Reviewerly/exHarmony huggingface dataset:
Explaining Machine Learned Relational Concepts in Visual Domains - Effects of Perceived Accuracy on Joint Performance and Trust

Author(s): Thaler, Anna Magdalena; Schmid, Ute | Abstract: Most machine learning based decision support systems are black box models that are not interpretable for humans. However, the demand for explainable models to create comprehensible and trustworthy systems is growing, particularly in complex domains involving risky decisions. In many domains, decision making is based on visual information. We argue that nevertheless, explanations need to be verbal to communicate the relevance of specific feature values and critical relations for a classification decision. To address that claim, we introduce a fictitious visual domain from archeology where aerial views of ancient grave sites must be classified. Trustworthiness among other factors relies on the perceived or assumed correctness of a system's decisions. Models learned by induction of data, in general, cannot have perfect predictive accuracy and one can assume that unexplained erroneous system decisions might reduce trust. In a 2×2 factorial online experiment with 190 participants, we investigated the effect of verbal explanations and information about system errors. Our results show that explanations increase comprehension of the factors on which classification of grave sites is based and that explanations increase the joint performance of human and system for new decision tasks. Furthermore, explanations result in more confidence in decision making and higher trust in the system.
Analysis of large class-size and its effects on teaching and learning process among students’ secondary schools in Potiskum local government area

The study was carried out on the evaluation of the effects of over-population on teaching and learning of among students in junior secondary schools in Potiskum Local Government Area. To guide the study, 3 research questions in line with purpose of the study were formulated. Descriptive Survey Research Design was adopted for the study. The target population of the study was the entire teachers in randomly selected four junior secondary schools in Potiskum Local Government Area. Stratified Random Sampling Technique was adopted to select 40 teachers from the 4 junior secondary schools to constitute the sample size of the study. Structure Questionnaire was the major instrument used to collect data for the study. The data collected was analyzed using Mean and Standard Deviation for the questionnaires. The findings of the study revealed that, inability of teachers to paid attention to individual students that need special attention, lack of classroom control and management at overcrowded classroom and teachers found it difficult in conducting effective continuous assessment in classroom are some of the problems faced by teachers and students in teaching and learning in over-populated classrooms in junior secondary schools of Potiskum Local Government Area. The findings of the study also revealed that, high numbers of the students in classroom affect academic performance and that smaller class’s size lead to improvement of academic performance are some of the effects of over-population on the quality of teaching and learning in junior secondary schools in the study area. The findings of the study further revealed that, Admission of the students in junior secondary schools should be based on minimum standard as specified National Junior Secondary School Curriculum and provision of modern teaching aids such as overhead projectors, power point presentation device that each and every individual student will view the content of the lesson in the classroom are some of the ways forwards for overcoming the problems faced by teachers and students in over-populated classroom during teaching and learning in junior secondary schools in the study area. It was recommended that, a ratio of 1:40 teachers to students is hereby suggested for junior secondary schools as stated in the National Policy of Education (2013).
Classification and Visualization Using Non-Linear Dimensionality Reduction

During the most recent couple of years we have encountered an explosive development in the measure of information that is being gathered, prompting the creation of extremely enormous databases, for example, commercial information distribution centers. New applications have risen that require the storage and retrieval of massive amounts of information; for instance: protein matching in biomedical applications, fingerprint acknowledgment, meteorological predictions, and satellite image repositories. In this paper we address the issue of utilizing local embeddings for information perception in two and three dimensions, and for grouping. We advocate their utilization on the premise that they give a productive mapping method from the first component of the information, to a lower intrinsic dimension. Authors portray how they can precisely catch the client's recognition of similarity in high-dimensional information for representation purposes. Also, authors exploit the low-dimensional mapping given by these embeddings, to grow new grouping strategies, and authors show tentatively that the grouping exactness is practically identical (though utilizing less dimensions} to various other classification procedures.
A neural dynamic process model of combined bottom-up and top-down guidance in triple conjunction visual search

