Reviewerly/exHarmony huggingface dataset:
Hubungan Antara Motivasi Belajar dengan Prestasi Belajar Siswa Kelas X Tata Boga Mata Pelajaran Boga Dasar di SMK Negeri 2 Depok

This study aims to determine: 1) the learning motivation of students in class X Catering Basic Catering Subjects at SMK Negeri 2 Depok; 2) the learning achievement of class X Culinary Administration in Basic Culinary Subjects at SMK Negeri 2 Depok; 3) the relationship between learning motivation and learning achievement of class X students in Basic Catering Subjects at SMK Negeri 2 Depok. This research is a correlation research using a quantitative approach. The population in this study were students of class X Catering Department at SMK Negeri 2 Depok. The sampling technique used was random sampling and the sample size in this study was determined using Isaac and Michael's Table, so the sample in this study was 84 students. Methods of data collection using questionnaires and tests. The instrument trial used validity and reliability tests. The data analysis technique used product moment correlation with the significance level of the analysis results determined at 5%. Based on the results of the study, it can be concluded as follows: 1) the learning motivation of class X students in Basic Catering subjects at SMK Negeri 2 Depok in the sufficient category is 57 students (67.9%), good category is 27 students (32.1%), and (0.0%) in the poor category; 2) the learning achievement of class X students in Basic Catering subjects at SMK Negeri 2 Depok in the good category as many as 77 students (91.7%), sufficient category as many as 7 students (8.3%) and (0.0%) in the poor category , 3) there is a positive and significant relationship between learning motivation and learning achievement of class X Basic Catering Subjects at SMK Negeri 2 Depok, this is evidenced by the value of r arithmetic greater than r table (0.504&gt; 0.213). Thus, there is a relationship between the learning motivation of class X students in the Basic Catering Subject at SMK Negeri 2 Depok.&#x0D; &#x0D; Keywords: Learning Motivation, Learning Achievement
Enhancing Fairness of Visual Attribute Predictors

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We address this problem by introducing fairness-aware regularization losses based on batch estimates of Demographic Parity, Equalized Odds, and a novel Intersection-over-Union measure. The experiments performed on facial and medical images from CelebA, UTKFace, and the SIIM-ISIC Melanoma classification challenge show the effectiveness of our proposed fairness losses on bias mitigation as they improve model fairness while maintaining high classification performance. To the best of our knowledge, our work is the first attempt to incorporate these types of losses in an end-to-end training scheme to mitigate biases of visual attribute predictors.
Comparison Knowledge Translation for Generalizable Image Classification

Deep learning has recently achieved remarkable performance in image classification tasks, which depends heavily on massive annotation. However, the classification mechanism of existing deep learning models seems to contrast to humans' recognition mechanism. With only a glance at an image of the object even unknown type, humans can quickly and precisely find other same category objects from massive images, which benefits from daily recognition of various objects. In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories. Specifically, we investigate a new task termed Comparison Knowledge Translation (CKT). Given a set of fully labeled categories, CKT aims to translate the comparison knowledge learned from the labeled categories to a set of novel categories. To this end, we put forward a Comparison Classification Translation Network (CCT-Net), which comprises a comparison classifier and a matching discriminator. The comparison classifier is devised to classify whether two images belong to the same category or not, while the matching discriminator works together in an adversarial manner to ensure whether classified results match the truth. Exhaustive experiments show that CCT-Net achieves surprising generalization ability on unseen categories and SOTA performance on target categories.
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences

Random Utility Models (RUMs), which subsume Plackett-Luce model (PL) as a special case, are among the most popular models for preference learning. In this paper, we consider RUMs with features and their mixtures, where each alternative has a vector of features, possibly different across agents. Such models significantly generalize the standard PL and RUMs, but are not as well investigated in the literature. We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability. For PL, we prove that when PL with features is identifiable, its MLE is consistent with a strictly concave objective function under mild assumptions, by characterizing a bound on root-mean-square-error (RMSE), which naturally leads to a sample complexity bound. We also characterize identifiability of more general RUMs with features and propose a generalized RBCML to learn them. Our experiments on synthetic data demonstrate the effectiveness of MLE on PL with features with tradeoffs between statistical efficiency and computational efficiency. Our experiments on real-world data show the prediction power of PL with features and its mixtures.
Multi-Agent Concentrative Coordination with Decentralized Task Representation

