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Articles 121 - 150 of 731
Full-Text Articles in Computer Engineering
Corrigendum: Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P. A. Hancock
Corrigendum: Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P. A. Hancock
Articles
In the published article, the name of Gabriella Hancock was incorrectly written as “Gabriela M. Hancock.” The correct name is “Gabriella Hancock.” In the published article, there was also an error in the author list as published. Gabriella Hancock was listed as the last author, but should have been listed as third author. P. A. Hancock was listed as third author but should be listed as the last author. The corrected author list appears below. Luca Longo1, Christopher D.Wickens, Gabriella Hancock and P. A. Hancock. The authors apologize for this error and state that this does not change the scientific …
A Big Data Smart Agricultural System: Recommending Optimum Fertilisers For Crops, Vuong Ngo, Thuy-Van T. Duong, Nguyen Nguyen, Cach N. Dang, Owen Conlan
A Big Data Smart Agricultural System: Recommending Optimum Fertilisers For Crops, Vuong Ngo, Thuy-Van T. Duong, Nguyen Nguyen, Cach N. Dang, Owen Conlan
Articles
Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart …
Subnetwork Ensembling And Data Augmentation: Effects On Calibration, A. Çağrı Demir, Simon Caton, Pierpaolo Dondio
Subnetwork Ensembling And Data Augmentation: Effects On Calibration, A. Çağrı Demir, Simon Caton, Pierpaolo Dondio
Articles
Deep Learning models based on convolutional neural networks are known to be uncalibrated, that is, they are either overconfident or underconfident in their predictions. Safety-critical applications of neural networks, however, require models to be well-calibrated, and there are various methods in the literature to increase model performance and calibration. Subnetwork ensembling is based on the over-parametrization of modern neural networks by fitting several subnetworks into a single network to take advantage of ensembling them without additional computational costs. Data augmentation methods have also been shown to enhance model performance in terms of accuracy and calibration. However, ensembling and data augmentation …
Decision Making For Process Control Management In Control Rooms: A Survey Methodology And Initial Findings, Chidera Winifred Amazu, Ammar N. Abbas, Micaela Demichela, Davide Fissore
Decision Making For Process Control Management In Control Rooms: A Survey Methodology And Initial Findings, Chidera Winifred Amazu, Ammar N. Abbas, Micaela Demichela, Davide Fissore
Articles
Control rooms and their operators are active elements in complex socio-technical systems such as process plants. Control room operators monitor process operations, respond to alarms, and manage process deviations until emergencies. The increase in automation of plants and equipment makes the operators less involved in manual process control or other physical roles while more exposed to cognitive load generated, for example, by increasing the number of alarms or potential system failures in abnormal situations. A shift in process control design and management techniques to holistically capture risks due to evolving process or monitoring capabilities and the related influencing factors is …
Persuasive Communication Systems: A Machine Learning Approach To Predict The Effect Of Linguistic Styles And Persuasion Techniques, Annye Braca, Pierpaolo Dondio
Persuasive Communication Systems: A Machine Learning Approach To Predict The Effect Of Linguistic Styles And Persuasion Techniques, Annye Braca, Pierpaolo Dondio
Articles
Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.
