Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Expression Classification using KNN and Decision Tree Classifiers

In recent years, facial emotional expression recognition attracts several researchers for developing intelligent human-machine interface (HMI) system. This present work classify six different facial expressions (happiness, sadness, anger, fear, disgust, and surprise) using two classifiers namely, K Nearest Neighbor (KNN) and Decision Tree (DT) classifiers. Fifty-five undergraduate university students (35 male and 20 female) with a mean age of 23.9 years voluntarily participated in the experiment to acquire six facial emotional expressions using ten virtual markers called Facial Action Units (FAUs). Firstly, Haar-like features are used for detecting the face and eyes in a video-frame using Viola-Jones adaboost classification method. These FAUs are placed on specific location on the subject’s face based on facial action coding system (FACS) using a mathematical model. Lucas-Kande optical flow algorithm is used to continuously track the markers positions. Here, the distance between the FAU at the center of the subject face to other markers are calculated and used as a feature for facial expression classification. The one-way analysis of variance with a significance level of p<0.01 is used to validate the extracted features and fivefold cross-validation is performed. Finally, these cross-validated features are used to map six different facial emotional expression using KNN and DT classifiers. In KNN, four distance measures are used to compare the performance of KNN classifier in emotion classification. The mean emotion classification accuracy of 98.03%, 97.21%, is achieved using KNN and DT, respectively. This accuracy of emotional expression detection using an optical flow algorithm give way for designing real-time systems for variety of applications.
The Protective Role of Self-Care in the Development of Compassion Fatigue among Health Care Professionals in Moi Teaching and Referral Hospital, Kenya

Compassion fatigue is a condition unique to the human service occupations, characterized by a state of tension and preoccupation with the traumatized clients by re-experiencing traumatic events. With increased incidences of traumatic events both nationally and globally, the greater burden of care and after-care is usually borne by health care professionals. The objective of this study was to determine the protective role of self-care in the development of compassion fatigue among health care professionals in Moi Teaching and Referral Hospital (MTRH) Eldoret, Kenya. The study was guided by Figley Model of Compassion Fatigue and adopted the Ex Post Facto research design. The target population was 76 doctors, 212 nurses and 33 counselors working in twelve units (grouped into more traumatizing and less traumatizing) offering specialized patient care services. A stratified simple random sampling technique was used to select a sample of 82 participants comprising of 19 Doctors, 54 Nurses and 9 Counselors. The independent variable was Years of Experience while Compassion Fatigue was the dependent variable. Data was collected using a demographic questionnaire, the ‘Professional Quality of Life Scale (ProQoL) version V’. Data was analyzed using the Pearson correlation r at α = 0.05 level of significance and data presented in form of percentages, frequencies and means while graphic presentation was in form of tables. Since p< 0.005 in the Pearson correlation statistic table, the study hypothesis was rejected. The consequent implication was that the lesser the self-care activities engagement, the higher the level of compassion fatigue among the health care professionals. Based on the findings of this study, it is recommended that regular training on self-care that encompasses all the six subsets of self-care (Physical, Spiritual, Emotional, Psychological, and Workplace Balance) should be introduced in the work place as it will help in both preventing compassion fatigue and managing it whenever it occurs.
Probing the Molecular Interactions of ATRX

Alpha thalassemia/mental retardation syndrome X-linked (ATRX) is linked to both developmental disease and cancer. ATRX functions in coordination with Death Domain Associated Protein 6 (DAXX) in a histone deposition pathway of a histone 3 variant at heterochromatin. While ATRX has recently been implicated in many alternative functions, mechanistic information is lacking. We performed proximity dependent biotinylation and protein identification to determine an unbiased list of labile ATRX interactors. As expected, we found transcriptional repressors and DAXX. Surprisingly, we also found many proteins involved in homologous recombination and nucleotide excision repair to be interacting with ATRX in the absence of DNA damaging events. Additional interactions include proteins involved in centromere maintenance and one nucleolar protein involved in RNA processing. These results suggest ATRX has a dynamic role in DNA damage repair and provide further support for its role as a nuclear protein involved in H3.3 deposition at endogenous retroviral elements.
Tank Culture of Tilapia.

