Computer Sciences Commons^™

Towards Achieving The Un Sustainable Development Goals: The Role Of Ai In Municipality Services, Thabit Sultan Mohammed, Karim Mohammed Aljebory, Ahmed Thabit Sultan

Mesopotamian Journal of Computer Science

In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) to end poverty, protect the planet, and ensure global peace and prosperity by 2030. However, progress has been hindered by challenges like the COVID-19 pandemic, climate change, funding shortages, political instability, and data limitations. Municipal services, crucial to achieving the SDGs, provide essential functions like waste management, healthcare, and public safety. Artificial Intelligence (AI) offers innovative solutions to enhance these services, improving urban sustainability and fostering public-private collaboration. This research examines AI's role in municipal services, analyzing its applications, benefits, challenges, and future potential through case studies and expert …

Automated Video Colorization Techniques For Enhanced Visual Realism And Computational Efficiency, Zahoor M. Aydam, Nidhal K. El Abbadi

Mesopotamian Journal of Computer Science

Automatic video colorization remains a challenging computer vision task, particularly when ensuring semantic accuracy and temporal coherence across dynamic, multi-scene content. Existing methods often rely on a single fixed reference image, which fails to adapt to abrupt scene changes or variations in lighting and texture. This study presents a hybrid deep learning framework that dynamically selects multiple reference images per scene using adaptive thresholds derived from the Structural Similarity Index Measure (SSIM) and deep features extracted via a ResNet50 backbone with Generalized Mean Pooling (GeM). The framework integrates three specialized modules pre-processing, reference image processing, and attention-based colorization—operating in the …

Enhancing Routing Efficiency In Vanets By Leveraging 5g To Mitigate Congestion, Yusor Rafid Bahar Al-Mayouf, Omar Adil Mahdi, Heba Hussain Hadi, Suleman Khan, Mazin Abed Mohammed

Mesopotamian Journal of Computer Science

Transportation networks impact millions of people daily. Their efficiency immediately affects travel time, safety, and environmental sustainability. Unfortunately, various issues hinder the expected performance and efficiency of these networks. Traffic congestion is an up-to-date issue in the urban environment. Fuel consumption is high because travel time has increased, which has a passive environmental impact. Extensive research has been conducted to progress the intelligent transportation systems installed on communication networks and information to treat this congestion. However, there is a significant amount of affront residue in combining real-time data, estimation analytics, and 5G abilities effectively. This paper offers a novel routing …

Roi – Enhancing Detection Of Citrus Disease Based On Yolov10, Raya N. Ismail, Armaneesa Naaman Hasoon, Israa Rafaa Abdulqader, Salwa Khalid Abdulateef

Mesopotamian Journal of Computer Science

One of the most important fruit crops in the world is citrus. However, some citrus diseases spread rapidly, which is why early detection at an accurate stage is important for timely intervention. YOLO-based object detection models, such as the latest YOLOv10, where small lesions are difficult to identify among noisy backgrounds, have recently been developed, yet their accuracy tends to degrade. Therefore, we proposed a citrus disease detection model by integrating the region of interest (ROI) for object segmentation with the YOLOv10 model, thus addressing the issues of low detection accuracy and slow inference time. The proposed model was trained …

End-To-End License Plate Detection And Recognition In Iraq Using A Detection Transformer And Ocr, Younis Al-Arbo, Hanaa F. Mahmood, Asmaa Alqassab

Mesopotamian Journal of Computer Science

Automatic License Plate Recognition (ALPR), DEtection TRansformer (DETR), Deep Learning for Object Detection, Optical Character Recognition (OCR), Region-Specific Vehicle Identification

Enhanced Tea Algorithm Performance Using Affine Transformation And Chaotic Arnold Map, Nada Hussein M. Ali, Mays M. Hoobi, Sura Abed Sarab Hussien

Mesopotamian Journal of Computer Science

In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing …

Dgen: A Dynamic Generative Encryption Network For Adaptive And Secure Image Processing, Mohammed Rajih Jassim, Qusay M. Salih, Ghada Al-Kateb

Mesopotamian Journal of Computer Science

Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public …

Using Wearable Technology For Context-Aware Of Pilgrimage Management System During/Post-Pandemic Of Covid-19, Fatina Shukur, Ahmed Al-Fatlawi, Safaa Jasim Mosa, Nasir Ibrahim

Mesopotamian Journal of Computer Science

Hajj is an annual event placed in Saudi Arabia. It is one of the largest religious gatherings, such that it gathers millions of pilgrims from all around the world. The pandemic of Covid-19 is not over yet, and could likely be part of our life for a long time. Also, it is negatively affecting such mass gatherings. The aim of this research is to facilitate the overall hajj event while maintaining peoples’ health, safety, and security. Therefore, we use a technology that helps to reduce direct contact between pilgrims themselves as well as with other entities, such that human interaction …

Ai Characterisations And Their Legal Implications, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

