Reviewerly/exHarmony huggingface dataset:
On the Learnability of Concepts

Word Embeddings are used widely in multiple Natural Language Processing (NLP) applications. They are coordinates associated with each word in a dictionary, inferred from statistical properties of these words in a large corpus. In this paper we introduce the notion of "concept" as a list of words that have shared semantic content. We use this notion to analyse the learnability of certain concepts, defined as the capability of a classifier to recognise unseen members of a concept after training on a random subset of it. We first use this method to measure the learnability of concepts on pretrained word embeddings. We then develop a statistical analysis of concept learnability, based on hypothesis testing and ROC curves, in order to compare the relative merits of various embedding algorithms using a fixed corpora and hyper parameters. We find that all embedding methods capture the semantic content of those word lists, but fastText performs better than the others.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE THINK PAIR SHARE UNTUK MENINGKATKAN PRESTASI BELAJAR IPS SISWA KELAS V SDI ENDE 14

The objectives achieved in this research are to find out the use of think pair share type cooperative learning models in Social Studies learning of Artificial Appearance in Indonesia, to find out the increase in students' learning achievement by using cooperative learning models of think pair share type in SDI Ende 14 Ende Timur district, Ende Regency. This type of research is Classroom Action Research, with a qualitative approach to grade 5 students of SDI Ende 14. Data collection techniques in this study were observation, interviews, learning outcomes tests and documentation. Data analysis uses descriptive qualitative data analysis techniques. Based on the results of the evaluation of the first cycle with a percentage of completeness of 54% and an average of 57.45%, the results of the evaluation of the second cycle with a percentage of completeness of 100% with an average of 90%, the application of the cooperative learning model type think pair share with the material appearance made in Indonesia can improve student learning achievement.
Model Digital Signature Pada Dokumen Formal Akademik

Digital signature system is implemented to improve the quality of document border services. A digital signature on a document means that the signatory knows and approves the document. On the other hand, previous research states that the digital signature model has weaknesses in terms of identifying and verifying the authenticity of the signature image. This study aims to develop a digital signature model with validation of the authenticity of good signatures and accelerate document binder services. The digital signature model was developed using modified agile methods. Model development is done through design, construction, testing, evaluation, and delivery. The test results show that the digital signature model developed in this study is proven to accelerate document publishing services with good validation and simplify the document publishing business process.
Building Synonym Sets for English WordNet with Robust Clustering using Links Method

English WordNet is an important synonym set to present the similarity of meanings between words. Synonym Set is built using Oxford Thesaurus which is accessed through lexico.com, which is a part of the lexical database that will be used. After using the extraction process through Oxford Thesaurus it will produce a synonym set with the same meaning between words. The difference between WordNet and ordinary dictionaries is that the word is interconnected with other words. One method employed for this approach is Robust Clustering Using Links method, which is similarity values and synonym sets that have been created to be used to build a lexical database. Therefore the main purpose of the development of the English WordNet is to produce an accurate synonym set using clustering techniques. The evaluation calculation will use the F-measure method and will use the gold standard for the calculation method. With the ROCK method, there is an increase in accuracy output from dataset input. Building the English wordnet is to improve words that can be used to help research and development of other language wordnets with role models using more accurate English wordnets. And the use of ROCK method there is an increase in the accuracy upon results of the development of English wordnet compared to the previous method, which is using hierarchical clustering. The outcome of this study resulted in improved accuracy so that the ROCK method is one of the good methods used in the development of the English wordnet.
Sampling Random Chordal Graphs by MCMC (Student Abstract)

Chordal graphs are a widely studied graph class, with applications in several areas of computer science, including structural learning of Bayesian networks. Many problems that are hard on general graphs become solvable on chordal graphs. The random generation of instances of chordal graphs for testing these algorithms is often required. Nevertheless, there are only few known algorithms that generate random chordal graphs, and, as far as we know, none of them generate chordal graphs uniformly at random (where each chordal graph appears with equal probability). In this paper we propose a Markov chain Monte Carlo (MCMC) method to sample connected chordal graphs uniformly at random. Additionally, we propose a Markov chain that generates connected chordal graphs with a bounded treewidth uniformly at random. Bounding the treewidth parameter (which bounds the largest clique) has direct implications on the running time of various algorithms on chordal graphs. For each of the proposed Markov chains we prove that they are ergodic and therefore converge to the uniform distribution. Finally, as initial evidence that the Markov chains have the potential to mix rapidly, we prove that the chain on graphs with bounded treewidth mixes rapidly for trees (chordal graphs with treewidth bound of one).
SAWARI: The Bikers Portal

