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On detecting stock price manipulation attacks: a comprehensive systematic literature review

Multimedia Tools and Applications 2025年 1-78页

作者： Alfajeer, Amal Altaweel, Ala Bouridane, Ahmed Mouheb, Djedjiga Aslam, Sidra College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Stock market is pivotal to economical systems. It is a platform where listed stocks of companies can be bought/sold by market participants to gain profit. In 2020, the total capitalization of all markets reached $95 trillion. Within such profiting platforms, malicious attacks like stock price/market manipulation are conducted. Stock price manipulation attack refers to the strategic act of trading either a stock, a future (derivative financial contracts), or a security (financial instrument) to increase or decrease their prices. Hence, making significant profits. Manipulation types vary by sources and/or exploited methods. To detect and deter such attacks/practices, extensive research have been conducted. To further assist the future studies, in this paper, we conduct a systematic literature review to extract research articles relevant to stock price/market manipulation attacks and their state-of-art detection systems/solutions. To the best of our knowledge, this is the first systematic literature review that is based on a predefined search methodology and systematically collects the relevant articles that were published between 2011 and 2021. After applying the systematic search methodology, we extracted and surveyed 66 articles and classified them into four main detection approaches. Namely, statistical-based, Machine Learning based, system-based/artefacts, and visualization-based/observation-based. We provide a thorough comparison among the detection systems in terms of utilized datasets, leveraged features, employed optimization techniques, and applied performance measures. Our survey also identifies and discusses a set of existing research gaps such as generality of detection approaches, near/real-time detection capability, validity of real/synthesised datasets, etc. We concluded our survey by proposing several research topics and recommendations to be investigated and addressed by future works. © The Author(s), under exclusive licence to Springer Science+Busine

关键词： Profitability

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DySARNet: a lightweight self-attention deep learning model for diagnosing dysarthria from speech recordings

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Multimedia Tools and Applications 2024年 1-49页

作者： Montalbo, Francis Jesmar P. College of Informatics and Computing Sciences Batangas State University Bantangas Philippines

Dysarthria, a neurological condition impairing speech, has garnered significant research interest in Deep Learning (DL). Curent studies underscore DL’s potential in automating dysarthria diagnosis and severity classification, utilizing well-known benchmark datasets like TORGO and UA-Speech. However, existing DL for dysarthria diagnosis, like Deep Neural Networks, are often computationally expensive, complex, and lack context awareness. This paper addresses these issues by proposing a Densely Squeezed-and-excited Attention-gated Residual convolutional neural Network (DySARNet) designed for dysarthria diagnosis and severity estimation. DySARNet aims to provide a cost-effective solution compatible with various devices, including mobile phones. DySARNet made this possible by integrating a Lean Separable Dense Block (LSDB) that enhances feature reuse while preventing a massive parameter increase through separable depthwise convolutions (SDWConv). For improved context understanding and awareness, DySARNet employs a Squeeze-and-Excited Lightweight Residual Attention-gated (SELRA) mechanism, a tailored self-attention mechanism that offers robust attention at a low cost by squeezing additional parameters through SDWConv and depthwise convolutions. DySARNet achieves 99.03% and 98.77% accuracy in diagnosing dysarthric and non-dysarthric speech recordings and 99.20% and 96.09% in classifying dysarthria severity using the TORGO and UA-Speech datasets. With only 33 K parameters and 0.04 G-FLOPs, DySARNet surpasses most state-of-the-art models in cost-effectiveness and overall performance. In conclusion, DySARNet, with its SELRA and LSDB components, reveals significant promise in enhancing dysarthria diagnosis by automatically recognizing the presence of dysarthria and classifying its severity more efficiently. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Audio signal processing

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Classifying Fake Profiles on X with Multinomial Naïve Bayes 10

Classifying Fake Profiles on X with Multinomial Naïve Bayes

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Azmin, Nur Akifah Zainuddin, Nurulhuda Mohamad, Masurah College of Computing Informatics & Mathematics Melaka Malaysia College of Computing Informatics & Mathematics Perak Malaysia

ISBN: (纸本)9798331521295

Online social networks have experienced rapid growth, facilitating massive connections among users. However, this expansion has also led to a significant increase in fake users, putting user privacy at risk as these deceptive entities exploit the platform's accessibility to gather and misuse personal data. The proliferation of fake profiles poses a serious threat to the social fabric of the X world, potentially undermining user interactions. This research aims to classify fake profiles that mimic genuine accounts, making detection challenging. The proposed solution utilizes a Multinomial Naïve Bayes model, offering an innovative method to combat the issue of fake profiles. The model demonstrates promising results, achieving 94.67% accuracy, 90% precision, 90% recall, and a 95% area under the curve (AUC). The study concludes by calling for further research to expand the range of training examples for the model, enhancing its ability to accurately differentiate between fake and genuine X profiles. It also emphasizes the importance of maintaining key features within the model to ensure an accurate representation of the subtle differences between genuine and fake profiles. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigating Developer Experiences with UI Components in Flutter: Challenges and Implications

Investigating Developer Experiences with UI Components in Fl...

