometric data from his\her smartphone. This system works when the user is 10 meters away from the access control system, after that the user will be asked to submit his\her biometric credentials. The database then compares the user's keys and grants access if authentication is successful. This design is intended to reduce the number of cases in which unauthorized access to a restricted area. The observed results clearly meet the required security of physical access control systems. © 2023 IEEE.

关键词： Biometrics

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A Tool for Security Risk Assessment for APT Attacks: using Scenarios, Security Requirements, and Evidence 31

A Tool for Security Risk Assessment for APT Attacks: using S...

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31st IEEE International Requirements engineering Conference, RE 2023

作者： Park, Sihn-Hye Kim, Dongyoon Lee, Seok-Won Ajou University Dept. of Artificial Intelligence Dept. Software and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326895

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems. © 2023 IEEE.

关键词： Risk assessment

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