Digital Commons Network^™

K-Means And Hierarchical Cluster Analysis As Segmentation Algorithms Of Ftir Hyperspectral Images Collected From Cutaneous Tissue, Cassio Lima, Luciana Correa, Hugh Byrne, Denise Zezell

Conference papers

Fourier Transform Infrared (FTIR) spectroscopy is a rapid and label-free analytical technique whose potential as a diagnostic tool has been well demonstrated. The combination of spectroscopy and microscopy technologies enable wide-field scanning of a sample, providing a hyperspectral image with tens of thousands of spectra in a few minutes. In order to increase the information content of FTIR images, different clustering algorithms have been proposed as segmentation methods. However, systematic comparative tests of these techniques are still missing. Thus, the present paper aims to compare the ability of K-means Cluster Analysis (KMCA) and Hierarchical Cluster Analysis (HCA) as clustering algorithms …

Outstanding Reviewers For Analyst In 2019, Royal Society Of Chemistry

Articles

We would like to take this opportunity to thank all of Analyst's reviewers, and in particular highlight the Outstanding Reviewers for the journal in 2019, as selected by the editorial team for their significant contribution to Analyst. We announce our Outstanding Reviewers annually and each receives a certificate to give recognition for their contribution. The reviewers have been chosen based on the number, timeliness and quality of the reports completed over the last 12 months.

Special Issue On The Third Workshop On Biological Mentality, Kenneth A. Augustyn

Michigan Tech Publications, Part 1

The Third Workshop on Biological Mentality was held from September 23, 2019 to March 2, 2020 as a series of twenty-one Monday online conferencing sessions, each consisting of a talk followed by a Q&A discussion. Like the two previous workshops [1, 2], the objective of this workshop was to seek a deeper level of understanding the physical foundations of biological mentality (whether conscious or nonconscious).

Consciousness As A Factor In Evolution, Kenneth A. Augustyn

Michigan Tech Publications, Part 1

What I call the mind began as a non-conscious robotic biochemical process control system in the very earliest forms of life. As life evolved, problems in control became more difficult and exceeded the computational capabilities of the organisms. Nature discovered a means of transcending computable physical processes resulting in non-computational subjective mental capabilities that, while still not conscious, had a degree of genuine autonomy from the physical world. These autonomous subjective wants and goals now affected the course of (but not the mechanism of) evolution. The integrated amalgam of robotic and transrobotic unconscious capabilities eventually gave rise to consciousness, which …

Vehicle Exhaust Remote Sensing Device Method To Screen Vehicles For Evaporative Running Loss Emissions, Gary A. Bishop, Timothy H. Defries, James A. Sidebottom, James M. Kemper

Fuel Efficiency Automobile Test Publications

Vehicle hydrocarbon (HC) emissions can be emitted from either tailpipe or non-tailpipe locations and understanding their fleet apportionment is important for successful air pollution policy. Vehicles initially misidentified as having elevated tailpipe HC emissions first indicated that roadside exhaust sensors could detect the presence of evaporative HC emissions as increased noise in the HC/carbon dioxide (CO₂) correlation measurement. The 90^th percentile of the largest residual of the HC/CO₂ correlation is defined as a running loss index (RLI) for each measurement. An RLI that is three standard deviations or greater above the instruments noise indicates possible evaporative …

Inspection And Maintenance Evaluation Using Historical U.S. Remote Sensing Measurements, Gary A. Bishop

Fuel Efficiency Automobile Test Publications

No abstract provided.

2-(Di­Phenyl­Methyl­Idene)-2,3-Di­Hydro-1h-Inden-1-One, Tao Zhang, Vilmar Bandero, Tom Mccabe, Neil Frankish, Helen Sheridan

Articles

In the title mol­ecule, C₂₂H₁₆O, the indanone ring system is approximately planar with a dihedral angle between the fused rings of 5.13 (14)°. Two benzene rings are linked together at one side of a double bond, sitting on either side of the indanone ring system and making dihedral angles of 70.30 (12) and 44.74 (13)° with it. In the crystal, hydrogen bonding is not present, but weak C—H⋯π or π–π inter­actions occur and mol­ecules form a sheet-like structure in the bc plane.

