bluuebunny/crossref_metadata_2025 huggingface dataset:
Perceptions of Problem-Based Learning and Attitudes Towards its Adoption Among K-12 Teachers in Seventh-day Adventist Schools in Florida
Authors:   ,   Andrews University, Pilliner Eileen
Published: May 2023

Problem One of the founders of Seventh-day Adventist education, Ellen G. White, advocated learning through, practical experiences and critical thinking. In 1997, the Seventh-day Adventist Church recommended problem-based learning (PBL) as a preferred teaching practice for its North American K-12 schools. However, Brantley and Ruiz observed that many Seventh-day Adventist educators feel inadequate to use this method of instruction. Little information exists anywhere concerning teachers’ awareness and perceptions o f problem-based learning (PBL) or factors related to its use. This study examined the relationship between PBL, philosophy of teaching, preferences for PBL teaching components, and perceived barriers to PBL adoption and use. Method An ex post facto survey was conducted among a convenience sample of 315 K-12 teachers in 50 schools in Florida. Four instruments were used to gather data to answer four research questions. The same instruments were administered to a group of experienced PBL teachers and results were compared to the Adventist group. Results The majority of Seventh-day Adventist K-12 teachers in Florida are unaware of problem-based learning (PBL). Teachers who embrace a student-centered teaching preference are more likely to be aware of PBL. Little more than half the teachers have a student-centered teaching philosophy, and less than half appreciate the student-centered teaching components of PBL. Teaching philosophy is related to the teachers’ age and preference for PBL teaching components. More female than male teachers embrace the student-centered components ofPBL. The greatest perceived barriers to teacher implementation ofPBL included (1) assessing and reporting student learning, (2) allowing students’ needs and interests to determine pace and content of curriculum coverage, (3) a loosely structured, sometimes noisy learning environment, and (4) system unwillingness to provide PBL support sources. The majority o f the teachers did not identify factors that would deter them from implementing problem-based learning. Conclusions Although most Seventh-day Adventist teachers are unaware ofPBL and seem to embrace a teacher-centered teaching philosophy, they appear willing to learn about the method and to implement it in their classrooms. However, they do not expect support from their school systems, parents, and colleagues, as preconditions to successful adoption. It should be noted that the major barriers to PBL adoption appear to be reflective of the teaching philosophy of the school systems, parents, and teachers. Addressing these barriers is likely to increase the possibility that successful adoption will take place.
The Impact of Early-Intervention/Prevention Services on the Northville Public Schools
Authors:   ,   Andrews University, Sornson Robert
Published: May 2023

Problem Whereas some school districts have continued to observe high rates of reading failure and increasing rates of special education identification, others have looked at program or system changes to reduce early learning failure. This study is an analysis of the impact of early intervention practices in the elementary schools of an upper-middle-class community. Method Cost analyses of district programs were compared to a hypothetical district with state average levels of special education identification. Special Education eligibility rates were tracked over a 10-year period. Data were collected from surveys given to Instructional-Support Team personnel, general education teachers, elementary administrators, and parents. Standardized data were collected on students in third and fifth grades who had been identified and served through the Instructional Support Team (1ST) process. Four research questions were posed: 1. Are early intervention and the use o f instructional Support Teams cost-effective? 2. Are early intervention and the use of ISTs associated with a reduction in special education identification rates? 3. Are early intervention and the use of ISTs effective from the viewpoints of principals, general education teachers, 1ST personnel and parents? 4. Are early intervention and the use of ISTs effective in increasing student achievement? Results Cost savings to the district were noted by comparing Northville costs to a hypothetical district with state-average special education identification rates. District special education rates were reduced from 10.2% in 1992-93 to 6.6% in 2001-02. This compares with state average levels o f 10.8% in 1992-93 and 13.3% in 2001-02. Parent, teacher, and administrator satisfaction rates were high. Students served by the early-intervention processes were found to be achieving, on average, at levels consistent with an expectation of success in general education. Conclusions Early-intervention efforts have contributed to reduced special education referrals and placements, and reduced long-term costs, and have been given high satisfaction ratings by teachers and parents. Students who were identified as at-risk and then served through the early-intervention process were found, on average, to be achieving above national averages, and near district-average levels.
The Phenomenon of Woman-on-Woman Abuse and its Relationship to Gender Profile and Personal Experiences of Women
Authors:   ,   Andrews University, Spence Deborah
Published: May 2023