Tank culture of tilapia is a good alternative to pond or cage culture if sufficient water or land is not available and the economics are favorable. Tilapia grow well at high densities in the confinement of tanks when good water quality is maintained. This is accomplished by aeration and frequent or continuous water exchange to renew dissolved oxygen (DO) supplies and remove wastes. Culture systems that discard water after use are called flowthrough systems while those that filter and recycle water are referred to as recirculating systems. Intensive tank culture offers several advantages over pond culture. High fish density in tanks disrupts breeding behavior and allows male and female tilapia to be grown together to marketable size. In ponds, mixed sex populations breed so much that parents and offspring compete for food and become stunted. Tanks allow the fish culturist to easily manage stocks and to exert a relatively high degree of environmental control over parameters (e.g., water temperature, DO, pH, waste) that can be adjusted for maximum production. With tanks, feeding and harvesting operations require much less time and labor compared to ponds. Small tank volumes make it practical and economical to treat diseases with therapeutic chemicals dissolved in the culture water. Intensive tank culture can produce very high yields on small parcels of land. Tank culture also has some disadvantages. Since tilapia have limited access to natural foods in tanks, they must be fed a complete diet containing vitamins and minerals. The cost of pumping water and aeration increases production costs. The filtration technology of recirculating systems can be fairly complex and expensive and requires constant and close attention. Any tank culture system that relies on continuous aeration or water pumping is at risk of mechanical or electrical failure and major fish mortality. Backup systems are essential. Confinement of fish in tanks at high densities creates stressful conditions and increases the risk of disease outbreaks. Discharges from flow-through systems may pollute receiving waters with nutrients and organic matter.
Corpulyzer: A Novel Framework for Building Low Resource Language Corpora

The rapid proliferation of artificial intelligence has led to the development of sophisticated cutting-edge systems in natural language processing and computational linguistics domains. These systems heavily rely on high-quality dataset/corpora for the training of deep-learning algorithms to develop precise models. The preparation of a high-quality gold standard corpus for natural language processing on a large scale is a challenging task due to the need of huge computational resources, accurate language identification models, and precise content parsing tools. This task is further exacerbated in case of regional languages due to the scarcity of web content. In this article, we propose a generic framework of Corpus Analyzer – Corpulyzer – a novel framework for building low resource language corpora. Our framework consists of corpus generation and corpus analyzer module. We demonstrate the efficacy of our framework by creating a high-quality large scale corpus for the Urdu language as a case study. Leveraging dataset from Common Crawl Corpus (CCC), first, we prepare a list of seed URLs by filtering the Urdu language webpages. Next, we use Corpulyzer to crawl the World-Wide-Web (WWW) over a period of four years (2016–2020). We build Urdu web corpus “UrduWeb20” that consists of 8.0 million Urdu webpages crawled from 6,590 websites. In addition, we propose Low-Resource Language (LRL) website scoring algorithm and content-size filter for language-focused crawling to achieve optimal use of computational resources. Moreover, we analyze UrduWeb20 using variety of traditional metrics such as web-traffic-rank, URL depth, duplicate documents, and vocabulary distribution along with our newly defined content-richness metrics. Furthermore, we compare different characteristics of our corpus with three datasets of CCC. In general, we observe that contrary to CCC that focuses on crawling the limited number of webpages from highly ranked Urdu websites, Corpulyzer performs an in-depth crawling of Urdu content-rich websites. Finally, we made available Corpulyzer framework for the research community for corpus building.
Data Analysis of Various Terrorism Activities Using Big Data Approaches on Global Terrorism Database

The field of data science is getting wide day by day and more areas are using this concept. This paper uses the concept of data science for analyzing patterns of terrorism globally. We use the “Global Terrorism Database (GTD)” having information of terrorist attacks around the world from 1970 to 2017. The data was preprocessed and we use “Hive Query Language (HiveQL)” and Hadoop concepts to make various predictions out of the database. HiveQL is run by intergrating with Hadoop which is installed on a linux system. Various interesting findings were made from this database which are represented in the form of queries that were shot on the database. The queries were decided upon by framing a few questions and finding suitable answers. The results obtained are presented graphically using tableau and python for a better understanding of the reader. In the last section, various inferences were drawn from the results obtained.
Software Effort Classification with Multilayer Perceptron Neural Networks

We target the problem of software effort estimation from a classification perspective. Our main goal is to build a classifier that can predict the required effort for a new project and assign it into one of the four classes: small, small-medium, medium-large, and large. A criterion is proposed for the aforementioned categorization and based on the amount of required effort. We study data sets that are based on three different categories: function points-based data sets, COCOMO-based data sets, and project characteristics-based data sets. Feature sets are prepared and fed to the multilayer perceptron (MLP) neural network algorithm. A hold-out test is implemented and ROC curve is used as a measure of the performance of the algorithm. In addition, we identify the important features for building the classification models across various data sets. Generally, MLP shows good performance across the six data sets. Moreover, there is a stability of performance within each category of data sets or across different categories with no dramatic differences in results. However, the performance of MLP seems to decrease with data sets that are based on project characteristics.
miRNAs as novel biomarkers for bone related diseases

