Reviewerly/exHarmony huggingface dataset:
Innovation Related Behaviour and Employment Embeddedness in Ranong Thailand

The study aimed to evaluate the impact of job embeddedness on innovative employee behaviour. The study considered the case of service industry operating in Thailand. The design of this research is quantitative as the instrument which was used to gather the responses was a survey questionnaire. Therefore, the method for data collection was from primary sources. The sample of 300 participants working in the service industry of Thailand was considered in this study. The analysis was carried out on SmartPLS in which the Structural Equation Modelling (PLS-SEM) was implemented. In view of this, the data was analysed using measurement model and path assessment with reflective constructs. The results revealed that there is an overall significant effect of job embeddedness on innovative employee behaviour in the service industry of Thailand. The study is limited to the service industry of Thailand and the results of this study can be implemented to increase the performance and innovative behaviour of employees.
BLENDED LEARNING METHOD TO OPTIMIZE ENGLISH LANGUAGE LEARNING IN NON-ENGLISH LANGUAGE EDUCATION DEPARTMENTS AT MUHAMMADIYAH UNIVERSITY OF ENREKANG

Limited time to direct learning in class often made learning English less effective, especially for classes with various levels of English proficiency. In a higher education climate that demands active learning, English teachers often find it difficult to help students whose English competency was below average to catch up with their peers who had good competence. In general, this compulsory course was only held for one semester. In fact, the target of learning English on campus was generally quite high, namely to make students had sufficient English proficiency so that they could write academic papers in English as an international language. To help overcome these obstacles, this study offers the use of the blended language learning method, which was a learning method that complements direct learning with distant learning using the internet. Blended language learning was able to complement the lack of direct time in class with distance learning using internet media. This research was the result of writer's experience in applying this method for one academic year along with the results obtained from the application of the blended language learning method in the teaching process. With the application of the blended language learning method, the writer found that language learning at the college level could be more effective, efficient, and more student-centered even though the author still faces some obstacles in its application.
Self-training with Few-shot Rationalization

While pre-trained language models have obtained state-of-the-art performance for several natural language understanding tasks, they are quite opaque in terms of their decision-making process. While some recent works focus on rationalizing neural predictions by highlighting salient concepts in the text as justifications or rationales, they rely on thousands of labeled training examples for both task labels as well as annotated rationales for every instance. Such extensive large-scale annotations are infeasible to obtain for many tasks. To this end, we develop a multi-task teacher-student framework based on self-training pre-trained language models with limited task-specific labels and rationales and judicious sample selection to learn from informative pseudo-labeled examples. We study several characteristics of what constitutes a good rationale and demonstrate that the neural model performance can be significantly improved by making it aware of its rationalized predictions, particularly in low-resource settings. Extensive experiments in several benchmark datasets demonstrate the effectiveness of our approach.
TEACHERS AND STUDENTS' PERCEPTION OF MAN 3 TULUNGAGUNG TOWARDS INTERNSHIP STUDENTS OF IAIN TULUNGAGUNG IN TEACHING ENGLISH

ABSTRACTMuktar, Faridatul. 2020. Registered Number, 17203163052. Teachers and Students’ Perception of MAN 3 Tulungagung Towards Internship Students of IAIN Tulungagung in Teaching English. Thesis. English Education Department. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Erna Iftanti, S.S, M.Pd.Keywords: Teachers’ Perception, Students’ Perception, Internship Program, Teaching English The internship program is a program that provides insight and practical experience to educate students regarding real learning activities in class. One of the purposes of Internship Program is to train the students to be competent teachers. The objective of this study is to know about the perception of teachers and students towards internship students of IAIN Tulungagung in teaching English. The research problems in this study were: 1) What is the teacher’s perception towards the internship students of IAIN Tulungagung in class? 2) What is the student’s perception towards the internship students of IAIN Tulungagung in class? The research design of this study was descriptive quantitative with a survey research design. The population of this study is English teachers and the students of the tenth grade who have been taught by an Internship student from the English Education Departement of IAIN Tulungagung. The total of the populations were 3 English teachers and 256 students. So all the population used as a sample. The data were collected using a questionnaire. The data were analyzed using MS. Excel and SPSS 16.0.The result showed that all the respondents gave a positive perception toward internship students in teaching English. Based on the findings, the teacher have positive perceptions toward Internship students make the lesson plan and design the materials which were very important when they conducted their teaching practice at school. This study also revealed that teachers perceive internship students helped the teachers on the pedagogical competence which was very important to improve the teaching skills. Beside that, the students also have positive perceptions toward Internship students can manage class well. They have good skill and knowledge as a teacher. Then, internship students also have a good attitude. The internship students act objectively to all of the students, they give attention to the students, communicate fluently, respect to all of the school’s member, cooperate with the students to solve the problem in the teaching process. Through the internship program, internship students develop their skills and knowledge that they can be an English teacher in the future. So, the Internship program gives internship students access to be effective teachers.
Deepfake Detection using a Two-Stream Capsule Network