Purpose of the Study. Female-on-female aggression is often inferred, or drawn from studies conducted with children or males. Little or no information is available that reports behaviors perceived as mistreatment or abuse among women. The purposes of this study were to investigate (a) behaviors demonstrated by women that women consider abuse or mistreatment; (b) the extent to which these perceptions of abuse/mistreatment were related to gender profiles; and (c) the extent to which personal experiences as victims or perpetrators of abuse were related to age, race, and education. Method. This study used the survey research method in which questionnaires were mailed and self-administered to a convenience sample of 1,700 Mary Kay™ personnel and their associates. Six hundred and twenty-six of the 640 respondents who chose to participate in this study were included for final data analysis. The questionnaire was designed to elicit demographic characteristics, gender profile, and overt and covert acts or behaviors that may be considered mistreatment/abuse. Results. Thirty-five percent of the women admitted to being perpetrators of abuse, while 59% reported being victims of abuse by other women. Only overt behaviors such as “sleeping with her husband to hurt her” were considered acts of abuse. Caucasians tended to view these overt acts as more abusive than other racial groups. In addition, women in the 40-49 age range perceived these acts to be more abusive. Perception of abuse was not related to gender profile. Conclusion. The phenomenon of woman-on-woman abuse is quite real. Unlike gender and education, race and age appear to play important roles in the perception of this phenomenon. Race, age and educational levels appeared to play important roles in the perception of victimization.
A Comparison of the Emotional Indicators on the House-Tree-Person Drawings and the Kinetic-House-Tree-Person Drawing
Authors:   ,   Andrews University, Stoddard Denise
Published: May 2023

Problem. Robert Burns, author of the Kinetic-Family- Drawing, - has devised a projective test called the Kinetic-House-Tree-Person drawing. He has taken the House-Tree-Person projective technique and changed the instructions to include all of the figures on one page and also to include an action. In Burns's book on the K-H-T-P he claimed that his drawing gives a better clinical picture of the subject than the H-T-P. However, there is no research that compares the two tests for clinical information. Method. The H-T-P and K-H-T-P were administered to 204 college students 18 years of age and older. These students came from one university in Michigan and one in Illinois. They were a non-clinical sample, meaning that they were not in therapy. Emotional indicators were extracted from the drawings, and then the indicators were analyzed using the Chi Square statistic called the McNemar test. Results. There were 499 different emotional indicators found on the drawings, and 108 indicators were found to be significant. There were 74 indicators that were found significantly more often on the H-T-P than on the K-H-T-P. There were 34 indicators that were found significantly more often on the K-H-T-P than on the H-T-P. The H-T-P had indicators in the categories of general drawing characteristic, house, tree, and person. The K-H-T-P had indicators in all of those categories and also in actions, styles, and symbols. The H-T-P had more than twice as many indicators as the K-H-T-P. The category that had the most indicators for both tests was the house, with 56% of the indicators on the H-T-P and 32% on the K-H-T-P. Conclusions. The H-T-P and K-H-T-P differ in the emotional indicators that are evoked from the drawings. With the H-T-P having more than twice as many indicators as the K-H-T-P, it is shown to be a stronger projective technique in eliciting information about the subject. Although similar in name, the tests are not interchangeable. Each test has been shown to have merit, and they could both be used in a psychological assessment battery to add the most amount of information to the clinical picture.
Genetic Variants Associated with Bronchial Asthma Specific to the Population of the Russian Federation
Authors: Akhmerova Yuliya N. , Shpakova Tatyana А. , Grammatikati Konstantin S. , Mitrofanov Sergey I. , Kazakova Polina G. , Mkrtchian Anastasia A. , Zemsky Pavel Yu. , Pilipenko Mariia N. , Feliz Nadezhda V. , Frolova Lidiya V. , Frolovskaya Alesya A. , Yudin Vladimir S. , Keskinov Anton A. , Kraevoy Sergey A. , Yudin Sergey M. , Skvortsova Veronika I.
Published: May 2023

