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Frontiers of Information Technology (FIT)

作者： Saima Rafi Muhammad Azeem Akbar Arif Ali Khan School of Computing Engineering & the Built Environment Edinburgh Napier University Scotland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Ensuring product security in software development is crucial, encompassing hardware, firmware, and holistic protection measures. With rising cyber threats, organizations face challenges in safeguarding software products, often viewing security as a bottleneck. To address these issues, we conducted an empirical study with 22 practitioners across various industries, exploring challenges and practices in product security. Our findings highlight both the applicability of security practices and the new challenges in their implementation. This research contributes valuable insights, enabling organizations to proactively address security concerns and develop robust strategies for protecting their software products.

关键词： Industries Organizations software Hardware Security software measurement Protection Information technology software development management Microprogramming

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Amplifying the Voice of Blacks in computing

Amplifying the Voice of Blacks in Computing

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Black Issues in computing Education (BICE)

作者： Cheryl Swanier Cheryl Seals Dale-Marie Wilson Manning College of Information and Computing Sciences University of Massachusetts USA Computer Science and Software Engineering Auburn University USA Department of Software and Information Systems University of North Carolina at Charlotte USA

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

This workshop is designed to intentionally share experiences and connect participants to their greatest asset, their voice. There is power in your voice. Engaging interaction, thought-provoking storytelling, and authentic truth telling will open the way for understanding the value of harnessing voice power when navigating professionally and personally. Despite diversity, equity, and inclusion efforts, stated commitments to diversity, the demographic characteristics of the professoriate and industry look remarkably like the faculty and professionals of 50 years ago for Blacks in computing. Blacks in computing constantly face discrimination in the form of microaggressions and white fragility. This discrimination has not translated to greater Black faculty and professional representation in their career advancement. Instead, the health of both faculty and professionals are being adversely impacted, as they are experiencing insomnia, anxiety, depression, emotional distress, and physical exhaustion. This disruption of norms has a negatively impacted the productivity of both Black men and women at work and can lead to their demotion and dismissal. Many times, these struggles are not shared, and their voices are silenced. This workshop primarily addresses how Blacks in computing can use their voice to advance their careers and utilize proven voice positioning strategies to harness their voice power and to maximize professional and personal wellbeing. These voice strategies will facilitate both Black men and women in their career advancement, skill development, and preparation for entry into their next career level. A failure to address these challenges has negative implications for both academia and industry

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Predicting Modalities of Dyslexic Students using Neuro-Linguistic Programming to Enhance Learning Method

Predicting Modalities of Dyslexic Students using Neuro-Lingu...

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2022 IEEE International Conference on Data Science and Information System, ICDSIS 2022

作者： Choube, Gaurav Dudhmande, Gauri Rahul Pushparaj, Jagalingam Anand, Christopher Suresh, Shilpa Software Engineer Optum Global Solutions Pvt Ltd Hyderabad India Software Engineer Intel Technology Pvt Ltd Aurangabad India Vellore Institute of Technology School of Computer Science and Engineering Vellore India McMaster University Department of Computing and Software Ontario Canada Manipal Institute of Technology Department of Mechatronics Manipal India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Dyslexia causes difficulty in reading, writing and learning. The children at a tender age have always suffered due to dyslexia. Dyslexia deceives student's perception and makes it difficult in the process of learning. In this paper, machine learning techniques like multi-layer perceptron, Decision tree and Gaussian NB approaches were implemented for the prediction of modalities. To enhance the learning approach for the students suffering from dyslexia, the predicted modalities can be adopted. The sampled data was trained, and the target labels were classified into three classes as visual, auditory, and kinesthetic. The data was processed and fed into multi-layer perceptron, decision tree and naive bayes machine learning algorithms using scikit-learn. Confusion matrix was used to evaluate the performance measure of the algorithms. It was observed that models achieved accuracy of 81.41% for MLP Classifier, 63.82% for Decision tree and 79.25% for Naive bayes. The best result was achieved by MLP Classifier. © 2022 IEEE.

关键词： Decision trees

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning

arXiv

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arXiv 2025年

作者： Zhang, Lili Zhu, Quanyan Ray, Herman Xie, Ying Hewlett Packard Enterprise United States Department of Electrical and Computer Engineering New York University United States College of Computing and Software Engineering Kennesaw State University United States

Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing practices of using analytics, machine learning, and artificial intelligence methods to detect the network threats, we propose an integrated modelling framework, where Knowledge Graph is used to analyze the users’ activity patterns, Imbalanced Learning techniques are used to prune and weigh Knowledge Graph, and LLM is used to retrieve and interpret the users’ activities from Knowledge Graph. The proposed framework is applied to Agile Threat Detection through Online Sequential Learning. The preliminary results show the improved threat capture rate by 3%-4% and the increased interpretabilities of risk predictions based on the users’ activities. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Cybersecurity Risks Quantification in the Internet of Things 7

