Databases and Information Systems Commons^™

Development Of Robot Feature For Stunting Analysis Using Long-Short Term Memory (Lstm) Algorithm, Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso

Journal of Informatics and Web Engineering

Stunting prevalence in Indonesia persists as a significant challenge necessitating concerted efforts from all stakeholders. We developed robot for stunting analysis using deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs …

Cyber-Securing Medical Devices Using Machine Learning: A Case Study Of Pacemaker, Suliat Toyosi Jimoh, Shaymaa S Al-Juboori

Journal of Informatics and Web Engineering

This study aims to enhance the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. The objectives are to pinpoint cybersecurity weaknesses, utilize machine learning to predict security breaches, and propose countermeasures based on analytical trends. The literature review highlights the transformation of pacemaker technology from basic, fixed-rate devices to sophisticated systems with wireless capabilities, which, while improving patient care, also introduce significant cybersecurity risks. These risks include unauthorized entry, data breaches, and life-threatening device malfunctions. The methodology in this study utilizes a quantitative research approach using the WUSTL-EHMS-2020 dataset, which includes network traffic features, patients' biometric …

Review On Automated Storage And Retrieval System For Warehouse, Alex Low Kai Jie, Sim Kok Swee, Lew Kai Liang

Journal of Informatics and Web Engineering

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context …

Editorial Preview, Su-Cheng Haw

Journal of Informatics and Web Engineering

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing …

Towards Analysable Chaos-Based Cryptosystems: Constructing Difference Distribution Tables For Chaotic Maps, Je Sen Teh, Abubakar Abba

Journal of Informatics and Web Engineering

Chaos-based cryptography has yet to achieve practical, real-world applications despite extensive research. A major challenge is the difficulty in analysing the security of these cryptosystems, which often appear ad hoc in design. Unlike conventional cryptography, evaluating the security margins of chaos-based encryption against attacks such as differential cryptanalysis is complex. This paper introduces a straightforward approach of using chaotic maps in cryptographic algorithms in a way that facilitates cryptanalysis. We demonstrate how a chaos-based substitution function can be constructed using fixed-point representation, enabling the application of conventional cryptanalysis tools such as the difference distribution table. As a proof-of-concept, we apply …

Mobitest – A Software For Mobile-Based Testing, Ayesha Anees Zaveri, Ramsha Mashood, Nabiha Faisal, Misbah Parveen, Naveera Sami, Mobeen Nazar, Saba Imtiaz

Journal of Informatics and Web Engineering

MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the …

Enhancing Cybersecurity Awareness Through Gamification: Design An Interactive Cybersecurity Learning Platform For Multimedia University Students, Adlil Khaliq Abdul Razack, Mohamad Firdaus Mat Saad

Journal of Informatics and Web Engineering

Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting …

Comparative Analysis Of Linear And Nonlinear Semg Methods For Detecting Muscle Fatigue During Dynamic Biceps Curls, Tang Ming, Ling Weay Ang, Sellappan Palaniappan

Journal of Informatics and Web Engineering

Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established …

Pi Class – A Revolutionary Step Forward In Hybrid Class Management System, Qasim Hassan, Nabiha Faisal, Ayesha Anees Zaveri

Journal of Informatics and Web Engineering

The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the …

Comparative Evaluation Of Machine Learning Models For Mobile Phone Price Prediction: Assessing Accuracy, Robustness, And Generalization Performance, Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata

Journal of Informatics and Web Engineering

These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting …

Investigation On Understanding The Numeracy Capacity Of Intellectual Disabled Students Using Enabling Technology Tools: Web Application, Ar And Ui/Ux, Vishvjit Thakar, Romany Thakar, Pratik Vyas

Journal of Informatics and Web Engineering

The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To …

Ddos Attack Detection With Machine Learning, Wei-Wu Tay, Siew-Chin Chong, Lee-Ying Chong

Journal of Informatics and Web Engineering

Nowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to …

Editorial: Artificial Intelligence And Cybersecurity In Pervasive Computing, Ji-Jian Chin

Journal of Informatics and Web Engineering

Pervasive computing, or ubiquitous computing, is rapidly increasing in capacity and capabilities. With the Internet of Things (IoT) becoming an integral part of daily life and the growing availability of edge computing resources, automation guided by data is advancing applications in healthcare, manufacturing, automotive, and other areas. It's natural that pervasive computing will intersect with artificial intelligence (AI) and cybersecurity. AI can improve detection, prediction, and anticipative responses to human needs, while cybersecurity addresses topics like misuse prevention, ethics, policies, and governance. This issue features seven articles on these intersections, including four AI articles exploring natural language processing and computer …