Nowadays, people have less time and more work to do which forces them to travel to different places for work, meetings, and tourism.If people don't have a private vehicle, they use public transports which is crowded and generally not running on time.Bikers Portal gives the solution to such problems. Bikers Portal can also be used as a complementary mode to other public transit such as buses, local trains. This will be a web application which will provide bike rental service. It will also provide a single platform for rental companies and users effectively and efficiently. This application is a combination of both sales and inventory management of the bike. From an individual's point of view, bike-renting systems eliminate the inconvenience of bike ownership, the need to find parking places, and the fear of theft. It helps in reducing traffic congestion as the number of vehicles on the road can be reduced significantly. Using this portal, users can rent a bike for tours, trips, and other such events that needs traveling with comfort and personal space. Also, the people who have private vehicles but they don't use it because of any reason, they can take advantage of this portal. For users, saving time will be another important benefit of this system.
The Research of Models of Online Distance Learning in the Conditions of Globalization

The article represents an analysis of distance learning models on the Internet in the context of globalization. The author gives a brief description of the analyzed type of education, as well as its advantages. It was found that the creation of an effective model of distance learning involves the redefinition of teaching methods, communication techniques and strategies for course development. The article also analyzes the step-by-step and hybrid models of distance learning and the role of the Internet in the process of their formation.
Streaming Batch Gradient Tracking for Neural Network Training (Student Abstract)

Faster and more energy efficient hardware accelerators are critical for machine learning on very large datasets. The energy cost of performing vector-matrix multiplication and repeatedly moving neural network models in and out of memory motivates a search for alternative hardware and algorithms. We propose to use streaming batch principal component analysis (SBPCA) to compress batch data during training by using a rank-k approximation of the total batch update. This approach yields comparable training performance to minibatch gradient descent (MBGD) at the same batch size while reducing overall memory and compute requirements.
Electrolaryngeal Speech Identification using GMM

People who have lost their larynx due to laryngeal cancer or due to other physical conditions cannot produce voice like other humans.As air is breathed out through the vocal folds, vocal folds are vibrated and sound is produced, heard as a speech voice.In situations where larynx is removed, air can never again go from the lungs into the mouth.The association between the windpipe and the mouth never again exists.Electrolarynx is a battery driven machine that produces sound to create a voice for laryngectomy patients.The electrolarynx could either function indirectly through contact with skin, that triggers pharyngeal vibrations or specifically by intraoral pressure, which produces vibrations of the vocal cavity.Articulation muscles are usually intact after total laryngectomy(TL) and therefore capable of transforming the stimulus noise supplied into understandable voice.Traditional electrolarynx produces a robotic voice and a mechanical humming when used.This paper focuses on increasing the quality of electrolaryngeal speech.Here, implementation of Mel Frequency Cepstral Coefficient (MFCC) Algorithm and Gaussian Mixture Model (GMM) in pair is used to achieve the target.We have considered MFCC with "tuned parameters" as the primary feature and delta-MFCC as secondary feature.And, we have implemented GMM with some tuned parameters to train our model.Speech with highest score is identified and corresponding normal speech is produced using python platform.
CULTURAL ASPECTS OF PEDAGOGICAL ACMEOLOGY

The relevance of the study is that the educational acmeology is very important in the process of the professional development of teachers. The purpose of the article is to consider pedagogical acmeology as the science of how to achieve professionalism and competence in the work of the teacher. It was reflected the preliminary results of the research work of acmeology of general and professional education. The results of a study of pedagogical acmeology are presented in two directions: cultural aspects of pedagogical acmeology and integrative communication of pedagogical acmeology. Recourse to unresolved and controversial issues of pedagogical acmeology allows the representatives of different scientific areas to broaden the horizons of scientific fields - perhaps the development of ecolinguistic, econoosphere pedagogics and acmeology of management and many others.
Analysis of the State of Elaboration of Thepreparing Future Teachers’ Problem for the Provision of Facilitated Pedagogicalsupport for Families in Ukraine