Cybersecurity Risks Quantification in the Internet of Things

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7th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Alagappan, Annamalai Baptist Andrews, Leo John Sampath Kumar, V. Raj, Raymon Antony Sarathkumar, D. Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Botho University Faculty of Computing Department of Software Engineering Botswana Grant Thornton Plot 50370 Acument Park Fair Grounds Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Tamilnadu India

ISBN: (纸本)9781665489102

A dynamic platform, the Internet of Things uses data, which in turn invites cybersecurity risks. In a dynamic and complicated world, many organisations are putting more of their attention into understanding cyber threats and attempting to quantify their exposure. This paper highlights and goes into great detail on standard cyber risk assessment methods while focusing on the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach employing machine learning algorithms. Based on the statistical research, it can be determined that the CAQ model utilising J48 classifies at 94 percent, while the Bayesian method performs in evaluating possible risks at 82 percent. The Bayes algorithm also aids in estimating complicated risks coming from various sources, aids in comprehending how risk variables originate and connect in an IoT environment, and aids in determining what controls are necessary for quantifying and reducing them. © 2022 IEEE.

关键词： Cybersecurity

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Socially-Critical software Systems: Is Extended Regulation Required? 1

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29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Dagg, Niall Kostick, Conor Fallon, James O’Neill, Alex Yilmaz, Murat Messnarz, Richard Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey ISCN the International Software Consulting Network Graz Austria Lero the Science Foundation Ireland Research Center for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Data has become a prevailing aspect of our daily lives, becoming ever more present since the beginning of the 21st century. It is a commodity in today’s world and the amount of data being produced has increased enormously. One of the major ways data is produced and collected is from the use of websites and web-based applications. This data is later used for many different purposes. This paper presents findings from a multivocal literature review, exploring the methods of how this data is collected, what the data is used for once it has been collected, the ethics of data and its collection, and the future of data collection. Among the possible futures, we introduce the concept of socially-critical applications, where data harvesting in web-based applications might require pre-market disclosure and evaluation by notified bodies (instructed by regulation) as a means to break the existing cycle of technology companies outpacing under-resourced and ill-equipped regulators. Rather than regulators continually falling short of enacting laws to satisfy the common good, a new class of socially-critical application could be created in law to permit pre-market evaluation of applications (or versions of applications) that could undermine or interrupt the common good. © 2022, Springer Nature Switzerland AG.

关键词： Websites

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A Feasibility Study of Fitting the Normal Distribution and Gamma Distribution to Rainfall Data at Kuantan River Basin 5th

A Feasibility Study of Fitting the Normal Distribution and G...

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5th International Conference on Water Resources, ICWR 2021

作者： Ghani, Nadiatul Adilah Ahmad Abdul Senawi, Azlyna Subramaniam, Roshan Department of Civil Engineering College Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Kuantan26300 Malaysia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia

ISBN: (纸本)9789811959462

Rainfall data can be very useful for decision makers in developing precautionary strategies to mitigate the impact of climate change in a variety of ways, such as forecasting floods. Prior to the data being utilised, it is critical to determine the best fit probability distribution of the data. Only after this has been done then it is possible to obtain reliable analysis and results. In this study, the feasibility of fitting the data with normal distribution was investigated. The investigation specifically used peak monthly values of rainfall data from ten hydrological stations of the Kuantan River Basin during the year 1975 to 2017. Four different test statistics were employed to assess the possibility of fitting the data to the normal and Gamma distributions: Skewness, excess kurtosis, Kolmogorov–Smirnov and Shapiro–Wilk. Results obtained show that normal distribution fits well with the annual daily maximum rainfall data for only two hydrological stations, but normal distribution is not a good fit for the seasonal daily maximum rainfall case for all stations involved. Gamma 1P distribution was found to be not an ideal fit for both annual and seasonal data for all stations, but Gamma 2P, on the contrary, was discovered to fit well for all stations except one station. The findings of the study are anticipated to improve rainfall forecasting in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Deepfake Detection Using EfficientNet and XceptionNet 11

Deepfake Detection Using EfficientNet and XceptionNet

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Yasser, Basma Hani, Jumana El-Gayar, Salma Amgad, Omar Ahmed, Nourhan Ebied, Hala M. Amr, Habiba Salah, Mohamed Ain Shams University Software Engineering Faculty of Computer and Information Sciences Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves preprocessing the Celeb- DF dataset by extracting frames and isolating faces, training the models, and evaluating their performance using log loss and Area Under the Curve (AUC) metrics. The study shows that both models are effective in accurately classifying real and fake videos and highlights the importance of continuously updating deepfake detection algorithms in response to evolving deepfake generation techniques. © 2023 IEEE.

关键词： Fake detection

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