Reviewerly/exHarmony huggingface dataset:
THE USE OF ICE BREAKER TO IMPROVE STUDENTS’ MOTIVATION IN LEARNING ENGLISH AT THE TENTH GRADE STUDENTS OF SMK YPKKP

There is a belief that ice breaking can be used in the classroom as it provides several advantages for the students. One of them is that it can improve students’ learning motivation. This study aims to prove this belief. In other words, it attempts to investigate the effectiveness of ice breaking in improving students’ learning motivation. Quantitative with one group pretest and posttest was used in this study. The population was the 10th grade students at SMK YPKKP. The sample was one class of office administration major. The data was collected through motivation test adapted from (Burhan, 2017). The result showed that null hypothesis of this study was rejected. It is proved by the data from t-test that shows the sigma 2 tailed is 0.00. This value is less than 0.05. Null hypothesis, therefore, was rejected. This means that ice breaking could definitely improve the students’ motivation in learning English. The teacher therefore can implement it in the classroom. in the title.
Predicting New Adopters via Socially-Aware Neural Graph Collaborative Filtering

We predict new adopters of specific items by proposing S-NGCF, a socially-aware neural graph collaborative filtering model. This model uses information about social influence and item adoptions; then it learns the representation of user-item relationships via a graph convolutional network. Experiments show that social influence is essential for adopter prediction. S-NGCF outperforms the prediction of new adopters compared to state-of-the-art methods by 18%.
Penetration Testing Untuk Deteksi Vulnerability Sistem Informasi Kampus

Security is an effort that can be done to protect the information contained in it which refers to confidentiality. Information systems that are centrally prone to various types of attacks such as DoS, SQL Injections, Cross Site Scripting (XSS), Clickjacking, CSRF / Cross-site request forgery and so on. This will be a polemic for the information service owner and manager. The method to be carried out in this study is to do penetration testing to audit the security of the campus information system webserver. This activity aims to identify and exploit vulnerabilities in the web server. In this study, several tools will be used as a tool, including WHOIS, NMAP and Acunetix Web Vulnerability Scanner. Tests carried out are to look for vulnerabilities on the web server while the level of vulnerability that will be detected in this test sawill be inter alia higt risk, Medium risk and low risk. The aim is to find out the weaknesses in the web server so that in the future it can avoid DoS attacks, CSRF / Cross-site request forgery, Cross Site Scripting (XSS) and clickjacking. The results of this test are expected to be an input for the management of campus information systems for the future can be made improvements to existing weaknesses.
Trade-off relation among genuine three-qubit nonlocalities in four-qubit systems

We study the trade-off relations satisfied by the genuine tripartite nonlocality in multipartite quantum systems. From the reduced three-qubit density matrices of the four-qubit generalized Greenberger-Horne-Zeilinger (GHZ) states and W states (4-qubit entangled state), we find that there exists a trade-off relation among the mean values of the Svetlichny operators associated with these reduced states. Namely, the genuine three-qubit nonlocalities are not independent. For four-qubit generalized GHZ states and W states, the summation of all their three-qubit maximal (squared) mean values of the Svetlichny operator has an upper bound. This bound is better than the one derived from the upper bounds of individual three-qubit mean values of the Svetlichny operator. Detailed examples are presented to illustrate the trade-off relation among the three-qubit nonlocalities.
ÚFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing

This paper describes the ÚFAL-Oslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. 2019).The submission is based on several third-party parsers.Within the official shared task results, the submission ranked 11 th out of 13 participating systems.
Graph Enhanced Cross-Domain Text-to-SQL Generation

Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.Existing deep learning approaches for semantic parsing have shown promise on a variety of benchmark data sets, particularly on textto-SQL parsing.However, most text-to-SQL parsers do not generalize to unseen data sets in different domains.In this paper, we propose a new cross-domain learning scheme to perform text-to-SQL translation and demonstrate its use on Spider, a large-scale cross-domain text-to-SQL data set.We improve upon a state-of-the-art Spider model, SyntaxSQLNet, by constructing a graph of column names for all databases and using graph neural networks to compute their embeddings.The resulting embeddings offer better cross-domain representations and SQL queries, as evidenced by substantial improvement on the Spider data set compared to SyntaxSQLNet.
Determination The Coefficient of Restitution in Object as Temperature Function in Partially Elastic Collision Using Phyphox Application on Smartphone

