Reviewerly/exHarmony huggingface dataset:
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection

Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set of tools exist for this purpose, there are currently no practical hyperparameter selection methods under the constraint of differential privacy (DP). We study honest hyperparameter selection for differentially private machine learning, in which the process of hyperparameter tuning is accounted for in the overall privacy budget. To this end, we i) show that standard composition tools outperform more advanced techniques in many settings, ii) empirically and theoretically demonstrate an intrinsic connection between the learning rate and clipping norm hyperparameters, iii) show that adaptive optimizers like DPAdam enjoy a significant advantage in the process of honest hyperparameter tuning, and iv) draw upon novel limiting behaviour of Adam in the DP setting to design a new and more efficient optimizer.
Modeling interpretation, or a humanistic alternative to data display

Review of Johanna Drucker, Visualization and Interpretation: Humanistic Approaches to Display, Cambridge, MA: The MIT Press, 2020, 195 pp. ISBN 978-0-262-044738.
Recurrent Attention for Neural Machine Translation

Recent research questions the importance of the dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. In this paper, we push further in this research line and propose a novel substitute mechanism for self-attention: Recurrent AtteNtion (RAN) . RAN directly learns attention weights without any token-to-token interaction and further improves their capacity by layer-to-layer interaction. Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time. Particularly, when apply RAN to the decoder of Transformer, there brings consistent improvements by about +0.5 BLEU on 6 translation tasks and +1.0 BLEU on Turkish-English translation task. In addition, we conduct extensive analysis on the attention weights of RAN to confirm their reasonableness. Our RAN is a promising alternative to build more effective and efficient NMT models.
ASSISTANCE IN THE UTILIZATION OF STORY-TELLING BOOK OF CHARACTER BUILDING FOR TEACHERS IN PAUD SURYA GEMILANG, MALANG

The aims of this study are: 1) to find out how the teacher training in PAUD Surya Gemilang, Malang, is carried out in implementing the book Let’s Respect Each Other: A Story-Telling Book of Character Building by using various methods and fun learning techniques, and 2) how the practice of using the book of Let’s Respect Each Other: A Story-Telling Book of Character Building. This research was conducted using a qualitative method in which the researcher described the process of the two aims aforementioned. The subjects of this study were all teachers, especially one homeroom teacher at PAUD Surya Gemilang. The results of this study found that in the first phase, namely teacher training, there were three stages. They were the provision of English for Young Learners material, the provision of story-telling material, and the provision of character education material. Then, in the second phase, namely the implementation of Let’s Respect Each Other: A Story-Telling Book of Character Building, it was found that teachers taught stories and character education by reading books in English and Bahasa alternately while showing the pictures that matched the story to students and retelling the stories by using puppets.
A Bayesian Framework for Information-Theoretic Probing

Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents—allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.
Commodities trend link prediction on heterogeneous information networks

Events can be defined as an action or a series of actions that have a determined theme, time, and place. Event analysis tasks for knowledge extraction from news and social media have been explored in recent years. However, there are still few studies that aim to enrich predictive models using event data. In particular, agribusiness events have multiple components to be considered for a successful prediction model. For example, price trend predictions for commodities can be performed through time series analysis of prices, but we can also consider events that represent knowledge about external factors during the training step of predictive models. In this paper, we present a method for integrating events into trend prediction tasks. First, we propose to model events and time-series information through heterogeneous information networks (HIN) that allow multiple components to be directly modeled through multi-type nodes and edges. Second, we learn features from HIN through network embedding methods, i.e., network nodes are mapped to a dense vector of features. In particular, we propose a network embedding method that propagates the semantic of the pre-trained neural language models to a heterogeneous information network and evaluates its performance in a trend link prediction. We show that the use of our proposed model language-based embedding propagation is competitive with state-of-art network embeddings algorithms. Moreover, our proposal performs network embedding incrementally, thereby allowing new events to be inserted in the same semantic space without rebuilding the entire network embedding.
Classroom Action Research on Thematic-Designed Online Learning for Contemporary Japanese Literature

