Societies’ Funds Management System Using Blockchain,
2025
Universiti Tun Hussein Onn Malaysia, Malaysia
Societies’ Funds Management System Using Blockchain, Jun Xiang Lau, Nur Ziadah Harun, Suziyanti Marjudi, Abd Samad Hasan Basari, Nur Amlya Abd Majid, Roziyani Setik
Journal of Informatics and Web Engineering
Lack of transparency of the funds and lack of immutability of the funds’ records are large problems today, especially in the societies’ funds. Leveraging the Ethereum Blockchain, the system ensures complete transparency and security in recording and accessing financial transactions for any society. Advanced encryption and blockchain consensus protocols guarantee data privacy and resilience against fraud or tampering. The information entered in the system helps the treasurer to effectively manage funds and accurately and transparently pass appropriate financial transactions. This project meets the challenges of transparency and immutability in society’s fund records, providing an assurance system and promoting growth for …
Implementation Of Conjugate Gradient Method For Estimating Inflation Rate In Malaysia,
2025
Universiti Teknologi Malaysia, Malaysia
Implementation Of Conjugate Gradient Method For Estimating Inflation Rate In Malaysia, Shin Yi Wong, Nur Syarafina Mohamed, Norhaslinda Zullpakkal
Journal of Informatics and Web Engineering
Optimization methods are valuable for making decisions and identifying the most suitable alternative based on a given objective function. One of the mathematical optimization methods is Conjugate Gradient (CG) method which is commonly used to solve large-scale unconstrained optimization systems with less storage space. Recently, various optimization methods have been studied and used in economics estimating. However, just a few studies have predicted inflation rate using modified CG method. Random initial points are tested on New Three-Terms (NTT) which are modified Rivaie-Mustafa-Ismail-Leong (RMIL+) and Umar Mustapha Waziri (UMW) CG method with ten optimization test functions suggested by Andrei using MATLAB. …
Conjugate Gradient Methods In Fitting Precipitation Of Rainfall Data In Malaysia,
2025
Universiti Teknologi Malaysia, Malaysia
Conjugate Gradient Methods In Fitting Precipitation Of Rainfall Data In Malaysia, Hua Ru Tang, Nur Syarafina Mohamed, Nurul Hajar Mohd Yusoff, Norhaslinda Zullpakkal
Journal of Informatics and Web Engineering
Conjugate gradient method (CGM) is one of the most efficient numerical methods for solving unconstrained optimization problems. It is also known as an iterative method with simple formulation. The classical CGM has always been an interest to the current researchers in improving the formulation which are categorized into three-term (TTCGM), spectral (SCGM), hybrid and scaled CGM. Although there are many variations of the CGM available, choosing the most efficient and effective one for a particular problem can be a time-consuming process. In this study, spectral Hestenes-Stiefel (sHS) CGM with the greatest NOI and central processing time per unit (CPU time) …
Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration,
2025
Universiti Tun Hussein Onn Malaysia, Malaysia
Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin
Journal of Informatics and Web Engineering
One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced class distribution. This results in fraudulently high accuracy without being able to detect minority cases. This bias is also most perilous in critical applications, where ignoring minority cases can be highly destructive. To overcome this problem, the Synthetic Minority Oversampling Technique (SMOTE) is one of the most widely used. SMOTE creates balanced class distribution by interpolating between existing minority samples. It creates samples that are too close …
Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems,
2025
Department of Electronics and Telecommunications Engineering, University of Dar es Salaam, P.O BOX 33336 Dar es Salaam, Tanzania
Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems, Kwame S. Ibwe
Tanzania Journal of Engineering and Technology (TJET)
