Reviewerly/exHarmony huggingface dataset:
Individual-Level Factors and Variation in Exposure to Online Hate Material: A Cross-National Comparison of Four Asian Countries

Internet has become one of the key drivers of social evolution. It has also provided an array of help forming certain kinds of negative behaviors against deviant groups through hate material. South Asia has witnessed a significant raise in online hate activities in recent past. To explore the possible explanations behind this spike, a survey method is used to collect the data from young adults (19-28) from different universities of Pakistan (n=457), India (n=523), Bangladesh (n=426), and Sri Lanka (n=381). The study found the conceptual roots in Routine Activity to understand the nexus between possible offenders (hate groups) and potential victims (internet users). The results showed that there was a significant difference between the exposures to online hate material across four Asian countries. The study theorized three of the possible explanations to account for this variation that include the score of each country on Inglehart-Welzel's self- expression scale, anti-hate-speech laws, and literacy rate. The data partially fits in the explanation of the variation by literacy rate better than the other two. Moreover, men are more prone to exposure to online hate material as well as the people with more social networking sites use, frequent visit to dangerous websites, using internet as anonymous, and having more Facebook friends. The study recommends taking into account the individual factors while formulating anti-hate-speech laws in South Asia. Also, the study recommends conducting a similar cross-national consideration in particularly those countries where the militant groups are using online space to make people radicalized.
PERANAN STAKEHOLDERS DALAM PENANGANAN BENCANA BANJIR DI KOTA SUNGAI PENUH (STUDI KASUS KECAMATAN HAMPARAN RAWANG)

ABSTRACTTitle. The Role of Stakeholders in Handling Flood Disasters in Sungai Penuh City. This research was conducted at the Sungai Penuh City Regional Disaster Management Agency (BPBD) Office. The handling of flood disasters has not been carried out to the maximum because it is suspected that there is still the handling of flood disasters that are lacking in the community affected by the flood disaster and the role of stakeholders in managing floods is not yet optimal. The research problem formulation is, what is the role of stakeholders in the framework of flood management in Sungai Penuh City? The research objective is to find out the role of stakeholders in the context of flood management in Sungai Penuh City. This research uses a qualitative approach where data is obtained through field interview guidelines to 5 informants who refer to the research indicators, namely 1. Prevention 2. Warning 3. Preparedness 4. Disaster Impact 5. Response. 6. Recovery 7. Development. The data obtained in the field were analyzed based on the classification of the informant's answers described in the results and discussion of the research which was then interpreted by the researcher. it is known that the Role of Stakeholders in Handling Flood Disasters in Sungai Penuh City there are 5 indicators of the framework of thinking that have not been maximally implemented, namely the indicators of Prevention, Warning, Preparedness, Disaster Impacts and Discipline while for the Response and Development indicators have been implemented to the maximum. For this reason, the authors suggest that the role of stakeholders in handling flood disasters in Sungai Penuh City can be improved so that facilities and infrastructure as well as houses that are damaged due to flood disasters can be overcome properly.
Penerapan Usability Website ESA 112 Menggunakan Metode System Usability Scale (SUS) pada DISKOMINFO Kota Palembang

Emergency service are the service that could be accessed by the public via gadgets and usually only consist of a three-digit number which was easy to remember and can be reached quickly. Emergency Service Application (ESA) 112 was a website for recording events or community emergency reports by calling 112 via gadgets and ESA 112 can be accessed by 112 emergency service employees only. The purpose of this study was to measure the usability of the website to determine whether the website could be used properly or not. This study uses the System Usability Scale (SUS) method to measure how well this website was used. Respondents who are directly involved will fill out a questionnaire containing 10 questions. The results of the system usability scale get an average score of 60.75, it was in an ok-good rating, grade D and it was at a marginal low. To increase the level of usability, these results still need the improvement.
IoT Based Fall Detection System for Elderly Population in Pakistan

A Fall is defined as an event that results in a person coming to rest inadvertently on the ground or floor or other lower level. Fall-related injuries may be fatal or non-fatal. It puts the elderly population, i.e., people of 65 years old, and over, in a dangerous situation. It is why we need a fast and efficient fall detection system to provide first aid and rescue the patient. The existing fall detection devices are not widely used because they do not meet the expectations of the users like the effectiveness of detecting a fall, time to inform guardian to provide necessary precautions and rescue the affected person on time. This device differs from other devices firstly in a way that it is a cheap portable device; secondly, as it was designed and assembled in a Muslim majority country, it should be able to distinguish fall from different movements of Salah. The tool uses a three-axis Accelerometer and Gyroscope sensor, which measures the acceleration and rotational motion in the X, Y, and Z-axis, respectively, which is controlled by a microcontroller called Arduino. The board uses a Wi-Fi module to send data wirelessly through the internet.
LibGuides: Test Preparation Materials: How Do I Prepare for the ACCUPLACER Test?