Detection Of Grape Clusters In Images Using Convolutional Neural Network, Mohammad Osama Shahzad, Anas Bin Aqeel, Waqar Shahid Qureshi
Detection Of Grape Clusters In Images Using Convolutional Neural Network, Mohammad Osama Shahzad, Anas Bin Aqeel, Waqar Shahid Qureshi
Articles
Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest …
Towards Automated Weed Detection Through Two-Stage Semantic Segmentation Of Tobacco And Weed Pixels In Aerial Imagery, S. Imran Moazzam, Umar S. Khan, Waqar Qureshi, Tahir Nawaz, Faraz Kunwar
Towards Automated Weed Detection Through Two-Stage Semantic Segmentation Of Tobacco And Weed Pixels In Aerial Imagery, S. Imran Moazzam, Umar S. Khan, Waqar Qureshi, Tahir Nawaz, Faraz Kunwar
Articles
In precision farming, weed detection is required for precise weedicide application, and the detection of tobacco crops is necessary for pesticide application on tobacco leaves. Automated accurate detection of tobacco and weeds through aerial visual cues holds promise. Precise weed detection in crop field imagery can be treated as a semantic segmentation problem. Many image processing, classical machine learning, and deep learning-based approaches have been devised in the past, out of which deep learning-based techniques promise better accuracies for semantic segmentation, i.e., pixel-level classification. We present a new method that improves the precision of pixel-level inter-class classification of the crop …
Comparing Poor And Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy In Acute Ischemic Stroke, Matthias A. Mutke, Vince I. Madai, Adam Hilbert, Esra Zihni, Arne Potreck, Charlotte S. Weyland, Markus A. Mohlenbruch, Sabine Heiland, Peter A. Ringleb, Simon Nagel, Martin Beendszus, Dietmar Frey
Comparing Poor And Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy In Acute Ischemic Stroke, Matthias A. Mutke, Vince I. Madai, Adam Hilbert, Esra Zihni, Arne Potreck, Charlotte S. Weyland, Markus A. Mohlenbruch, Sabine Heiland, Peter A. Ringleb, Simon Nagel, Martin Beendszus, Dietmar Frey
Articles
Outcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.
Exploring The Impact Of Noise And Degradations On Heart Sound Classification Models, Davoud Shariat Panah, Andrew Hines, Susan Mckeever
Exploring The Impact Of Noise And Degradations On Heart Sound Classification Models, Davoud Shariat Panah, Andrew Hines, Susan Mckeever
Articles
The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise …
Comparing And Extending The Use Of Defeasible Argumentation With Quantitative Data In Real-World Contexts, Lucas Rizzo, Luca Longo
Comparing And Extending The Use Of Defeasible Argumentation With Quantitative Data In Real-World Contexts, Lucas Rizzo, Luca Longo
Articles
Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential …
Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed
Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed
Articles
The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Articles
Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …
Understanding And Predicting Cognitive Improvement Of Young Adults In Ischemic Stroke Rehabilitation Therapy, Helard Becerra Martinez, Katryna Cisek, Alejandro Garcia-Rudolph, John Kelleher, Andrew Hines
Understanding And Predicting Cognitive Improvement Of Young Adults In Ischemic Stroke Rehabilitation Therapy, Helard Becerra Martinez, Katryna Cisek, Alejandro Garcia-Rudolph, John Kelleher, Andrew Hines
Articles
Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected …
A Multidimensionality Reduction Approach To Rainfall Prediction, Menatallah Abdel Azeem, Prasanjit Dey, Soumyabrata Dev
A Multidimensionality Reduction Approach To Rainfall Prediction, Menatallah Abdel Azeem, Prasanjit Dey, Soumyabrata Dev
Articles
The rainfall has an impact on various fields and industries, including transportation, construction, tourism, health, and wildlife preservation. Accurate rainfall prediction is essential for mitigating the negative impact of rainfall on these sectors. However, previous studies on rainfall prediction have been mainly based on datasets from North America, Europe, Australia, and Central Asia, covering different periods. This study proposes using weather datasets covering the past 5 to 10 years to capture recent patterns in weather data. Additionally, the curse of dimensionality can impact model performance and lead to overfitting. Therefore, this study proposes utilizing dimensionality reduction techniques to ensure that …
Nesnet: A Deep Network For Estimating Near-Surface Pollutant Concentrations, Prasanjit Dey, Bibhash Pran Das, Yee Hui Lee, Soumyabrata Dev
