Computer Sciences Commons^™

Gamerfit-Asd Beta Test: Adapting An Evidence-Based Exergaming And Telehealth Coaching Intervention For Autistic Youth, Daniel P. Hatfield, Aviva Must, Winston Kennedy, Amanda E. Staiano, James Slavet, Rachael A. Sabelli, Carol Curtin, Linda G. Bandini, Phillip Nauta, Christopher Stuetzle, April Bowling

Computer and Data Science Faculty Publications

Background: Health disparities faced by autistic youth are exacerbated by inadequate physical activity (PA) and sleep, whereas healthy PA and sleep may improve mood and function. Adaptive Game Squad (AGS) is an evidence-based telehealth coaching and exergaming intervention to improve PA and sleep for adolescents with diverse neurodevelopmental and psychiatric conditions. This study aimed to adapt AGS for autistic youth ages 10–15 years; beta-test the modified intervention for feasibility, accessibility, and engagement; and further refine the intervention for a larger planned demonstration pilot.

Methods: Interdisciplinary experts adapted AGS to create GamerFit-ASD, a 12-week intervention that included a progressive exergame schedule, …

In Vitro Antioxidant, Anti-Inflammatory, And Photoprotective Activities Of Aqueous Extract Of The Endemic Plant Hammada Scoparia L. From Algeria, Benine Chaima, Djahra Ali Boutlelis, Laiche Ammar Touhami, Tlili Hajer, Tliba Ali, Ben Arfa Abdelkarim, Najjaa Hanen

Karbala International Journal of Modern Science

This study aimed to analyze the chemical profile and evaluate the biological activities of the aqueous extract of H. Scoparia, an endemic plant found in Southeastern Algeria. The aqueous extract was subjected to phytochemical screening, RP-HPLC, and FT-IR analysis. The anti-inflammatory activity was examined using the protein denaturation method, and the photoprotective activity was evaluated using UV-Visible spectrophotometry. The antioxidant activity was assessed using ABTS, DPPH, and FRAP assays. The study found the presence of various secondary metabolites such as polyphenols, tannins, and saponins. This study suggests that H. Scoparia extract has the potential as a natural source of bio-actives …

Disease Progression Modelling Of Alzheimer's Disease Using Probabilistic Principal Components Analysis, Martin Saint-Jalmes, Victor Fedyashov, Daniel Beck, Timothy Baldwin, Noel G. Faux, Pierrick Bourgeat, Jurgen Fripp, Colin L. Masters, Benjamin Goudey

Natural Language Processing Faculty Publications

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the …

Frank At Checkthat! 2023: Detecting The Political Bias Of News Articles And News Media Notebook For The Checkthat! Lab At Clef 2023, Dilshod Azizov, Preslav Nakov, Shangsong Liang

Machine Learning Faculty Publications

This paper addresses the challenge of detecting political bias in news articles and media outlets from CheckThat!lab Task 3 [1, 2] by proposing an automated method for classifying these as left, center, or right-leaning. As mass media consumption continues to grow, the capability to identify bias in news reporting is crucial due to the potential societal impact of unaddressed political bias. To tackle this issue, we present a comprehensive approach employing machine learning techniques to detect political leaning in news media and articles. Our model, CatBoost, is evaluated on a diverse dataset comprising over 55,000 news articles sourced from AllSides1 …

Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi

Applied Mathematics & Information Sciences

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits …

Neutrosophic Adaptive Lsb And Deep Learning Hybrid Framework For Ecg Signal Classification, Abdallah Rezk, Ahmed S. Sakr, H. M. Abdulkader

Applied Mathematics & Information Sciences

This paper proposes a novel hybrid framework for ECG signal classification and privacy preservation. The framework includes two phases: the first phase uses LSTM+CNN with attention gate for ECG classification, while the second phase utilizes adaptive least signal bit with neutrosophic for hiding important data during transmission. The proposed framework converts data into three sets of degrees (true, false, and intermediate) using neutrosophic and passes them to an embedding layer. In the sender part, the framework hides important data in ECG signal as true and false degrees, using the intermediate set as a shared dynamic key between sender and receiver. …

Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu

Applied Mathematics & Information Sciences

In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …

The Influence Of Supply Chain Management Strategies On Organizational Performance In Hospitality Industry, Omar Jawabreh, Abdullah Mahfoud Baadhem, Basel J. A. Ali, Anas Ahmad Bani Atta, Anis Ali, Fahmi Fadhl Al- Hosaini

Applied Mathematics & Information Sciences

The studys primary goal is to analyze the connection between SCM practices and organizational performance, and it also aims to evaluate the moderating role of management type. Quantitative data collected from Jordans hotel and restaurant workers via questionnaire. Structural equation modeling is used to examine the hypothesized relationships. Organizational Performance is positively impacted by effective information sharing. Information Quality (IQ) positively affects Organizational Performance (OP), and Strategic Supplier Partnerships (SSP) play a crucial role. Customer Relationship Management (CRM) had no discernible effect on OP, according to the study. OP is positively impacted by Postponement (POS) techniques. When implemented, postponement increases …

The Effect Of System Quality And User Quality Of Information Technology On Internal Audit Effectiveness In Jordan, And The Moderating Effect Of Management Support, Ahmad Yahiya Ahmad Bani Ahmad (Ayassrah), Anas Ahmad Mahmoud Bani Atta, Hanan Ali Alawawdeh, Nawaf Abdallah Aljundi, Amer Morshed, Saleh Amin Dahbour

Applied Mathematics & Information Sciences

The goal of this study is to ascertain the moderating role that management support has in internal audit effectiveness in Jordan, as well as the impact of system quality and user quality of information technology. There were 172 responders in all, and they were split across Jordanian auditors. In the data analysis process, the quantitative analysis test— which consists of the validity test, reliability test, test of conventional assumptions, and hypothesis test—is applied. Information technology system and user quality are independent variables in this study. The dependent variable in this study is internal audit effectiveness, and the moderating variable is …

Optimal Control Analysis Of The Dynamics Of Covid-19 With Application To Ethiopian Data, Temesgen Duresa Keno, Fekadu Mosisa Legesse, Ebisa Olana Bajira

Applied Mathematics & Information Sciences

In this paper, we proposed an optimal control of the COVID-19 transmission dynamics. First, we investigated system features such as solution boundedness, positivity, disease-free and endemic equilibrium, and the local and global stability of equilibrium points. Besides, a disease-free equilibrium point is globally asymptotically stable if the basic reproduction number is less than one, and an endemic equilibrium point exists otherwise. Secondly, we have shown the sensitivity analysis of the basic reproduction number. Also the model is then fitted using COVID-19 infected reported in Ethiopia from February 1,2023 to March 2,2023. The values of model parameters are then estimated from …

Nexus Between Intellectual Capital And Financial Performance Sustainability: Evidence From Listed Jordanian Firms, Ali M. Alrabei, Leqaa N. Al-Othman, Thaer A. Abutaber, Mustafa S. Alathamneh, Tareq M. Almomani, Mohammed H. Qeshta

Applied Mathematics & Information Sciences

Purpose: The authors observe the effect of exploring the reality of Intellectual Capital (IC) and its impact on the financial performance of Jordanian industrial firms in Amman Stock Exchange. This empirical research explores the effect of intellectual capital on financial performance using data from 36 Jordanian industrial firms listed in Amman Stock Exchange for the period 2016-2020. The Value-Added Intellectual coefficient (VAIC) was adopted to measure the intellectual capital, while the return on assets (ROA), return on equity (ROE), and earnings per share (EPS) were adopted as measures of the companys financial performance. The effect of IC was tested by …

