Reviewerly/exHarmony huggingface dataset:
LibGuides: Social Work: Citations and Writing Help

This guide is designed to assist students in finding resources for their Social Work classes.
Writing Anxiety and Writing Performance: A Descriptive-Correlational Study of Grade 11 Students at Centro Escolar Integrated School Malolos

Using the descriptive-correlational approach, the researchers sought to establish a connection between writing anxiety and writing performance of Grade 11 students at Centro Escolar Integrated School in Malolos, Bulacan, Philippines. The results showed that majority of the respondents experience a “moderate” level of psychological symptoms of anxiety while experiencing “lack of focus,” and “fear of failure” which has the highest weighted average of among ten symptoms. In addition, no correlation was established between writing performance based on grades and level of anxiety based on physical and psychological symptoms of the respondents.
Helping Early Childhood Education Teacher Teach English with an E-Book Guide

This paper aims to identify the responses of early childhood education teacher (ECET) toward E-book guide for English teaching practices which was developed by the writer.By identifying their responses properly, it is assumed that they can be helped to be competent English practitioners with the e-book independently.The survey technique was conducted with 19 respondents from the ECET from Ponorogo, Indonesian through a questionnaire.The analysis data was done by calculating the percentage of responses and descriptively explored.The result showed that only 12% of the respondents were fairly interested.Meanwhile as many as 88% of them was very interested in the ebook designed.The model was very effective to motivate the especially when it was used in the pandemic covid-19 situation.Therefore, e-book guide as an alternative that can be offered for their want.
Graph analytics in the exascale era

Emergence of large-scale data sets has ushered in a new era of data-driven discovery in science and beyond that is enabled by advances in artificial intelligence techniques and high-performance computing. Graph analytics is a rapidly emerging area of research and application that enables several classes of applications. Generalization of graph algorithms in the form of combinatorial optimization has numerous applications in scientific computing and data-driven discovery. Despite widespread use, efficient parallel tools for graph analytics are hard to come by, especially when targeting the hybrid CPU-Graphics Processing Unit architectures at extreme scales. In this talk, we will present our ongoing work on distributed multi-GPU systems for two prototypical graph problems: graph clustering and influence maximization. We will demonstrate substantial gains in performance not only on PNNL systems but also on the current # 2 supercomputer, Summit. We will also present case studies from several scientific domains of importance to the DOE.
Predicting Human Responses to Syllogism Tasks Following the Principle of Maximum Entropy

Syllogistic reasoning is one of the major research domains in cognitive science. Syllogisms are quantified semi-logical statements that consist of two premises, each relating two terms by one quantifier out of "All", "No", "Some", and "Some not". While one of the terms is mentioned in both premises, one is interested in what conclusion can be drawn about the relationship between the other two terms. For example, a well-formed syllogism task is "If all A are B and no B is a C, what, if anything, follows about the relationship between A and C?" While some syllogism tasks have a logically valid conclusion (in the example above, "No A is a C." is logically valid), some have not, like "If all A are B and some B are C, what follows about A and C?" In cognitive science, human responses to syllogism tasks are studied in order to better understand the human understanding of quantification and uncertainty in reasoning. In order to predict human responses to syllogism tasks, we develop a probabilistic model of syllogisms based on the principle of maximum entropy. For this, we translate the premises of syllogisms into probabilistic conditional statements and derive the probability distribution that satisfies the conditional probabilities while having maximal entropy. Then, we calculate the probabilities of all possible conclusions and compare them with the respective quantifier. As a prediction, we basically choose the option with the best matching. Based on empirical data, we show that our maximum entropy model predicts human responses better than established cognitive models.
이공계대학 교양영어의 수준별 수업 운영과 선호도에 관한 연구

The purpose of this study was to investigate university learners’ preferences and other related factors for two general English courses, one course focusing on reading and another on conversation, with students divided by their language proficiency level. The participants were 554 students at an engineering university. A 4-point Likert-type scale was used to record general tendency. Students were evaluated and divided into three and four proficiency levels for the reading and conversation course, respectively. 88.1% of participants had a favorable opinion of the level-divided conversation course, and 87.7% had a positive view of the level-divided reading course. Cross tabulation analysis was conducted to examine any relationships between the preferences and other factors such as sex, favorable feelings toward English, students’ majors, and English proficiency. The chi-square analysis showed no significant relationship between level-divided course preference and factors like sex or fondness for English. However, there appears to be a significant relationship between students’ majors and level-divided course preference for both the conversation and reading courses as well as a preference relationship between English proficiency and the reading course. The participants liked the effectiveness of the level-divided course content and the tailoring of educational content according to students’ proficiency.
Preschool teacher's role at addressing the family topic in kindergarten

