Reviewerly/exHarmony huggingface dataset:
THE INFLUENCE OF THINK PAIR SHARE STRATEGY TOWARDS STUDENTS’ SPEAKING ABILITY OF ENGLISH DEPARTMENT OF STKIP PGRI BANDAR LAMPUNG IN 2017/2018

The problem of this research was many students got difficulties in speaking. In this research the researcher tried to investigate about think pair share in teaching speaking. The objective of this research were to know and describe the significant of the influence of think pair share strategy towards students’ speaking ability and to know and describe the average score of students’ speaking ability which is taught through think pair share is higher than which is taught through conventional technique. The research was conducted at second semester students of English Department of STKIP PGRI Bandar Lampung in 2017/2018. In this research the researcher used experimental method. The population of this research was 94 students in 2 classes. The sample of the research were two classes, the first class was ENG IIB consisted of 47 students as the experimental class and the second class was ENG IIA consisted of 47 students as the control class. In the experimental class was the students were taught by think pair share strategy and in control class the students were taught by using the conventional technique. In conducting the research, the writer used Cluster Random Sampling technique to establish the experiment class and the control class, because all students were the sample is homogeneous. In data collecting technique, it was used spoken test, it is included instruction that students should make a composition based on the topic chosen. In data analysis, the writer used t-test formula. It was found that ttest > ttable = 2.98 > 1.67 for significant level 5% and ttable 2.39 for significant level 1%. It means that Hα is accepted. So, the researcher concluded that there is the influence of using think pair share strategy towards students’ speaking ability of English Department of STKIP PGRI Bandar Lampung in 2017/2018
A Mental Health Database Creation Method with Neuroscience-Inspired Search Functions

Mental health, an essential factor for maintaining a high quality of life, is determined by one’s nutritional, physical, and psychological situations. Since mental health is influenced by multiple factors, a multidisciplinary approach is effective. Due to the complexity of this mechanism, most non-specialists have little knowledge and access to the related information. There are multiple factors that influence one’s mental health, such as nutrition, physical activities, daily habits, and personal cognitive characteristics. Because of this complexity, it can be hard for non-specialists to find and implement appropriate methods for improving their mental health. This paper presents the 2-Phase Correlation Computing method for interpreting the characteristics of each emotion/mental state, nutrients, exercises, life habits with a vector space. The vector space reflects the roles of neurotransmitters. The 2-Phase Correlation Computing extracts the information expected to be most relevant to the user’s request. In this method, expert knowledge, characteristics of emotions, and mental states are defined in the “Requests” Matrix, and each stimulus into “Nutrients”, “Exercises”, and “Life Habits” Matrixes. “Nutrients”, “Exercises”, and “Life Habits” are expressed and computed to as “Stimuli”. In short, this method introduces logos to the chaotic world of decision making in mental health.
Weight-of-evidence 2.0 with shrinkage and spline-binning

In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of classes are required to be both precise as well as interpretable. Linear modeling methods such as logistic regression are often adopted, since they offer an acceptable balance between precision and interpretability. Linear methods, however, are not well equipped to handle categorical predictors with high-cardinality or to exploit non-linear relations in the data. As a solution, data preprocessing methods such as weight-of-evidence are typically used for transforming the predictors. The binning procedure that underlies the weight-of-evidence approach, however, has been little researched and typically relies on ad-hoc or expert driven procedures. The objective in this paper, therefore, is to propose a formalized, data-driven and powerful method. To this end, we explore the discretization of continuous variables through the binning of spline functions, which allows for capturing non-linear effects in the predictor variables and yields highly interpretable predictors taking only a small number of discrete values. Moreover, we extend upon the weight-of-evidence approach and propose to estimate the proportions using shrinkage estimators. Together, this offers an improved ability to exploit both non-linear and categorical predictors for achieving increased classification precision, while maintaining interpretability of the resulting model and decreasing the risk of overfitting. We present the results of a series of experiments in a fraud detection setting, which illustrate the effectiveness of the presented approach. We facilitate reproduction of the presented results and adoption of the proposed approaches by providing both the dataset and the code for implementing the experiments and the presented approach.
Problems of teaching Physical Training by means of distance learning technologies in educational institutions of the Ministry of Internal Affairs of Russia