Assessing The Moderating Effect Of Innovation On The Relationship Between Information Technology And Supply Chain Management: An Empirical Examination, Heba Hatamlah, Mahmoud Allahham, Ibrahim A. Abu-Alsondos, Alaa S. Mushtaha, Ghadeer M. Al-Anati, Mustafa Al-Shaikh

Applied Mathematics & Information Sciences

This study examines how innovation (INN) influences the relationship between supply chain management and information technology in Jordan. 211 employees of Jordanian industrial enterprises who work in the Operations Department provided information for the study, which examines this subject. The findings indicate a close connection between information technology and supply chain management. Innovation also dramatically modifies the interaction between supply chain management and information technology. Management help may be the subject of future research.

The Role Of Business Intelligence Adoption As A Mediator Of Big Data Analytics In The Management Of Outsourced Reverse Supply Chain Operations, Heba Hatamlah, Mahmoud Allahham, Ibrahim A. Abu-Alsondos, Alaa Al-Junaidi, Ghadeer M. Al-Anati, Mustafa Al-Shaikh

Applied Mathematics & Information Sciences

The fluctuating and disorganized state of todays global markets is the result of several factors. COVID-19 is an illustration. Supply chain managers should re-evaluate their competitive strategy and leverage big data analytics in light of the rising volatility in demand and supply, rivalry among supply chain partners, and the requirement to deliver tailored goods and services (BDA). Supply chain firms require sophisticated BDA processes and procedures to provide useful insights from big data to better decision-making and supply chain operations, as many leaders in the sector have acknowledged the necessity for improving with data" (SCO). This research gives theoretical justification …

The Artificial Intelligence As A Decision-Making Instrument For Modeling And Predicting Small Cities’ Attractiveness: Evidence From Morocco, Sohaib Khalid, Driss Effina, Khaoula Rihab Khalid, Mohamed Salem Chaabane

Applied Mathematics & Information Sciences

This study analyzes residential attractiveness in small Moroccan cities using statistical models. Net migration rates are commonly used to assess attractiveness. The study estimated net migration rates for each city and employed a structural econometric model with logistic regression to identify influential variables that affect the net migration rate. These variables were then used in a predictive model with an artificial neural network algorithm. The logistic model revealed insights, highlighting the complexity of residential attractiveness influenced by factors like job supply, accessibility, and housing conditions. The artificial neural network model provided accurate predictions (over 80%), aiding policymakers in decision-making and …

Improving The Performance Of A Series-Parallel System Based On Lindley Distribution, Abdelfattah Mustafa, M. I. Khan, Maher. A. Alraddadi

Applied Mathematics & Information Sciences

In this article, the performance of a series-parallel system is improved. The system components are assumed to follows independently and identically Lindley distributed with three parameters. The system reliability for the given system will be improved by using reduction method, hot, cold and imperfect duplication method. Some reliability measures are derived. Two types of reliability equivalence factors and gamma fractiles are calculated. A numerical example is introduced to explain the theoretical results.

Quantization Of Fractional Constrained Systems With Wkb Approximation, Ola A. Jarabah

Applied Mathematics & Information Sciences

In this paper the constrained systems with two primary first class constraints are studied using fractional Lagrangian, after that we find the fractional Hamiltonian and the corresponding Hamilton Jacobi equation. Using separation of variables technique, we can find the action function S this function helps us to formulate the wave function which describe the behavior of our systems also from the action function S we can find the equations of motion and the corresponding momenta in fractional form. This work is illustrated using one example.