Bronchial asthma (BA) is a disease that still lacks an exhaustive treatment protocol. In this regard, the global medical community pays special attention to the genetic prerequisites for the occurrence of this disease. Therefore, the search for the genetic polymorphisms underlying bronchial asthma has expanded considerably. As the present study progressed, a significant amount of scientific medical literature was analyzed and 167 genes reported to be associated with the development of bronchial asthma were identified. A group of participants (n = 7,303) who had voluntarily provided their biomaterial (venous blood) to be used in the research conducted by the Federal Medical Biological Agency of Russia was formed to subsequently perform a bioinformatic verification of known associations and search for new ones. This group of participants was divided into four cohorts, including two sex-distinct cohorts of individuals with a history of asthma and two sex-distinct cohorts of apparently healthy individuals. A search for polymorphisms was made in each cohort among the selected genes, and genetic variants were identified whose difference in occurrence in the different cohorts was statistically significant (significance level less than 0.0001). The study revealed 11 polymorphisms that affect the development of asthma: four genetic variants (rs869106717, rs1461555098, rs189649077, and rs1199362453), which are more common in men with bronchial asthma compared to apparently healthy men; five genetic variants (rs1923038536, rs181066119, rs143247175, rs140597386, and rs762042586), which are more common in women with bronchial asthma compared to apparently healthy women; and two genetic variants (rs1219244986 and rs2291651) that are rare in women with a history of asthma.
Prediction of Pneumonia Patient Proportions Using Kriging and Inverse Distance Weighted Methods
Authors: Okma Arnilia  , Muhammad Nur Aidi  , Indahwati  , Fitrah Ernawati
Published: May 2023

Pneumonia is a respiratory tract disease caused by bacteria, viruses, or fungi. In Indonesia, pneumonia is one of the diseases with the second highest number of cases after malaria from 2007 to 2015. Mapping of regions with pneumonia prevalence needs to be done so that the government can pay more attention to areas with high pneumonia rates, and the public can be more aware of areas that are prone to pneumonia. Spatial methods that can be used to predict the distribution of pneumonia patients are ordinary kriging and inverse distance weighted. The research results showed that the best method chosen for predicting the proportion of pneumonia patients in Sumatra, Java, Kalimantan, and Sulawesi islands is the ordinary kriging method with its theoretical semivariogram model being exponential, and the obtained RMSE value is 0.0178. The average proportion of pneumonia patients in Sumatra Island is 0.01655, with the highest proportion value of 0.02504 in Simalungun District, North Sumatra. Then, the average proportion of pneumonia patients in Java Island is 0.01672, with the highest proportion value of 0.02443 in Garut District, West Java. The proportion of pneumonia patients in Kalimantan Island is 0.01657, with the highest proportion value of 0.01835 in Balikpapan District, East Kalimantan. The average proportion of pneumonia patients in Sulawesi Island is 0.0166, with the highest proportion value of 0.02397 in South Minahasa District, North Sulawesi.
Cuidado ao Paciente Oncológico na Perspectiva da Oncologia Integrativa
Authors: Santos William Messias Silva , Santos Jaqueline Silva , Machado Gilmar Antonio Batista , Maia Maria Ambrosina Cardoso , Andrade Raquel Dully
Published: May 2023

Introdução: A oncologia integrativa é um campo recente e promissor que visa ao cuidado integral centrado no paciente, com uma abordagem baseada em evidências. Objetivo: Verificar o conhecimento científico produzido sobre a oncologia integrativa na atenção hospitalar. Método: Revisão integrativa com buscas nas bases de dados PubMed, LILACS, SciELO e MOSAICO, realizadas de fevereiro a março de 2022. A partir dos critérios de elegibilidade (estudos referentes ao tema publicados de 2000 a 2022, disponíveis na íntegra no acesso público, nos idiomas inglês, português e espanhol), foram selecionados sete artigos, analisados de acordo com a modalidade temática. Resultados: Os artigos elegíveis foram publicados entre 2018 e 2021, com destaque para a produção europeia, que corresponde a quatro (57,14%) artigos. O conteúdo dos estudos foi organizado em dois temas: a implementação de cuidados de oncologia integrativa e o acesso equitativo à oncologia integrativa e outros desafios. Foi observado que a oncologia integrativa tem sido praticada em diferentes cenários, com variadas ações realizadas, e tem como desafio central a ampliação do acesso ao usuário, por meio do desenvolvimento de diretrizes baseadas em evidências e da implementação de políticas de financiamento e qualificação profissional. Conclusão: O conhecimento científico produzido aponta que a implementação de cuidados de oncologia integrativa na atenção hospitalar ainda é limitada, com desafios relacionados ao acesso equitativo, ao financiamento, à gestão e à qualificação dos profissionais de saúde.
АДМІНІСТРАТИВНО-ПРАВОВЕ РЕГУЛЮВАННЯ ЗДІЙСНЕННЯ ПРОЦЕДУРИ БАНКРУТСТВА
Authors: ПАЛКІН Андрій
Published: May 2023