This study aims to implement contemporary Japanese literature learning with thematic designs as a learning strategy for 60 students in literary study classes (Poetry, Prose, and Drama) at Darma Persada University that were conducted online during the pandemic. This study was conducted using Kurt Lewin's Classroom Action Research model consisting of planning, acting, observing, and reflecting; particularly for the observing section, the assessment was measured using two indicators based on students’ understanding of the material and satisfaction with online learning. Data was collected using questionnaires and filled out via google form. The findings of this study indicate the level of student understanding of the material, the constraints of online learning, and feedback from experts. The findings are useful for improving Japanese literature learning, to be more precisely during the online course
Effective Cataloging over Diverse Algorithms for Automatic Text Summarization and Its Survey

In these days, the measure of information and data accessible over the web is gigantic which prompted the making of automatic text summarization. Text Summarization is the route toward obtaining eminent information from a substantial text record. Automatic Text summarization is being a need of great importance and a fascinating theme with regards to NLP. Given the expansion in size and number of archives accessible on the web, an effective automatic text summarizer is significant. The fundamental test is to cause the computer to comprehend the report given with any expansion and create the outline as the primary rationale. The fundamental main impetus behind this work is to diminish the time and exertion spent by the client on perusing the whole document to understand what is the issue here. Subsequently the paper focuses on procedures accessible in delivering the significant rundown by utilizing different abstractive and extractive techniques
Alteration mapping for geochemical exploration in Niazgholi-Moshiran area,Using Aster data

Niaz Gholi-Moshiran region is located in the 1: 100000 of Lahroud in the northwest of Ardabil province.Geologically, most of the area is covered with Paleocene-Eocene rocks which consist of volcanic sections with andesite, tephrite and andesite trachyte parts which are observed along with andesitic-tephritic pillow lava.The main image analysis techniques in this study were false color combination and color composite ratio.Due to the presence of Mg-OH bond, montmorillonite, kaolinite, muscovite and illite minerals (Index of phyllic and argillic alteration zones) show maximum reflection in band 4 of SWIR and low reflection in band 6(due to the presence of Al-OH bond),.so in the color combination of 468, argillic and phyllic alterations are red to pink and propylitic alterations are green because of high reflection of index minerals (chlorite and epidote) in band 5 and 6 beside of low reflection in band 8(due to the presence of Mg-OH bond)( FIG1).Also the color composite ratio method (bands 4/5, 9/8 , 5+7/6 respectively in RGB) was used to highlight alteration mapping (the argillic in red, propylitic in green and phyllic in blue)(FIG2).FIG1: Aster ratio image (bands 4,6,8 in RGB) FIG2: Aster ratio image (bands 4/5, 9/8, 5+7/6 in RGB)
Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression

We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-ofwords encoding after processing with subword segmentation together with nonlexical features.The model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research.The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy.Our code and training data are publicly available 1 .
Sistem Pengelompokan Siswa Berdasarkan Tingkat Kedisiplinan Menggunakan Metode Naïve Bayes Classifier

Discipline is a very important thing in the educational process. Discipline will succeed if it is applied to students correctly. Student discipline is that every student follows every rule and order that has been set by the school. At SMK Muhammadiyah 2 Sukoharjo student discipline. Declining discipline at SMK Muhammadiah 2 Sukoharjo is marked by the increase in points of violation from students. The purpose of this study was to apply the nave Bayes method in the classification of student discipline levels at SMK Muhammadiyah 2 Sukoharjo. With this information will be obtained that can be used for information on which students need to be given Counseling Guidance to provide direction and guidance to students. The attributes used are cases of fights, not attending apples, not carrying out picket, not entering without explanation, arriving late, noisy in class. Test results with 490 records with a portion of 75% training data and 25% test data. And produces an accuracy of 76%.
Cascaded PID Control System for UAV with Gain Factor Prediction Using ML