This chapter examines the difficult legal characterisation problems that artificially intelligent systems raise and explores how different characterisations of artificial intelligence (AI) shape practical legal outcomes. Three reasons are offered for the legal difficulty with characterising AI. First, answers to characterisation problems are inherently subjective and perspective-driven, particularly when the subject is an intangible technological system. Second, AI technology is especially difficult to define since the field typically proceeds on inexact anthropomorphic metaphors. Third, AI characterisation problems raise difficult sub-problems, particularly in determining how autonomous an AI system is. The chapter thus argues that a range of plausible AI characterisations …

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

Analyzing Visual Attention In Virtual Crime Scene Investigations Using Eye-Tracking And Vr: Insights For Cognitive Modeling, Wen-Chao Yang, Chih-Hung Shih, Jiajun Jiang, Sergio Pallas Enguita, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A 360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated …

Fostering Critically Conscious Lesson Planning In A Generative Artificial Intelligence Era, Derek Riddle, Paula Cristina Azevedo, Catharyn Shelton, Jaime Colwell, Jori Beck

Teaching & Learning Faculty Publications

Teacher candidates (TCs) use digital resources and social media to plan and develop learning material, and with publically accessible generative artificial intelligence (GAI), TCs are able to generate lesson plans within seconds rather than hours or days. While there is research on how to support TCs' evaluation of reliable digital media, there is little known on how to prepare TCs for GAI content. Using the complementary frameworks of Freire’s (1970) critical consciousness and Jonnasen’s (1991) theory of constructivism, this in-progress design based research seeks to develop an adaptable framework that addresses the evolving nature of technology, specifically GAI, and the …

Copyright And Artificial Intelligence, Part 2: Copyrightability

Copyright, Fair Use, Scholarly Communication, etc.

This report by the United States Copyright Office addresses the legal and policy issues related to artificial intelligence (AI) and copyright as outlined in the Office’s August 2023 Notice of Inquiry (NOI).

The report will be published in several parts each one addressing a different topic. This part addresses the copyrightability of works created using generative AI. The first part, published in 2024, addresses the topic of digital replicas—the use of digital technology to realistically replicate an individual’s voice or appearance. A subsequent part will turn to the training of AI models on copyrighted works, licensing considerations, and allocation of …

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …

Llms-Based Augmentation For Domain Adaptation In Long-Tailed Food Datasets, Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun

Research Collection School Of Computing and Information Systems

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment. In addition to this domain-shift problem, the real-world food datasets tend to be long-tailed distributed and some dishes of different categories exhibit subtle variations that are difficult to distinguish visually. In this paper, we present a framework empowered with large language models (LLMs) to address these challenges in food recognition. We first leverage LLMs to parse food images to generate food titles and ingredients. Then, we project the …

Demo2test: Transfer Testing Of Agent In Competitive Environment With Failure Demonstrations, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Dandan Wang, Qing Wang, Fanjiang Xu

Research Collection School Of Computing and Information Systems

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of …

Interpreting Topic Models In Byte-Pair Encoding Space, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Byte-pair encoding (BPE) is pivotal for processing text into chunksize tokens, particularly in Large Language Model (LLM). From a topic modeling perspective, as these chunksize tokens might be mere parts of valid words, evaluating and interpreting these tokens for coherence is challenging. Most, if not all, of coherence evaluation measures are incompatible as they benchmark using valid words. We propose to interpret the recovery of valid words from these tokens as a ranking problem and present a model-agnostic and training-free recovery approach from the topic-token distribution onto a selected vocabulary space, following which we could apply existing evaluation measures. Results …

Empowering Crisis Information Extraction Through Actionability Event Schemata And Domain-Adaptive Pre-Training, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on social media, including requirement gathering, selection of machine learning tasks, data preparation, and integration with existing resources, providing guidance for governments, civil services, emergency workers, and researchers on supplementing existing channels with actionable information from social media. Our solution leverages an actionability schema and domain-adaptive pre-training, improving upon the state-of-the-art model by 5.5% and 10.1% in micro and macro F1 …

Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Research Collection School Of Computing and Information Systems

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …

Automated Program Refinement: Guide And Verify Code Large Language Model With Refinement Calculus, Yufan Cai, Zhe Hou, David Sanan, Xiaokun Luan, Yun Lin, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Recently, the rise of code-centric large language models (LLMs) appears to have reshaped the software engineering world with low-barrier tools like Copilot that can generate code easily. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult for users to understand and trust the generated code. Addressing these challenges is both necessary and critical. In contrast, program refinement transforms high-level specification statements into …

Financial Named Entity Recognition: How Far Can Llm Go?, Yi-Te Lu, Yintong Huo

Research Collection School Of Computing and Information Systems

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight …

Gamified Mhealth System For Evaluating Upper Limb Motor Performance In Children: Cross-Sectional Feasibility Study, Md Raihan Mia, Sheikh Iqbal Ahamed, Samuel Nemanich

Computer Science Faculty Research and Publications

Background: Approximately 17% of children in the United States have been diagnosed with a developmental or neurological disorder that affects upper limb (UL) movements needed for completing activities of daily living. Gold-standard laboratory assessments of the UL are objective and precise but may not be portable, while clinical assessments can be time-intensive. We developed MoEvGame, a mobile health (mHealth) gamification software system for the iPad, as a potential advanced technology to assess UL motor functions.

