Physical Sciences and Mathematics Commons^™

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-Of-The-Art Methods, Yingna Zhong, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, Kohbalan Moorthy

All Works

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics' interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. …

Survey On Outdoor Navigation Applications For People With Visual Impairment, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Datasets

Outdoor navigation is a very challenging activity for People who suffer from Blindness or Visually Impairment (PBVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PBVI undertake a journey in an outdoor environment and what their main challenges and obstacles are. To throw some light on this gap, we conducted a questionnaire in collaboration with the National Council for the Blind Ireland (NCBI) for 49 PBVI. Our questionnaire gathers information about key aspects related to PBVI outdoor navigation such as support tools/devices, hazards, journey preparation, crossing roads, and understanding …

Csc 71010/Csci 77100: Programming Languages/Software Engineering, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

Wala Quick Start, Raffi T. Khatchadourian

Open Educational Resources

Setting up and trying the TJ Watson Library for Analysis (WALA).

Building An Ast Eclipse Plug-In, Raffi T. Khatchadourian

Open Educational Resources

Complete the Building an AST Eclipse Plug-in assignment. Once it works, find a medium-sized open-source Java project to run your plugin on. You may want to explore GitHub. Import the project into Eclipse and run your plug-in on it. Report on the following, which may require you to change some of the source code so that it is convenient:

1. Project name.
2. Project URL.
3. Project description.
4. The number of classes in the project.
5. The number of user-defined methods in the project.
6. For each class, the number of method calls.
7. Statistics about the method calls:
   1. The total number of method calls …

Working With Control-Flow Graphs, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

An Experiment On The Effects Of Using Color To Visualize Requirements Analysis Tasks: Supplemental Material, Yesugen Baatartogtokh, Irene Foster, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper: "An Experiment on the Effects of using Color to Visualize Requirements Analysis Tasks".
This paper is a scientific evaluation of the effectiveness and usability of EVO. We conduct an experiment to measure any effect of using colors to represent evidence pairs.

Visualizations For User-Supported State Space Exploration Of Goal Models: Supplemental Material, Yesugen Baatartogtokh, Irene Foster, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the research paper entitled, "Visualizations for User-supported State Space Exploration of Goal Models". This paper presents a technique for valuation-based filtering and coloring to assist users in understanding a solution space and selecting custom states from it. This supplement contains the data from our initial evaluation and associated models.

Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

All Works

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Accuracy Of Spectral Indices Assessing Fire Severity Utilizing Maximum And Minimum Pixel Values, Jarrad Mckercher, David Blake, Eddie Van Etten

Research Datasets

This data set contains all Spectral Indices created using Google Earth Engine through Google Collaborate. 16 Spectral Indices were created that utilise different image collection and pixel value parameters to map the burn severity of the 2021 Wooroloo Bushfire.

Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

All Works

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …

Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin

All Works

Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential …

Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

All Works

No abstract provided.

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

The Dope Distance Is Sic: A Stable, Informative, And Computable Metric On Ordered Merge Trees, Jose Arbelo, Antonio Delgado, Charley Kirk, Zach Schlamowitz

Mathematics Summer Fellows

When analyzing time series data, it is often of interest to categorize them based on how different they are. We define a new dissimilarity measure between time series: Dynamic Ordered Persistence Editing (DOPE). DOPE satisfies metric properties, is stable to noise, is as informative as alternative approaches, and efficiently computable. Satisfying these properties simultaneously makes DOPE of interest to both theoreticians and data scientists alike.

Reverse-Engineering The Design Rules For Cloud-Based Big Data Platforms, Ravi S. Sharma, Purna N. Mannava, Stephen C. Wingreen

All Works

Big Data's 5 V complexities are making it increasingly difficult to develop an understanding of the end to end process. Big Data platforms play a crucial role in many critical systems, combining with Internet-of-Things, Artificial Intelligence and Business Analytics. It is both relevant and important to understand Big Data systems to identify the best tools that fit the requirements of heterogeneous platforms. The objective of this paper is to "discover" a set of design principles and rules for Cloud-based Big Data platforms for complex, heterogeneous environments. The design scope comprises Big Data's significance, challenges and architectural impacts. Using a methodology …

A Divide & Concur Approach To Collaborative Goal Modeling With Merge In Early-Re: Supplemental Material, Kathleen R. Hablutzel, Anisha Jain, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper:
"A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE"
This paper proposes a formal approach to the problem of merging the attributes of intentions and actors, once these elements have been matched.