Applications Of The Ara-Residual Power Series Technique To Physical Phenomena, Aliaa Burqan

Applied Mathematics & Information Sciences

In this paper, a new analytical method called the ARA-Residual power series method (ARA- RPSM) is implemented to solve some fractional physical equations. The methodology of the proposed method based on applying the ARA-transform to the given fractional differential equations, followed by the creation of approximate series solutions using Taylor’s expansion. Then the series solution is transformed using the inverse of the ARA-transform to get the solution in the original space. Accuracy, effectiveness, and validity of the suggested method are demonstrated through the discussion of three attractive applications. The solution obtained using ARA-RPSM demonstrates good agreement when compared to the …

Generalization Of Renyi’S Entropy And Its Application In Source Coding, Ashiq Hussain Bhat, Niyamat Ali Siddiqui, Ismail A Mageed, Shawkat Alkhazaleh, Vidyanand Rabi Das, M. A. K Baig

Applied Mathematics & Information Sciences

In this paper, we introduce a new generalization of Renyis entropy β(P) and the most important feature of this generalized entropy Rαβ (P) is that it derives most important entropies that are well known and influence information theory and applied mathematics. Some significant properties of Rαβ (P) has been undertaken in this article. In addition, we introduce a new generalized exponentiated mean codeword length Lβα (P) in this article then determine how Rβα (P) and Lβα (P) are related in terms of source coding theorem.

Asynchronous Fdrl-Based Low-Latency Computation Offloading For Integrated Terrestrial And Non-Terrestrial Power Iot, Sifeng Li, Sunxuan Zhang, Zhao Wang, Zhenyu Zhou, Xiaoyan Wang, Shahid Mumtaz, Mohsen Guizani, Valerio Frascolla

Machine Learning Faculty Publications

Integrated terrestrial and non-terrestrial power internet of things (IPIoT) has emerged as a paradigm shift to three-dimensional vertical communication networks for power systems in the 6G era. Computation offloading plays key roles in enabling real-time data processing and analysis for electric services. However, computation offloading in IPIoT still faces challenges of coupling between task offloading and computation resource allocation, resource heterogeneity and dynamics, and degraded model training caused by electromagnetic interference (EMI). In this article, we propose an asynchronous federated deep reinforcement learning (AFDRL)-based computation offloading framework for IPIoT, where models are uploaded asynchronously for federated averaging to relieve network …

Overview Of The Clef-2023 Checkthat! Lab Task 1 On Check-Worthiness Of Multimodal And Multigenre Content, Firoj Alam, Alberto Barrón-Cedeño, Gullal S. Cheema, Gautam Kishore Shahi, Sherzod Hakimov, Maram Hasanain, Chengkai Li, Rubén Míguez, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov

Natural Language Processing Faculty Publications

We present an overview of CheckThat! Lab’s 2023 Task 1, which is part of CLEF-2023. Task 1 asks to determine whether a text item, or a text coupled with an image, is check-worthy. This task places a special emphasis on COVID-19, political debates and transcriptions, and it is conducted in three languages: Arabic, English, and Spanish. A total of 15 teams participated, and most submissions managed to achieve significant improvements over the baselines using Transformer-based models. Out of these, seven teams participated in the multimodal subtask (1A), and 12 teams participated in the Multigenre subtask (1B), collectively submitting 155 official …

Gpachov At Checkthat! 2023: A Diverse Multi-Approach Ensemble For Subjectivity Detection In News Articles, Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task 2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered …

Enriched Pre-Trained Transformers For Joint Slot Filling And Intent Detection, Momchil Hardalov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

Detecting the user's intent and finding the corresponding slots among the utterance's words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset. Here, we leverage such models, and we design a novel architecture on top of them. Moreover, we propose …

Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe

Natural Language Processing Faculty Publications

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense …

Linear Regression Under Partial Information, Tho M. Nguyen, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

Often, we need to know how to estimate the value of a difficult-to-directly estimate quantity y -- e.g., tomorrow's temperature -- based on the known values of several quantities x1, ..., xn. In many practical situations, we know that the relation between y and xi can be accurately described by a linear function. So, to find this dependence, we need to estimate the coefficients of this linear dependence based on the known cases in which we know both y and xi; this is known as linear regression. In the ideal situation, when in each case, we know all the inputs …