Author(s): Grieben, Raul; Schoner, Gregor | Abstract: The surprising efficiency of triple conjunction search has created a puzzle for modelers who link visual feature binding to selective attention, igniting an ongoing debate on whether features are bound with or without attention. Nordfang and Wolfe (2014) identified feature sharing and grouping as important factors in solving the puzzle and thereby established new constraints for models of visual search. Here we extend our neural dynamic model of scene perception and visual search (Grieben et al., 2020) to account for these constraints without the need for preattentive binding. By demonstrating that visual search is not only guided top-down, but that its efficiency is affected by bottom-up salience, we address a major theoretical weakness of models of conjunctive visual search (Proulx, 2007). We show how these complex interactions emerge naturally from the underlying neural dynamics.
PROBLEM-BASED WORKSHEET DEVELOPMENT IN THE MAKING OF LIQUID SOAP FOR LEMON PEEL (CITRUS LIMON)

The experiment in making innovative products that have benefits for everyday life is one of the efforts to face the challenges of the 21st century. One of the innovative products related to the concept of colloids and which can be made easily by students is antimicrobial liquid soap. During its development, the experiment required worksheets to direct students to the concepts to be studied and make it easier for students to carry out experiments. As for answering the challenges of the 21st century, the experimental worksheets that are made must be able to construct students' critical and creative thinking skills. To achieve this goal, the experimental worksheet is arranged with problem-based learning stages. This study aims to describe the stages of the development of problem-based worksheets on the manufacture of antimicrobial liquid soap from lemon zest (Citrus limon) extract and to analyze the results of the validation test. The research method used is design based research (DBR) and literature. Worksheets are arranged through analysis and development stages. The design stage is replaced by the library method. The worksheets that are arranged have a display in the form of discourse and questions that are arranged based on the stages of problem-based learning. Based on the results of the validation test, an average rcount of 0.76 was obtained, which means that the worksheets prepared were valid and could be used in Basic Chemistry practicum on colloid material.
TELL-US Journal

Focus of TELL- US Journal TELL-US Journal publishes articles in the field of English, with the emphasis on four sub-categories: English Linguistics, English Literary Research, or English Education in the context of pluralistic Indonesian society. The system of this concentration is meant to be giving medium to all Indonesian lecturers or those coming from overseas in terms of scientific article publication. The name of TELL-US Journal is the abbreviation of the words: Teaching, English, Linguistics, Literature - Usage. The meaning of TELL-US is that the published articles in this journal is geared toward one particular concept, which is Usage, or closer in meaning to Applied. Although lexically, TELL-US Journal seems to appear as collection of four concentration of field of study, but the context of this journal is concentrated on Applied, or Usage. Therefore, the focus of this journal is to publish research article that deals with Application, Usage, Significance of the result of this research in the field of English Literary Research, and General English Studies within the context of Indonesia, or the overseas context depending on the background of the writer. Â Â  Scope of TELL-US Journal When we view the approved science and legalized knowledge categories in Indonesia within the context of Indonesia via Direktorat Jenderal Pendidikan Tinggi Republik Indonesia, Â TELL-US Journal is listed within the category of English Language (and Literature) Education. The research articles in this journal dominantly topics that are of around English Language Teaching and Learning, English Linguistics, English Literature, and English Literary Research, that all of them are framed within the concept of Application or Usage in Indonesia. TELL-US Journal, in this case, declares that the research articles published in this journal are not stiff, or not as narrow as the meaning of the words above literally, where in fact the journal opens chances for all lecturers in Indonesia, and or those who are overseas to publish about any subject, but it must be connected to the four-categories in this journal. Thus, this journal has scope in the form of English Language (and Literature) Education.Â
EFEKTIVITAS METODE EXPLORE ASK READ TELL HARVEST (EARTH) UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PEMAHAMAN BAGI SISWA BERKESULITAN BELAJAR KELAS IV (CLASSROOM ACTION RESEARCH DI SDN 024/XI KEL. DUSUN BARU)

This research is motivated by the problems found in class IV SDN 024 / XI Kel.Dusun Baru, Sungai Penuh City, Jambi Province. There are three students who have difficulty understanding the contents of a discourse reading text.Basically, students are already able to read, it's just that there are frequent additions, subtractions and omissions of letters when reading. During the learning process the teacher uses a demonstration method that explains the sub-theme of the three environments around me, then the teacher instructs the students to read the text entitled The Sun as a Source of Energy. After the reading activity is complete, there are three students who have problems understanding the contents of the discourse reading text.Therefore, researchers will use the explore ask read tell harvest (earth) method to improve reading comprehension skills for students having difficulty learning grade IV SD. The implementation of this research will be carried out in two cycles, each of which consists of: planning, implementing actions, observing and reflecting. Data collection techniques used were observation, documentation, and action tests. Based on the results obtained by the three fourth grade students on the ability to understand the contents of the discourse reading text, in the first cycle the results obtained were F 39%, Z 35% and R 52%. and in the second cycle the results obtained were F 65%, Z 66% and R 70%. Based on the data above, it can be concluded that students have improved in understanding the contents of a reading using the explore ask read tell harvest (earth) method.
Aspect Based Sentiment Analysis on Beauty Product Review Using Random Forest