У статті визначено особливості адміністративно-правового регулювання здійснення процедури банкрутства. Метою статті є встановлення змісту адміністративно-правового регулювання здійснення процедури банкрутства. Автором до прогалин здійснених науково-практичних досліджень у сфері банкрутства віднесено фрагментарність висвітлення проблематики специфіки визнання неплатоспроможними державних підприємств. Встановлено, що відповідно до чинного законодавства до державних підприємств може бути застосована судова процедура розпорядження майном. З’ясовано, що чинним законодавством України встановлюється імперативна заборона із недопущення застосування процедури неплатоспроможності казенних підприємств. Автором встановлено, що для таких підприємств застосовується процедури санації чи ліквідації після їх вилучення із переліку об’єктів, що не підлягають приватизації, тобто встановлення нормативного дозволу на їх вилучення із об’єктів права державної власності. Автором зроблено висновок, що здійснення адміністративно-правового регулювання відносин у сфері банкрутства має досягати ефективності реалізації ряду функцій. До спеціальних функцій адміністративно-правового регулювання у сфері банкрутства віднесено: 1) функція аналітичного дослідження внутрішніх та зовнішніх чинників прогнозування економічного розвитку суб’єктів господарювання; 2) функція розробки, обґрунтування та реалізації плану заходів санації боржника; 3) функція кореляції розподілу здобутих від продажу майна та акумуляції ресурсів; 4) функція співробітництво на національному та міжнародному рівнях із забезпечення взаємодії судів, спрямоване на захист інтересів та прав суб’єктів господарювання; 4) функція відновлення платоспроможності боржника; 5) функція здійснення контролю за своєчасністю та об’єктивністю справляння податків, інших загальнообов’язкових платежів та соціальних внесків.
ЗАРУБІЖНИЙ ДОСВІД ЗАПОБІГАННЯ КОРУПЦІЙНИХ РИЗИКІВ У СФЕРІ НАДАННЯ ПУБЛІЧНИХ ПОСЛУГ
Authors: ЦАРЕВИЧ Оксана
Published: May 2023

Наукова стаття присвячена висвітленню зарубіжного досвіду запобігання корупційних ризиків у сфері надання публічних послуг. Враховуючі нормативно-правові акти зарубіжних країн виокремлено напрямки удосконалення запобігання корупційних ризиків у сфері надання публічних послуг. Встановлено, в Німеччині, слід назвати створення реєстру корумпованих фірм як дієвим метод боротьби з корупцією, в тому числі у сфері надання публічних послуг. У даному разі Німеччина йде шляхом іноземного досвіду, зокрема ізраїльського. Його сутність полягає у тому, що фірма, яка включена до такого реєстру, позбавляється права виконувати будь-які державні замовлення, стає об’єктом більш пильної уваги з боку правоохоронних органів. Такі заходи, за оцінками парламентарів та урядовців, повністю себе виправдовують. У науковій літературі Німеччини прийнято поділити корупційні діяння на три групи. Ситуативна корупція – це, як правило, спонтанні дії, що відбуваються без планування і є неадекватною реакцією на безпосередньо проведену службову дію на чиюсь користь поза законом та норм або таку, що безпосередньо відбувається за корупційною дією. Структурна корупція – має місце, коли продажність політиків службовців та інших посадовців субсидується протягом тривалого часу або якщо корупція має неодноразовий характер. Корупція як складова частина організованої злочинності є найвищою й найбільш небезпечною формою корупції. Вона виявляється в діях організованої злочинності, що мають на меті поставити державні, суспільні, економічні та політичні установи під контроль злочинних організацій. Зроблено висновок, що наступними шляхими успішної боротьби з корупцією, в тому числі у сфері надання публічних послуг, вироблені світовим співтовариством є: чітке регламентування дій та етичних стандартів діяльності посадових осіб усіх рівнів, в тому числі у сфері надання публічних послуг та відповідальність за їх порушення; відкритість влади, прозорість та зрозумілість процедур прийняття державних рішень, в тому числі у сфері надання публічних послуг; дієві механізми контролю за діяльністю державних органів з боку громадянського суспільства; свобода слова, свобода та незалежність засобів масової інформації.
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
Authors: Ahmad Zeeshan , Khan Naimul
Published: May 2023