The article contains the results of the research aimed at studying the problems of teaching Physical Training by means of distance learning technologies in educational organizations of the Ministry of Internal Affairs of Russia during the period of additional health and hygiene measures taken in order to protect public health in conditions of COVID-19 spreading. The research attempts at finding the ways to resolve the contradiction between the necessity to use distance learning technologies for teaching Physical Training in educational organizations of the Ministry of Internal Affairs of Russia, dictated by the current socio-economic situation, and the objective limitations of these methods in terms of the practice-oriented character of the discipline. Using theoretical analysis of the conclusions of the previously held studies of the topic and&amp;nbsp; their own observations and their comparison with the current practice of Physical Training teaching, the researchers detect the main obstacles of distance learning technologies and offer the ways to overcome them. The research results can be used for planning and organizing the educational process for Physical Training discipline in educational institutions of the Ministry of Internal Affairs of Russia.&amp;nbsp; &amp;nbsp;
Representation Problems in Linguistic Annotations : Ambiguity, Variation, Uncertainty, Error and Bias

The development of linguistic corpora is fraught with various problems of annotation and representation. These constitute a very real challenge for the development and use of annotated corpora, but as yet not much literature exists on how to address the underlying problems. In this paper, we identify and discuss five sources of representation problems, which are independent though interrelated: ambiguity, variation, uncertainty, error and bias. We outline and characterize these sources, discussing how their improper treatment can have stark consequences for research outcomes. Finally, we discuss how an adequate treatment can inform corpus-related linguistic research, both computational and theoretical, improving the reliability of research results and NLP models, as well as informing the more general reproducibility issue.
Is the User Enjoying the Conversation? A Case Study on the Impact on the Reward Function

The impact of user satisfaction in policy learning task-oriented dialogue systems has long been a subject of research interest. Most current models for estimating the user satisfaction either (i) treat out-of-context short-texts, such as product reviews, or (ii) rely on turn features instead of on distributed semantic representations. In this work we adopt deep neural networks that use distributed semantic representation learning for estimating the user satisfaction in conversations. We evaluate the impact of modelling context length in these networks. Moreover, we show that the proposed hierarchical network outperforms state-of-the-art quality estimators. Furthermore, we show that applying these networks to infer the reward function in a Partial Observable Markov Decision Process (POMDP) yields to a great improvement in the task success rate.
$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era

Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal recommendation techniques are proposed to improve the efficiency of information retrieval on mobile devices by means of accurately recommending items via learning temporal interests with short-term user interaction behaviors. However, the enforcement of privacy-preserving laws and regulations, such as GDPR, may overshadow the successful practice of temporal recommendation. The reason is that state-of-the-art recommendation systems require to gather and process the user data in centralized servers but the interaction behaviors data used for temporal recommendation are usually non-transactional data that are not allowed to gather without the explicit permission of users according to GDPR. As a result, if users do not permit services to gather their interaction behaviors data, the temporal recommendation fails to work. To realize the temporal recommendation in the post-GDPR era, this paper proposes $C^3DRec$, a cloud-client cooperative deep learning framework of mining interaction behaviors for recommendation while preserving user privacy. $C^3DRec$ constructs a global recommendation model on centralized servers using data collected before GDPR and fine-tunes the model directly on individual local devices using data collected after GDPR. We design two modes to accomplish the recommendation, i.e. pull mode where candidate items are pulled down onto the devices and fed into the local model to get recommended items, and push mode where the output of the local model is pushed onto the server and combined with candidate items to get recommended ones. Evaluation results show that $C^3DRec$ achieves comparable recommendation accuracy to the centralized approaches, with minimal privacy concern.
LibGuides: Tutorial Storage: Resources and Databases

This guide stores tutorials so that librarians can easily access them and incorporate them into their research guides.
불확실한 환경하의 FMEA에서 Fuzzy DST/AHP, TOPSIS, Grey이론을 이용한 리스크 평가 적용방안

Failure Mode and Effect Analysis (FMEA) is a widely used method to assess the failure risk using the Risk Priority Number (RPN) of occurrence, detectability, and severity by following a tiered structure, such as the cause-mode-effect causal relationship. However, FMEA has an uncertainty under four circumstances in terms of; (1) lack of information on failure modes, (2) difference in risk criteria importance, (3) vagueness in linguistic terminologies for risk scale, and (4) limited sample number of failure mode and risk criteria. This study focuses on evaluating the failure risk in FMEA under uncertain conditions, and presents an implementation strategy of the evidential Technique for Order Preference by Similarity Ideal Solution (TOPSIS) and evidential Grey theory with group fuzzy Dempster Shafer Theory (DST)/ Analytic Hierarchy Process (AHP). Particularly, this work demonstrates the application of Basic Belief Assignment (BBA) using the weighted average with risk criteria importance and the risk scale obtained defuzzified value from corresponding linguistic terminologies. Integrated Electronic Control Unit (ECU) of Vehicle Item illustrates an implementation strategy of evidential TOPSIS and evidential Grey theory, and compares them with traditional RPN methods.
Problems in Writing Scholarly Articles in English among Indonesian EFL Post-Graduate Students