Cosmetics and beauty products (including skincare) are the products used as body care or face care and used to accentuate the body alure. A product could give diverse sentiment to the consumers including positive and negative sentiment. Many consumers of beauty products are sharing their reviews to help other consumers to find the right products to buy and to give feedback to the brand of the beauty product itself. The number of reviews is inversely proportional to the lack of opinion identification towards product’s aspects. Hence, a study has been conducted to analyze beauty products reviews as toner, serum, sun protection, and exfoliator. The analysis process is conducted aspect based to determine sentiment towards aspect of beauty products based on the reviews. The result is addressed to people using skincare and beauty product brands in deducting consumer’s opinion. The solution to this problem is by using Random Forest with hyperparameters tuning as classification method, and TF-IDF and n-gram as feature extraction methods. The multi-aspect sentiment analysis in this study obtained highest accuracy for 90.48%, precision for 87.27%, recall for 70.13%, and F1-Score for 71.77%.
On the Gaussian Approximation to Bayesian Posterior Distributions

The present article derives the minimal number N of observations needed to approximate a Bayesian posterior distribution by a Gaussian.The derivation is based on an invariance requirement for the likelihood p(x|ξ).This requirement is defined by a Lie group that leaves the p(x|ξ) unchanged, when applied both to the observation(s) x and to the parameter ξ to be estimated.It leads, in turn, to a class of specific priors.In general, the criterion for the Gaussian approximation is found to depend on (i) the Fisher information related to the likelihood p(x|ξ), and (ii) on the lowest non-vanishing order in the Taylor expansion of the Kullback-Leibler distance between p(x|ξ) and p(x|ξ ML ), where ξ ML is the maximum-likelihood estimator of ξ, given by the observations x.Two examples are presented, widespread in various statistical analyses.In the first one, a chi-squared distribution, both the observations x and the parameter ξ are defined all over the real axis.In the other one, the binomial distribution, the observation is a binary number, while the parameter is defined on a finite interval of the real axis.Analytic expressions for the required minimal N are given in both cases.The necessary N is an order of magnitude larger for the chi-squared model (continuous x) than for the binomial model (binary x).The difference is traced back to symmetry properties of the likelihood function p(x|ξ).We see considerable practical interest in our results since the normal distribution is the basis of parametric methods of applied statistics widely used in diverse areas of research (education, medicine, physics, astronomy etc.).To have an analytical criterion whether the normal distribution is ap-plicable or not, appears relevant for practitioners in these fields.
Graph-Based Semi-Supervised Learning through the Lens of Safety

Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.
THE INSTITUTE OF VULNERABILITY IN THE TIME OF COVID-19 PANDEMIC: ALL SHADES OF THE HUMAN RIGHTS SPECTRUM

The vulnerability thesis is one of the most important legal concepts in contemporary legal theory. Apart from being studied by legal scholars, the notion of vulnerability has been embodied in concrete legal rules and transferred to national case law allowing courts to set its boundaries by the power of judicial interpretation. Even though it would be hard to contest Schroeder and Gefenas’s statement that it is not necessary for an academic to say what vulnerability is because common sense dictates the existence of it, recent scholarly analysis clearly shows that the concept itself has become intolerably vague and slippery. More precisely, it is not quite clear what the essence of vulnerability is and what the effects of its gradation as well as repercussions are on other constitutional institutes across the human rights spectrum. The noted vagueness poses a great concern, particularly in the time of COVID-19, the greatest social stressor that humanity has faced in recent months. The COVID-19 crisis has had untold consequences on our health, mental well-being, educational growth, and economic stability. In order for the state to bear the COVID-19 social burden and adequately protect the vulnerable, it is of the utmost importance to set clear guidance for the interpretation and implementation of the vulnerability concept. Seeking to contribute to literature on these issues, the author brings light to constitutional and criminal legal standards on vulnerability set within the current jurisprudence and doctrine. Bearing in mind the influence of the European Court of Human Rights (hereinafter, the ECtHR or the Court) on developments in human rights law, 196 judgments related to vulnerability have been retrieved from the HUDOC database using a keywords search strategy. The quantitative analysis was supplemented with more in-depth qualitative linguistic research of the Court’s reasoning in cases concerning vulnerable children, persons suffering from mental illness and victims of family violence. Although the vulnerability reasoning has considerably expended their rights within the ambit of the Convention, the analysis has shown that inconsistencies and ambiguities emerge around the formulation of the applicant’s vulnerability and its gradation with respect to positive obligations. The full creative and transformative potential of the institute of vulnerability is yet to be realized.
A the Translates into turkish languages creation of a model of neuron machine translation