Abstract: Drones are not inherently stable, necessitating the use of a flight controller. If the UAV is properly tuned, the drone will fly steadily; otherwise, it won’t. Hence, we have used a PID (proportional, integral, differential) controller for a stable flight. A well-functioning PID controller should enable amazing climbs and long-range flights. But, when used singly, PID controllers can provide poor performance, resulting in a long settling time, overshoot, and oscillation. Here, we propose a new approach to maneuver UAVs using a PID control system and overcome the shortcomings of using PID controllers in UAVs. This disadvantage is resolved using the Machine Learning polynomial regression model. The gain factors in a PID control system, which is otherwise ideally constant, should be changed in order to reduce the minor instabilities for a smooth flight. Our method has been elaborated and illustrated with suitable diagrams in the following work. When simulated in Gazebo on a Robot Operating System (ROS), our technique is proven to be successful. Keywords: Control Systems, PID, UAV, Drones, Polynomial Regression, Gain Factor, Prediction Algorithm.
Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric

It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts—in what is known as the gold standard for reference-based metrics—or a separate annotated dataset to fine-tune the reference-less metric. Reference-based systems have a low correlation with human judgement, cannot capture all the ways in which a sentence can be corrected, and require substantial work to develop a test dataset. We propose a reference-less GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. The proposed system relies solely on commonly available tools. Additionally, currently available reference-less metrics do not work properly when part of a sentence is repeated as opposed to reference-based metrics. In our proposed system, we look to address issues inherent in reference-less metrics and reference-based metrics.
نموذج تعليم مهارة الكلام عبر الإنترنت فى الجامعات الإسلامية الحكومية بجاوى الشرقية

Learning interaction is an essential element during the online learning process. Therefore, this study offers an internet-based speaking skill learning model. This study aims to describe the process, the problem, and the learning model of learning internet-based speaking skills. This study uses a descriptive qualitative approach with case studies, focusing on learning at state Islamic universities in East Java, Indonesia, The State Islamic University of Maulana Malik Ibrahim Malang, and The State Islamic University of KH. Ahmad Shiddiq Jember. For data collection, the researcher used three methods: first, observation of online speaking learning activities conducted at two universities to find out the steps of learning, secondly interviews were conducted with lecturers who teach speaking courses and also students in the class to find out the obstacles faced. and thirdly documentation in the form of photos of activities and platforms used in the learning process to complete the data. For data analysis, the researcher used the Miles and Huberman method: data collection, data classification, data presentation, and concluding. The findings of the research : (1) that the internet-based speaking skill learning process is based on Personal Learning, (2) There are two problems in learning internet-based speaking skills: Internal Problems that is from the lecturer side cannot explain the whole theme and from the student side; bored and External Problems: electronic constraints related to internet signals and data, (3) internet-based speaking skill learning model That is the interaction between students - students and interactions between lecturers - students. The formal findings of this study are: learning maharah kalam with an internet-based personal learning model based on interaction with synchronous media shows its effectiveness in Islamic universities.
Adopting a New Marking and Reporting System (MaRS) at Faculty Level: A Survey of Lecturers’ Level of Readiness and Acceptance

At the end of each semester, academic and administrative staff of a faculty have to analyse students’ results for a myriad of factors such as calculating performance of students and lecturers, and for the university’s senate confirmation. Unfortunately, many relevant personnel at the faculty level lack the clearance to access students’ results directly. To circumvent this issue, individual lecturers were required to print out and submit hardcopies of students’ results and channel the data to relevant personnel. When the Covid-19 pandemic hit and lecturers were unable to submit the hardcopies, the faculty had to find an alternative. The introduction of the Marking and Reporting System (MaRS) marks a change from manual hardcopy submission of results following travel restrictions necessitated by the Malaysian government during the Covid-19 pandemic. As with any technological shift and innovation, the level of readiness and acceptance of academic and administrative staff at a faculty must be examined. This paper uses the Technology Acceptance Model (TAM) (Davis, 1986; Davis, Bagozzi &amp; Warshaw, 1989) to determine the level of readiness and investigate the level of acceptance among academic and administrative staff in using MaRS via a questionnaire. The findings suggest that with adequate guidance and assistance, the respondents were positive towards using MaRS as the system offers advantages to them such as being cost effective, environmentally friendly, and more convenient in recording students' results. In short, the respondents are ready and willing to use MaRS to manage students’ results despite facing some difficulties in handling the system, because positively overcoming challenges will yield positive outcomes.
Категорія «душа» в іспанській психології на межі ХІХ – ХХ століть

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Virtual look around: comparing presence, cybersickness and usability for virtual tours across different devices