Value-based multi-agent reinforcement learning (MARL) methods hold the promise of promoting coordination in cooperative settings. Popular MARL methods mainly focus on the scalability or the representational capacity of value functions. Such a learning paradigm can reduce agents' uncertainties and promote coordination. However, they fail to leverage the task structure decomposability, which generally exists in real-world multi-agent systems (MASs), leading to a significant amount of time exploring the optimal policy in complex scenarios. To address this limitation, we propose a novel framework Multi-Agent Concentrative Coordination (MACC) based on task decomposition, with which an agent can implicitly form local groups to reduce the learning space to facilitate coordination. In MACC, agents first learn representations for subtasks from their local information and then implement an attention mechanism to concentrate on the most relevant ones. Thus, agents can pay targeted attention to specific subtasks and improve coordination. Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods.
Analyzing sentiments of human through twitter comments

This disquisition is to find the people behaviour through their comments or post on twitter what actually like or dislike about a particular product or topic . In this we use Hybrid algorithm to build more well grounded and sturdy process. In this growing epoch of online community users. A huge numeral of people every day pin (up) their point of view in the form of tweets. This approach is useful to organization in many different ways who get introduced or marked in the tweet. By classifying those tweets into positive or negative according to the sentences by converting the unstructured tweet in structured format .tweets are going through cleaning phase first of all and after that the retrieve of tweets have been adept through libraries by using the API of Twitter.
Non – cognitive factors that influence reading competency

This study aimed to find out what factors influenced the reading proficiency of first-year college students at Isabela State University, San Mariano in SY 2020-2021.This study employed descriptive statistics.A questionnaire was utilized to collect data from 125 respondents.Analysis used frequency % and ranking.Concerning student-related aspects, more than half of them stated that subject interest and selfmotivation influenced their reading proficiency.Regarding teacher-related aspects, respondents indicated that instructors' job experience influenced their reading competency.Parental involvement in home academic activities ranked #1 as a factor influencing their reading skills.Among the schoolrelated characteristics that influenced their reading ability, discipline scored first.Under studentrelated criteria, interest in a subject influenced reading proficiency, followed by teacher effectiveness with over half of the respondents.Most respondents said that discipline was the most important element influencing their reading ability.Instructors should use a variety of learning activities, and parents should help their children with homework.The government must also focus on good reading teacher training.Aside from providing a conducive area for reading enhancement activities, book borrowing must be encouraged.
Fabric-GC: A Blockchain-based Gantt chart system for cross-organizational project management

Large-scale production is always associated with more and more development and interaction among peers, and many fields achieve higher economic benefits through project cooperation. However, project managers in the traditional centralized approach cannot rearrange their activities to cross-organizational project management. Thanks to its characteristics, the Blockchain can represent a valid solution to the problems mentioned above. In this article, we propose Fabric-GC, a Blockchain-based Gantt chart system. Fabric-GC enables to realize secure and effective cross-organizational cooperation for project management, providing access control to multiple parties for project visualization. Compared with other solutions, the proposed system is versatile, as it can be applied to project management in different fields and achieve effective and agile scheduling. Experimental results show that Fabric-GC achieves stable performance in large-scale request and processing distributed environments, where the data synchronization speed of the consortium chain reached four times faster than a public chain, achieving faster data consistency.
Penerapan Metode Time Series Terhadap Analisis Kebutuhan Air Bersih Kota Sukabumi

Water is a source of life for living things, especially for humans, water is a basic need whose existence is needed to support all activities such as bathing, washing, cooking and so on. The purpose of this study is to calculate the amount of water discharge that must be provided to meet water needs in the city of Sukabumi for the long term, so that time series methods such as arithmetic, geometric, regression, moving average and exponential smoothing methods can be used. The results obtained based on the time series method used, it is known that the need for water has increased every year while for the production of water discharge has decreased so that water production cannot meet the needs, so additional water discharge is needed. With the addition of water discharge, it is estimated that water needs will be met.
The Effectiveness of Probing Techniques in Learning Biology on Learning Outcomes and Perceptions in Class IX Students of SMP Negeri 2 Sengkang, Wajo Regency