Nesnet: A Deep Network For Estimating Near-Surface Pollutant Concentrations, Prasanjit Dey, Bibhash Pran Das, Yee Hui Lee, Soumyabrata Dev
Articles
Atmospheric pollution has become a serious threat in recent years. The advancements and expansion of industrial activity and civilization have been the major catalysts. With serious consequences like climate change and global warming, the onset of which is already being observed, keeping a check on atmospheric pollutant levels is now more important than ever. Trace gases play a major role in atmospheric chemistry. Many of these are also regarded as major atmospheric pollutants. The concentration of gases, such as (SO2), (O3), (NO2), etc., are indicators of air quality. Therefore, in this study, we primarily concern ourselves with concentrations of NO2, …
Performance Evaluation Of Ingenious Crow Search Optimization Algorithm For Protein Structure Prediction, Ahmad M. Alshamrani, Akash Saxena, Shalini Shekhawat, Hossam Zawbaa, Ali Wagdy Mohamed
Performance Evaluation Of Ingenious Crow Search Optimization Algorithm For Protein Structure Prediction, Ahmad M. Alshamrani, Akash Saxena, Shalini Shekhawat, Hossam Zawbaa, Ali Wagdy Mohamed
Articles
Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application …
An Integrated Model For Information Adoption&Trust In Mobile Social Commerce, Fulya Acikgoz, Abdelsalam Busalim, James Gaskin, Shahla Asadi
An Integrated Model For Information Adoption&Trust In Mobile Social Commerce, Fulya Acikgoz, Abdelsalam Busalim, James Gaskin, Shahla Asadi
Articles
ABSTRACT Despite the growing importance of mobile social commerce (ms-commerce), little research has been conducted on the effects of informational and social factors on users’ post-adoption behavior. We, therefore, build on the understanding of mobile social commerce in the UK market and how it affects users’ post-adoption behaviors. Our theoretical model leverages the information adoption model, social support theory, and social influence theory. Data was gathered from 377 ms-commerce users from the UK and analyzed via Partial Least Squares (PLS-SEM). The research findings show that both informational and social factors have a positive impact on information adoption in ms-commerce apps. …
Learnings From A National Cyberattack Digital Disaster During The Sars-Cov-2 Pandemic In A Pediatric Emergency Medicine Department, Fiona Leonard, Hugh O'Reilly, Carol Blackburn, Laura Melody, Dani Hall, Eleanor Ryan, Kate Bruton, Pamela Doyle, Bridget Conway, Michael Barrett
Learnings From A National Cyberattack Digital Disaster During The Sars-Cov-2 Pandemic In A Pediatric Emergency Medicine Department, Fiona Leonard, Hugh O'Reilly, Carol Blackburn, Laura Melody, Dani Hall, Eleanor Ryan, Kate Bruton, Pamela Doyle, Bridget Conway, Michael Barrett
Articles
Objective: The primary objective was to analyze the impact of the national cyberattack in May 2021 on patient flow and data quality in the Paediatric Emergency Department (ED), amid the SARS-CoV-2 (COVID-19) pandemic. Methods: A single site retrospective time series analysis was conducted of three 6-week periods: before, during, and after the cyberattack outage. Initial emergent workflows are described. Analysis includes diagnoses, demographic context, key performance indicators, and the gradual return of information technology capability on ED performance. Data quality was compared using 10 data quality dimensions. Results: Patient visits totaled 13 390. During the system outage, patient experience times …
Assessing The Impact Of Contact Tracing With An Agent-Based Model For Simulating The Spread Of Covid-19: The Irish Experience, Elizabeth Hunter, Sudipta Saha, Jwenish Kumawat, Ciara Carroll, John Kelleher, Claire Buckley, Conor Mcaloon, Patricia Kearney, Michelle Gilbert, Greg Martin
Assessing The Impact Of Contact Tracing With An Agent-Based Model For Simulating The Spread Of Covid-19: The Irish Experience, Elizabeth Hunter, Sudipta Saha, Jwenish Kumawat, Ciara Carroll, John Kelleher, Claire Buckley, Conor Mcaloon, Patricia Kearney, Michelle Gilbert, Greg Martin
Articles
Contact tracing is an important tool in managing infectious disease outbreaks and Ireland used a comprehensive contact tracing program to slow the spread of COVID-19. Although the benefits of contact tracing seem obvious, it is difficult to estimate the actual impact contact tracing has on an outbreak because it is hard to separate the effects of contact tracing from other behavioural changes or interventions. To understand the impact contact tracing had in Ireland, we used an agent-based model that is designed to simulate the spread of COVID-19 through Ireland. The model uses real contact tracing data from the first year …