Sibling Discrimination Using Linear Fusion On Deep Learning Face Recognition Models, Rita Goel, Maida Alamgir, Haroon Wahab, Maria Alamgir, Irfan Mehmood, Hassan Ugail, Amit Sinha

Journal of Informatics and Web Engineering

Facial recognition technology has revolutionised human identification, providing a non-invasive alternative to traditional biometric methods like signatures and voice recognition. The integration of deep learning has significantly enhanced the accuracy and adaptability of these systems, now widely used in criminal identification, access control, and security. Initial research focused on recognising full-frontal facial features, but recent advancements have tackled the challenge of identifying partially visible faces, a scenario that often reduces recognition accuracy. This study aims to identify siblings based on facial features, particularly in cases where only partial features like eyes, nose, or mouth are visible. Utilising advanced deep learning …

Hybrideval: An Improved Novel Hybrid Metric For Evaluation Of Text Summarization, Raheem Sarwar, Bilal Ahmad, Pin Shen Teh, Suppawong Tuarob, Tipajin Thaipisutikul, Farooq Zaman, Naif R. Aljohani, Jia Zhu, Saeed-Ul Hassan, Raheel Nawaz, Ali R Ansari, Muhammad A B Fayyaz

Journal of Informatics and Web Engineering

The present work re-evaluates the evaluation method for text summarization tasks. Two state-of-the-art assessment measures e.g., Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Bilingual Evaluation Understudy (BLEU) are discussed along with their limitations before presenting a novel evaluation metric. The evaluation scores are significantly different because of the length and vocabulary of the sentences, this suggests that the primary restriction is its inability to preserve the semantics and meaning of the sentences and consistent weight distribution over the whole sentence. To address this, the present work organizes the phrases into six different groups and to evaluate “text summarization” problems, a …

Conditional Deployable Biometrics: Matching Periocular And Face In Various Settings, Jihyeon Kim, Tiong-Sik Ng, Andrew Beng Jin Teoh

Journal of Informatics and Web Engineering

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and …

In Search Of Suitable Methods For Cost-Benefit Analysis Of Cyber Risk Mitigation In Offshore Wind: A Survey, Yvonne Hwei-Syn Kam, Kevin Jones, Robert Rawlinson-Smith, Kimberly Tam

Journal of Informatics and Web Engineering

In recent years, notable incidents have highlighted the vulnerability of wind energy infrastructure, making cybersecurity crucial for the offshore wind industry. However, justifying the costs of cybersecurity measures is essential. A cost benefit analysis (CBA) is commonly utilised to support decision-making for risk mitigation. With a cost benefit analysis, risk mitigation strategies that strike an optimal balance between the costs of mitigation measures and the resulting risk reduction can be identified. This survey of literature was carried out to identify the existing proposed solutions for cost benefit analysis on cyber risk mitigation measures for offshore wind cyber physical systems. After …

Empowering Entrepreneurial Evolution: A Beyond Founder Strategic Approach To Small Business Growth, D. Jared Knisley

USF Tampa Graduate Theses and Dissertations

Growing a business beyond its founder’s capacities and talents presents a challenging undertaking. When a founder is no longer involved or motivated to grow the business, firm decline is a likely outcome. Small businesses often have a deep and, at times, hindering reliance on their early founders for development. Therefore, a continuous engaging plan to advance entrepreneurial success is needed to grow a small business.

My research objective aims to find methods and designs, through academic literature and practitioner interviews, that transition leadership responsibilities and increase team empowerment within a small business, enabling these firms to continue growing as the …

Research On Vector Database And Its Application, Yusheng Sun, Junhao Zeng

Journal of Scientific Information Research

[Purpose/significance]The article reveals the theoretical systems, technological systems, and applied systems of vector databases, aiming to promote innovation in the research and practice of multimodal AI related theories, technologies, and applications. [Method/process]This article elaborates on the evolution of vector databases and defines its core concepts through literatures tracing and content analyzing. Subsequently, it compares and analyzes their characteristics and values, and based on this, sorts out their application mechanisms, functions, corresponding key technologies and application modes. Simultaneously, it discusses the challenges and countermeasures faced by vector databases, and looks forward to their development trends from theoretical, technical, and application perspectives. …

Calibrated One-Class Classification For Unsupervised Time Series Anomaly Detection, Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang

Research Collection School Of Computing and Information Systems

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty …

D2sr: Decentralized Detection, De-Synchronization, And Recovery Of Lidar Interference, Darshana Rathnayake, Hemanth Sabbella, Meera Radhakrishnan, Archan Misra

Research Collection School Of Computing and Information Systems

We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination among nearby LiDAR devices. D2SR comprises three stages: (a) Detection, which identifies interfered frames, (b) Mitigation, which performs time-shifting of a LiDAR’s active period to reduce interference, and (c) Recovery, which corrects or reconstructs the depth values in interfered regions of a depth frame. Key contributions include a lightweight interference detection algorithm achieving an F1-score of 92%, a simple …

Cisc 3130 Data Structures, Moshe Lach

Open Educational Resources

Container classes: their design, implementations, and applications. Sequences: vectors, linked lists, stacks, queues, deques, lists. Associative structures: sets, maps and their hash and tree underlying representations. Sorting and searching techniques. Collection frameworks and hierarchies.

Themis: Automatic And Efficient Deep Learning System Testing With Strong Fault Detection Capability, Dong Huang, Tsz On Li, Xiaofei Xie, Heming Cui

Research Collection School Of Computing and Information Systems

Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has incorrect outputs (i.e., faults). DLS testing techniques (e.g., DeepXplore) detect such faults by generating perturbed inputs to explore data flows that induce faults. Since a DLS often has infinitely many data flows, existing techniques require developers to manually specify a set of activation values in a DLS’s neurons for exploring fault-inducing data flows. Unfortunately, recent studies show that such manual effort is tedious and can detect only a tiny proportion …

Desk2desk : Optimization-Based Mixed Reality Workspace Integration For Remote Side-By-Side Collaboration, Ludwig Sidenmark, Tianyu Zhang, Leen Al Lababidi, Jiannan Li, Tovi Grossman

Research Collection School Of Computing and Information Systems

Mixed Reality enables hybrid workspaces where physical and virtual monitors are adaptively created and moved to suit the current environment and needs. However, in shared settings, individual users’ workspaces are rarely aligned and can vary significantly in the number of monitors, available physical space, and workspace layout, creating inconsistencies between workspaces which may cause confusion and reduce collaboration. We present Desk2Desk, an optimization-based approach for remote collaboration in which the hybrid workspaces of two collaborators are fully integrated to enable immersive side-by-side collaboration. The optimization adjusts each user’s workspace in layout and number of shared monitors and creates a mapping …

Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu

Research Collection School Of Computing and Information Systems

With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …

Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun

Research Collection School Of Computing and Information Systems

Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …

Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu

Research Collection School Of Computing and Information Systems

Despite efforts to mitigate aggressive financial reporting, earnings management remains challenging to parties interested in inhibiting its dysfunctional effects. Using linguistic algorithms to assess CEO agreeableness personality from their unscripted texts in conference calls, we find that it is a determinant that mitigates a firm's real earnings management. Furthermore, such an effect is more pronounced when firms confront intensive market competition and financial distress and have weaker managerial entrenchment or when CEOs face stronger internal governance. Our findings persist even after we utilize several alternative real earnings management metrics and control other confounding personalities in prior earnings management studies. The …

Retrofitting A Legacy Cutlery Washing Machine Using Computer Vision, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed andutilization. With the existing operational constraints of …

Motif Graph Neural Network, Xuexin Chen, Ruicui Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao

Research Collection School Of Computing and Information Systems

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order …

Temporal Relational Graph Convolutional Network Approach To Financial Performance Prediction, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang

Research Collection School Of Computing and Information Systems

Accurately predicting financial entity performance remains a challenge due to the dynamic nature of financial markets and vast unstructured textual data. Financial knowledge graphs (FKGs) offer a structured representation for tackling this problem by representing complex financial relationships and concepts. However, constructing a comprehensive and accurate financial knowledge graph that captures the temporal dynamics of financial entities is non-trivial. We introduce FintechKG, a comprehensive financial knowledge graph developed through a three-dimensional information extraction process that incorporates commercial entities and temporal dimensions and uses a financial concept taxonomy that ensures financial domain entity and relationship extraction. We propose a temporal and …