Reviewerly/exHarmony huggingface dataset:
Exploring the Use and Awareness of Web 2.0 Tools by MLIS Students of Universities in Odisha

This paper explores the awareness level, use and competency in using Web 2.0 tools by students’ of Master in Library and Information Science in Universities of Odisha, India. Particularly, the study found the demographic profile of MLIS students, explored the students’ level of the acquaintance of Web 2.0 tools, and level of competency with Web 2.0 technologies, and finally reported different activities they do while using Web 2.0 tools. Survey data were collected using a well-thought-out questionnaire from 170 MLIS students out of 181 enrolled during the academic year 2016–2017. Data were analysed by applying descriptive statistical methods like, frequency of occurrences, percentage and mean value. Findings show that around 54.1% (92) of the students were female, whereas 45.8% (78) were male. The study also found that students are aware of Web 2.0 tools (Facebook, Wiki and Blog) and Facebook being the very frequently used tools with ‘excellent’ skill level, followed by skill level ‘very good’ using the Wiki. The activities of MLIS students reported that they are more reading Blog and Wiki to gather knowledge, than having own Blog and Wiki. But they are equally engaged in all activities related to Facebook. The study further recommends that a suitable policy need to frame regarding its use and training needs to improve the use of Web 2.0 tools for the educational purpose.
ANALYSIS OF BIG DATA FACTORS ON SOCIAL MEDIA USER AND POPULARITY BRAND

The purpose of writing this paper is to look at how factors of big data affect the users of social media and brand popularity. The method used in the writing of this paper is by using path analysis and also by doing literature study to find reference material. The results of the analysis indicate that there is influence of big data factors that affect the popularity of the brand. With this research, we get big data model that influence the brand popularity Keywords: big data. Brand popularity, path analysis, model, factors
Analysis of the Development of Emotional Quotient of Students in Medical Colleges Based on WeChat Platform

Emotional quotient plays a vital role in the growth of college students. At present, the current situation of the emotional quotient of medical college students is not optimistic. And WeChat platform has incomparable advantages for the development of the emotional quotient of medical college students. Therefore, it is of great practical significance for medical colleges to make good use of WeChat platform to cultivate and improve the emotional quotient of students.
Manajemen referensi penulisan karya tulis ilmiah Mahasiswa STKIP-PI Makassar

This program was driven by the need for students and lecturers in utilizing an application tool in order to help them at managing the references in the scientific paper. The purpose of this training was to provide students of STKIP-PI Makassar in writing scientific papers, especially managing references used by using Zotero 5.0 software. The results of this training showed that the students and lecturers were very interesting to participate in the training as well as using the Zotero 5.0 in managing their references. The director of STKIP-PI and teaching staff encourage their students to use the Zotero 5.0 software in writing scientific papers.
Colchester Institute’s Braintree Campus opens its doors for National Apprenticeship Week

Apprenticeships are being put in focus and celebrated at 12th National Apprenticeship Week, which commences on Monday 4th March 2019.
Consideration of Introduction of a Correlated Double Samping to a Two-Dimensional Magnetic Sensor and Its Noise Suppresion

A CDS (correlated double sampling) method was applied to two-dimensional magnetic sensors. The two-dimensional integrated magnetic sensor used for the consideration of CDS was composed of a 64×64 array of Hall sensors. The size of a Hall element was 2.7×2.7µm2 and a pixel size was 7×7µm2. By the results of meausrement, FPN(fixed pattern noise) in the reproduced image sampled at 3.2µs was suppressed to 1.27mVrms.
ViDi: Virtual Director of Video Lectures

Many educational institutes are making lecture recordings widely available to improve the students’ learning experience, but the viewing experience is quite poor (e.g., fixed camera pointed to the classroom and/or slideshow). This can jeopardize the benefits of the learning process based on video lectures. In this paper, we propose Virtual Director, a system designed to improve the viewing experience through an automated direction of video lectures. Virtual Director captures the classroom scenario, understands the semantic content of the lecture and produces a video with a layout that changes according to the semantic content and according to movie theory rules. A prototype, developed with off-the-shelf technologies, shows the feasibility of the approach and a preliminary evaluation confirmed that Virtual Director might improve the viewing experience of video lectures.
CALL:The Use of Winnerclass Professionals V.3.0 Software in Teaching Listening

The use of multimedia is very crucial thing nowday. By using multimedia such as software program can aid the teacher to improve students’English skill. Specially for listening, multimedia is important since without using multimedia the teaching process can not be done. Winner class professional V.3.0 software is one of software that can be used in teaching listening. It is a classroom management program that can be utilized for language and computer laboratories that has main features where the teachers can send sound and video to the students’ computer for all multimedia files, interacting activities, quiz, broadcast, etc. This article will discuss the way to use this software in teaching listening.
Proposed Digital Surveillance Software