Objective: This feasibility study examines whether MoEvGame can assess children’s whole-limb movement, fine motor skills, manual dexterity, and bimanual coordination. The specific aims were to …

Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi

Theses and Dissertations

Electronic Health Records (EHRs) constitute a comprehensive and high-dimensional repository of clinical data, encompassing a wide array of patient-level information such as diagnoses, procedures, medications, laboratory results, and unstructured clinical narratives. These data hold immense potential for advancing predictive modeling in healthcare, including tasks such as disease progression modeling, hospital readmission prediction, and length of stay (LoS) estimation. However, the intrinsic complexity of EHR data—manifested in its heterogeneity, sparsity, and temporal dynamics—poses significant analytical challenges that limit the generalizability and interpretability of conventional machine learning models. Recent methodological advancements in deep learning and graph-based learning, particularly Graph Neural Networks (GNNs), …

Relationship Extraction Using Retrieval Augmented Generation For Biomedical Dataset, Jannat .

Theses and Dissertations

With the increasing number of structured and unstructured data, obtaining reliable information effectively has become crucial. In the biomedical domain, extracting information from the scientific papers is crucial in order to stay up-to-date with accurate information, given the increased pace by which new research studies are published. This work focuses on identifying relationships between entities that are extracted from the abstracts and titles of biomedical research papers. In this work, we developed a Retrieval Augmented Generation (RAG) based system to automatically identify relations between biomedical entities. We evaluate multiple open source Large Language Models (LLMs) and the number of examples …

Learning From Non-Stationary Data Streams, Gabriel Jonas Aguiar

Theses and Dissertations

The rapid growth of data from sources such as mobile applications, sensors, and network monitoring has increased the need for machine learning algorithms capable of handling non-stationary data streams. However, learning from such streams presents significant challenges due to their evolving nature and the presence of concept drift. One of the most complex issues is learning from imbalanced data streams, where shifting data distributions, combined with feature space drifts, complicate continuous adaptation. These challenges become even more pronounced in multi-class scenarios, which are common in real-world applications. Detecting concept drift in such contexts is particularly demanding, as it requires tracking …

Applying Machine Learning And Optimization Algorithms To Perform Feature Selection, Shizhao Yu

Theses and Dissertations (Comprehensive)

The objective of feature selection in the realms of machine learning and data mining is integral, serving as an efficient mechanism to eradicate redundant or irrelevant features, and subsequently augmenting the performance of predictive models. In the contemporary landscape of big data, with the escalating dimensionality of datasets, the efficacy of traditional feature selection methodologies is compromised, due to their computational complexity and ineptitude in addressing the curse of dimensionality. This thesis posits a pioneering feature selection framework that amalgamates machine learning with advanced optimization algorithms. The methodology employs a Support Vector Machine (SVM), in conjunction with a cutting-edge metaheuristic …

Multi-Lingual And Cross-Domain Frontiers In Machine-Generated Content Detection, Gurunameh Singh Chhatwal

Theses and Dissertations (Comprehensive)

The rapid advancement of generative artificial intelligence, particularly Large Language Models (LLMs) such as GPT-4 and their multilingual capabilities, has significantly blurred the distinction between human-authored and machine-generated content. This technological evolution introduces critical challenges concerning the detection and attribution of textual authenticity and authorship, exacerbating societal issues like misinformation proliferation and compromising academic and professional integrity. Traditional detection methodologies, predominantly monolingual and heuristic-based, have demonstrated inadequate generalizability and efficacy against the sophisticated, multilingual capabilities of contemporary generative models.

This thesis addresses two major problems arising from these advancements. Firstly, it introduces novel multilingual detection methodologies explicitly designed to differentiate …

Reinforcement Learning For Scheduling And Routing: Electric Vehicles Charging And Patrol Scheduling, Majid Ghasemi

Theses and Dissertations (Comprehensive)

This thesis offers a comprehensive exploration of Reinforcement Learning (RL), beginning with fundamental theoretical constructs, Markov Decision Processes, Dynamic Programming, Monte Carlo, and Temporal Difference methods, and extending into state-of-the-art deep RL approaches such as Deep Q-Networks (DQN) and policy-gradient algorithms. Through analytical experiments in controlled environments, the thesis demonstrates how distinct algorithmic choices (e.g., exploration techniques, eligibility traces, or network architectures) influence convergence and stability. These foundational insights pave the way for two in-depth case studies, which apply RL techniques to critical, real-world scheduling and routing challenges.

The first case study tackles the Electric Vehicle (EV) routing and charging …

Information Retrieval In The Age Of Generative Ai: A Mismatch That Matters, Alex Zhang

Faculty Scholarship

This short piece explores a widespread and yet underexamined or even overlooked misconception, that is, large language models (LLMs) function like traditional legal research databases. They do not. As a matter of fact, information retrieval from databases functions very differently from LLMs in terms of inputs, retrieval processes, and outputs. These differences have significant implications for transparency, traceability, and overall effectiveness in AI-driven legal research. Without intentional oversight and adaption, these changes could profoundly affect how we develop research skills and a cumulative knowledge base, both of which are essential skills for lifelong learning in the legal field.

This article …