MicroRNAs (miRNAs) are single stranded, non-coding RNAs that regulate mRNA expression and the amount of gene products. miRNAs regulate mRNA expression through direct binding and influence various signalling pathways in the organism. miRNAs have been found to play key roles in cell proliferation, differentiation, organ and tissue development and the regulation of bone homeostasis. They contribute to bone formation and resorption, bone remodelling and the differentiation of bone cells. Therefore, altered expression of distinct miRNAs may influence the pathology of bone diseases, such as osteoporosis, renal osteodystrophy and malignant bone tumours. miRNAs are released into the bloodstream, partly through active secretion, and are bound to structures, such as proteins and exosomes, that protect the miRNAs from degradation. Therefore, miRNAs are easy to sample, and they are stable with great potential as biomarkers for several diseases, including osteoporosis. This review is giving a short overview on the background and the features of the miRNAs and other non-coding RNAs, highlighting examples for their relevance in bone diseases and summarizing knowledge on miRNAs as biomarkers for osteoporosis.
Consumo simbólico en la intensidad de uso de las redes sociales digitales y el valor percibido de las experiencias vacacionales en jóvenes bogotanos

Esta investigacion busca identificar la influencia que tiene el consumo simbolico en la intensidad de uso de las redes sociales digitales y el valor percibido de las experiencias vacacionales de jovenes Bogotanos, por medio de un diseno de corte descriptivo correlacional y transversal, ya que se recolectaron los datos una unica vez por medio de encuesta; los participantes fueron 200 jovenes, hombres y mujeres, universitarios, de edad entre 18 y 32 anos.Los resultados muestran que cuando un consumidor se encuentra satisfecho con la experiencia vacacional, mejora la identidad frente a su entorno social, y si eso ocurre, la satisfaccion con la experiencia sera mayor generando la intencion de repetirla; tambien los resultados de la investigacion muestran que cuando el consumidor esta satisfecho con su experiencia vacacional desea compartirla con amigos y familiares, por medio de las redes sociales, y si su identidad mejora frente a ellos, lo hara de forma mas frecuente para sentirse parte de un grupo social; sin embargo en los resultados se evidencia que el hecho de que el individuo comparta informacion de forma frecuente en sus redes sociales no genera una mayor satisfaccion con la experiencia.
Genetic Variability and Marker Trait Association analysis of Various Phenological and Yield Related Traits for Heat Tolerance in Chickpea (Cicer arietinum L.)

Increasing incidence of heat stress (HS) is receiving serious attention as it causes significant yield reduction in various crops including chickpea worldwide. Here, we investigated the existing genetic variability for various yield related crucial traits for developing heat tolerant genotype under field condition in a panel of seventy eight chickpea genotypes under normal and HS condition via conducting augmented design analysis. Analysis of variance (ANOVA) exhibited significance difference among the checks for first flowering (FF), days to 50% flowering (50F), days to pod initiation (DPI), days to maturity (MAT), plant height (PH), empty pod (EP), yield plant-1 (YPP), biological yield (BioY), harvest index (HI%), and 100 seed weight (100 SW) under normal condition. While, under HS condition significance difference among the checks for the following traits FF, 50F, PH, EP, YPP and 100SW were recorded. Additionally, to seek marker trait association (MTA), we examined MTAs for the given phenological and yield related traits under both normal and late sown condition via employing 81 simple sequence repeat (SSR) markers in the given set of genotypes. A total of 37 significant MTAs (under normal condition) and 38 significant MTAs (under HS condition) were obtained for various phenological and yield related traits. Additionally, eighteen MTAs for heat tolerance index (HTI), stress susceptibility index (SSI), yield index (YI), mean productivity (MP) and geometric mean productivity (GMP) were recorded.
DAMPAK SINKRONISASI BERAHI MENGUNAKAN PROGESTERON TERHADAP BERAHI DAN KEBUNTINGAN SAPI BETINA MILIK RAKYATKABUPATEN SUKOHARJO

The aim of this study was to determine the effect of estrus synchronization using progesterone for old heifers and cowsof breeder in Sukoharjo District. The research materialswere 12 old heifers and38 cows. Thematerials of research weresynchronized used progesterone hormone on vagina sponge. The hormone progesterone was placing in the vagina and pulled out the sponge on day 18. Allestrus heifers and cows were inseminated used frozen semen. The parameter of the research was number of estrus and pregnant. The data obtained were analyzed used Chi-square and statistics of descriptive. The results of the study showed 10 (91.67%) old heifers and 25 (65.79%) cows were estrus.The gestation rate for old heifers and cows showed 4 (44.44 %) and 13 (68.42%), respectively. The chi-square analysis showed highly significant different (P<0.01) for the number estrus between heifers and cows.In other hand, significantly different (P<0.05) were showed for chi-square analysis of pregnancy between heifers and cows. The conclusion The Conclusion, that thesuccess ofestrusoldheifersnotin linewiththe success ofpregnant. Cowsareestrus, becamepregnantmorethanoldheifers. Keywords: synchronizationestrus,progesterone, gestation, beefcows.
Corporate Social Responsibility and Market Efficiency: Evidence from ESG and Misvaluation Measures