How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo
How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo
Articles
Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers …
A Comparison Of Feature Selection Methodologies And Learning Algorithms In The Development Of A Dna Methylation-Based Telomere Length Estimator, Trevor Doherty, Emma Dempster, Eilis Hannon, Jonathan Mill, Richie Poulton, David Corcoran, Karen Sugden, Ben Williams, Avshalom Caspi, Terrie E. Moffitt, Sarah Jane Delany, Therese Murphy Dr
A Comparison Of Feature Selection Methodologies And Learning Algorithms In The Development Of A Dna Methylation-Based Telomere Length Estimator, Trevor Doherty, Emma Dempster, Eilis Hannon, Jonathan Mill, Richie Poulton, David Corcoran, Karen Sugden, Ben Williams, Avshalom Caspi, Terrie E. Moffitt, Sarah Jane Delany, Therese Murphy Dr
Articles
The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine–guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based …
New Fxlmat-Based Algorithms For Active Control Of Impulsive Noise, Alina Mirza, Farkhanda Afzal, Ayesha Zeb, Abdul Wakeel, Waqar Shahid Qureshi, Ali Akgul
New Fxlmat-Based Algorithms For Active Control Of Impulsive Noise, Alina Mirza, Farkhanda Afzal, Ayesha Zeb, Abdul Wakeel, Waqar Shahid Qureshi, Ali Akgul
Articles
In the presence of non-Gaussian impulsive noise (IN) with a heavy tail, active noise control (ANC) algorithms often encounter stability problems. While adaptive filters based on the higher-order error power principle have shown improved filtering capability compared to the least mean square family algorithms for IN, however, the performance of the filtered-x least mean absolute third (FxLMAT) algorithm tends to degrade under high impulses. To address this issue, this paper proposes three modifications to enhance the performance of the FxLMAT algorithm for IN. To improve stability, the first alteration i.e. variable step size FxLMAT (VSSFxLMAT)algorithm is suggested that incorporates the …
Argframe: A Multi-Layer, Web, Argument-Based Framework For Quantitative Reasoning, Lucas Rizzo
Argframe: A Multi-Layer, Web, Argument-Based Framework For Quantitative Reasoning, Lucas Rizzo
Articles
Multiple systems have been proposed to perform computational argumentation activities, but there is a lack of options for dealing with quantitative inferences. This multi-layer, web, argument-based framework has been proposed as a tool to perform automated reasoning with numerical data. It is able to use boolean logic for the creation of if-then rules and attacking rules. In turn, these rules/arguments can be activated or not by some input data, have their attacks solved (following some Dung or rank-based semantics), and finally aggregated in different fashions in order to produce a prediction (a number). The framework is implemented in PHP for …
Enhancing The Prediction For Shunt‑Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using A Machine Learning Approach, Dietmar Frey, Adam Hilbert, Anton Früh, Vince Istvan Madai, Tabea Kossen, Julia Kiewitz, Jenny Sommerfeld, Peter Vajkoczy, Meike Unteroberdörster, Esra Zihni, Sophie Charlotte Brune, Stefan Wolf, Nora Franziska Dengler
Enhancing The Prediction For Shunt‑Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using A Machine Learning Approach, Dietmar Frey, Adam Hilbert, Anton Früh, Vince Istvan Madai, Tabea Kossen, Julia Kiewitz, Jenny Sommerfeld, Peter Vajkoczy, Meike Unteroberdörster, Esra Zihni, Sophie Charlotte Brune, Stefan Wolf, Nora Franziska Dengler
Articles
Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid haemorhage (a SAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (Cat Boost) algorithm, a Nave Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of …
Interpreting Disentangled Representations Of Person-Specific Convolutional Variational Autoencoders Of Spatially Preserving Eeg Topographic Maps Via Clustering And Visual Plausibility, Taufique Ahmed, Luca Longo
Interpreting Disentangled Representations Of Person-Specific Convolutional Variational Autoencoders Of Spatially Preserving Eeg Topographic Maps Via Clustering And Visual Plausibility, Taufique Ahmed, Luca Longo
Articles
Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction. In most of the studies, VAE latent space interpretation is used to detect only the out-of-order distribution latent variable for anomaly detection. However, the interpretation and visualisation of all latent space components disclose information about how the model arrives at its conclusion. The main contribution of this study is interpreting the disentangled representation of VAE by activating only one latent component at a time, whereas the values for the remaining components are …