Multimodal fusion frameworks for Human Action Recognition (HAR) using depth and inertial sensor data have been proposed over the years. In most of the existing works, fusion is performed at a single level (feature level or decision level), missing the opportunity to fuse rich mid-level features necessary for better classification. To address this shortcoming, in this paper, we propose three novel deep multilevel multimodal fusion frameworks to capitalize on different fusion strategies at various stages and to leverage the superiority of multilevel fusion. At input, we transform the depth data into depth images called sequential front view images (SFIs) and inertial sensor data into signal images. Each input modality, depth and inertial, is further made multimodal by taking convolution with the Prewitt filter. Creating "modality within modality" enables further complementary and discriminative feature extraction through Convolutional Neural Networks (CNNs). CNNs are trained on input images of each modality to learn low-level, high-level and complex features. Learned features are extracted and fused at different stages of the proposed frameworks to combine discriminative and complementary information. These highly informative features are served as input to a multi-class Support Vector Machine (SVM). We evaluate the proposed frameworks on three publicly available multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTD-MHAD Kinect V2. Experimental results show the supremacy of the proposed fusion frameworks over existing methods.
A Survey on Physiological Signal-Based Emotion Recognition
Authors: Ahmad Zeeshan , Khan Naimul
Published: May 2023

Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.
Transfer Learning With Intelligent Training Data Selection for Prediction of Alzheimer’s Disease
Authors: Khan Naimul , Hon Marcia , Abraham Nabila
Published: May 2023

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning has been a subject of intense research in recent years. Recent success of deep learning in computer vision has progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where the state-of-the-art VGG architecture is initialized with pre-trained weights from large benchmark datasets consisting of natural images. The network is then fine-tuned with layer-wise tuning, where only a pre-defined group of layers are trained on MRI images. To shrink the training data size, we employ image entropy to select the most informative slices. Through experimentation on the ADNI dataset, we show that with training size of 10 to 20 times smaller than the other contemporary methods, we reach state-of-the-art performance in AD vs. NC, AD vs. MCI, and MCI vs. NC classification problems, with a 4% and a 7% increase in accuracy over the state-of-the-art for AD vs. MCI and MCI vs. NC, respectively. We also provide detailed analysis of the effect of the intelligent training data selection method, changing the training size, and changing the number of layers to be fine-tuned. Finally, we provide Class Activation Maps (CAM) that demonstrate how the proposed model focuses on discriminative image regions that are neuropathologically relevant, and can help the healthcare practitioner in interpreting the model's decision making process.
Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset
Authors: Ewen Nicolas , Khan Naimul
Published: May 2023

Traditionally, convolutional neural networks need large amounts of data labelled by humans to train. Self supervision has been proposed as a method of dealing with small amounts of labelled data. The aim of this study is to determine whether self supervision can increase classification performance on a small COVID-19 CT scan dataset. This study also aims to determine whether the proposed self supervision strategy, targeted self supervision, is a viable option for a COVID-19 imaging dataset. A total of 10 experiments are run comparing the classification performance of the proposed method of self supervision with different amounts of data. The experiments run with the proposed self supervision strategy perform significantly better than their non-self supervised counterparts. We get almost 8% increase in accuracy with full self supervision when compared to no self supervision. The results suggest that self supervision can improve classification performance on a small COVID-19 CT scan dataset. Code for targeted self supervision can be found at this link: this https URL 
Inertial Sensor Data To Image Encoding For Human Action Recognition
Authors: Ahmad Zeeshan , Khan Naimul
Published: May 2023

Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework. These four types of activity images are Signal Images (SI), Gramian Angular Field (GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP) Images. Furthermore, for creating a multimodal fusion framework and to exploit activity image, we made each type of activity images multimodal by convolving with two spatial domain filters : Prewitt filter and High-boost filter. Resnet-18, a CNN model, is used to learn deep features from multi-modalities. Learned features are extracted from the last pooling layer of each ReNet and then fused by canonical correlation based fusion (CCF) for improving the accuracy of human action recognition. These highly informative features are served as input to a multiclass Support Vector Machine (SVM). Experimental results on three publicly available inertial datasets show the superiority of the proposed method over the current state-of-the-art.
Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
Authors: Khan Naimul , Abraham Nabila , Hon Marcia , Guan Ling
Published: May 2023

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.
A Scene-based Augmented Reality Framework for Exhibits
Authors: Li-Chee-Ming Julien , Wu Zheng , Tan Randy , Tan Ryan , Khan Naimul , Ye Andy , Guan Ling
Published: May 2023

This paper presents a novel augmented reality (AR) framework specifically targeting scene-based exhibits. Unlike traditional AR libraries that rely on specific image targets, the proposed framework utilizes a bag-of-words model for scene recognition, which enables recognition and subsequent launch of AR experiences under challenging environments. Moreover, the proposed framework utilizes relocalization capabilities of the popular ORB-SLAM algorithm to track the user’s movement after recognition of a particular exhibit. We demonstrate the efficacy of the proposed framework through a complete mobile app designed for a local museum, where artifacts are enhanced through AR.
Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals
Authors: Khan Naimul , Imtiaz Md Niaz
Published: May 2023