Detecting Road Intersections Automatically From Satellite Images Using A Deep Learning Approach, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Datasets

Automatic detection of road intersections is an important task in various domains such as navigation, route planning, traffic prediction, and road network extraction. Road intersections range from simple three-way T-junctions (degree 3) to complex large-scale junctions with many branches. The location of intersections and their complexity is an important consideration in route planning, such as the requirement to avoid complex intersections on pedestrian journeys. This is relevant to vulnerable road users such as People with Blindness or Visually Impairment (PBVI) or children. Route planning applications, however, do not give information about the location or complexity of intersections as this information …

Technology, Values, And Faith With Computer Scientist Derek Schuurman, Derek Schuurman

University Faculty Publications and Creative Works

Dr. Schuurman is a fellow of the American Scientific Affiliation, part of the leadership team for the West Michigan ASA chapter, an associate fellow of the The Kirby Laing Centre for Public Theology in Cambridge, senior member of the IEEE, member of the ACM, CES, ACMS, a book review editor for Perspectives on Science and Christian Faith, a regular contributor to the Christian Scholars Review blog, and a regular columnist for Christian Courier.

Amplification Of Hidden Periodic Motions In 3d Videos, Thomas Boccuto, Seraiah Kutai, Kristen Mosley, Samuel Kirk

Mathematics Summer Fellows

Ordinary videos capture a surprising amount of hidden, visually imperceptible information. For instance, videos of peoples' faces may capture color changes in the skin and artery motion from heartbeats, while videos of mechanical systems can capture subtle vibrations indicating imminent failure. Algorithms can extract and exaggerate these signals for visualization on top of the original videos. In particular, Eulerian magnification algorithms sidestep the need to track hidden motions directly and instead devise multiscale bandpass filters to amplify signals in local spatial regions. In this work, we extend these techniques beyond color videos to geometric video data captured by 3D depth …

Standard Non-Uniform Noise Dataset, Andres Imperial, John M. Edwards

Browse all Datasets

Fixed Pattern Noise Non-Uniformity Correction through K-Means Clustering

Fixed pattern noise removal from imagery by software correction is a practical approach compared to a physical hardware correction because it allows for correction post-capture of the imagery. Fixed pattern noise presents a unique challenge for de-noising techniques as the noise does not present itself where large number statistics are effective. Traditional noise removal techniques such as blurring or despeckling produce poor correction results because of a lack of noise identification. Other correction methods developed for fixed pattern noise can often present another problem of misidentification of noise. This problem can result …

Script For Estimating Error And Bias In Offline Evaluation Results, Mucun Tian, Michael D. Ekstrand

Computer Science Faculty Scripts and Data

This publication contains scripts to reproduce the paper “Estimating Error and Bias in Offline Evaluation Results” by Muncun Tian and Michael D. Ekstrand in Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR '20).

Script For Exploring Author Gender In Book Rating And Recommendation, Michael D. Ekstrand, Daniel Kluver

Computer Science Faculty Scripts and Data

This publication contains scripts to reproduce the paper:

Ekstrand, M.D. and Kluver, D. (2021). Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction. https://doi.org/10.1007/s11257-020-09284-2

*Date reflected refers to the publisher's online early release date.

Csci 40500/77100: Software Engineering, Raffi T. Khatchadourian

Open Educational Resources

This course is intended to be an introductory survey on the fundamental concepts and principles that underlie current and emerging methods, tools, and techniques for the efficient engineering of high-quality software systems. This may include understanding and appreciating problems in large-scale software development such as functional analysis of information processing systems, system design concepts, timing estimates, documentation, and system testing.

Csci 40500/77100: Software Engineering, Raffi T. Khatchadourian

Open Educational Resources

This course is intended to be an introductory survey on the fundamental concepts and principles that underlie current and emerging methods, tools, and techniques for the efficient engineering of high-quality software systems. This may include understanding and appreciating problems in large-scale software development such as functional analysis of information processing systems, system design concepts, timing estimates, documentation, and system testing.

Privacy-Preserving Non-Participatory Surveillance System For Covid-19-Like Pandemics, Mahmoud Nabil, Ahmed Sherif, Mohamed Mahmoud, Waleed Alsmary, Maazen Alsabaan

Faculty Publications

COVID-19 pandemic has revealed a pressing need for an effective surveillance system to control the spread of infection. However, the existing systems are run by the people’s smartphones and without a strong participation from the people, the systems become ineffective. Moreover, these systems can be misused to spy on people and breach their privacy. Due to recent privacy breaches, people became anxious about their privacy, and without privacy reassurance, the people may not accept the systems. In this paper, we propose a non-participatory privacy-preserving surveillance system for COVID-19-like pandemics. The system aims to control the spread of COVID-19 infection without …

Pothole Detection Under Diverse Conditions Using Object Detection Model, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever

Datasets

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalizable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles and resolutions. In this paper we present our …

Csci 49380/79526: Fundamentals Of Reactive Programming - Assignment 5, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

Collections In Scala, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.