Virtual Reality has become readily available in the last few years through different devices, from desktop computers to head­-mounted displays (HMD). Also, virtual tours became popular with 360º panoramic photographs and video clips on online social media, so people could visit remote locations without being exposed to crowded transportation or long travels. Also, virtual tours demonstrate considerable potential as a form of escapism and even for remote teaching. Since we lack studies that evaluate the User Experience (UX) in virtual tours on different devices, this article aims to compare aspects of the User Experience (regarding sense of presence, cybersickness, and usability) in a virtual tour website developed in WebXR across different devices. To achieve our objective, we developed a virtual tour based on 360º pictures using WebXR API and React 360 framework and conducted an experiment with 41 undergraduate students using four different devices: a laptop computer, a smartphone, a Google Cardboard headset, and a Samsung Gear VR HMD. We evaluated users’ perceptions by adapting and translating the Suitability Evaluation Questionnaire (SEQ) and users’ performance by measuring the time to fulfill a set of tasks. The main findings from this study include that (i) the overall self-­reported experience using Google Cardboard is worse than using other devices, (ii) the users’ performance is quite similar between the platforms, (iii) there is evidence of unexpected cybersickness symptoms in tests with the smartphone, and (iv) the development of a plausible hypothesis concerning low usability having an effect upon the sense of presence. Additional contributions of our research are the adaptation, translation into Portuguese, psychometric analysis, and revised scoring procedures of the SEQ.
ENGLISH EDUCATION MASTER STUDENTS’ PERCEPTIONS ON THEIR SELF-EFFICACY IN EFL SPEAKING LEARNING CONTEXTS

AbstractSelf-efficacy should be fully integrated as one of the mainstays assisting multiverse EFL academicians to achieve greater speaking learning achievements, improve their actual communicative competencies, and justify their misconceptions of L2 speaking learning enterprises. Corresponding with these 3 aforementioned advantages, the full activation of robust self-efficacy will potentially allow EFL academicians to show greater effortful actions, resilience, motivation, and commitment to relentlessly forge their L2 communicative competencies in the presence of adverse speaking learning situations. This qualitative study was conducted in the support of the narrative inquiry approach to yield rejuvenated facts, trustworthy truth, and apparent evidence for worldwide EFL educationalists concerning the significance of self-efficacy implementations in diverse wide-ranging L2 speaking classroom vicinities. Two major positive influential factors are deemed as both internal and external factors affecting the luxuriant proliferation of striking L2 speaking skills as well as self-efficacy levels advancement namely the induction of long-life speaking learning endeavor and supportive speaking learning environments. For the future betterment of self-efficacy and EFL speaking studies, varied relevant research instruments need to be mutually accompanied by a considerable number of research participants to produce more significant research results contributing a richer understanding of affective second language learning fields. Keywords: Self-efficacy, EFL speaking, narrative inquiry
Application of Cohesion Theory in CET-4 Listening Comprehension

Cohesion is a very important part of learning English. The Cohesion Theory of Halliday and Hasan is of great importance for people to acquire the knowledge of cohesion. This study is aimed at applying this Cohesion Theory in CET-4 listening comprehension which is an important test for Chinese college students. This study uses the test papers of CET-4 in June 2021 as examples to demonstrate that it is obvious that there are certain cohesive relations between the listening materials and the correct answers. It is also practicable to apply the Cohesion Theory in listening comprehension of CET-4. It mainly analyzes the cohesive devices applied in figuring out the correct choices by understanding the cohesive relations between the listening materials with the right answers. It demonstrated obviously that there exists the cohesive relations between the listening materials with the right answers in the test, and both the grammatical device and lexical device are often used interactively. The cohesive devices can serve as the important clues for students to make the right choices. The findings of this study suggest that the application of the Cohesion Theory in CET-4 listening comprehension is practicable and effective. It can not only help the students get good performance in the CET-4 tests, the same approach can also be used in other important tests.&amp;nbsp;
Provably efficient multi-task reinforcement learning with model transfer

We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze an algorithm based on the idea of model transfer, and provide gap-dependent and gap-independent upper and lower bounds that characterize the intrinsic complexity of the problem.