This paper aims to address the problem of Deepfake Detection using a Two-Stream Capsule Network. First we review methods used to create Deepfake content, as well as methods proposed in the literature to detect such Deepfake content. We then propose a novel architecture to detect Deepfakes, which consists of a two-stream Capsule network running in parallel that takes in both RGB images/frames as well as Error Level Analysis images. Results show that the proposed approach exhibits the detection accuracy of 73.39 % and 57.45 % for the Deepfake Detection Challenge (DFDC) and the Celeb-DF datasets respectively. These results are, however, from a preliminary implementation of the proposed approach. As part of future work, population-based optimization techniques such as Particle Swarm Optimization (PSO) will be used to tune the hyper parameters for better performance.
Dual Features Extraction Network for Image Super-Resolution

With the development of deep convolutional neural network, recent research on single image super-resolution (SISR) has achieved great achievements. In particular, the networks, which fully utilize features, achieve a better performance. In this paper, we propose an image super-resolution dual features extraction network (SRDFN). Our method uses the dual features extraction blocks (DFBs) to extract and combine low-resolution features, with less noise but less detail, and high-resolution features, with more detail but more noise. The output of DFB contains the advantages of low- and high-resolution features, with more detail and less noise. Moreover, due to that the number of DFB and channels can be set by weighting accuracy against size of model, SRDFN can be designed according to actual situation. The experimental results demonstrate that the proposed SRDFN performs well in comparison with the state-of-the-art methods.
Avoiding Excess Computation in Asynchronous Evolutionary Algorithms

Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast-evaluating solutions at the expense of higher-quality, slow-evaluating ones. We introduce a steady-state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. This gives slow-evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We find that SWEET appears effective in simulated take-over time analysis, but that its benefit is confined mostly to early in the run, and our preliminary study on an autonomous vehicle controller problem that involves tuning a spiking neural network proves inconclusive.
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits

Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or different levels of quality or complexity. Naturally, these facets do not occur with equal frequency, nor are they equally important for the test scenario at hand. In this work, we propose to optimize this balance jointly with MT model parameters to relieve system developers from manual schedule design. A multi-armed bandit is trained to dynamically choose between facets in a way that is most beneficial for the MT system. We evaluate it on three different multi-facet applications: balancing translationese and natural training data, or data from multiple domains or multiple language pairs. We find that bandit learning leads to competitive MT systems across tasks, and our analysis provides insights into its learned strategies and the underlying data sets.
Pengembangan Media Pembelajaran Augmented Reality Pada Mata Pelajaran Instalasi Motor Listrik

Teachers are expected to provide media in debriefing pupils to meet the present industrial revolution 4.0, one of which is by leveraging technology and automation in the delivery of learning. As a result, media development as a support in the teaching and learning process is required. In class XI TITL at SMKN 5 Padang, this study intends to provide a legitimate and practical Augmented Reality learning media on the subject of electric motor installation. The research method used in this study was research and development using a 4D model. The participants in this study were students in class XI TITL 1 at SMKN 5 Padang. The validation of AR learning media carried out by the validator team in the media area was carried out by two validators who were Lecturers of Electrical Engineering and were certified legitimate, according to the study's findings. The authenticity of the material field was then tested and pronounced genuine by two teachers in charge of the subject of electric motor installation at SMKN 5 Padang. The practicality test, which was done online and offline on 32 students in class XI TITL 1 SMKN 5 Padang, was pronounced Very Practical. The effectiveness test, which was carried out on 32 students from class XI TITL 1 SMKN 5 Padang and consisted of working on 20 questions, revealed that the media used was deemed to be very effective.
Analysis of Language Change in Collaborative Instruction Following

We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.
Implementasi Jaringan Syaraf Tiruan Backpropagation untuk Mengidentifikasi Jenis Tanaman Mengkudu Berdasarkan Tekstur Buah

Noni plants are plants that are widely used for herbal medicine. Noni plants have various types. Noni has a type of fruit that is poisonous to distinguish is difficult because noni fruit has a similar texture in terms of fruit. Identification of the type of noni fruit using backpropagation artificial neural network based on fruit texture to identify the type of noni in order to distinguish which non-toxic and poisonous fruit types. Characteristics used include in terms of homogeneity, energy, entropy, and contrast. The results of the testing process obtained a numerical value of 86% accuracy on the results of the 10 cm MSE 1e-10 distance ANN test.
Computational informatics in the training of future informatics teachers in the context of the development of modern education

It is known that the huge computing resources of modern computers are used primarily to solve problems that arise in various applied areas in the process of mathematical modeling of real objects. The aim of this study is to present a theoretical justification of the need to train informatics future teacher in the field of computational informatics and to develop a methodology for teaching him/her in the context of the development of modern education. The competence-based and contextual approaches to teaching and the concept of pedagogical education funding are used in the research. The necessity of training future informatics teacher in the field of computational informatics has been justified and a methodology for teaching it based on web quests has been developed and experimentally tested.The implementation of the developed methodology for teaching computational informatics on the basis of modern educational technology web-quest will equip him/her with the necessary knowledge and ensure the development of his/her information and computational competence.. &#x0D; Key words: training of informatics teacher, competence-based approach in teaching, computational informatics, information and computational competence, digitalization of education
Implementation of Enhance Confix Stripping Stemmer Algorithm for Multiclass Dataset Classification in News Text using K-Nearest Neighbor