Education is not only about the discourse on how to shape young people into competent generations of the nation, but education also includes the practical realm of how the process is applied. The type of research used in this research is experimental research that seeks to find the influence of other variables under tightly controlled conditions. The research method used in this research is quantitative analysis method, namely study that emphasizes its analysis on numerical data or numbers obtained by statistical methods and is carried out in inferential study or the context of hypothesis testing so that the significance of the relationship between the variables studied is obtained. This research is a process that begins with observations in the form of prior experiences on the implementation of learning in class IX.2 SMP Negeri 2 Sengkang, Wajo Regency in the way of preliminary data collection. The results in this study are that students 'perceptions of learning using probing techniques are positive, students feel learning becomes more interesting; students' thinking skills become more trained by answering questions from the teacher. The teacher's perception is excellent about learning using probing techniques; the teacher feels happy to guide students to further improve their thinking skills with questions that are by the level of thinking ability. Reproductive systems learning material in biology learning using probing techniques is more effective than ordinary learning.
Dance Movement Interference Suppression Algorithm Based on Wearable Sensors in a Smart Environment

Wearable device (Wearable device) is a hardware device that can be installed on clothes, clothing accessories or directly worn on the body for easy carrying.It can collect a large amount of user data and behavior habits in real time, and complete some by using software information technology.This paper aims to study the dance movement interference suppression algorithm based on wearable sensors in a smart environment.This paper proposes a method of human action recognition based on wearable sensors, using the original motion capture data collected by the wearable sensors to perform three-dimensional reconstruction, which shows that the basic link of constructing action words is the extraction of key gestures.In addition, this article also proposed an interference suppression algorithm, introduced wavelet noise reduction processing, calculated wavelet coefficients, noisy signals and other basic information, which proved the effectiveness of the interference suppression algorithm.The experimental results of this article show that the wearable sensor dance movement recognition in a smart environment has been greatly improved compared with the traditional dance movement recognition.Among them, the recognition rate of dance movements has increased by 25%, and the error of dance movements has become smaller and smaller.The results show that the interference suppression algorithm has obvious effects on the dance movements of the wearable sensor.
Corporate Social Responsibility and Accounting Restatement: Evidence from Pakistan

The study aims to analyze the relationship between corporate social responsibility and financial restatements.The study is conducted to check whether a CSR firm has to make less financial restatement.For this purpose, a sample of 150 non-financial firms are selected over the period of 2011-2018.The data are gathered from annual reports of these firms.Results of Logit regression analysis shows that CSR and financial statements are not significantly related with each other.
An Evaluative Study for First Year Primary School Textbook

The present study aimed to investigate the appropriateness of Egyptian primary schools textbook "Connect 1" from teachers" perspectives.For this purpose, 30 male and female experienced English language teachers were randomly selected from different primary schools in Beni Suef, Egypt.They were chosen based on the availability sampling method.The evaluation of the textbook was conducted quantitatively through a 4-point Likert scale questionnaire which was developed by the researcher.The textbook evaluation scheme contained 27 items which relied upon seven main criteria namely a) layout and design, b) subject and content, c) vocabulary, d) grammar, e) phonetics, f) activities, and g) skills.The results indicated that there was an incongruity between the presumed educational purposes , the teachers and learners' needs.It is believed that the results of the study could provide intriguing hints for the curriculum designers and material' developers to think of adapting the available materials or adopting an alternative textbook which contains an organized layout, useful and real-life subjects and contents, contains relevant and appropriate vocabulary, grammar and phonetics, covers relevant skills and activities, and corresponds to teachers" and learners' emerging needs.
Modeling Word Strings

The SynthaCorpus methods for generating words (Chapters 4 and 5) are concerned with generating a series of integers Ri representing the ranks of the words in a Zipf-style ordering. If we are given a lexicon (for example the lexicon of a corpus being emulated), we can convert each Ri to a string by simple look-up. If we have no lexicon and no interest in the actual textual rep-resentations, we can emit strings such as “t27,” representing the 27th most frequently occurring word.
Argument mining as rapid screening tool of COVID-19 literature quality: Preliminary evidence