Pilot Scale Pyrolysis Of Activated Sludge Waste From Milk Processing Factory, Marzena Kwapinska, Alen Horvat, Yupeng Liu, James Leahy

Articles

The majority of the sludge from the treatment of wastewater in milk processing plants is land spread. The drawbacks of land spreading include local oversupply due to high transport costs, which results in sludge being spread on lands in the vicinity of the dairy factories. Local oversupply can lead to accumulation of certain substances in soil through annual application over many years. Therefore, in the long term, there is a need for alternative methods to recover energy and nutrients from increasing volumes of sludge generated from dairy processing. Pyrolysis ofers a potential alternative to land spreading, which can reduce health …

Dissipation Of Pesticide Residues On Grapes And Strawberries Using Plasma-Activated Water, Chaitanya Sarangapani, Laurence Scally, Miroslav Gulan, P. J. Cullen

Articles

In this study, we present a novel atmospheric air plasma discharge for the generation of plasma-activated water (PAW), with the aim of reducing pesticide residues on fresh fruit. For this purpose, a large discharge volume pin-to-plate cold plasma reactor was employed. The pesticide-spiked grapes and strawberries were processed with varying PAW concentrations to study their efficacies for pesticide degradation combined with an evaluation of any induced changes in key nutritional and quality attributes. The results suggest that the reduction of chlorpyrifos was 79% on grapes and 69% on strawberries while that of carbaryl was 86% on grapes and 73% on …

A Mathematical Model For Visco-Ferromagnetic Materials, Giovambattista Amendola, Mauro Fabrizio, John Murrough Golden

Articles

Visco-ferromagnetic materials represented by non-local constitutive equation are considered in the paper. We use fractional derivatives in order to describe memory and spatial effects. Also, thermodynamic principles are formulated and studied.

Fast Preprocessing For Optimal Orthogonal Range Reporting And Range Successor With Applications To Text Indexing, Younan Gao, Meng He, Yakov Nekrich

Michigan Tech Publications, Part 1

Under the word RAM model, we design three data structures that can be constructed in O(n√lg n) time over n points in an n×n grid. The first data structure is an O(n lg n)-word structure supporting orthogonal range reporting in O(lg lg n + k) time, where k denotes output size and is an arbitrarily small constant. The second is an O(n lg lg n)-word structure supporting orthogonal range successor in O(lg lg n) time, while the third is an O(n lg n)-word structure supporting sorted range reporting in O(lg lg n + k) time. The query times of these …

Competencies For Educators In Delivering Digital Accessibility In Higher Education, John Gilligan

Conference Papers

The aim of this paper is to critically review the capabilities of the European Framework for the Digital Competence of Educators (DigCompEdu) and the UNESCO ICT Competency Framework for in delivering greater accessibility for students with disabilities in a Higher Education landscape undergoing Digital Transformation. These frameworks describe what it means for educators to be digitally competent. However are there other competencies required to deliver Digital Accessibility in education. The particular focus of this paper is the role of the teachers in delivering Digital Accessibility in higher education. What should be expected of them and what are the required competencies …

Studies On The Synthesis Of Unusual Amino Acids And Their Incorporation Into New Peptide Antibiotics, Agnieszka Wadolowska

Doctoral

As microorganisms continue to develop resistance and survive against many different forms of antimicrobial solutions, such as antibiotics, there is urgent need for new classes of antimicrobial peptides to mitigate the threat. Bacteria are a rich source of ribosomally-synthesized antimicrobial peptides known as bacteriocins, which are produced by one bacterial species to kill other bacterial strains. Lanthionine-containing lantibiotics and sactibiotics are post-translationally modified bacteriocins which display extremely potent antibacterial activity.

Thuricin CD, a two-peptide system belonging to the sactibiotic family, shows high activity, but most importantly excellent selectivity, against C. difficile infection. The Thuricin CD peptides contain a number of …

Named Entity Recognition Based On A Graph Structure, David Muñoz, Fernando Pérez Téllez, David Pinto

Articles

The identification of indirect relationships between texts from different sources makes the task of text mining useful when the goal is to obtain the most valuable information from a set of texts. That is why in the field of information retrieval the correct recognition of named entities plays an important role when extracting valuable information in large amounts of text. Therefore, it is important to propose techniques that improve the NER classifiers in order to achieve the correct recognition of named entities. In this work, a graph structure for storage and enrichment of named entities is proposed. It makes use …

Exploring Composite Dataset Biases For Heart Sound Classification, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Conference papers

