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arXiv 2023年

作者： Tanyel, Toygar Alkurdi, Besher Ayvaz, Serkan Department of Computer Engineering Yildiz Technical University Turkey Department of Biomedical Engineering Istanbul Technical University Turkey Centre for Industrial Software Maersk Mc-kinney Moeller Institute University of Southern Denmark Denmark

With the proliferation of social media, there has been a sharp increase in offensive content, particularly targeting vulnerable groups, exacerbating social problems such as hatred, racism, and sexism. Detecting offensive language use is crucial to prevent offensive language from being widely shared on social media. However, the accurate detection of irony, implication, and various forms of hate speech on social media remains a challenge. Natural language-based deep learning models require extensive training with large, comprehensive, and labeled datasets. Unfortunately, manually creating such datasets is both costly and error-prone. Additionally, the presence of human-bias in offensive language datasets is a major concern for deep learning models. In this paper, we propose a linguistic data augmentation approach to reduce bias in labeling processes, which aims to mitigate the influence of human bias by leveraging the power of machines to improve the accuracy and fairness of labeling processes. This approach has the potential to improve offensive language classification tasks across multiple languages and reduce the prevalence of offensive content on social media. © 2023, CC BY-NC-SA.

关键词： Data mining

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Digital Evidence and Forensic Tools in Cyber Law: A Comprehensive Analysis

Digital Evidence and Forensic Tools in Cyber Law: A Comprehe...

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Information Technology (ACIT)

作者： Ala’A Saeb Al-Sherideh Samah Suleiman Al-Zou’Bi Fuad Alshraiedeh Esraa Abu Elsoud Suha Afaneh Mohammad Rasmi Al-Mousa Raed Alazaidah Mohammed Khouj Ghassan Samara Department of Cybersecurity Zarqa University Zarqa Jordan Department of Software Engineering Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Data Science and AI Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The exponential rise in hacking, phishing, information theft, and electronic crime has become an actual concern with the increased use of digital devices and technology. By 2023 the number of IoT devices expectation will be 32.1 billion devices, leading to a significant increase in cybercrimes. These illegal behaviors are specifically connected to computers and communication technology. Consequently, there has been an urgent need for the development of cyber law which is a legal system closely related to digital evidence. This paper aim to provides delve into the significance and multifaceted applications of digital evidence within the context of cyber law, shedding light on the complexities and nuances inherent in its acceptance and reliability. The paper explores the importance and numerous uses of digital evidence in the context of cyber law, highlighting the challenges associated with its legitimacy and acceptance. Additionally, it focuses on the challenges that prevent digital evidence from being seamlessly incorporated into court processes and offers solutions and strategic advice to overcome these challenges.

关键词： computers Phishing Digital forensics Personal digital devices Communications technology Reliability Internet of Things computer crime Information technology Faces

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M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans

arXiv

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arXiv 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on the widely known NIH pancreas dataset and MSD pancreas dataset. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53%±1.82 and an Intersection Over Union (IOU) score of up to 81.16±0.03% for the NIH pancreas dataset, and 88.60%±1.48 DSC and 79.90%±2.19 IOU for the MSD Pancreas dataset. © 2024, CC BY-NC-SA.

关键词： computerized tomography

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Reinforcement Learning for Testing Android Applications: A Review

Reinforcement Learning for Testing Android Applications: A R...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Asmau Usman Moussa Mahamat Boukar Muhammed Aliyu Suleiman Ibrahim Anka Salihu Ndukwe-Oke Eke Department of Computer Science Abdu Gusau Polytechnic Talata Mafara Nigeria Department of Computer Science Nile University of Nigeria Abuja Nigeria Department of Software Engineering Nile University of Nigeria Abuja Nigeria

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

This paper offers a review of current research studies that use reinforcement learning (RL) to test Android applications. The primary purpose of this study is to simplify future research by collecting and investigating the current state of Android app testing approaches using the RL technique. We provide a well-defined criterion comprising of seven key points. The key points are: addressed problems, reasons for using the RL technique, RL algorithms, supported events, testing techniques, validation, and evaluation methods. In the literature, we have analyzed various techniques to evaluate their efficiency. This study showed that model-based testing is the most commonly used testing technique. Q-learning is the best algorithm in terms of predictive accuracy. We identified that code coverage is the most widely used evaluation metric and comparison with other tools and techniques is the preferred validation approach.

关键词： Measurement Q-learning Codes Prediction algorithms Testing Graphical user interfaces

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Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction 7

Ensemble Online Sequential Extreme Learning Machine for Air ...

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7th International Conference on Control Science and Systems engineering, ICCSSE 2021

作者： Liu, Ye Cao, Weipeng Liu, Yiwen Li, Dachuan Wang, Qiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of International Exchange Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665444057

Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms. © 2021 IEEE.