R-peak detection is crucial in electrocardiogram (ECG) signal processing as it is the basis of heart rate variability analysis. The Pan-Tompkins algorithm is the most widely used QRS complex detector for the monitoring of many cardiac diseases including arrhythmia detection. However, the performance of the Pan-Tompkins algorithm in detecting the QRS complexes degrades in low-quality and noisy signals. This article introduces Pan-Tompkins++, an improved Pan-Tompkins algorithm. A bandpass filter with a passband of 5--18 Hz followed by an N-point moving average filter has been applied to remove the noise without discarding the significant signal components. Pan-Tompkins++ uses three thresholds to distinguish between R-peaks and noise peaks. Rather than using a generalized equation, different rules are applied to adjust the thresholds based on the pattern of the signal for the accurate detection of R-peaks under significant changes in signal pattern. The proposed algorithm reduces the False Positive and False Negative detections, and hence improves the robustness and performance of Pan-Tompkins algorithm. Pan-Tompkins++ has been tested on four open source datasets. The experimental results show noticeable improvement for both R-peak detection and execution time. We achieve 2.8% and 1.8% reduction in FP and FN, respectively, and 2.2% increase in F-score on average across four datasets, with 33% reduction in execution time. We show specific examples to demonstrate that in situations where the Pan-Tompkins algorithm fails to identify R-peaks, the proposed algorithm is found to be effective. The results have also been contrasted with other well-known R-peak detection algorithms.
Lightweight and Interpretable Left Ventricular Ejection Fraction Estimation using Mobile U-Net
Authors: Muldoon Meghan , Khan Naimul
Published: May 2023

Accurate LVEF measurement is important in clinical practice as it identifies patients who may be in need of life-prolonging treatments. This paper presents a deep learning based framework to automatically estimate left ventricular ejection fraction from an entire 4-chamber apical echocardiogram video. The aim of the proposed framework is to provide an interpretable and computationally effective ejection fraction prediction pipeline. A lightweight Mobile U-Net based network is developed to segment the left ventricle in each frame of an echocardiogram video. An unsupervised LVEF estimation algorithm is implemented based on Simpson's mono-plane method. Experimental results on a large public dataset show that our proposed approach achieves comparable accuracy to the state-of-the-art while being significantly more space and time efficient (with 5 times fewer parameters and 10 times fewer FLOPS).
ECG Heartbeat Classification Using Multimodal Fusion
Authors: Ahmad Zeeshan , Tabassum Anika , Guan Ling , Khan Naimul
Published: May 2023

Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the state-of-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN). In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information necessary for better performance of classifier. These informational features are finally used to train a Support Vector Machine (SVM) classifier for ECG heart-beat classification. We demonstrate the superiority of the proposed fusion models by performing experiments on PhysioNet's MIT-BIH dataset for five distinct conditions of arrhythmias which are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for Myocardial Infarction (MI) classification. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively. Source code at https://github.com/zaamad/ECG-Heartbeat-Classification-Using-Multimodal-Fusion.
Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability
Authors: Zafar Muhammad Rehman , Khan Naimul
Published: May 2023

Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically creates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g., linear classifier) around the prediction through generating simulated data around the instance by random perturbation, and obtaining feature importance through applying some form of feature selection. While LIME and similar local algorithms have gained popularity due to their simplicity, the random perturbation methods result in shifts in data and instability in the generated explanations, where for the same prediction, different explanations can be generated. These are critical issues that can prevent deployment of LIME in sensitive domains. We propose a deterministic version of LIME. Instead of random perturbation, we utilize Agglomerative Hierarchical Clustering (AHC) to group the training data together and K-Nearest Neighbour (KNN) to select the relevant cluster of the new instance that is being explained. After finding the relevant cluster, a simple model (i.e., linear model or decision tree) is trained over the selected cluster to generate the explanations. Experimental results on six public (three binary and three multi-class) and six synthetic datasets show the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME), where we quantitatively determine the stability and faithfulness of DLIME compared to LIME.
GRIHA: synthesizing 2-dimensional building layouts from images captured using a smart phone
Authors: Goyal Shreya , Khan Naimul , Chattopadhyay Chiranjoy , Bhatnagar Gaurav
Published: May 2023