This research aims to determine the coefficient of restitution in object experiencing partially elastic collision when the object falls free using the phyphox application on smartphone. In terms of experiment, this experiment was carried out five times with different temperature. The smartphone is connected on PC through the same wifi network or using tethering by entering the search address that available on phyphox application. After that, the smartphone placed on the board that used as a base when the object dropped, then the object released from the certain height. Data collecting process started by running the phyphox application through a PC to collect the measurement data. The data collected on smartphone is exported to Ms. Excel to fit the data and then the results are plotted in graphical form. The result of the analysis shows the coefficient of restitution value of the object that had variations based on experiment with each temperatures at 32 oC, 28 oC, 24 oC, 20 oC 18 oC and the coefficient of restitution values are 0.889 ± 0.006, 0.890 ± 0.008, 0.891 ± 0.003, 0.892 ± 0.003 and 0.893 ± 0.003. The result of research shows that the lower of the temperature so theres increasing on the coefficient of restitution. So that the coefficient of restitution has avarage’s value is 0.891±0.002.&#x0D; Keywords: coefficient of restitution ,smartphone, phyphox application
Semi-Supervised Graph Based Embedding With Non-Convex Sparse Coding Techniques

We consider the problem of semi-supervised graph-based learning upon multimodal and mixmodal data. Since in semi-supervised settings, the labeled information is very limited, we first propose a non-convex sparse-coding based label propagation (αα-SLP) method to estimate the soft labels, and thereby to enrich the supervised information. By considering the structural properties of multimodal and mixmodal data, we present a semi-supervised graph-based embedding (SGE) approach that incorporates the soft label information with the hierarchical local geometric information of within-class, between-class, and overall-class data. Based on this, subspaces characterizing the multimodal and mixmodal data structure can be derived by maximizing the weighted between-class separability and minimizing the locality-preserved within-class as well as overall-class distances of the training samples. We further extend SGE into semi-supervised sparse subspace learning scenarios and present an αα-structural-regularization-induced SGE (αα-SSGE) model, which can give better results in extracting discriminative groups of features by utilizing the non-convex structural regularization techniques. Experiments for multimodal and mixmodal digit as well as face recognition verify the validity and effectiveness of the proposed models.
Informational and digital competence of the future primary school teacher: forming factors

In the article, the main factors of forming the informational and digital competence of the future primary school teacher have been specified. It has been concluded that the informational and digital competence of the primary school teacher should be considered in three aspects.&#x0D; It has been generalized that universal personal competence is formed throughout person’s life on all stages of education. The tasks of pedagogical education in colleges consist in providing the consistency of its development; studying the condition of competence of the student; correction and development of the student's competence; stimulation and orientation of the graduate to improve it. Formation and evolution of this type of competence relates to the tasks of each discipline that is studied within the curriculum of pedagogical education at colleges. Therefore, it has been summarized that the informational and educational environment of the college «works» for the development of universal personal competence for a future teacher.&#x0D; It has been figured out that pedagogical ICT competence is based on the universal component of this competence, in particular, on such components as valuable, communicative and technological, and all the general competences, which are set out in the educational standard of a new generation. The task of preparing primary school teacher within the pedagogical education in colleges is about the rigorous formation of the aspect of competence throughout the whole learning process by studying both special informational disciplines and disciplines of vocational training. The pedagogical ICT competence of the primary school teacher, while preserving the common characteristics of any other pedagogical specialty, has essential peculiarities. It has been summarized that these peculiarities are characterized by the versatility of the refraction of the researched competence in the multidisciplinary training of the primary school teacher. It has been stated that methodical ICT competence is predetermined by the specifics of educational activity of the primary school teacher and is based on such components as valuable, communicative, technological, motivational, and reflexive.&#x0D; Based on the results of the conducted research the author offers the ways of effective development of informational and digital competence of students.
On-the-fly extrinsic calibration of multimodal sensing system for fast 3D thermographic scanning

The fusion of three-dimensional (3D) geometrical and two-dimensional (2D) thermal information provides a promising method for characterizing temperature distribution of 3D objects, extending infrared imaging from 2D to 3D to support various thermal inspection applications. In this paper, we present an effective on-the-fly calibration approach for accurate alignment of depth and thermal data to facilitate dynamic and fast-speed 3D thermal scanning tasks. For each pair of depth and thermal frames, we estimate their relative pose by minimizing the objective function that measures the temperature consistency between a 2D infrared image and the reference 3D thermographic model. Our proposed frame-to-model mapping scheme can be seamlessly integrated into a generic 3D thermographic reconstruction framework. Through graphics-processing-unit-based acceleration, our method requires less than 10&#x00A0;ms to generate a pair of well-aligned depth and thermal images without hardware synchronization and improves the robustness of the system against significant camera motion.
A Personalized Sentiment Model with Textual and Contextual Information