The number of Nigeria’s mobile subscribers reached 150 million users in 2017 [1]. For forensic experts and security experts, this is a great concern in terms of using the right security tools to investigate these devices when they are used for crime purposes. More strangely, most of the crimes that occur in Nigeria are performed on these digital devices and there is lack of evidence from these devices for admissibility in the court of law. Due to this challenge, a digital surveillance software (A-BOT) is proposed to secure Nigeria’s cyber space and mobile devices from being used for cyber crimes and related crimes. Proper usage of A-BOT will serve as digital surveillance and prevent digital crimes in Nigeria.
Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models

Rao-Blackwellisation is a technique that provably improves the performance of Gibbs sampling by summing-out variables from the PGM. However, collapsing variables is computationally expensive, since it changes the PGM structure introducing factors whose size is dependent upon the Markov blanket of the variable. Therefore, collapsing out several variables jointly is typically intractable in arbitrary PGM structures. In this paper, we propose an adaptive approach for Rao-Blackwellisation, where we add parallel Markov chains defined over different collapsed PGM structures. The collapsed variables are chosen based on their convergence diagnostics. However, adding a new chain requires burn-in, thus wasting samples. To address this, we initialize the new chains from a mean field approximation for the distribution, that improves over time, thus reducing the burn-in period. Our experiments on several UAI benchmarks shows that our approach is more accurate than state-of-the-art inference systems such as Merlin that implements algorithms that have previously won the UAI inference challenge.
MGA-TSP: Modernized Genetic Algorithm for the Traveling Salesman Problem

This paper proposes a new enhanced algorithm called modernised genetic algorithm for solving the travelling salesman problem (MGA-TSP). Recently, the most successful evolutionary algorithm used for TSP problem, is GA algorithm. The main obstacles for GA is building its initial population. Therefore, in this paper, three neighbourhood structures (inverse, insert, and swap) along with 2-opt is utilised to build strong initial population. Additionally, the main operators (i.e., crossover and mutation) of GA during the generation process are also enhanced for TSP. Therefore, powerful crossover operator called EAX is utilised in the proposed MGA-TSP to enhance its convergence. For validation purpose, we used TSP datasets, range from 150 to 33,810 cities. Initially, the impact of each neighbouring structure on the performance of MGA-TSP is studied. In conclusion, MGA-TSP achieved the best results. For comparative evaluation. MGA-TSP is able to outperform six comparative methods in almost all TSP instances used.
An Expertise Recommender System based on Data from an Institutional Repository (DiVA)

Finding experts in academics is an important practical problem, e.g., recruiting reviewers for reviewing conference, journal or project submissions, partner matching for research proposals, finding relevant MSc or PhD supervisors, etc. In this work, we discuss an expertise recommender system that is built on data extracted from the Blekinge Institute of Technology (BTH) instance of the institutional repository system DiVA. The developed prototype system is evaluated and validated on information extracted from the BTH DiVA installation, concerning thesis supervision of researchers affiliated with BTH. The extracted DiVA classification terms are used to build an ontology that conceptualizes the thesis domain supported by the university. The supervisor profiles of the tutors affiliated with the BTH are constructed based on the extracted DiVA data. These profiles can further be used to identify and recommend relevant subject thesis supervisors.
Deep Topic Models for Multi-label Learning

We present a probabilistic framework for multi-label learning based on a deep generative model for the binary label vector associated with each observation. Our generative model learns deep multi-layer latent embeddings of the binary label vector, which are conditioned on the input features of the observation. The model also has an interesting interpretation in terms of a deep topic model, with each label vector representing a bag-of-words document, with the input features being its meta-data. In addition to capturing the structural properties of the label space (e.g., a near-low-rank label matrix), the model also offers a clean, geometric interpretation. In particular, the nonlinear classification boundaries learned by the model can be seen as the union of multiple convex polytopes. Our model admits a simple and scalable inference via efficient Gibbs sampling or EM algorithm. We compare our model with state-of-the-art baselines for multi-label learning on benchmark data sets, and also report some interesting qualitative results.
Characterizing hysteresis in 2D materials via heavy-tail switching transients in black phosphorous (Conference Presentation)