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2024 International Visualization, informatics and Technology Conference, IVIT 2024

作者： Donglan, Zou Bin Darus, Mohamad Yusof Ramli, Azlin Binti College of Computing Informatics and Mathematics Malaysia

ISBN: (纸本)9798350354447

The rapid proliferation of smartphones, particularly those using the Android operating system, has positioned Android as the leading platform in the mobile market. With the advent of various mobile app development frameworks such as Ionic, Flutter, Corona SDK, and Xamarin, developers face the challenge of selecting the optimal framework for their projects. This study focuses on the Flutter framework, a cross-platform development tool by Google, exploring developers' experiences with User interface (UI) construction, specifically in handling common components such as network images and animations. A comprehensive survey was conducted, targeting Flutter developers from diverse backgrounds, including engineers, university students, and instructors. The survey aimed to identify the challenges associated with using Flutter's NetworkImage feature and Animation component. Results indicate significant issues with performance, network connectivity, image quality, dependence on third-party libraries, cache management, and traffic consumption for network images. Similarly, animations in Flutter pose challenges related to memory insufficiency, varying effects across different devices, and performance impacts on user experience. The findings highlight the necessity for enhanced support and innovative solutions within the Flutter framework to address these limitations. This study serves as a resource for developers to better understand and navigate the complexities of Flutter, and it underscores the importance of further research to improve mobile application development technologies. Future investigations should broaden the scope to include more diverse application cases and larger sample sizes to build on these initial insights. © 2024 IEEE.

关键词： Cache memory

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Classification of Privacy Preserved Medical Data with Fractional Tuna Sailfish Optimization Based Deep Residual Network in Cloud

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Annals of Data Science 2025年第3期12卷 829-854页

作者： Shikalgar, Shabanam K. Kumar, N. V. S. Pavan Singh, Gavendra Rashid, Faizur Sharad Institute of Technology College of Engineering Yadrav India Koneru Lakshmiah Education Foundation Vijayawada India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia

Nowadays, with the growth of emerging technologies, increased attention has been paid to the classification of privacy-preserved medical data and development of various privacy-preserving models for the promotion of online medical pre-diagnosis systems. Medical data is highly sensitive and it is essential to ensure privacy of medical records from third-party users to increase service quality, satisfy patients and earn trust. The classification of medical preserved data is helpful to build a clinical decision system by classifying patients based on their disease and symptoms. In this article, a hybrid optimization-based deep learning model named Fractional Tuna Sailfish Optimization–Deep Residual Network (FractionalTSFO-DRN) is designed to precisely classify the privacy preserved medical data. A privacy utility coefficient matrix is used to ensure the privacy of medical data by generating a key matrix using Tuna Sailfish Optimization (TSFO) algorithmic technique. The privacy-preserved medical data is allowed for the classification process using DRN and the introduced Fractional TSFO is used to optimize and enhance the classification in DRN. The assessment followed by using heart disease prediction databases proved that the employed classification technique recorded an accuracy of 94.67%, a True Positive Rate of 93.56%, and a True Negative Rate of 89.68% respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Classification (of information)

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MH-HMR:Human mesh recovery from monocular images via multi-hypothesis learning

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1263-1274页

作者： Haibiao Xuan Jinsong Zhang Yu-Kun Lai Kun Li College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Informatics Cardiff UniversityCardiffUK

Recovering 3D human meshes from monocular images is an inherently ill-posed and challenging task due to depth ambiguity,joint occlusion,and ***,most existing approaches do not model such uncertainties,typically yielding a single reconstruction for one *** contrast,the ambiguity of the reconstruction is embraced and the problem is considered as an inverse problem for which multiple feasible solutions *** address these issues,the authors propose a multi-hypothesis approach,multi-hypothesis human mesh recovery(MH-HMR),to efficiently model the multi-hypothesis representation and build strong relationships among the hypothetical ***,the task is decomposed into three stages:(1)generating a reasonable set of initial recovery results(i.e.,multiple hypotheses)given a single colour image;(2)modelling intra-hypothesis refinement to enhance every single-hypothesis feature;and(3)establishing inter-hypothesis communication and regressing the final human ***,the authors take further advantage of multiple hypotheses and the recovery process to achieve human mesh recovery from multiple uncalibrated *** with state-of-the-art methods,the MH-HMR approach achieves superior performance and recovers more accurate human meshes on challenging benchmark datasets,such as Human3.6M and 3DPW,while demonstrating the effectiveness across a variety of *** code will be publicly available at https://***/faculty/likun/projects/MH-HMR.