In this paper, a blind symbol synchronization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems, and a timing function based on the redundancy of the cyclic prefix (CP) is introduced. The existing algorithms rely on the prior knowledge of the channel energy distribution i.e. channel power profile. In practical environment the channel power profile is unknown to the receiver and its statistics are expected to be highly changing. Nevertheless, the use of pilot symbols in channel profile estimation reduces efficiency as data subcarriers are used to carry pilots instead of payload. In this paper a timing function that accounts …
Cnn-Based Hybrid Model For Detecting Blight Diseases In Potato Crops With Advanced Image Processing Techniques,
2025
Department of Informatics and Information Technology, College of Natural and Applied Sciences, Sokoine University of Agriculture, P.O. Box 3000, Morogoro, Tanzania
Cnn-Based Hybrid Model For Detecting Blight Diseases In Potato Crops With Advanced Image Processing Techniques, Farian S. Ishengoma
Tanzania Journal of Engineering and Technology (TJET)
Potato production plays a vital role in global agriculture as a major food source for large populations. However, potato crops are highly susceptible to diseases, particularly Early Blight and Late Blight, which result in substantial yield losses. Timely detection and effective control of these diseases are essential for maintaining stable crop output. This study explores the integration of Convolutional Neural Networks (CNNs) and advanced image processing techniques to differentiate between diseased and healthy potato plants accurately. Two datasets comprising original and enhanced images were used to train four CNN models: InceptionV3, Xception, Densenet201, and Resnet152V2. The original images underwent background …
Building An Inclusive Ai Chatbot For Diverse Student Communities At Cal Poly: Uplift Ai,
2025
California Polytechnic State University, San Luis Obispo
Building An Inclusive Ai Chatbot For Diverse Student Communities At Cal Poly: Uplift Ai, Gideon Telahun
College of Engineering Summer Undergraduate Research Program
This research project will investigate the ability of advanced Large Language Models (LLMs) to identify and assess misinformation across diverse forms of media, including text, images, and video. In an age where misleading content spreads rapidly across digital platforms, evaluating the reliability and integrity of AI systems tasked with fact-checking is critical. We will develop a comprehensive dataset composed of factual and misleading examples drawn from various well-known and reliable fact-checking organizations. Each item will be independently reviewed and transparently labeled to ensure reproducibility. We will then prompt a curated group of state-of-the-art LLMs—including GPT-4, Claude, Gemini, Perplexity, Grok, and …
Filterfl: Knowledge Filtering-Based Data-Free Backdoor Defense For Federated Learning,
2025
Singapore Management University
Filterfl: Knowledge Filtering-Based Data-Free Backdoor Defense For Federated Learning, Yanxin Yang, Ming Hu, Xiaofei Xie, Yue Cao, Pengyu Zhang, Yihao Huang, Mingsong Chen
Research Collection School Of Computing and Information Systems
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned …
Deep Learning For Hate Speech Detection: A Comparative Study,
2025
University of Adelaide
Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …
Impact Of Original Versus Reposted Social Endorsements On Content Consumption: The Moderating Role Of Endorsers’ Network Characteristics,
2025
Singapore Management University
Impact Of Original Versus Reposted Social Endorsements On Content Consumption: The Moderating Role Of Endorsers’ Network Characteristics, Anqi Zhao, Qian Tang
Research Collection School Of Computing and Information Systems
Social endorsements broadcast endorsers’ positive attitudes toward content or products, especially to their social ties. Original endorsements created by endorsers can be propagated further as reposted endorsements. Both are important marketing tools to increase content consumption, yet their differences are unclear. This study compares the impacts of original and reposted endorsements on content consumption and their contingencies on the endorsers’ network characteristics. Using data on social endorsements of YouTube videos on Twitter, we find that original endorsements (i.e., original tweets) significantly boost content consumption, and the effect is positively moderated by the endorsers’ network size but not their tie strength. …
Honors Faculty Research Talk Ml Tlachac,
2025
Bryant University
Honors Faculty Research Talk Ml Tlachac, Ml Tlachac
Honors Programs
PowerPoint Slides of Honors Research Talk by ML Tlachac.