Needs for news information has increased since the change from physical media to online media. News is grouped according to categories to making it easier for readers to get the news as desired. Grouping to determine the category of news information is known as text classification. The number of words in the news text create diversity of words that appear and can be minimized by the stemming process, which is changing an affixed word into its root word. This study comparing between use of stemming and without stemming and finding the best value of K and optimum distance calculation of K-Nearest Neighbor. The best accuracy is 0.9671 which is obtained when stemming algorithm not applied, number of K=9 and cosine distance is used as distance metric. This result is greater than the classification that applies stemming algorithm in condition K=7 using cosine distance which resulted accuracy in 0.9660.
The Influence of Verbal-Linguistic Intelligence on Students Learning Outcomes in English at SMA Negeri 1 Sidrap

This study aims to determine verbal linguistic intelligence, to determine the results learning English especially on students' speaking, and to find out the influence of verbal linguistic intelligence on learning outcomes in English students of class X IPA 1 of SMAN 1 Sidrap. This research is quantitative research; this type of research is a pre-experimental design research. The population in this study was all students of class X SMAN 1 Sidrap, amounting to 182 people, while the study sample was the entire population, namely students of class X IPA 1 SMAN 1 Sidrap, amounting to 32 people. The instruments used in this study were interviews, questionnaires, treatment and recording tools. The data analysis technique used is descriptive statistical analysis and simple regression test through the application of SPSS. Based on the results of descriptive statistical analysis, verbal linguistic intelligence obtained an average value of 84.94, the lowest value obtained 0, and the highest value 107 and are at a percentage of 56.25% are in the average category. The results of learning English in this case speaking students obtained an average value of 96, the lowest value of 0, and the highest value of 96 and are in the good category with a percentage of 65.625%. The simple regression test results obtained sig &lt;α (0,000 &lt;0.05). This means that Ho is rejected and Ha is accepted, which means there is a positive influence between verbal linguistic intelligence on learning outcomes of students of class X Science 1 of SMAN 1 Sidrap.
Correlation of the concepts “educational” and “intellectual” tourism: main similarities and differences

Authors analyze various points of view that have formed in the scientific community in relation to educational and intellectual tourism. There are fairly well-established views regarding the first type; scientists highlight the goal of implementing educational tourism, its subjects, etc. As for intellectual tourism, the situation is much more complicated due to the fact that in the Russian literature, there have been only a few studies devoted to theoretical research on this issue. Naturally, this creates gaps and, therefore, problems in the practical sphere. With this in mind, we attempted to clarify the conceptual apparatus of intellectual tourism. Based on the results of the study, it was concluded that educational and intellectual tourism have similar goals. However, they differ significantly from each other, which does not allow equating these two terms.
Consistent Accelerated Inference via Confident Adaptive Transformers.

We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs – Confident Adaptive Transformers – in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier. To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction. We demonstrate the effectiveness of this approach on four classification and regression tasks.
Poems that Solve Puzzles

This article contains book reviews of Poems that Solve Puzzles by Chris Bleakley, Quick Calculations by Trevor Davis Lipscombe, The Error of Truth by Steven Osterling, Bernoulli's Fallacy by Aubrey Clayton, and A History of Data Visualization and Graphic Communication by Michael Friendly and Howard Wainer.
Trend of the spread of COVID-19 in Indonesia using the machine learning prophet algorithm

Based on information on the website www.bnpb.go.id on September 2, 2020, the positive rate for covid-19 in Indonesia reached 25.25% on August 30, 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by WHO is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the Prophet's machine learning algorithm can be used to see trends in the spread of covid-19 in the next one year. This algorithm has a fairly high level of accuracy because the data contains time variables which are adjusted to the dataset. In several previous research, the dataset was vast uncertain and small. Meanwhile, in this research, data was taken from March 2, 2020 to February 12, 2021 on the kawalcovid19.id. This data is used to predict from February 13, 2021 to February 12, 2022. There are 3 data used, namely data confirmed, recovered and died. Based on the analysis, the confirmed patient was 22.60%-42.11%, died amounted to 21.67%-39.00%, and recovered by 22.53%-41.82%. The prediction percentage that the average cases died was 2.43% every day. The accuarcy of data confirmed was 43.97%, died was 72.50% and recovered was 84.24%.
An Exploratory Analysis of Virtual Delivery Alternatives for University-Based Animal Assisted Activities During COVID-19

Animal-assisted activities (AAAs) are being offered progressively in universities to support studentsr well-being. However, with the recent health restrictions due to COVID-19, all classes and health services are being delivered remotely. Due to this, many postsecondary institutions have...
Resolving dark web identities

Criminals continue to pose a significant threat on the dark web; a threat that will continue for decades. As cybersecurity professionals, we often hear and read about many forms of breached data fo...