关键词： 3-D computer vision human reconstraction

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A Review of Deep Learning Methods for Automatic Pain Assessment 7

A Review of Deep Learning Methods for Automatic Pain Assessm...

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7th International Conference on Data Science and Information Technology, DSIT 2024

作者： Zhicui, Lu Mat Jasin, Aisyah Binti Akma Binti Zulkifle, Farizuwana Ul-Saufie Bin Mohamad Japeri, Ahmad Zia Universiti Teknologi MARA College of Computing Informatics and Mathematics Selangor Shah Alam Malaysia Universiti Teknologi MARA College of Computing Informatics and Mathematics Raub Malaysia Universiti Teknologi MARA College of Computing Informatics and Mathematics Kuala Pilah Malaysia

ISBN: (纸本)9798350384093

Pain is a complex phenomenon that incorporates both physical sensations and emotional responses. The use of automated pain assessment is crucial in order to develop effective medical diagnostic systems for pain management. Thus, it is important to conduct a study on the outcomes achieved by utilising deep learning algorithms for the detection of pain expression. This study aims to provide reliable and unbiased methods for the automated assessment of pain. The aim of this systematic review is to discuss the models, methods and data types used to build the foundation of deep learning-based automated pain assessment systems, with a focus on analysing improved strategies and methods based on deep modelling techniques used to enhance feature extraction accuracy and the accuracy of pain level assessment. As a result, the literature explores the application of facial expression recognition techniques in the field environment for addressing various challenges in clinical testing. Consequently, the systematic review identifies the limitations of the current research on automated pain assessment and offers an outlook on the potential future research directions. © 2024 IEEE.

关键词： Contrastive Learning

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Evaluating Machine Learning Algorithms for Predicting Financial Aid Eligibility: A Comparative Study of Random Forest, Gradient Boosting and Neural Network 18

Evaluating Machine Learning Algorithms for Predicting Financ...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Ismail, Mohammad Hafiz Razak, Tajul Rosli Noor, Noorfaizalfarid Mohd Aziz, Azlan Abdul College of Computing Informatics and Mathematics Universiti Teknologi MARA Arau Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9798350331011

Financial aid ensures equitable access to higher education, irrespective of students' social or economic backgrounds. However, as the financial aid source are limited, the administrators are burdened with the task of determining the student eligibility for financial aid in a fair and unbias manner. Additionally, the process of determining eligibility by human evaluators can benefit from machine learning assisted decision support tools. This study investigates the feasibility of using machine learning algorithms to achieve this goal. Three algorithms were selected for this comparative study with Decision Tree acts as a baseline. The algorithms are trained against a highly imbalanced dataset provided by ZAWAF. The training process incorporates k-fold cross-validation and employs stratified sampling techniques. It was found that all machine learning models outperformed the baseline, with the MLP-ANN model exhibiting the highest accuracy and precision scores. This demonstrates the potential for machine learning models to be integrated as decision support for the distribution of financial aid. © 2024 IEEE.

关键词： Decision trees

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ISWMbot: Chatbot with an Intent Classification Model using Supervised Algorithm 6

ISWMbot: Chatbot with an Intent Classification Model using S...

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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Istihat, Nur Nazirah Ibrahim Teo, Noor Hasimah Roslan, Rosniza Hamzah, Raseeda Shahbudin, Fadilah Ezlina Sulaiman, Mohd Suffian Universiti Teknologi Mara College of Computing Informatics and Mathematics Melaka Malaysia Universiti Teknologi Mara College of Computing Informatics and Mathematics Shah Alam Malaysia

ISBN: (纸本)9798350353853

Effective solid waste management is essential for environmental best practices, but public participation is hindered by a lack of proper knowledge about waste management. People face difficulties in finding relevant answers about waste management, whether through social media or the internet, leading to time-consuming searches. To address these challenges, the project aims to implement Intent Solid Waste Management Chatbot (iSWMbot), a web-based chatbot, providing users with answers and solutions to their solid waste management queries. The project proposes the implementation of iSWMbot, a user-friendly web-based chatbot designed to provide prompt and relevant answers to users' inquiries on solid waste management. The proposed system utilizes machine learning techniques, incorporating Natural Language Processing for keyword extraction and the Multinomial Naïve Bayes algorithm for classification. The iSWMbot consists of three modules which are query answering, answer suggestions and feedback. The system effectively fulfills its purpose by providing appropriate responses to users' queries and offering helpful suggestions. By implementing the iSWMbot system, it streamlines the workload for waste management authorities and provides timely and accurate information to the public, reducing their concerns and promoting sustainable waste management practices. © 2024 IEEE.

关键词： Natural language processing systems

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