Background The COVID-19 pandemic prompted the scientific community to share timely evidence, also in the form of pre-printed papers, not peer reviewed yet. Purpose To develop an artificial intelligence system for the analysis of the scientific literature by leveraging on recent developments in the field of Argument Mining. Methodology Scientific quality criteria were borrowed from two selected Cochrane systematic reviews. Four independent reviewers gave a blind evaluation on a 1–5 scale to 40 papers for each review. These scores were matched with the automatic analysis performed by an AM system named MARGOT, which detected claims and supporting evidence for the cited papers. Outcomes were evaluated with inter-rater indices (Cohen's Kappa, Krippendorff's Alpha, s * statistics). Results MARGOT performs differently on the two selected Cochrane reviews: the inter-rater indices show a fair-to-moderate agreement of the most relevant MARGOT metrics both with Cochrane and the skilled interval scores, with larger values for one of the two reviews. Discussion and conclusions The noted discrepancy could rely on a limitation of the MARGOT system that can be improved; yet, the level of agreement between human reviewers also suggests a different complexity between the two reviews in debating controversial arguments. These preliminary results encourage to expand and deepen the investigation to other topics and a larger number of highly specialized reviewers, to reduce uncertainty in the evaluation process, thus supporting the retraining of AM systems.
The Structural Relationship between Emotional Support, Grit, and Psychological Well-being: Focusing on the Moderating Effect of the Type of Early Childhood Education Institutions

Objectives The purpose of this study is to examine the structural relationship between emotional support, grit, and psychological well-being perceived by early childhood teachers and to investigate the moderating effect of the type of early childhood education institutions. Methods The participants of the study were 670 teachers working at kindergartens and daycare centers in Seoul and Gyeonggi, Incheon Province. The collected data were analyzed using SPSS and AMOS 23.0 program. We analyzed the structural relationship between social support, reflective thinking, and teacher efficacy by applying a structural equation model. The bootstrapping was conducted to verify the significance of the mediating effect. A multi-group analysis was conducted to analyze the moderating effect of the type of early childhood education institution. Results As a result of analyzing the structural relationship between emotional support, grit, and psychological well-being, emotional support was found to have a positive effect on grit and psychological well-being, respectively, and grit was analyzed to have a positive effect on psychological well-being. Also, grit was found to mediate emotional support and psychological well-being positively. The moderating effect of the type of early childhood education institutions was significant only in the kindergarten teacher group in the relationship between emotional support and psychological well-being. Conclusions The above results suggest that it be necessary to support the recognition of emotional support in order to promote psychological well-being of the kindergarten teacher group while it need to enhance grit through emotional support in order to facilitate psychological well-being of the daycare center teacher group.&#x0D;
Analisis Kesalahan Penulisan Abstrak Tugas Akhir (TA) dan Skripsi Mahasiswa STMA Trisakti

The purpose of this research is to find out the STMA TRISAKTI students’ errors in writing their abstracts in English, both in the form of the Writing Format, Grammatical or Sentence Structure, the Use of Tense, and Lexical or Word Structure/Word Order, Choice of Words/Diction. The research method used is Anakes or Error Analysis which uses the qualitative and comparative approach, and a simple quantitative approach. The quantitative approach is used to identify the existence of error frequency, while the qualitative approach is used to describe the existence of error forms. The results of the research show that 90% of 100% of samples that are analyzed have errors. From the total errors of all items analyzed, there are 71 errors or 100%, and the highest errors in the number of 33 errors or 46,47are is on the Lexical item, and the lowest errors in the number of 10 errors or 14,08% is on the Grammatical item. For that reason, it is suggested and necessary that the institution determine a standard for abstract writing in English, make an improvement of learning and teaching strategy for composition writing in English, specifically is correct and right abstract writing in English.
Essentialism: A Philosophy in AR Product Development

Abstract Adopting a philosophy of essentialism enables the delivery of minimally viable AR products in the eyewear form factor, and the ability to learn and iterate based on customers’ evolving needs in comfort, privacy, productivity, entertainment, and affordability.
A Tsetlin Machine Framework for Universal Outlier and Novelty Detection

Outlier and novelty detection are two of the most active study areas where a huge amount of research effort has been made over the past decades. Although there are several well-known outlier and novelty detection methods, it is difficult to find one that can effectively and simultaneously deal with both tasks across different data types. When studied in detail, outliers and novelties exhibit different characteristics. In this paper, we introduce a universal Tsetlin Machine (TM) framework for novelty and outlier detection. The framework consists of a TM generator and a machine learning classifier. To this end, we enhance the vanilla TM with a generator to produce a novelty score. The generator consists of the conjunctive clauses of the TM, which are used to form a representative pattern of a given input. We demonstrate that the clauses provide a succinct interpretable description of the trained input and that our scoring mechanism enables us to discern outlier and novel input. Empirically, we evaluate our TM framework on nine outlier datasets, five novelty tasks, and a one-class classification setup. In all experiments, we were able to either outperform or closely match state-of-the-art methods, with the added benefit of an interpretable propositional logic-based representation.