The directive from the Indonesian Directorate General of Institutional Science, Technology, and Higher Education that graduates from the universities in Indonesia must publish at least one scholarly article in English as a prerequisite to complete their study is seen as a move towards upgrading the quality and the credential of graduates in the country.However, for EFL students, this is not an easy task.This study aimed at examining the problems faced by the post-graduate students in Indonesia in writing scholarly articles in English and to identify the support needed by these students to enhance their writing skills.Using survey questionnaire and interview as methods of data collection, the findings indicate that
Comparative Analysis of Text Difficulty in the Reading Section of TOEIC, TOEFL, TEPS, G-TELP, and FLEX: Focusing on Lexical, Syntactic, and Discourse Level and Readability

This study conducted a corpus-based analysis to examine the difficulty level of texts used in the reading section of the five standardized EFL tests: TOEIC, TOEFL, TEPS, G-TELP, and FLEX. For the data analysis of the five EFL tests, three actual tests were extracted from each test preparation book. and were all scanned to convert PDF files to Text files. As for the analysis tools, Lexical Complexity Analyzer (LSA) and Coh-Metrix 3.0 were used for lexical, syntactic, and discourse levels and readability. As a result of the study, there were statistically significant differences in all sub-indicators except ADVV in lexical analysis including lexical density, lexical sophistication, and lexical variation. As for syntactic analysis, there was no statistically significant difference in syntactic complexity and syntactic pattern density except for SYNSTRUTa, DRPP, and DRNEG. In the case of discourse analysis, there were statistically significant differences in all indicators in the connectives except for LSASS1 and LSASSp and the latent semantic analysis. Finally, there was a statistically significant difference in readability. The overall findings suggest that TOEFL contains the most difficult texts, followed by FLEX, G-TELP, TEPS, and TOEIC. The results of this study can be used by domestic test developers to control the text difficulty, and by English learners to select and prepare for the five EFL tests.
Video-Based Android Application Development as an Educational Facility for E-Journal Users at the Library of Universitas Negeri Malang

According to the data, there was a decrease access of e-journal by the academics of higher education institutions in Indonesia.Universitas Negeri Malang (UM) Library as a college library also had a few internal problems of students in terms of library e-journal service user education.This problem can be solved by developing the user education of library e-journal service.There are a lot of data which mention the opportunities that can be used as basic research and development include 90% of the world community accessing videos, 45.3% of internet users in Indonesia like video content, and 75% of gadget users in Indonesia use devices with the Android operating system.This research method used research and development methods of Rapid Application Development (RAD) model which consists of four stages, namely (1) planning needs, (2) user design, (3) construction, and (4) switching.The results of research and development show, educational products for Android-based ejournal users are considered "very valid" by media experts, material experts, and practitioners with a percentage of 95.77% in the media aspect, and 87.5% in the material aspect.The product was also rated "valid" by students with a percentage of 82.07% in the media aspect, and 83.04% in the material aspect.Product revision is done after the validity test and field trial based on suggestions by the validator and students.The main features of the video-based android application are three ejournal user education videos of UM Library consist of ejournal service video, video of access way electronic journal services, and video of searching methods on e-journal sites.The benefits of product are (1) helping the UM Library in order to introduce e-journal services, (2) encouraging users to access ejournal services as a reference source for writing quality scientific work, (3) helping UM Library participate in the development of era.
Utilization of Picture Story for Consumer-Economic Education

This study analyzed the contents of children’s picture books to increase their utility for consumer and economic education. This study focused on the economic roles, such as workers, buyers-family financial managers, and consumer citizens, shown in picture books. For this purpose, 28 children’s picture books were selected and examined for their usefulness as learning materials.BR In 12 picture books related to workers and their occupations, the following concepts were discussed: human capital, job characteristics and uniqueness, labor value, worker’s responsibility, workers of industrial or traditional societies, and unemployment. In 12 picture books related to buyers and family financial managers, consumer rights and responsibilities, relationships between consumers and producers, decision-making processes, and household financial management were discussed. Finally, in 10 picture books related to consumer citizens, community behavior and interdependence, ethical consumption such as donation and sharing, green behavior, decision making for the community, and consumer sovereignty were discussed.BR The study results showed that useful contents and concepts that can be used to improve individuals’ roles in the economic society were included in selected children’s picture books. Because of these contents and concepts, these books can be utilized for various consumer and economic education issues. Therefore, children’s picture books can be good consumer and economic education materials for various target students, subjects, and educational areas.
Regional geological framework of New Zealands mineral deposits