Indoor scene reconstruction and generating a 2D/3D floor plan is a widely explored problem. In the recent years a few algorithms have been proposed, which either use RGB-D images, requiring a depth capturing camera or depend upon panoramic images, assuming little to no occlusion in the room. In this work, we propose a framework named GRIHA (Generating Room Interior of a House using ARcore), which takes advantage of RGB images taken from a conventional mobile phone camera. The proposed work uses Simultaneous Localization And Mapping (SLAM) technology to estimate the 3D transformations required for layout generation. GRIHA uses SLAM based Google ARcore library for camera pose estimation while capturing the images. It gives the user freedom to generate a layout by merely taking a few conventional photos, rather than relying on specialized depth hardware or occlusion-free panoramic images. We have compared GRIHA with other existing methods and obtained superior results. Moreover, the system is tested on multiple hardware platforms to test the dependency and efficiency.

 
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
Authors: Mrabah Nairouz , Khan Naimul , Ksantini Riadh , Lachiri Zied
Published: May 2023

In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static during the training process. The absence of concrete supervision suggests that smooth dynamics should be integrated. Compared to classical static cost functions, dynamic objective functions allow to better make use of the gradual and uncertain knowledge acquired through pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a novel model for deep clustering that overcomes a clustering-reconstruction trade-off, by gradually and smoothly eliminating the reconstruction objective function in favor of a construction one. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to the most relevant deep clustering methods.
Real-Time System for Human Activity Analysis
Authors: Tan Randy , Khan Naimul , Guan Ling
Published: May 2023

We propose a real-time human activity analysis system, where a user's activity can be quantiatively evaluated with respect to a ground truth recording. We use two Kinects to solve the ptorblem of self-occlusion through extraction optimal joint positions using Singular Value Decomposition (SVD) and Sequential Quadratic Programming (SQP). Incremental Dynamic Time Warping (IDTW) is used to compare the user and expert (ground truth) to quantiatively score the user's performance. Furthermore, the user's performance is displayed through a visual feedback system, where colors on the skeleton represent the user's score. Our experiements use a motion capture suit as ground truth to compare our dual Kinect setup to a single Kinect. We also show that with out visual feedback method, users gain statistically significant boost to learning as opposed to watching a simple video.
A novel Accelerated Greedy Snake Algorithm for active contours
Authors: Khan Naimul , Raahemifar Kaamran
Published: May 2023

In this paper, we propose a new Accelerated Greedy Snake Algorithm (AGSA) for faster convergence of the active contour optimization problem. The new algorithm takes advantage of the similarity in image pixel gradients to take larger steps in the initial stages of the snake. Due to its fast convergence, the snake can be initialized far away from the object without any issues. This algorithm also uses some intelligent techniques (e.g. re-sampling, relaxation) to maintain a regular shape of the snake at all times while approaching the final contour. Experimental results on three test cases are presented, where the convergence efficiency of our method has been compared with three contemporary algorithms in terms of number of iterations and computational time.
Stakeholder engagement on fair revenue sharing between digital platforms and news media
Authors: Lindgren April
Published: May 2023

Canadians need access to news and information on matters of public importance, including information regarding health, education and political life, in order to fully benefit and participate in democratic society. News and journalism serve to inform communities, drive civic engagement and counter the rise of disinformation on digital platforms – thereby playing an essential role in the vitality of democracy in Canada. However, the health of the Canadian news and information ecosystem is at risk. At least one-third of Canadian journalism jobs have disappeared since 2010, and many Canadian news media businesses are shrinking, having to cut costs and jobs, and even closing altogether. The COVID-19 pandemic has amplified the crisis and exacerbated the decline in advertising revenue which, historically, has been a key source of revenue for many news outlets.
Local news is being decimated during one of its most important moments
Authors: Lindgren April
Published: May 2023

Readership has surged during this pandemic crisis, but keeping local news alive will require a collective solution, however imperfect it is.
Population decline reduces cooperative breeding in a spatially heterogenous population of superb fairy-wrens
Authors: Backhouse Fiona , Osmond Helen , Doran Bruce , Stein John , Kruuk Loeske , Cockburn Andrew
Published: May 2023