We study the impact of corporate social responsibility (CSR) on firm misvaluation in the US. Our results indicate that a firm’s Environmental, Social and Governance (ESG) profile significantly affects valuation: an improvement of a firm’s CSR leads to a higher ratio of actual to true firm value. Analyzing the relation between ESG and misvaluation separately, we find that ESG expands existing overvaluation whereas it reduces undervalued firms' deviation from the true value. We argue that both valuation effects are attributable to the worldwide trend of sustainable investing. Further analyses reveal a moderating role of market sentiment towards sustainability in the ESG-misvaluation relationship.
Microridges are apical projections formed of branched F-actin networks that organize the glycan layer

Apical projections are integral functional units of epithelial cells. Microvilli and stereocilia are cylindrical apical projections that are formed of bundled actin. Microridges on the other hand, grow laterally long, forming labyrinthine patterns on surfaces of various kinds of squamous epithelial cells. So far, the structural organization and functions of microridges have remained elusive. We have analyzed microridges on zebrafish epidermal cells using confocal and electron microscopy methods including electron tomography, to show that microridges are formed of a network of F-actin and require the function of the Arp2/3 complex for their maintenance. During development, microridges begin as F-actin punctae showing signatures of branching and requiring an active Arp2/3 complex. Using inhibitors of actin polymerization and the Arp2/3 complex, we show that microridges organize the surface glycan layer. Our analyses have unraveled the F-actin organization supporting the most abundant and evolutionarily conserved apical projection, which functions in glycan organization.
Digital Health Needs of Women With Postpartum Depression: Focus Group Study

Background Although approximately 10% of new mothers in Canada develop postpartum depression (PPD), they face many barriers when accessing care. eHealth offers a unique opportunity to provide psychosocial skills and support to new mothers; however, patient populations are not consistently engaged in eHealth development processes. Thus, the diversity of women’s backgrounds and needs are often not reflected in existing tools. Objective This study aims to engage women from a variety of backgrounds and locations around British Columbia (BC) who have previously experienced PPD to determine the unmet psychoeducational needs of women with PPD and how a web-enabled platform used to deliver psychosocial skills and education to assist in the management of PPD could fulfill those needs. Methods Focus groups were conducted in 7 cities across BC with a total of 31 women (mean age 34.5 years, SD 4.9), with each group ranging from 2-7 participants. Focus groups were cofacilitated by the study coordinator and a local service provider in each community using a semistructured guide to discuss participants’ needs, ideas, and opinions as they relate to the use of technology in PPD management. Transcripts were approached inductively using thematic analysis to identify themes and qualitative description to frame what was observed in the data. Results A total of 5 themes were identified: bridging gaps to meet needs; providing validation to combat stigma; nurturing capacity to cope, manage, and/or reach wellness; empowering people to take ownership over their mental health; and offering customization to ensure relevance. Each theme identified a need (eg, combatting stigma) and a way to address that need using a web-enabled intervention (eg, providing validation). At the intersection of these themes was the overarching value of promoting agency for women experiencing PPD. Conclusions Ultimately, new mothers require accessible mental health care that promotes their agency in mental health care decision making. Our participants believed that a web-enabled intervention could help meet this need. These data will be used to guide the design of such an intervention, with the eventual implementation of this resource as a first-line management option for PPD.
Music Therapy by Analyzing EEG Signals

The role emotions play in our life is inevitable right from the way we interact with other humans, in our decision-making process and even in the way we see the people around us. In this technological world, human computer interaction is almost everywhere. Right from fingerprint access in our mobile phones to face recognition- based attendance patterns at work places, human computer interaction has formed a strong base in day-today activities of human life. In such human computer interaction, EEG has gathered attention since it can provide simple, cheap, precise methods of emotion recognition. In this proposed work, a Music app is created which plays songs based on the emotion recognized from the given EEG signal. In MATLAB, the trained EEG signals are given and it is further processed to extract the features. The extracted features are classified to find the emotion and the result is sent to the music app. The music app which contains the playlist plays accordingly.
Learning Kinematic Machine Models from Videos

VR/AR applications, such as virtual training or coaching, often require a digital twin of a machine. Such a virtual twin must also include a kinematic model that defines its motion behavior. This behavior is usually expressed by constraints in a physics engine. In this paper, we present a system that automatically derives the kinematic model of a machine from RGB video with an optional depth channel. Our system records a live session while a user performs all typical machine movements. It then searches for trajectories and converts them into linear, circular and helical constraints. Our system can also detect kinematic chains and coupled constraints, for example, when a crank moves a toothed rod.