关键词： Distribution functions

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Personality and demographic correlates of support for regulating artificial intelligence

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AI and Ethics 2023年第2期4卷 419-426页

作者： Bartneck, Christoph Yogeeswaran, Kumar Sibley, Chris G. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand Department of Psychology University of Canterbury Christchurch New Zealand School of Psychology University of Auckland Auckland New Zealand

The arrival of artificial intelligence (AI) in our society has sparked many hopes and fears, with people having diverging views on the need to strictly regulate AI. The current study investigates how demographic and personality traits are associated with a desire to strictly regulate AI using a representative sample of adults from New Zealand (N = 47,951 participants). Data revealed that support for strict regulation of AI is positively related with agreeableness, neuroticism, and honesty–humility. However, it is negatively related to openness to experiences. A wide range of demographic factors including gender, age, ethnicity, religiosity, neighbourhood level economic deprivation, living rural, relationship status, and parental status were additionally related to support for regulation of AI. However, all these effects were fairly small suggesting that both personality and socio-demographic factors contribute to support for regulating AI, but other factors beyond these characteristics should also be considered for understanding people’s support for regulating AI.

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Decoding Sentiments: An Efficient Trans-Long Short Term Memory to Understand Viewer Reactions on Ad-Supported Video Contents

Decoding Sentiments: An Efficient Trans-Long Short Term Memo...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Shashishekhar Ramagundam Niharika Karne Department of Computer Science Software Development & Engineering in Content Discovery & AI COMCAST USA Troy University AL Department of Electrical Engineering Sr. Principal Systems Engineer in C & RS Platform Services DT Amtrak USA University of South Alabama

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Viewer reaction analysis in ad-supported video content focuses on the use of advanced techniques to understand and interpret the emotional responses expressed by viewers during ad viewing experiences. This analysis goes beyond simple positive or negative sentiment and delves into the nuances of viewer reactions, allowing for a more comprehensive understanding of the emotional impact of ads. With this information, advertisers can make data-driven decisions to optimize their advertising strategies, create more engaging content, and ultimately enhance the viewer experience. One of the challenges in sentiment analysis for viewer reaction analysis is the inherent subjectivity and variability of human emotions. Emotions can be complex and subjective, making it difficult to accurately capture and interpret them solely based on text or viewer feedback. Different individuals may perceive and express emotions differently, adding another layer of complexity to the analysis process. Addressing these challenges requires a combination of advanced deep learning model refinement. Overcoming these challenges can provide valuable insights into viewer reactions, enabling advertisers and content creators to create more engaging and impactful ad-supported video content. In this paper, an Artificial Intelligence (AI)-based sentiment analysis is introduced to analyze the viewer's reaction while watching the ad in the video content. Here, an efficient model named Trans-Long Short-Term Memory (Trans-LSTM) is developed to perform the sentiment analysis. The developed Trans-LSTM is helpful in supervising the emotional data in the online video content and identifying the popularity of the corresponding video. The suggested model effectively analyzes the reviewer’s actions while watching all types of video content. Finally, the experimental analysis is performed to find the effectiveness of the developed sentiment analysis model via various metrics.

关键词： Measurement Deep learning Analytical models Sentiment analysis Logistic regression Memory management Emotional responses Decoding Security Artificial intelligence

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Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

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CodeSafari: A Customized Mobile Solution for Introducing Programming Concepts to Children

CodeSafari: A Customized Mobile Solution for Introducing Pro...

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International Conference on Information Technology Research (ICITR)

作者： Fahmi M.F.A. Sanjeevan M.C.M.A. Samanthi E.R. Siriwardana Mihiri Samaraweera Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Following the COVID-19 pandemic, Sri Lanka's economy faced significant setbacks, particularly in key sectors such as tea exports, garment manufacturing, and tourism, which experienced severe downturns. However, the IT sector showed resilience amid these challenges, highlighting its potential to drive economic growth. Despite this potential, Sri Lanka has struggled to capitalize fully on global demand for IT services due to a skills gap among Sri Lankan youth compared to their Indian counterparts. India's IT outsourcing industry has thrived partly because of the early involvement of young professionals in open-source projects such as the Android Open Source Project (AOSP), Linux Kernel and React. This trend underscores the importance of foundational programming education to develop competitive technical skills from a young age. To address this gap, authors developed a mobile learning solution CodeSafari, designed to introduce core programming concepts data types, conditional statements, and loops to young Sri Lankan learners. The platform uses Personalized Storytelling and a Customized Kids Assisting System in students' native language, making programming more accessible. Programming training with the app improved test scores for 58 percent of participants. By addressing the skills gap early, this research aims to build a strong IT foundation for Sri Lankan students, positioning them to compete more effectively in the global outsourcing market. These findings underscore the essential role of programming education in fostering economic resilience and preparing a workforce capable of thriving in an evolving technology landscape.

关键词： Training Economics Pandemics Prototypes software Outsourcing Usability Programming profession Testing Resilience

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