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information.In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective.Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones.Psychological studies have also shown that individual variation exists in how easily people change their sentiments.In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a userspecific design.Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.
An Overview of Learning Design and Analytics in Mobile and Ubiquitous Learning

Mobile and ubiquitous learning models have been widely adopted in technology-enhanced learning (TEL) practices. Apart from potential benefits, these models introduce additional complexity in designing, monitoring and evaluating learning activities, as learning happens across different spaces. In recent years, literature on learning design (LD) and learning analytics (LA) has started to address these issues. This paper presents a systematic review on how LD and LA communities understand mobile and ubiquitous learning, as well as their respective contributions in these fields. The search included seven main academic databases in TEL, resulting in 1722 papers, from which 54 papers were included in the final analysis. Results point out the lack of common definitions for mobile and ubiquitous learning, raises research trends and (unexploited) synergies between LD and LA communities, and identifies areas that require further attention from these communities.
Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition

In the past few years, Neural Architecture Search (NAS) has exhibited remarkable advances in terms of neural architecture design, especially on mobile devices. NAS normally use hand-craft MBConv as building block. However, they mainly searched for block-related hyperparameters, and the structure of MBConv itself was largely overlooked. This paper investigates that factorization and reconstitution can promote the efficiency of large-kernel MBConv and thus proposes FR-MBConv (Factorizing and Reconstituting large-kernel MBConv). Compared to large-kernel MBConv with the same receptive field, our FR-MBConv has fewer number of parameters and less computational cost, dramatically increased depth and nonlinearity. In addition, from the perspective of feature generation mechanism, FR-MBConv can be equivalent to more regular convolutions. We combine FR-MBConv with MobileNetV3 to build a lightweight face recognition model. Extensive experiments on face recognition benchmark demonstrate that our lightweight face recognition model outperforms the state-of-the-art lightweight model. Even on large scale face recognition benchmark IJB-B, IJB-C and MegaFace, our lightweight model also achieves comparable performance with large models.
Predicting Tamil Movies Sentimental Reviews Using Tamil Tweets

Recently people are more frequently using their mother tongue to express their opinion and view in the social media.Especially Indian languages are often used in social media messages.Tamil is one of the oldest language which has been used slightly higher percentage in micro blogs.Sentiment analysis has gained incredible development in recent times mostly for English language.However very less work of sentiment analysis has done for Indian languages like Hindi, Tamil, Kannada etc., In this paper we focus on Tamil language tweets.It is essential to analyse the Tamil language content for tweets and get perception of opinion expressed by the tweets.Our objective is to classify the sentiment of the Tamil movies based on Tamil tweets using Tamil SentiWordNet (TSWN).We proposed Term Frequency -Inverse Document Frequency (TF-IDF) method to find the sentiment polarity of the Tamil movie dataset.This method provides baseline for our research.Domain specific ontology is used to identify the primary sentiment categorization of the Tamil movies.In contextual semantic, the sentiment of a word may flip based on the neighbouring word.In this research, sentiment-bearing terms and its neighbouring terms in Tamil tweets are evaluated using contextual semantic sentiment analysis to get more accurate result for the movie sentimental classification.
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense

This paper describes our model for COmmonsense INference in Natural Language Processing (COIN) shared task 1: Commonsense Inference in Everyday Narrations.This paper explores the use of Bidirectional Encoder Representations from Transformers(BERT) along with external relational knowledge from Con-ceptNet to tackle the problem of commonsense inference.The input passage, question and answer are augmented with relational knowledge from ConceptNet.Using this technique we are able to achieve an accuracy of 73.3 % on the official test data.
Using time series analysis to forecast the health-related quality of life of post-menopausal women with non-metastatic ER+ breast cancer: A tutorial and case study