In our work we see that the quality of translation has improved due to the creation of models of translation from Turkish to English and from English to Turkic languages. Turkic-speaking languages ​​are structurally similar. Therefore, studying one of the Turkic languages, you can assemble a corpus for other languages ​​and apply it to the same model. This is done using the OpenNMT model (open neural machine translation). The article shows the morphological, lexical, semantic increase of BlEU (translation index) words and sentences of Turkic languages ​​using OpenNMT. To increase the value of BLEU it is necessary to increase the base in the case. In addition, the work provides a detailed description of the construction of OpenNMT models. Experiments with the Kazakh language, one of the Turkic languages, were conducted and the results were obtained. Words in the Kazakh language taken from the news. The scientific work includes a review of the work of scientists who studied neural machine translation. It is shown that the results of this work outperform the work of other researchers. Having created the neural model of OpenNMT, you will see that the result of the translation is not the same as the online translation from Google, Yandex. OpenNMT also takes less time to read data and saves memory. The results of the experimental study show that the Kazakh-English and English-Kazakh language pairs gave good results in translation.
The Difficulties Facing Of The Mechanic Otomotif (Mo) Student At Smk Yapim Kabanjahe Using Participle In Sentences

Abstract. This study deals with the student’s difficulties in using participle in sentences. The purposes of the study were to find out whether or not the students found difficulties in using participle in sentence and to find out the type of difficulties they faced.The population of the study was the 2020/2021 of the Mechanic Otomotif (MO) students at SMK YAPIM Kabanjahe. In this sampling, all the population has equal chance to be selected for the sample. The total numbers of samples was 30 students. The instrument used to collect the data was multiple choice test. This research was conducted by applying the descriptive quantitative design. The reliability of the test is counted by using KR21 formula. The formula testing result showed that the reliability of the test was 0,89, it means that the test was very good. The finding showed that the students found some difficulties in using participle, they were : Present Participle (8,67 %), Past Participle (9,34 %) and Perfect Participle (9,33 %). Perfect Participle was regarded as the most difficult type for them, especially in using it after certain verbs and in replacing relative pronoun, and then followed Past Participle by especially in using in it replacing relative pronoun and after certain verb. And the last was Present Participle. The percentage of each difficulty was taken by dividing the wrong answer to the total correct answer of the test.
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers

With the growing footprint of ecommerce worldwide, the role of contact center is becoming increasingly crucial for customer satisfaction.To effectively handle scale and manage operational cost, automation through chatbots and voice-bots are getting rapidly adopted.With customers having multiple, often long list of active orders -the first task of a voicebot is to identify which one they are calling about.Towards solving this problem which we refer to as order identification, we propose a two-staged real-time technique by combining search and prediction in a sequential manner.In the first stage, analogous to retrieval-based question-answering, a fuzzy search technique uses customized lexical and phonetic similarity measures on noisy transcripts of calls to retrieve the order of interest.The coverage of fuzzy search is limited by no or limited response from customers to voice prompts.Hence, in the second stage, a predictive solution that predicts the most likely order a customer is calling about based on certain features of orders is introduced.We compare with multiple relevant techniques based on word embeddings as well as ecommerce product search to show that the proposed approach provides the best performance with 64% coverage and 87% accuracy on a large real-life data-set.A system based on the proposed technique is also deployed in production for a fraction of calls landing in the contact center of a large ecommerce provider; providing real evidence of operational benefits as well as increased customer delight.