1. Reproductive performance in birds can be affected by both social environment and small-scale environmental heterogeneity via food abundance, availability of nesting sites, and predation risk. However, to date, the best studies of effects of microhabitat variation on avian populations have been on northern hemisphere passerines using nestboxes, where birds have limited control over nest sites and have a comparatively simple social structure. 2. Here we utilise a multi-decade dataset on the superb fairy-wren, Malurus cyaneus, a southern hemisphere passerine with facultative cooperative breeding. We monitored territory characteristics, nest locations and breeding success, and used GIS to relate these to social organisation and a survey of vegetation characteristics throughout the study area.3. There was a long-term nearly two-fold decline in population density over the study period (1994-2015). This was associated with a corresponding decline in the mean number of helpers per group, and hence in the extent of cooperative breeding: in the first four years of the study (1994-1997), 56% of groups had at least one helper, but in the final four years (2012-2015), this was reduced to 28%. Mean territory size also increased (from 0.74ha in the first four years to 1ha in the final four years) such that on average, years with lower numbers of helpers per territory had larger territory sizes. However, helper number was positively correlated with territory size within years.4. Reproductive performance was related to microhabitat heterogeneity: fledgling production was lower and nest predation higher in territories with dense midstorey vegetation, possibly because avian predators using visual information to detect nests can conceal themselves from nesting birds. Predation during the nesting phase decreased over time, indicating that the population decline was not driven by increased predation.5. The causes of overall population decline remain to be determined, however our analyses have uncovered both microspatial patterns in nesting behaviour of birds, and temporal changes in population density and social group dynamics. From a methodological perspective, the study demonstrates the utility of GIS methods for investigating fine-scale habitat dynamics over time.
Transformation of Payment Systems in the Digital Economy
Authors:   Drohobych Ivan Franko State Pedagogical University, Kozyr Yury R.
Published: May 2023

The article examines the evolutionary development of payment systems, which takes place in the unity of technological and organizational modernization through the innovatization of payment instruments. It is substantiated that one of the fundamental factors for the stable development of the State’s economy is the availability of an efficient and smoothly functioning modern national payment system that allows real-time payments in accordance with generally accepted standards. It is specified that payment systems ensure uninterrupted circulation of money flows of the State, the commercial and non-commercial sectors, and individuals. Under the influence of scientific and technological progress, the payment market is being digitalized, and new non-financial participants are being involved in the standard processes of providing payment services. It is shown that the functional significance of payment systems in the development of the digital economy is that they are the most convenient and universal financial intermediaries that create a digital culture and instill digital skills, rules and traditions that represent the core of the digital environment. Practice shows that payment systems are characterized by a rapid response to changing demands in servicing economic relations, the speed of diffusion of scientific and technological progress, and a high degree of adaptability to the risk environment, which is manifested in accelerating the penetration of financial innovations and their spread between system elements. It is underlined that the payment system in a broad sense is a universal financial category that unites a totality of financial institutions that carry out systemic activities in the field of monetary circulation of the economy and its individual sectors to ensure sustainable macroeconomic development and socioeconomic efficiency of functioning of its subjects.
Features of the Development of the National IT market as Part of the Global Information Space
Authors:   Simon Kuznets Kharkiv National University of Economics, Ptashchenko Olena V. , Kurtsev Oleksiy Yu. ,   Simon Kuznets Kharkiv National University of Economics, Denysiuk Olga V. ,   Institute of Innovative Education Kyiv National University of Civil Engineering and Architecture, Vovk Volodymyr A. ,   Simon Kuznets Kharkiv National University of Economics
Published: May 2023

The publication considers the main features of the formation of the information space of the present day, considers the features of information services and trends in the development of the IT market. The global information technology market is characterized by a high level of competition, which encourages IT companies operating in this market to use all available marketing means in order to withstand the constantly growing competition. In the context of activities in the global information technology market, effective marketing communication activities are of major importance, which is the key to ensuring the competitiveness of an IT company for successful operation in this highly competitive market. It is specified that the IT market is one of the elements of the formation of a modern high-tech market in Ukraine. In a broad sense, IT covers all areas of transfer, storage and perception of information. At this, IT is often associated with computer technology, and this is no coincidence: the advent of computers has brought IT to a new level. Further on, the basis of IT is the technology of processing, storing and perceiving information. The main trends in the development of the IT market have been identified, which include: competence (Ukraine has highly qualified specialists in the field of programming, software testing, data analysis and other related fields); competitiveness (Ukrainian IT companies provide quality services at more reasonable prices compared to developed countries of the West); speed of development (the IT industry in Ukraine is developing rapidly and growing, which makes it possible to be on the way to technological leadership); the presence of a startup ecosystem (Ukraine has an active startup ecosystem that promotes the creation and development of young technology companies); geographical location (Ukraine is in a convenient geographical position for cooperation with Europe and the United States, which makes it easy to enter the world market). It is concluded that one of the key factors for success is to ensure a favorable business climate in Ukraine, which depends on government policy, legislation and infrastructure.