The article is devoted to the issues of the preparing future teachers for providing facilitative pedagogical support to families. The analysis of the conditions of the formation and the development of a teacher of a new formation, a teacher – as a facilitator has been done. Key research concepts are revealed: facilitator, facilitation, teacher – facilitator, support, pedagogical support. The attention is focused on the important psychological qualities of the facilitator teacher that contribute to increasing student motivation and help to create the conditions for discovering the potential of all participants in the educational process. The author’s definition of “facilitative pedagogical support” has been developed; a comparative analysis of various types of pedagogical support, the practical elaboration of the problem of preparing future teachers for the provision of facilitated pedagogical support for families in Ukraine has been carried out. It is taught that understanding the important aspects of the facilitative pedagogical support of the family and its tools will allow the teacher to build trusting relationships, communicate productively, taking into account the individual characteristics of all participants in the educational process, to realize more successfully the goals of the New Ukrainian School.
Research on the Transformation and Development of College English Teachers under the Mode of Applied Talents Training

With the rapid development of economic globalization and the transformation of higher education from elite education to popular education, all employers require college students' English ability is getting higher and higher?especially for the ability to work in English. Teachers not only play a major role in the process of training students' application ability, but also determine the success or failure of the transformation and development of local colleges and universities. This also puts forward higher requirements for College English teachers. The majority of English teachers must realize their own transformation and development from the overall situation of the transformation and development of colleges and the actual needs of students to learn English. College English teachers should adapt to the new situation, change teaching ideas and establish the concept of lifelong learning, so as to increase their own practical experience and industry experience, so as to promote the development of College English teaching.
Abstract Constraints for Safe and Robust Robot Learning from Demonstration

My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.
Blockchain based Information Architecture for Medical Product Supply Chain

The medical product supply chain is the most complex and fragmented of all supply chains. The production is found all over the world both on land and in water. A lot of the producers and intermediaries are difficult to identify and track. For all the participants in the production chain this creates uncertainty and risk. Mitigating this uncertainty comes at a cost, and the outcome may still be insufficient. Examples of problems that have been difficult or impossible to solve with current technologies include establishing reliable provenance and preventing fraud and counterfeiting. These issues can have knockon effects on public health and the environment, and reduce financial costs of unnecessary recalls of Medical products. To overcome the above challenges, a Blockchain based Medical Product traceability system (BIMPTS) is proposed in this study, to achieve the following: To integrate blockchain technology for effective and efficient traceability, and To support shelf life adjustment and quality decay evaluation for improving quality assurance. For the sake of better computational load, the blockchain is modified as a lightweight blockchain to be associated with cloud computing to support monitoring, and can be vaporized after the entire life cycle of traceability to release computational resources of the system. By using such a reliable data source, the decision support in product quality can be made by using fuzzy logic to determine adjustment of shelf life, rate, and order of quality decay, according to different situations for each batch of perishable products at processing sites. Therefore, the proposed traceability model is extended to the modern Medical Product supply chain environment, resulting in reliable and intelligent monitoring, product tracking, and quality assurance.
ANALISIS POLA LAYOUT APLIKASI MOBILE COMMERCE POPULER DI INDONESIA DENGAN METODE KUALITATIF HEURISTIK

The development of the internet and smartphones, making almost allactivities can be accessed very easily for example shopping onlineusing a mobile commerce application. In Indonesia, there is much ecommerce with many users too, but it does not rule out the possibilityto build new e-commerce because Indonesia is the best market for ecommerce. UI / UX designers are needed to build new mobilecommerce because it requires research on interaction design. UI /UX layout guidelines needed by designers in the development ofmobile commerce. This study analyzes the layout patterns of mobilecommerce applications in 4 popular online stores in Indonesia.This study used qualitative research methods. The analysis is carriedout using the principles of pattern and graphic layout, the heuristicmethod of the 10 Nielsen principles used to evaluate a device orapplication.The results of this study successfully proved that the 4 Nielsenprinciples are used by mobile commerce applications (Bukalapak,Shopee, Tokedia, Lazada), namely Match between system and thereal world, Consistency and standards, Error prevention,Recognition rather than recall. The mobile commerce applicationhas the same shopping flow pattern and interface layout that is likethe navigation buttons on the top and bottom of the page, visualweight for promotional features, the concept of finger touch for thesafe size of a button measuring 1x1 cm, visual objects entered on thethumb zone mostly contain elements important and promotions suchas digital bill payments and discounts.
Multi-View Deep Attention Network for Reinforcement Learning (Student Abstract)