Time series models are widely used forecasting techniques in health care for long time series and are typically built in commercial statistical packages. However, for short time series data, such as health-related quality of life (HRQoL), guidance on how to select and use appropriate time series models is lacking. This tutorial provides a step-by-step guide adopting a time series analysis framework for HRQoL forecasting. We walk through a case study examining the forecasting of the effects of adjuvant endocrine therapy on the HRQoL of post-menopausal women with non-metastatic ER + breast cancer using data from the HRQoL sub-protocol of the Tamoxifen arm of the Arimidex, tamoxifen, alone or in combination (ATAC) trial. The forecasting of HRQoL consists of four steps: 1) data extraction and accuracy check, 2) forecasting horizon definition and identification of data pattern, 3) forecasting model identification and fitting using five forecasting approaches appropriate for short time series ((i) double exponential smoothing, (ii) double moving average, (iii) fuzzy forecasting, (iv) grey forecasting, and (v) Volterra series), 4) forecasting model selection. A user-friendly visual basic for applications (VBA) Excel add-in is made available to interested users to facilitate the application of the tutorial. The Grey method and Volterra series appeared to be good candidates to forecast the effects of adjuvant endocrine therapy on the HRQoL of post-menopausal women with non-metastatic ER + breast cancer enrolled in the ATAC trial. It is feasible to forecast the effects of treatments on HRQOL even when the time series is short.
Designing Indonesian Learning Materials for Communicative Purposes for Foreign Learners

This study aimed to develop the language learning materials design for communicative purposes for foreign learners. This study focused on designing the language material and the feasibility of language learning materials for communicative purposes for foreign learners. The study involved participants of 8 program managers, 11 language teachers, and 3 experts experienced in language learning for foreign students, also 17 beginner-level foreign students studying Indonesian in Indonesia. Data collected includes verbal and numerical data. Verbal data was in the form of information about teaching-learning materials have been used by Indonesian learning program for foreign learners. The data was used for designing language teaching materials and collected through interviews with the program mangers and documents studies. Numerical data in the form of validation score of the learning materials design used to determine the feasibility of the language teaching materials as learning media for foreign students. The data was obtained by the validation test. The verbal data was analyzed by using qualitative analysis techniques and its results were used to make a design for Indonesian learning material. The numerical data was analyzed by simple statistical analysis. Using these analytical techniques, this study produced a design of learning materials that are appropriate for learning Indonesian for foreign learners for communicative purposes. The learning materials for communicative purposes of Indonesian foreign learners more emphasize on the language used in communication. Cultural materials and language skills were the main choices in the development of learning materials. The choice of content and variety of language learning materials was based on the analysis of students' learning needs. The development of learning materials based on empirical studies can produce material designs that are feasible for learning. https://doi.org/10.26803/ijlter.18.10. 7
Diachronic Analysis of Entities by ExploitingWikipedia Page revisions

In the last few years, the increasing availability of large corpora spanning several time periods has opened new opportunities for the diachronic analysis of language.This type of analysis can bring to the light not only linguistic phenomena related to the shift of word meanings over time, but it can also be used to study the impact that societal and cultural trends have on this language change.This paper introduces a new resource for performing the diachronic analysis of named entities built upon Wikipedia page revisions.This resource enables the analysis over time of changes in the relations between entities (concepts), surface forms (words), and the contexts surrounding entities and surface forms, by analysing the whole history of Wikipedia internal links.We provide some useful use cases that prove the impact of this resource on diachronic studies and delineate some possible future usage.
INFORMATION TECHNOLOGY GOVERNANCE PERFORMANCE MEASUREMENT AT NATIONAL LIBRARY USING COBIT FRAMEWORK 5

National Library of Indonesia (Perpusnas) is a public library located in Jakarta. In its operations Perpusnas already implemented Information Technology (IT) to support its activities, in IT implementation good IT Governance is needed to ensure that the IT is working consistently with business and produce the expected output. IT Governance is structure, process and mechanism that also includes objective making of the organization while considering the organization’s capability. With the good IT Governance Perpusnas can allocate its resource more efficiently and measure the running performance. This research will use COBIT 5 as the framework, the reason why COBIT 5 is chosen because COBIT 5 provides comprehensive framework that could help Perpusnas in achieving its IT Governance and IT management objectives. COBIT 5 enables IT to be organized and managed in the whole organization, it also could provide balance between the realization of benefits and risk optimization. Observation and interview is done in order of data collection in this research, the data then is analyzed and measured with the capability level in COBIT 5. Based on the analysis conducted the result obtained is. The average capability level of EDM01 is 1, on EDM02 is 3, on EDM03 is 2, on EDM04 is 3 and on EDM05 is 3. This research also provides recommendations that can be used as reference for Perpusnas to increase the performance of its IT Governance