New Zealand is the emergent part of a 4.9 Mkm2, mainly submerged continent in the SW Pacific Ocean that was formerly part of the Gondwana supercontinent. The geology can be described in terms of two main Cambrian-Early Cretaceous basement units (Western and Eastern provinces) and a Late Cretaceous-Holocene sedimentary and volcanic cover (Zealandia Megasequence and Rūaumoko Volcanics). New Zealand contains a large number of different kinds of mineral deposits and occurrences. The metallogenic map of New Zealand lists more than 600 metallic mineral deposits classified into 25 different types. This mineral diversity for a land area of only 270 500 km2 is a function of a varied and complex geology resulting from New Zealands location at or near active plate boundaries for the past half billion years. Since the 2006 overview of New Zealand’s mineral deposits in the AUSIMM Monograph series, several advances have been made in our understanding of the country’s regional geological framework and development. This paper provides a brief overview of New Zealand’s mineral deposits and occurrences in the context of the currently understood geological and tectonic framework.
Robot Adaptation for Generating Consistent Navigational Behaviors over Unstructured Off-Road Terrain

Terrain adaptation is an essential capability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected robot behaviors generated by terrain adaptation methods cannot always be executed accurately due to setbacks such as wheel slip and reduced tire pressure. To address this problem, we propose a novel approach for consistent behavior generation that enables the ground robot's actual behaviors to more accurately match expected behaviors while adapting to a variety of unstructured off-road terrain. Our approach learns offset behaviors that are used to compensate for the inconsistency between the actual and expected behaviors without requiring the explicit modeling of various setbacks. Our approach is also able to estimate the importance of the multi-modal features to improve terrain representations for better adaptation. In addition, we develop an algorithmic solver for our formulated regularized optimization problem, which is guaranteed to converge to the global optimal solution. To evaluate the method, we perform extensive experiments using various unstructured off-road terrain in real-world field environments. Experimental results have validated that our approach enables robots to traverse complex unstructured off-road terrain with more navigational behavior consistency, and it outperforms previous methods, particularly so on challenging terrain.
Using Hydrological Mapping to Evaluate the Effectiveness of the Bener Dam Development in Reducing Flood Risk in Purworejo Regency, Central Java

The Bogowonto Hulu sub-watershed has high flooding potential, especially during the rainy season. This flooding causes physical, social, and economic losses for the local community and so the Bener Dam was built to reduce the volume of flooding in the area. This research aims to find out the effectiveness of the Bener Dam in overcoming the problem of flooding around the Bogowonto Hulu sub-watershed. The effectiveness of dams can be analysed by comparing the volume of the dam reservoir with the peak discharge amount generated. The value of peak discharge can be obtained using remote-sensing data and mathematical calculations following the rational method. Using this method, the estimated peak discharge value obtained for the watershed was 302.3 m3/s while the value of dam capacity was only 210 m3/s, giving 92.3 m3/s of flooding potential. It is necessary to reduce the potential for flooding by building an IR reservoir in the catchment area, so that rainwater is accommodated rather than being immediately depleted, and to reduce concentration time.
Learning-based calibration of a high-precision monocular vision system for macro-micro manipulation

Calibration is critical for the optical vision measurement system (OVMS) that requires high measuring accuracy within a large field of view. In this paper, we propose a practical two-step calibration method that improves measuring accuracy for the multi-scale vision measurement system based on a learning-based approach. In this method, an OVMS combined with a degenerated perspective-n-points algorithm is established for macro-micro manipulation systems. After the OVMS is coarsely calibrated by the analytic technique, the fine calibration is performed using the Artificial Nerual Network to compensate for both geometric and non-geometric measuring errors. Simulations, experiments and comparisons are carried out with a laser tracker, and the results show that the proposed two-step calibration method can remarkably minimize measuring errors and shows stronger robustness and stability, which enables the OVMS to be capable of macro-micro manipulation tasks.
Filling the ___-s in Finnish MWE lexicons

This paper describes the automatic construction of FinnMWE: a lexicon of Finnish Multi-Word Expressions (MWEs). In focus here are syntactic frames: verbal constructions with arguments in a particular morphological form. The verbal frames are automatically extracted from FinnWordNet and English Wiktionary. The resulting lexicon interoperates with dependency tree searching software so that instances can be quickly found within dependency treebanks. The extraction and enrichment process is explained in detail. The resulting resource is evaluated in terms of its coverage of different types of MWEs. It is also compared with and evaluated against Finnish PropBank.