In two-dimensional (2D) materials, such as black phosphorus, the hysteresis attributed to surface and interfacial disorder can severely limit applications in electronics. In this work, we characterize the hysteresis in Al2O3-encapsulated black phosphorous samples by studying conductivity switching transients in response to an applied step gate bias. Using the dispersive diffusion model for relaxation in disordered systems, the so-called bimolecular and unimolecular recombination limits were observed in low-disorder pristine and high-disorder oxidized BP samples, respectively. Two different heavy-tail lineshapes ( the algebraic decay and the stretched exponential relaxation ) were clearly distinguished in the low- and high-disorder limits, respectively. The parameterization of these transients allows temperature dependence of the line-fit parameters to be tracked. If interpreted under the continuous time random walk model, the observed temperature dependence of the dispersion parameter beta would result from a disorder-induced tail of localized trap states.
Mixed reality homework system

Mixed reality has potential to transform many industries, including education. The following research aims to provide a framework for mixed reality implementation into a homework system so that data collection can occur in a single location for teachers. The research attempts to demonstrate that mixed reality learning manipulatives may be deployed directly from and scored into a homework system. Applets for repetition are suggested as an extension to the mixed reality activities. The system is detailed so that replication and modifications can be done via core components. Interoperability connections allow for data collected from users during the activities and lessons to be deployed directly from the institutional learning management systems or the homework system. Advantages and limitations of the system are identified. Future studies of the system are presented.
On the Succinctness of Atoms of Dependency

Propositional team logic is the propositional analog to first-order team logic. Non-classical atoms of dependence, independence, inclusion, exclusion and anonymity can be expressed in it, but for all atoms except dependence only exponential translations are known. In this paper, we systematically compare their succinctness in the existential fragment, where the splitting disjunction only occurs positively, and in full propositional team logic with unrestricted negation. By introducing a variant of the Ehrenfeucht-Fra\"{i}ss\'{e} game called formula size game into team logic, we obtain exponential lower bounds in the existential fragment for all atoms. In the full fragment, we present polynomial upper bounds also for all atoms.
Identification of Implementing Secondary Education Affirmation Program (ADEM) in Education Board Region at East Java Province

The program is a policy affirmation designed by government through special educational institutions and specialized services (PK-LK) under the auspices of Kemendikbud (Ministry of education and culture) to equitable access in education for middle-down citizens across the country. In order to evaluate the implementation of the program, this current research is conducted. The aims of this research are to identify the problems and to find obstacles so as to discover alternative solutions. Focus group discussion (FGD) was employed in collecting the data. The results have found that the implementation of Secondary Education Affirmation Program (ADEM), namely: [1] regulations and implementation programs which include socialization, the recruitment process, and the student placement process, [2] the obstacles faced by schools namely, student unpreparedness, finance, and human resources. The novelty of the research is related to the implementation of ADEM programme in the East Java region that had not been carried out by previous studies.
REFLECTING THINKING LEVEL FOR GYMNASTIC STUDENTS IN THE FACULTY OF SPORT EDUCATION IN YARMOUK UNIVERSITY, JORDAN – A COMPARATIVE STUDY

The study aimed to identify the level of reflective thinking among physical education students enrolled in gymnastics training classes at Yarmouk University in Jordan, and to identify the impact of gender upon reflective thinking. The sample was taken from the students of the physical education college including males and females enrolled in gymnastics training course, with a total of (80). 40 were male students, whereas 40 were females. They were chosen using the intentional method. The results show a high level of reflective thinking among students enrolled in the gymnastics training course. Also male students was better than female students in reflective thinking just on meditation after the event domain. Whereas the degree of reflective thinking was similar in both male and female student on the total average score of the test and on the other domains. Article visualizations:
Introduction to Wearable Sensors

The use of inertial sensors in sporting and other applications where human locomotion is concerned has grown steadily ever since a spring loaded weight was attached to the body segments to determine its movement characteristics. This monograph has been developed to address these barriers by providing a practical introduction to the technologies, a practical guide to its usage together with case studies to aid in its deployment in applications of interest to the reader. This is presented against a background of its development and likely trends going forward. Like any technology, this work will likely show signs of age eventually, but should prove a helpful companion for the wearable sensor whether they be from an engineering discipline or the sports sciences. Onward!!!
A Yang Data Model for VN Operation

This document provides a YANG data model generally applicable to anymode of Virtual Network (VN) operation.
A Descriptive Study to Assess the Level of Knowledge regarding Sound Waves Regulating Systems (Radiation) in Mobiles and its Effects on Health among Engineering College Students in Selected Colleges (Boys and Girls) at Eluru, W.G. Dist., A.P.

A Descriptive Study To Assess The Level of Knowledge Regarding Sound Waves Regulating Systems (Radiation) In Mobiles And Its Effects On Health Among Engineering College Students In Selected Colleges (Boys and Girls) At Eluru.
CLASSIFICATION MODEL USING ANFIS ALGORITHM FOR ESTIMATION OF LANDCOVER CLASS AMBIGUITIES (Study Case: Mixed Garden and Private Forest)

The spatial information is importance in natural resources management for sustainable development. It is a land cover map which is update and comprehensive, to describe phenomenon of land use changed in Indonesia. This research was done by applying the classification of land cover ambiguities between mixed garden and private forest using ANFIS algorithm. This algorithm compute spectral, physic and socio-economy factors in to fuzzy model. The best classification model which chosen is a model scenario with K-fold 3 as training data by 80 data rows, with type of membership function on triangle distribution, with the number of membership functions as follow; the distance to settlement is at 4 mf (near, medium, far, very far), the distance to rice field is at 4 mf (near, medium, far, very far), the elevation is at 2 mf (low and high), the near infrared band is at 2 mf (low and high), the thermal infrared band is at 2 mf (low and high). It shows that error value is downward trend and stable at 0.311646 and learning iteration at 3108. It give the overall accuracy of data test at 85%, and data validation at 69.23%. This model is adequate to data input characteristic, so the degree of membership calculated firing strength that was satisfied to generate the best of premise statement on rule classifier.
Metric video retrieval speedup by irrelevant data elimination

Under content-based video search in arbitrary metric space, there arises necessity to deal with the problems concerning various aspects of Big Data specificities. Since most of the available data do not match with a query (usually in the form ‘ad exemplum’), it comes into being a construction of elimination regions of such data, which allows to significantly reduce the number of necessary comparisons for metric search. It is proposed the use of solely pivot points to calculate the distances to the query. Such approach gives possibility to exclude the entire clusters from consideration without computationally capacious operations. General case of elimination regions scheme is considered.
A Study on the Change of Communication Competence of Middle School Students Running Mathematics Experience Exhibition

Recently, there is more emphasis on the importance of activities focused on experience. One of these activities is the Mathematics experience exhibition. The purpose of this study is to explore how students' communication competences change during the preparation and running of the mathematics experience exhibition. The research problem is as follows: How do students' communication competences change during the running of the mathematics experience exhibition? In order to solve the research problem, four middle school mathematics club students took part in the exercise. All of them were in charge of running booths on exploring the polyhedron compound and Latin square in the mathematics experience exhibition. The results of the exercise are summarized as follows. The students showed the changes of their communication competences. First, they could make logical and clear explanations. They also showed the use of mathematical terms properly with everyday language. Second, the students were able to concentrate on others' explanations and changed their explanations according to the level of the opponent. From the results, implications for developing communication competences of students running mathematics experience exhibitions are suggested.
Statistical API Completion Based on Code Relevance Mining

While Application Programming Interface (API) enables an easy and flexible software development process, selecting a best-fit API is often non-straightforward in practice due to misunderstanding on the API specification or a complex programming context etc.. Consequently, the API selection has always been time-consuming and error-prone. In recent years, API recommendation systems have been introduced to help developers choose an API automatically, e.g., Eclipse and IntelliJ can generate an internal or user-defined API on the fly. Other research leveraged language models to capture the regularity in API usage and further guide the completion of APIs. While existing approaches provided a general support for API usage, they suffer from the lack of semantic awareness (e.g., Eclipse) and code relevance (e.g., language model based methods). To overcome these limitations, we proposed Crmac in this paper. The key insight of Crmac is a combination of a cache language model which learns code regularity from both open-source projects and local projects, as well as a relevance mining engine that identifies similar code to enable a weighted language model training. In our empirical evaluation, Crmac overwhelmed n-gram approaches, with an improvement of 5.28% in terms of top 10 accuracy. Moreover, over 79% APIs were correctly predicted in the top 10 guesses of Crmac.
How to record learning activity with a smartphone on lesson study program

This study was motivated by the desire to digitally record and document students' learning activities in a program namely lesson study since so far, the observation on lesson study has been conducted manually. Thus, the purpose of the study was to identify on how to record students' behaviors during lesson study using a smartphone. There are two techniques of learning activity recording. First, to record learning activities classically, the technique used is placing a smartphone camera horizontally from the back of the classroom. Second, to have more detailed recording for each individual, there needs to be a tripod to help zoom in and zoom out during the lesson. The results of the study show that the digital observation using a smartphone enabled the teachers have more detailed and focused documentation. In addition, there are to other benefits that the recording could be replayed to see the lesson reflection in accordance with the lesson plan and it could also be discussed further in the context of collaborative lesson study.