In the last few years, the automatic classification of heart sounds has been widely studied as a screening method for heart disease. Some of these studies have achieved high accuracies in heart abnormality prediction. However, for such models to assist clinicians in the detection of heart abnormalities, it is of critical importance that they are generalisable, working on unseen real-world data. Despite the importance of generalisability, the presence of bias in the leading heart sound datasets used in these studies has remained unexplored. In this paper, we explore the presence of potential bias in heart sound datasets. Using a small …

Language Model Co-Occurrence Linking For Interleaved Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

Synthesising Tabular Datasets Using Wasserstein Conditional Gans With Gradient Penalty (Wcgan-Gp), Manhar Singh Walia, Brendan Tierney, Susan Mckeever

Conference papers

Deep learning based methods based on Generative Adversarial Networks (GANs) have seen remarkable success in data synthesis of images and text. This study investigates the use of GANs for the generation of tabular mixed dataset. We apply Wasserstein Conditional Generative Adversarial Network (WCGAN-GP) to the task of generating tabular synthetic data that is indistinguishable from the real data, without incurring information leakage. The performance of WCGAN-GP is compared against both the ground truth datasets and SMOTE using three labelled real-world datasets from different domains. Our results for WCGAN-GP show that the synthetic data preserves distributions and relationships of the real …

A Framework For Validating And Testing Agent-Based Models: A Case Study From Infectious Diseases Modelling., Elizabeth Hunter, John D. Kelleher

Conference papers

In this paper we present a framework for validating and testing an agent-based model that includes determining the appropriate number of runs to account for variation in model runs, validating the model and showing that the model can be used to learn about the system. To demonstrate the framework we use a case study of an agent-based model for the spread of infectious diseases.

Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio

Conference papers

On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to “Get BREXIT Done”. The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4thDecember, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach …

Mutual Information Decay Curves And Hyper-Parameter Grid Search Design For Recurrent Neural Architectures, Abhijit Mahalunkar, John Kelleher

Conference papers

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.

Novice Learner Experiences In Software Development: A Study Of Freshman Undergraduates, Catherine Higgins, Ciaran O'Leary, Claire Mcavinia, Barry J. Ryan

Conference papers

This paper presents a study that is part of a larger research project aimed at addressing the gap in the provision of educational software development processes for freshman, novice undergraduate learners, to improve proficiency levels. With the aim of understanding how such learners problem solve in software development in the absence of a formal process, this case study examines the experiences and depth of learning acquired by a sample set of novice undergraduates. A novel adaption of the Kirkpatrick framework known as AKM-SOLO is used to frame the evaluation. The study finds that without the scaffolding of an appropriate structured …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Design And Evaluation Of An Adventure Videogame Based In The History Of Mathematics, Mariana Rocha, Pierpaolo Dondio

Conference papers

The present paper describes the design and evaluation of an adventure videogame developed to cover the mathematics primary school curriculum. The narrative of the game is based in the history of mathematics and, to win, the player needs to travel through time, starting from the ancient Egypt and finishing at the modern world. To achieve that, the player interacts with real-life characters, such as Pythagoras of Samos, learning about their contributions to the field and using this knowledge to solve puzzles. The aim of the research presented in this paper is to understand the effects of the game on students’ …

Smpl-Based 3d Pedestrian Pose Prediction, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche, Lorraine D'Arcy

Conference papers

Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently …

Start Of Epidemy In A City: Short-Term Forecast Of Covid-19 With Gmdh-Based Algorithms And Official Medical Statistics, Anna Boldyreva, Mikhail Alexandrov, Olexiy Koshulko, Svetland Popova

Conference papers

The sudden onset and quick development of an unknown epidemic may lead to tragic consequences: panic of population due to victims and unpreparedness of authorities for effectively help to population. These circumstances define extremely high requirements to the tools for short-term operational forecast. Namely, such tools should provide reliable results when model of phenomenon is unknown (factors of disease spreading) and data are limited (time series of observations). GMDH-based algorithms just meet these requirements unlike modern differential or advanced statistical models. In this study we test different algorithms from GMDH Shell platform on the example of Covid-19 epidemic in Moscow …

Brexit: Psychometric Profiling The Political Salubrious Through Machine Learning: Predicting Personality Traits Of Boris Johnson Through Twitter Political Text, James Usher, Pierpaolo Dondio

Conference papers

Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that people's psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnson’s Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN …

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of …

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia

Dissertations

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution.