Digital Commons Network^™

Intelligent Abstractive Summarization Of Scholarly Publications With Transfer Learning, Farooq Zaman, Munaza Afzal, Pin Shen Teh, Raheem Sarwar, Faisal Kamiran, Naif R. Aljohani, Raheel Nawaz, Muhammad Umair Hassan, Fahad Sabah

Journal of Informatics and Web Engineering

Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially …

Development Of Virtual Reality-Based Left Brain System, Kok An Teo, Kok Swee Sim, Chean Khim Toa, Kai Liang Lew

Journal of Informatics and Web Engineering

This paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and …

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 …

Ai And Future-Making: Design, Biases, And Human-Plant Interactions, Maliheh Ghajargar

Art Faculty Articles and Research

Design researchers and practitioners are turning to generative AI (genAI) to support activities such as ideation and concept development in pursuit of preferred futures. At the same time, genAI is known to have biases, which prompts questions about how these biases might adversely affect design practices. In the domain of sustainable HCI, with its recent trends in human-nature interactions and more-than-human design, the question can be further refined into whether and how genAI biases might perpetuate anthropocentric biases that these practices are increasingly seeking to confront. In the present research, we conducted three workshops, focusing on genAI for human-plant interactions; …

"The Words We Do Not Yet Have." A Creative Inquiry Into Human-Plant Relationships, Maliheh Ghajargar

Art Faculty Articles and Research

Climate change, loss of plant biodiversity, and ocean pollution signal the drastic changes in our ecology that call us to attend to the needs of more than human forms of life on Earth. Sustainable design and HCI research are responding to this call by offering methods and approaches to design more sustainable products and systems and recently, more than human design is building momentum. This agenda seeks to reform traditional design processes by decentering the creative agency of the dominant socio-economical group of humans and foregrounding those of diverse Others. In this paper, I focus on plants as a nonhuman …

Predictive Residual Neural Networks For Optical Trapping Of Small Particles, Nasim Mohammadi Estrakhri, Ponthea Zahraii, Saman Kashanchi, Nooshin M. Estakhri

Engineering Faculty Articles and Research

Optical tweezers provide a non-contact method to trap, move, and manipulate micro- and nano-sized objects. Using properly designed dielectric and plasmonic nanostructure configurations, optical tweezers have been tailored to create stable and precise trapping for nanoscale objects. Recent advances in numerical optimization techniques allow further enhancement in nanoscale optical traps through inverse optimization of such configurations. One of the main challenges in such optimization approaches is the time-consuming nature of full-wave simulation of nanostructures and postprocessing steps to extract optical forces. To address this challenge, we introduce a surrogate solver based on residual neural networks that can accurately predict the …

Instructional Systems Design: The Diffusion And Adoption Of Technology: (Volume 2), Cassandra Celaya (Author), Pamela J. Downing (Author), Jessica Shifflett (Author), Debbie Gdula (Author), Tracie Barr (Author), Miguel Ramlatchan (Author & Editor)

University Administration Bookshelf

Instructional designers, instructional systems designers, and other educational technologists are, by their nature, innovators. These professionals apply and extend the applied science of learning, systems, communication, and instructional design theory to help students learn. Technology in some capacity is used to make the connections between subject matter experts, teachers, instructors, and their learners. It is common for instructional designers to seek new tools, techniques, and innovations for the improvement of learning, access, quality, and student satisfaction. However, the adoption and diffusion of new educational technology and innovation is a complex process that depends on many variables. Understanding these processes and …

Bi-Directional Transformers Vs. Word2vec: Discovering Vulnerabilities In Lifted Compiled Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier

Research & Publications

Detecting vulnerabilities within compiled binaries is challenging due to lost high-level code structures and other factors such as architectural dependencies, compilers, and optimization options. To address these obstacles, this research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoBERTa to learn semantics from intermediate representation (LLVM IR) code. Long short-term memory (LSTM) neural networks were trained on embeddings from encoders created using approximately 48k LLVM functions from the Juliet dataset. This study is pioneering in its comparison of word2vec models with multiple bidirectional transformers (BERT, RoBERTa) embeddings built using LLVM code to train neural …

Using Machine Learning To Predict State Compliance With International Legal Obligations For Registration Of Space Objects: Comparative Performance Of Logistic Regression And Dense Neural Network Models, Jonathan K. Sawmiller

Student Publications

Approximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.

Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best …

The Psychological Impacts Of Algorithmic And Ai-Driven Social Media On Teenagers: A Call To Action, Sunil Arora, Sahil Arora, John Hastings

Research & Publications

This study investigates the meta-issues surrounding social media, which, while theoretically designed to enhance social interactions and improve our social lives by facilitating the sharing of personal experiences and life events, often results in adverse psychological impacts. Our investigation reveals a paradoxical outcome: rather than fostering closer relationships and improving social lives, the algorithms and structures that underlie social media platforms inadvertently contribute to a profound psychological impact on individuals, influencing them in unforeseen ways. This phenomenon is particularly pronounced among teenagers, who are disproportionately affected by curated online personas, peer pressure to present a perfect digital image, and the …

Confronting The Reproducibility Crisis: A Case Study Of Challenges In Cybersecurity Ai, Richard H. Moulton, Gary A. Mccully, John D. Hastings

Research & Publications

In the rapidly evolving field of cybersecurity, ensuring the reproducibility of AI-driven research is critical to maintaining the reliability and integrity of security systems. This paper addresses the reproducibility crisis within the domain of adversarial robustness—a key area in AI-based cybersecurity that focuses on defending deep neural networks against malicious perturbations. Through a detailed case study, we attempt to validate results from prior work on certified robustness using the VeriGauge toolkit, revealing significant challenges due to software and hardware incompatibilities, version conflicts, and obsolescence. Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation to ensure the …

Measurement Automation & Measurement System Research Endowment, Brian Bivinetto, Beneda Loya, Shiron Bendrihem

College of Engineering Summer Undergraduate Research Program

Road travel safety is always the most important issue in transportation systems. In general, several factors cause road accidents, such as human error, vehicle mechanical failure, roadway limitations (e.g. pavement, lane geometry, etc.), and inclement weather conditions. The major focus of today’s transportation developments is related to making highway transportation safer, smarter, and greener to enhance livability. Many accidents are caused when drivers lack a better understanding of the surrounding traffic conditions because the driver not only needs to control his/her vehicle but also needs to be aware of the movements of the vehicles around him/her. A driver cannot be …

Surrogate Models For Stress-Strain Mapping Of Microscale Physics, Samuel Roach, Dr. Eric Ocegueda

College of Engineering Summer Undergraduate Research Program

•Learn the difference between different neural networks within machine learning (ML) •Develop a working understanding of the ML tool Pytorch and machine learning operator: Recurrent Neural Operator •Use MATLAB to create and process time dependent stress/strain matrices to display the hyper-parameters for different RNOs •Apply RNO to train the strain-stress mapping of tri-laminate and granular cases

Enhancing Place-Based Interaction With Emotion Ai And Augmented Reality, Jake Maier, Ivan Martinez

College of Engineering Summer Undergraduate Research Program

This project explores the integration of augmented reality (AR) and Emotion AI technologies to enhance user experiences in physical environments. By seamlessly merging virtual elements with real-world contexts, we aim to deepen individuals’ interactions and perceptions of their surroundings. Leveraging AR technology enables users to access contextual information, engage with interactive content, and navigate spaces with heightened immersion and understanding. Additionally, Emotion AI enhances these experiences by detecting and responding to users’ emotional states, fostering personalized and emotionally resonant interactions. We aim to integrate digital content within physical environments using mixed-reality headsets equipped with eye-tracking capabilities and consumer-grade wireless EEG …