The representation approximated by a single deep network is usually limited for reinforcement learning agents. We propose a novel multi-view deep attention network (MvDAN), which introduces multi-view representation learning into the reinforcement learning task for the first time. The proposed model approximates a set of strategies from multiple representations and combines these strategies based on attention mechanisms to provide a comprehensive strategy for a single-agent. Experimental results on eight Atari video games show that the MvDAN has effective competitive performance than single-view reinforcement learning methods.
THE ANALYSIS OF ENGLISH DEPARTMENT STUDENTS’ ABILITY IN MASTERING READING TEXT OF MUHAMMADIYAH ACEH UNIVERSITY

The aim of this study is at examining how well the students’ ability in mastering the text and the main idea, and how they could improve their ability in understanding the text especially to find out the main idea from the text. The object of this study was 10 students from semester I in Intensive Reading Comprehension class. The data was obtained from the result of students’ worksheets and questionnaires distribution. In finding out the result, The data was analyzed by using descriptive qualitative. The result shows that the students’ ability in mastering reading text was categorized in poor level of understanding main idea. Besides, the result of questionnaire indicated that the main problem in reading lesson is that the methods used and the class situation. Therefore, the students do self motivation to enrich and memorize new vocabularies as an effort in improving their reading understanding. The implication of this study is to give the input to the lecturer of Reading subject to apply the appropriate method and technique, and create a nice class environtment.
The Application of Affective Filter Hypothesis Theory in English Grammar Teaching

Krashen's theories of second language acquisition play a vital role in English teaching, in which "affective filter hypothesis" analyzes the influence of various affective factors on English learning. This paper focuses on Klashen's "affective filter hypothesis", analyzes the application of affective factors in the teaching practice of English grammar, and puts forward some teaching suggestions. The purpose of the study is to reduce the anxiety of the students in the second language grammar learning class, and then to find the most conducive state of English grammar learning, and to achieve the goal of English grammar teaching.
Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability

Learning a second language (L2) usually progresses faster if a learner's L2 is similar to their first language (L1). Yet global similarity between languages is difficult to quantify, obscuring its precise effect on learnability. Further, the combinatorial explosion of possible L1 and L2 language pairs, combined with the difficulty of controlling for idiosyncratic differences across language pairs and language learners, limits the generalisability of the experimental approach. In this study, we present a different approach, employing artificial languages and artificial learners. We built a set of five artificial languages whose underlying grammars and vocabulary were manipulated to ensure a known degree of similarity between each pair of languages. We next built a series of neural network models for each language, and sequentially trained them on pairs of languages. These models thus represented L1 speakers learning L2s. By observing the change in activity of the cells between the L1-speaker model and the L2-learner model, we estimated how much change was needed for the model to learn the new language. We then compared the change for each L1/L2 bilingual model to the underlying similarity across each language pair. The results showed that this approach can not only recover the facilitative effect of similarity on L2 acquisition, but can also offer new insights into the differential effects across different domains of similarity. These findings serve as a proof of concept for a generalisable approach that can be applied to natural languages.
A Multi-thread Business License Identification System Based on Modified DenseNet

There emerges an increasing need to stimulate trustworthy computer-aided authentication to provide solid technical support in commerce. Recently, remarkable advances have been made in text recognition, primarily driven by the success of Faster R-CNN [1] and DenseNet [2]. However, these networks failed to realize superb identification of business license, exposing accurate object detection as a bottleneck. In this work, we have introduced the multi-thread business license identification system based on modified DenseNet that achieves improvement of precision, simultaneously enabling specific management of license number. Experimental results validate that our system surpasses the original result by a relatively large margin in accuracy, further emphasizing the significance of the independent branch for license number.