For children it is important to feel secured, loved and accepted by their families. All of thisslowly build our behavior, values, personality and the way we think when we are adults.Through the family we learn how to socialize.In the theoretical part of the thesis, I first presented the definition of family and defined it.Family role is to transmit manners and habits from parents to children. There are several typesof families, as well as several ways of raising a child in the family. I presented and describedfamily types and parenting styles. In thesis I also described the most important tasks andfunctions performed by the family, which help to make our everyday life easier. In the secondpart of theoretical part, the red thread is the educator's work in kindergarten. I described howkindergarten teacher should work, what principles and methods should be used and what heshould take into account when planning activities. I find it important that he is critical of hiswork and that he prepares a stimulating learning environment for children in which they activelyparticipate and learn.In the second part of the tesis I described activities I played with children. In the end of thethesis I made evalvations of the children’s responses to the various questions I asked before andafter the activities.
A Performance Aware Technique Towards Security and Integrity in Cloud and Internet of Things

Cloud Computing and Distributed Computing is one of the prominent domains of research with the high-performance applications for assorted domains including web services, e-governance, telemedicine, citizen portals and many others. All these applications are performance aware and needs huge attention towards the security and integrity so that the citizen, social and corporate services can be delivered with huge level of performance with minimum error rate. In this research manuscript, the integration of high-performance research implementation is integrated using advanced simulation tools for Internet of Things (IoT) and cloud-based integrations and thereby the outcomes are attained with elevated effectiveness on assorted parameters.
The Electronic Resources Troubleshooting Guide

The Electronic Resources Troubleshooting Guide provides readers with an in-depth look at planning for and responding to access problems in library systems and resources. The authors make the case t...
Research on Lean Management Optimization of Intelligent Storage for Emergency Materials Based on Big Data

The Covid-19 once again warns us to take precautions before they happen. With the outbreak of the epidemic, all kinds of medical supplies and emergency rescue supplies have been in short supply or even fuse. Based on the background of big data analysis. Through research, it is found that due to the outbreak of the epidemic, the classic material allocation model cannot be applied to actual situations, and there is no way to achieve actual results. Therefore, based on the classic material allocation model, this paper-established the demand growth model and added GIS route planning to form a new material allocation, route selection, and cost comparison model to respond to the decision-making of rescue materials allocation and route selection in public emergency (epidemic, flood, fire), establish a mathematical model of material combination optimization and a mathematical model of route optimization planning, and use computers to solve the optimization plan.
Teacher's Strategies in Teaching English for The Hearing – Impaired Students

This study is aimed at investigating the teacher’s strategies in teaching English for the hearing-impaired students of a special education school in Surakarta, to find out the teacher’s problems in teaching English, and to find out how the teaching strategies help the hearing-impaired students to learn English. The method used in this research is a case study. The qualitative data used in this research were collected from observation, interview, and documentation. The techniques used to analyze the data are from Miles and Huberman (1994), which include data reduction, data display and conclusion drawing or verification. From the result of this research, it showed that the strategies the teacher used in teaching English for the hearing-impaired students were drilling and visual scaffolding. The result of this research also showed the strategies the teacher used can help the students in memorizing new vocabulary and help the students in understanding better.
Visualisasi Letak Geografis Provinsi Di Indonesia Berbasis Augmented Reality Untuk Siswa SD

The learning implementation in the subject of ¬social science¬, especially the topic of Indonesia's geographical location at SD Negeri 99 Pongrakka is still carried out in a classical way, namely using the lecture method with learning media in the form of depictions on the blackboard or just observing pictures through textbooks. These methods make the learning process less interesting and seem passive because there is no direct interaction between the topic and the students. To create interactive learning, we propose the development of learning media by utilizing Augmented Reality technology. The system proposed in this study can display the geographical location of the province in 3 dimensions by means of the user pointing the marker at the camera then the camera detects the marker and the object will be displayed directly on the monitor screen according to the marker used. After testing the system based on the Software Requirement Specification, it was able to be concluded that the application of visualizing the geographical location of the province based on Augmented Reality could not only make learning more interactive but also more interesting, so that it could be used as an option for learning methods.
INSTANCE SEGMENTATION OF VIDEO USING MASK R-CNN