A guide to the test preparation materials available in the OHS Library, Frederick County Public Libraries, and online.
Multiple Face Tracking using Kalman and Hungarian Algorithm to Reduce Face Recognition Computational Cost

Currently, research in face recognition systemsmainly utilized deep learning to achieve high accuracy. Usingdeep learning as the base platform, per frame image processingto detect and recognize faces is computationally expensive,especially for video surveillance systems using large numbers ofmounted cameras simultaneously streaming video data to thesystem. The idea behind this research is that the system does notneed to recognize every occurrence of faces in every frame. Weused MobileNet SSD to detect the face, Kalman filter to predictface location in the next frame when detection fails, andHungarian algorithm to maintain the identity of each face.Based on the result, using our algorithm 87.832 face that mustbe recognized is reduced to only 204 faces, and run at the realtime scenario. This method is proven to be used in surveillancesystems by reducing computational cost.
Amortized inference and model learning for probabilistic programming

Probabilistic modeling lets us infer, predict and make decisions based on incomplete or noisy data. The goal of probabilistic programming is to automate inference in probabilistic models that are expressed as probabilistic programs---programs that can draw random values and condition the resulting stochastic execution on data. The ability to define models using programming constructs such as recursion, stochastic branching, higher-order functions, and highly-developed simulation libraries allows us to more easily express and perform inference in models that have simulators, a dynamic number of latent variables, highly structured latent variables or nested probabilistic models. The key to success of probabilistic programming is efficient inference and model learning in such models. The most powerful black-box approximate inference algorithms for probabilistic programs are either sampling-based (Monte Carlo simulations) or optimization-based (variational methods). In both cases, one must re-run the inference algorithm for new data. Hence, our first goal is developing algorithms for amortized inference which, here, refers to reducing the run-time cost of inference by pre-training a neural network that maps from observed data to efficient parameters for performing fast, repeated inference. Our second goal is developing algorithms for model learning that are advantageous for two classes of models that one might typically want to write as probabilistic programs: models of sequential data and models that contain discrete latent variables. In this thesis, we discuss the general topic of Bayesian machine learning and probabilistic programming before moving on to our methodological contributions in amortized inference and model learning.
Library and Information Science Research Outcomes in MINTs Countries

Member countries of MINT (Mexico, Indonesia, Nigeria and Turkey) have long history of LIS education and research. This study focus on the pattern on literature growth, research trend in LIS, citation, self cites of documentation and international collaboration. The present study conceived to assesses the quantitative aspect of research output of MINT nations for the period of nineteen years i.e. 1996–2016 in the subject of Library and Information Science. To undertake this study, data was retrieved from SJR – SCImago Journal & Country Rank on March 2018, and data was entered in MS-Excel and analyzed.
SUMAK KAWSAY: EDUCARNOS PARA EL BUEN VIVIR. Desde el ciclo lectivo hacia el ciclo de experiencias educativas

Today, in the current pandemic situation caused by Covid-19, the education of our province, our country and the world has had to suspend its traditional practices, having replaced them with other non-face-to-face alternative forms, and thus try to continue its functions by ensuring rights and obligations in reflective and constitutive access to cultural and knowledge assets; functions on which, historically, it has been questioned and discussed and for what we ask ourselves in this article: Will this reality be a possibility to think, debate and finally try in action other possibilities of configuration of educational systems?
Ideal-Theoretic Explanation of Capacity-Achieving Decoding.