A Data-Driven Framework For Optimal Retail Store Location,
2025
Singapore Management University
A Data-Driven Framework For Optimal Retail Store Location, Ming Hui Tan
Dissertations and Theses Collection (Open Access)
This study develops a data-driven framework for optimal retail store location planning that integrates road network analysis, mobility data and optimization techniques. By addressing the limitations of traditional approaches that rely on outdated census data and manual site selection, this research offers a scalable and adaptable solution for retail expansion in diverse urban environments. Chapters 1 and 2 establish the foundational context and theoretical underpinnings of this research. Chapter 1 introduces the research problem and motivation, highlighting the limitations of existing approaches and defining three key research objectives: automating candidate site identification, improving footfall estimation, and developing a scalable multi-site …
Website Owner Identification Through Multi-Level Contrastive Representation Learning,
2025
Singapore Management University
Website Owner Identification Through Multi-Level Contrastive Representation Learning, Cheng Tu, Yunshan Ma, Yang Li, Min Zhang, Miao Hu, Fan Shi, Xiang Wang
Research Collection School Of Computing and Information Systems
Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable …
Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs,
2025
Singapore Management University
Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs, Jiaolong Kong, Xiaofei Xie, Mingfei Cheng, Shangqing Liu, Xiaoning Du, Qi Guo
Research Collection School Of Computing and Information Systems
Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recentstrides in Large Language Models (LLM), such as ChatGPT, have yielded encouraging outcomes in APR,especially within the conversation-driven APR framework. Nevertheless, the efficacy of conversation-drivenAPR is contingent on the quality of the feedback information. In this article, we propose ContrastRepair, anovel conversation-based APR approach that augments conversation-driven APR by providing LLMs withcontrastive test pairs. A test pair consists of a failing test and a passing test, which offer contrastive feedback tothe LLM. Our key insight is to minimize the difference between the generated passing test and the …
Factors Influencing The Use Of Gis-Enabled Public E-Participation For Municipal Solid Waste Management,
2025
Makerere University
Factors Influencing The Use Of Gis-Enabled Public E-Participation For Municipal Solid Waste Management, Irene Arinaitwe, Agnes Nakakawa, Gilbert Maiga
The African Journal of Information Systems
Due to rapid global population growth and urbanization, approximately two billion metric tons of waste are generated annually. Municipal solid waste management has become a critical function for urban authorities. However, many urban authorities in low- and middle-income economies cannot provide efficient municipal solid waste management services because of suboptimal stakeholder participation in governance processes and inadequate information exchange. Therefore, this study sought to determine factors that influence the implementation of GIS-enabled public e-participation using Enhanced Adaptive Structuration Theory. A descriptive field study was conducted among staff of municipal authorities and residents in Uganda’s Kampala Metropolitan Area. Data were analyzed …
Lighttransfer: Your Long-Context Llm Is Secretly A Hybrid Model With Effortless Adaptation,
2025
Singapore Management University
Lighttransfer: Your Long-Context Llm Is Secretly A Hybrid Model With Effortless Adaptation, Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, Min Lin
Research Collection School Of Computing and Information Systems
Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation. In this work, we propose LightTransfer, a lightweight method that transforms models such as LLaMA into hybrid variants. Our approach identifies lazy layers -- those focusing on recent or initial tokens -- and replaces their full attention with streaming attention. This transformation can be performed without any training for long-context …
Ponzilens+: Visualizing Bytecode Actions For Smart Ponzi Scheme Identification,
2025
Singapore Management University
Ponzilens+: Visualizing Bytecode Actions For Smart Ponzi Scheme Identification, Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen, Jun Sun, Feida Zhu, Yong Wang
Research Collection School Of Computing and Information Systems
With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive …
Managing Rumors On Electronic Interaction Platforms: How Management Responses Affect Investor Reaction,
2025
Singapore Management University
Managing Rumors On Electronic Interaction Platforms: How Management Responses Affect Investor Reaction, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang
Research Collection School Of Computing and Information Systems
This study investigates how listed firms respond to investors’ rumor-related inquiries and examines the impact of these responses on investor reactions, as indicated by subsequent daily abnormal stock returns (ARs). Using a unique dataset of question-and-answer (Q&A) interactions from China’s major e-interaction platforms, established by the stock exchanges, our study provides insights into regulated firm-investor communications in a structured Q&A setting. Unlike informal social media channels, these platforms enable official responses from firm representatives, typically board secretaries, under direct regulatory oversight. By analyzing rumor-related Q&A pairs with regression models and several robustness checks, we find that firms can benefit from …
Deep Graph Anomaly Detection: A Survey And New Perspectives,
2025
Singapore Management University
Deep Graph Anomaly Detection: A Survey And New Perspectives, Hezhe Qiao, Hanghang Tong, Nanyang Technological University, Irwin King, Charu Aggarwal, Guansong Pang
Research Collection School Of Computing and Information Systems
Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model …
Recurrent Autoregressive Linear Model For Next-Basket Recommendation,
2025
Singapore Management University
Recurrent Autoregressive Linear Model For Next-Basket Recommendation, Tereza Zmeskalova, Antoine Ledent, Martin Spisak, Pavel Kordik, Rodrigo Alves
Research Collection School Of Computing and Information Systems
Next-basket recommendation aims to predict the (sets of) items that a user is most likely to purchase during their next visit, capturing both short-term sequential patterns and long-term user preferences. However, effectively modeling these dynamics remains a challenge for traditional methods, which often struggle with interpretability and computational efficiency, particularly when dealing with intricate temporal dependencies and inter-item relationships. In this paper, we propose ReALM, a Recurrent Autoregressive Linear Model that explicitly captures temporal item-to-item dependencies across multiple time steps. By leveraging a recurrent loss function and a closed-form optimization solution, our approach offers both interpretability and scalability while maintaining …