Object detection in the digital images, videos plays an important role in the real world. At present object detection is done in the images with the help of the models like Fast RCNN. In the object detection the objects in the image are identified with the help of bounding boxes around objects. The object detection is done by generating the bounding box around the object with (x,y) coordinates but with this we cannot identify which pixel belongs to the background and which pixel belongs to the foreground. Image classification only categorizes the objects in the input image. Object detection localizes every object in the input image. Whereas the semantic segmentation can classify all the similar objects as a single instance, the main drawback of this is we cannot identify how many similar objects are present in the single instance.In semantic segmentation we can only segment objects by using bounding boxes and cannot identify individual instances of even same classes. For each instance of an object in an image the model generates bounding boxes and segmentation masks.In the Instance segmentation objects of same class will assign a different instance. Here we can compute a mask (pixel level) for each and every object in the input image this can be achieved with the help of Mask R-CNN In this paper the Mask R-CNN is proposed to add the mask feature to every object at pixel level along with the bounded box. The Mask R-CNN can perform the instance segmentation along with the object detection. It outperforms all the other previous models.
Factors associated with psychic symptomatology in diabetics during the COVID-19 pandemic/ Fatores associados à sintomatologia psíquica em diabéticos durante a pandemia da COVID-19

Abstract Objectives: to determine the frequency and factors associated with severe / extreme signs and symptoms of stress, anxiety and depression in diabetic patients during the COVID-19 pandemic. Methods: cross-sectional study conducted in April /May 2020, which included 162 individuals with diabetes mellitus and over 18 years old. An online questionnaire was applied on social networks, composed of biological, sociodemographic, clinical characteristics and the Stress, Anxiety and Depression Scale (DASS-21). For statistical analysis, a multivariate logistic regression model was applied with a 5% significance level. Results: frequency of 37.7%, 43.3% and 45.1% were found for some symptom of stress, anxiety and depression, respectively. The factors associated with sign and symptoms of severe / extreme psychic disorders were: not having religion (stress, anxiety and depression); be at graduation (stress and anxiety); history of anxiety and depression (anxiety and depression); not practicing or decreasing physical exercises and starting, increased or continue taking sleeping medications (stress); history of contact with a suspected case of COVID-19, absence or decreased leisure (anxiety); female gender, absence of a partner, decreased family income and work or study (depression). Conclusion: the frequency of psychological disorders was higher than described in the literature on diabetic patients, being associated with biological, sociodemographic, clinical factors and aspects related to COVID-19 during the pandemic
IMPLEMENTATION OF 3D SOFTWARE TOWARDS REPRESENTATION MICROSCOPIC AND SPATIAL INTELLIGENCE OF PROSPECTIVE BIOLOGY TEACHERS

The aim of this research is to find out the differences in spatial intelligence and microscopic representation of prospective biology teachers in 3D software lectures on plant anatomy using Blender and 3DS Max applications. The research method used was quasi-experimental using the research design of The Matching Only Pretest-Posttest group design group. The population in this study is a prospective biology teacher in the department of biology education faculty of teaching and educational sciences in the academic year 2017/2018 at Siliwangi University who contracted the course of plant anatomy. Sampling is done by purposive sampling techniques as many as 2 classes by looking at the level of activeness in the same learning process. The results showed both classes showed a low spatial intelligence N-Gain category, as well as getting an average value of microscopic representation with a score of 3.7 for the use of 3Ds Max and 3.82 for Blender. The conclusion of this study is that there is no difference in spatial intelligence and microscopic representation of students, in addition students are still having difficulty for those who use max 3Ds software, because it requires a high enough computer specification, so the use of 3-dimensional blender application is more recommended used for aspiring biology teachers who want to make 3D forms of biological objects especially plant anatomy.
A Contemporary Study of Deep Learning in Diagnosis and Prediction of Diseases