Ontology-Based Case Study Management Towards Bridging Training And Actual Investigation Gaps In Digital Forensics, Hung Q. Ngo, Nhien-An Le-Khac
Ontology-Based Case Study Management Towards Bridging Training And Actual Investigation Gaps In Digital Forensics, Hung Q. Ngo, Nhien-An Le-Khac
Articles
The training programs in digital forensics have contributed many case study models to guide digital forensic analyses. However, they only account for a small number of real cases and they are usually too abstract while actual cybercrime investigations are more diverse and complex. This gap leads to difficulties in giving immediate and straightforward actions for law enforcement during cybercrime investigations. In this paper, we propose an ontology-based knowledge map model, which is a foundation model for building a case study management system for Digital Forensic Intelligence (DFINT) and Open Source Intelligence (OSINT) in digital forensics. The main idea of this …
Using Machine Learning To Identify Patterns In Learner-Submitted Code For The Purpose Of Assessment, Botond Tarcsay, Fernando Perez-Tellez, Jelena Vasic
Using Machine Learning To Identify Patterns In Learner-Submitted Code For The Purpose Of Assessment, Botond Tarcsay, Fernando Perez-Tellez, Jelena Vasic
Conference papers
Programming has become an important skill in today’s world and is taught widely both in traditional and online settings. Instructors need to grade increasing amounts of student work. Unit testing can contribute to the automation of the grading process but it cannot assess the structure or partial correctness of code, which is needed for finely differentiated grading. This paper builds on previous research that investigated machine learning models for determining the correctness of programs from token-based features of source code and found that some such models can be successful in classifying source code with respect to whether it passes unit …
Analysis Of Attention Mechanisms In Box-Embedding Systems, Jeffrey Sardina Jeffrey Sardina, Callie Sardina, John Kelleher, Declan O’Sullivan
Analysis Of Attention Mechanisms In Box-Embedding Systems, Jeffrey Sardina Jeffrey Sardina, Callie Sardina, John Kelleher, Declan O’Sullivan
Conference papers
Large-scale Knowledge Graphs (KGs) have recently gained considerable research attention for their ability to model the inter- and intra- relationships of data. However, the huge scale of KGs has necessitated the use of querying methods to facilitate human use. Question Answering (QA) systems have shown much promise in breaking down this human-machine barrier. A recent QA model that achieved state-of-the-art performance, Query2box, modelled queries on a KG using box embeddings with an attention mechanism backend to compute the intersections of boxes for query resolution. In this paper, we introduce a new model, Query2Geom, which replaces the Query2box attention mechanism with …
Action Classification In Human Robot Interaction Cells In Manufacturing, Shakra S.M. Mehak, Maria Chiara Leva, John Kelleher, Michael Guilfoyle
Action Classification In Human Robot Interaction Cells In Manufacturing, Shakra S.M. Mehak, Maria Chiara Leva, John Kelleher, Michael Guilfoyle
Conference papers
Action recognition has become a prerequisite approach to fluent Human-Robot Interaction (HRI) due to a high degree of movement flexibility. With the improvements in machine learning algorithms, robots are gradually transitioning into more human-populated areas. However, HRI systems demand the need for robots to possess enough cognition. The action recognition algorithms require massive training datasets, structural information of objects in the environment, and less expensive models in terms of computational complexity. In addition, many such algorithms are trained on datasets derived from daily activities. The algorithms trained on non-industrial datasets may have an unfavorable impact on implementing models and validating …
Music For Visual Media Of The 21st Century What Role Does Music Have In Visual Media, Sigrid Heugen
Music For Visual Media Of The 21st Century What Role Does Music Have In Visual Media, Sigrid Heugen
Doctoral
This research consists of a composition portfolio which examines new perspectives on musical experiences in virtual environments, through the exploration and development of immersive adaptive music for a visual medium. The research addresses the usage, adaptation, and creation of music in visual media such as Virtual Reality (VR) and other Extended Realities (XR).