In this work, we present an abstract framework for some algebraic error-correcting codes with the aim of capturing codes that are list-decodable to capacity, along with their decoding algorithm. In the polynomial ideal framework, a code is specified by some ideals in a polynomial ring, messages are polynomials and their encoding is the residue modulo the ideals. We present an alternate way of viewing this class of codes in terms of linear operators, and show that this alternate view makes their algorithmic list-decodability amenable to analysis. Our framework leads to a new class of codes that we call affine Folded Reed-Solomon codes (which are themselves a special case of the broader class we explore). These codes are common generalizations of the well-studied Folded Reed-Solomon codes and Multiplicity codes, while also capturing the less-studied Additive Folded Reed-Solomon codes as well as a large family of codes that were not previously known/studied. More significantly our framework also captures the algorithmic list-decodability of the constituent codes. Specifically, we present a unified view of the decoding algorithm for ideal theoretic codes and show that the decodability reduces to the analysis of the distance of some related codes. We show that good bounds on this distance lead to capacity-achieving performance of the underlying code, providing a unifying explanation of known capacity-achieving results. In the specific case of affine Folded Reed-Solomon codes, our framework shows that they are list-decodable up to capacity (for appropriate setting of the parameters), thereby unifying the previous results for Folded Reed-Solomon, Multiplicity and Additive Folded Reed-Solomon codes.
Θ(1) time complexity parallel local binary pattern feature extractor on a graphical processing unit

Local Binary Pattern (LBP) feature is used widely as a texture feature in object recognition, face recognition, real-time recognitions, etc. in an image. LBP featureextraction time is crucial in many real-time applications. To accelerate feature extrac�tion time, in many previous works researchers have used CPU-GPU combination. In this work, we provide a θ(1) time complexity implementation for determining the LocalBinary Pattern (LBP) features of an image. This is possible by employing the full capa�bility of a GPU. The implementation is tested on LISS medical imag
Renominative logics with extended renomination, equality and predicate complement

A new class of program-oriented logical formalisms is investigated – renominative logics with extended renominations, equality predicates, and predicate complement composition. Composition algebras and languages of such logics are described; their semantic properties are investigated. For these logics, a number of logical consequence relations are proposed and investigated, in particular, the logical consequence relations with undefinedness conditions. Properties of these relations form the semantic basis for further construction of sequent-type calculi for the proposed logics.
Video Management System

The “Video Management System” provides a platform for both teachers and students where subject teachers can easily upload videos and students can easily access videos on a given topic. This application is very useful for tracking the progress of students on an individual basis by the teacher. In this application first students have to register for the given subject or course, there are two login buttons. Students can log in to the students' login button of the subject or course they want and then they are only able to watch videos related to that subject. Similarly, teachers can also log in using the Admin button and are able to see the number of students who have watched the video and their number, as well as the time to identify whether the students will watch the full video.
Adversarial auto-encoder for rating prediction with ratings and reviews

Recommender systems have been widely used in our life in recent years to facilitate our life. And it is very important and meaningful to improve recommendation performance. Generally, recommendation methods use users’ historical ratings on items to predict ratings on their unrated items to make recommendations. However, with the increase of the number of users and items, the degree of data sparsity increases, and the quality of recommendations decreases sharply. In order to solve the sparsity problem, other auxiliary information is combined to mine users’ preferences for higher recommendation quality. Similar to rating data, review data also contain rich information about users’ preferences on items. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of the rating and review relation between a user and an item. The empirical studies on real-world datasets show that the proposed method improves the recommendation performance.
The content of psycho-physiological features of improving the process of developing teamwork skills of students in grades 1-4 in general secondary schools

The following article deals with the recommendations to improve educational process in secondary schools. Pedagogical leadership within the collective life and collective's activities were analyzed as well. The traditional school is aimed at solving two main problems. Firstly, it must provide students with a great knowledge, and secondly, prepare them for life, forming the most important skills and abilities. Thus, in the traditional course of study, development has been relegated to the background for a long time. New social demands force teachers to abandon traditional teaching methods; there is a need to master new developing educational technologies.
The Teaching Method of Science Subject in Primary Schools Based on Students’ Competency Development: Results From a Field Study

Renovating the General Education Curriculum is one of the most important goals of the education renovation in Vietnam at the present. Particularly, the renovation of teaching methods has been receiving increasing attention during the past few years. This originates from the growing demands for social developments and the inadequacies of the current General Education curriculum. This article explores the current teaching methods used to teach the science subject with an orientation toward the development of students’ competencies in primary schools in Ho Chi Minh City, Vietnam. This scientific and objective account will provide the foundation for improving teaching methods, enhancing the quality of science education in primary schools with an orientation toward students’ competency development to meet the demands for the renovation of the General Education Curriculum at the primary level.
The effectiveness of using role play videos to enhance students' speaking performance: a case study of UiTM Cawangan Kelantan interim students / Anis Idayu Aziz... [et al.]

Speaking competence marks an evident proof of success in the process of second languageacquisition. Hence, the syllabus of speaking has become a burgeoning aspect of many English courses.Realizing the importance of speaking fluency and accuracy in the target language, language professionals andinstructors have never stopped to find practical ways to enhance the learners’ speaking skill. Reflecting uponthe many sessions of teaching English to students at UiTM Kelantan Branch, more often than not, the studentsadmitted to having fear in speaking lessons as compared to lessons on other skills. They find it difficult toexpress their feelings in the target language as their native language stands as a barrier. This has prompted somelanguage instructors to put much effort into providing various communicative activities to help studentsovercome their fear, among which is through role-play activities. In this study, role-plays are compiled in theform of video recordings and prepared as supplementary material for both teachers and students to be used inand out of classrooms. The research’s main objective is to determine the relationship between the effectivenessof role-play videos and students’ speaking performance. 127 respondents of the interim semester at UiTMKelantan Branch were selected as the samples for this research. Simple random sampling was used, andquestionnaires were distributed for data collection. The finding shows that the correlation value between theeffectiveness of role play videos and students’ speaking performance is 0.933 (p=0.000 < 0.05). Thus, thismeans there is a significant, strong positive relationship between the effectiveness of role play videos andstudents’ speaking performance
Extractive Summarization Based on Dynamic Memory Network

We present an extractive summarization model based on the Bert and dynamic memory network. The model based on Bert uses the transformer to extract text features and uses the pre-trained model to construct the sentence embeddings. The model based on Bert labels the sentences automatically without using any hand-crafted features and the datasets are symmetry labeled. We also present a dynamic memory network method for extractive summarization. Experiments are conducted on several summarization benchmark datasets. Our model shows comparable performance compared with other extractive summarization methods.
Pengembangan Modul Pembelajaran Kimia Berbentuk Komik Berbasis Augmented Reality pada Materi Pokok Ikatan Kimia

This study is a development research that aims to develop and produce learning module in the form of comics based on augmented reality in Chemical Bonds learning subject. The development model used in the study refers to the 4D development model by Thiagarajan et al. The limited trial of this learning module was conducted at UPT SMAN 11 Pinrang. The results of the study reveal that (1) the development process of learning module employed the 4-D model which included 4 stages, namely (a) defining (define), (b) design, (c) development (develop), and (d) disseminating (disseminate), (2) the learning module is valid, effective, and practical. The validity of the module by the material experts obtained moderate validity and by the media experts obtained high validity categories. Limited field trials to 33 students of class X MIPA 1 obtained 81% class completeness after using the module. The learning motivation after using the module obtained an average score of 86 which was higher than before using the module which was 75. The effectiveness of learning module was also obtained by comparing the learning motivation of students based on the paired sample t-test with a significance p value smaller than α (0.00 &lt; 0.05). The responses of students and teachers to the learning module developed also obtained a very practical category so that the media is confirmed to be practical.
An Ontology Based Methodology for Capability Requirements Analysis and Verification of the Military Information System

To solve the problem of capability requirements analysis and verification of the military information system, a new method based on ontology is proposed in this paper. Firstly, meta ontology of capability requirements is designed based on its analysis process. And then meta ontology is instantiated to construct the capability requirements model according to the scenario of specific applications. Secondly, the data completeness and data consistency rules of the model are extracted from the ontology directly according to the relations among the data entities of the ontology, and the data entities are used to develop the verification matrixes, which are utilized to judge the data completeness and data consistency of requirements model. Finally, a typical example is given to illustrate the implementation process of our method, and the results demonstrate its feasibility and validity.
Lifelong Learning Sentiment Classification of Continuously Increasing Data

Social media gives a platform for users to communicate with family, friends, and colleagues. Social media contain a massive amount of data ranging from a different topic like an opinion about any product, services, or personality. Lifelong learning helps to analyze and classify the sentiment of continuously increasing movie review data. The proposed approach is a dictionary-based technique i.e. a dictionary sentiment bearing words was used to classify the text classes like the positive, negative or neutral opinion. Achieving a high level of accuracy poses a challenge due to the large volume of data sets. Proper classification of emotion and sentiment can greatly influence productivity and quality of the different product in the market. To classify, there is a need, to have enough labeled data to perform sentiment classification task. The proposed system consists of one algorithms, the Naive Bayes algorithm. The objective of this dissertation is to improve the accuracy by Naive Bayes classifier for better results. Naive Bayes Algorithm gives 82% accuracy. It is also observed that the Naive Bayes algorithm is more accurate than Support Vector Machine algorithms.