Synthetic Image Generation And The Use Of Virtual Environments For Image Enhancement Tasks, Neil Patrick Del Gallego

Software Technology Dissertations

Deep learning networks are often difficult to train if there are insufficient image samples. Gathering real-world images tailored for a specific job takes a lot of work to perform. This dissertation explores techniques for synthetic image generation and virtual environments for various image enhancement/ correction/restoration tasks, specifically distortion correction, dehazing, shadow removal, and intrinsic image decomposition. First, given various image formation equations, such as those used in distortion correction and dehazing, synthetic image samples can be produced, provided that the equation is well-posed. Second, using virtual environments to train various image models is applicable for simulating real-world effects that are …

Thinking Beyond Chatbots’ Threat To Education: Visualizations To Elucidate The Writing Or Coding Process, Badri Adhikari

Educator Preparation & Leadership Faculty Works

Despite overwhelming evidence to the contrary, educational practices continue to be predominantly centered around outcome-oriented approaches. These practices are now thoroughly disrupted by the recent accessibility of online resources and chatbots. Among the most affected subjects are writing and computer programming. As educators transform their teaching practices to account for this disruption, it is important to note that writing and computer programming play a critical role in the development of logical and computational thinking. For instance, what and how we write shapes our thinking and sets us on the path of self-directed learning. Likewise, computer programming plays a similar role …

A Roadmap For The Human Gut Cell Atlas, Matthias Zilbauer, Kylie R. James, Mandeep Kaur, Sebastian Pott, Zhixin Li, Albert Burger, Jay R. Thiagarajah, Joseph Burclaff, Frode L. Jahnsen, Francesca Perrone, Alexander D. Ross, Gianluca Matteoli, Nathalie Stakenborg, Tomohisa Sujino, Andreas Moor, Raquel Bartolome-Casado, Espen S. Bækkevold, Ran Zhou, Bingqing Xie, Ken S. Lau, Shahida Din, Scott T. Magness, Qiuming Yao, Semir Beyaz, Mark Arends, Alexandre Denadai-Souza, Lori A. Coburn, Jellert T. Gaublomme, Richard Baldock, Irene Papatheodorou, Jose Ordovas-Montanes, Guy Boeckxstaens, Anna Hupalowska, Sarah A. Teichmann

School of Computing: Faculty Publications

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human …

When Is It Beneficial To Merge Two Companies? When Is It Beneficial To Start A Research Collaboration?, Miroslav Svitek, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Merging two companies or splitting a company into two, teaming of two researchers or two research groups -- or splitting a research group into two -- these are frequent occurrences. Sometimes these actions lead to increased effectiveness, but sometimes, contrary to the optimistic expectations, the overall effectiveness decreases. To minimize the possibility of such failures, it is desirable to replace the current semi-intuitive way of making the corresponding decisions with a more objective approach. In this paper, we propose such an approach.

Autoconf: Automated Configuration Of Unsupervised Learning Systems Using Metamorphic Testing And Bayesian Optimization, Lwin Khin Shar, Goknil Arda, Erik Johannes Husom, Sagar Sen Sen, Naing Tun Yan, Kisub Kim

Research Collection School Of Computing and Information Systems

Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the com- plexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for config- uring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a …

The Devil Is In The Tails: How Long-Tailed Code Distributions Impact Large Language Models, Xin Zhou, Kisub Kim, Bowen Xu, Jiakun Liu, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Learning-based techniques, especially advanced Large Language Models (LLMs) for code, have gained considerable popularity in various software engineering (SE) tasks. However, most existing works focus on designing better learning-based models and pay less attention to the properties of datasets. Learning-based models, including popular LLMs for code, heavily rely on data, and the data's properties (e.g., data distribution) could significantly affect their behavior. We conducted an exploratory study on the distribution of SE data and found that such data usually follows a skewed distribution (i.e., long-tailed distribution) where a small number of classes have an extensive collection of samples, while a …