With the introduction of big data and its incredible advancement in image procurement devices, the transformation of medical data into valuable knowledge has become an important challenge in the area of bioinformatics. The medical images procured require huge analysis and diagnosis of images which can be done using Artificial Intelligence techniques like Machine learning and Deep Learning that yields automated diagnosis solutions. Deep learning methods can provide optimized and precise solutions for medical image diagnosis and can be an important methodology for imminent health care applications. In this paper, some contemporary deep learning neural networks will be discussed and their application in detecting various diseases.
Drone Monitoring System to Detect Human Posture Using Deep Learning Algorithm

Artificial Intelligence is taking part in many forms of daily human life. Many innovations have been embedded in technology to ensure they reach the same or even higher than human capability. One of the trending innovations in the market is image recognition. Image recognition is now implementing machine learning and deep learning for better image detection. Usual issues come for any image recognition technology about the low ability to detect various object classes. Too much research on image recognition also led to difficulty in applying the best algorithms. Although there are many technologies regarding image recognition, there is still not much work on human pose detection. Therefore, this paper proposed an application for detecting human pose related to drone control using a deep learning algorithm. This research aims to review the type of deep learning algorithm for human pose detection, develop an enhanced algorithm based on deep learning algorithm for human pose classification, and evaluate the proposed human pose classification algorithm's performance based on accuracy using drone technology. This paper proposes using Convolutional Neural Network (CNN) as a selected deep learning algorithm suitable for pattern recognition. This research expects an accurate result for detecting the human pose involving controlling the drone.
Practical Study and Implementation of an Isolated Word Speech Recognition System.(Dept.E)

The art and science of speech recognition have been advanced to the state where it is now possible to communicate reliably with a machine by speaking to it in a disciplined manner using a vocabulary of moderate size. It is the purpose of this paper to present a hardware implementation of a simple real time speech recognition system for isolated words. The system determines the energy of the spoken word through a bank of four channels band pass filters. The output from each filter is passed through a rectifier followed by a low pass filter to provide an average level which is proportional to the total signal energy within the particular filter band. The energy measurements from each channel is then passed to a personal computer where a recognition algorithm program is then executed. The program detects the starting and ending points of each spoken word. compares the energy features with the reference stored in the library, and chooses the most close pattern that matches the pattern of the spoken word. Results from experiments conducted by simulation and by the hardware prototype using Arabic speech show that the system provides recognition score between 76 % and 94%.
Report of the ALCTS/LITA Linked Library Data Interest Group Meeting. ALCTS Virtual Interest Group Week, June 2020

The Linked Library Data Interest Group session “Thinking Outside the Box: Linked Data Initiatives to Connect Users to Content” took place on June 8, 2020 during the ALCTS Virtual Interest Group Wee...
The Impact of Electronic Marketing Strategies on the Effectiveness of Communication with Customers: Case Study of Higher Education Services Offices in Muscat, Sultanate of Oman

Recent advances in technology have led to the emergence of electronic marketing (e-marketing). E-marketing depends on the strategies used by the organizations. Marketing strategy is a marketing plan designed to achieve marketing goals. The purpose of this study is to identify the impact of e-marketing strategies on the effectiveness of customer communication with higher education services offices, and to measure the effectiveness of this impact, identify the challenges facing both customers and offices in the process of implementing the e-marketing process and find the most appropriate solutions to overcome them. The study used both the descriptive and exploratory research design with application of quantitative and qualitative research methods. The quantitative research method was facilitated through the conduct of survey with 159 customers of five higher education service offices located in Muscat, Oman. The findings revealed that most offices used appropriate strategies to communicate with their customers and there were effective responses from higher education service offices. The collected qualitative data revealed that there are no challenges hindering their e-marketing.&#x0D; The suggestion for organization is to change the electronic marketing strategies used to meet the needs and desires of customers over time, and to keep pace with all the changes and trends in marketing; improving procedures for building confidence and a sense of safety on the site, increasing customers' awareness of the importance of e-marketing and developing intensive awareness programs targeting schools, universities and institutions. It is concluded from this study that organizations should evaluate the impact of e-marketing strategies on the effectiveness of customer communication with them by collecting the necessary information and data on these customers. The focus should be on the most satisfied customers because they are the ones who can make long-term impacts, and influence the future of the organization and its investments.