Reviewerly/exHarmony huggingface dataset:
DRAMOHERMENEUTICS: BASIC PROVISIONS, METHODOLOGICAL GUIDELINES AND EDUCATIONAL PRACTICE

A living understanding (according to Losev) of the topic studied in the lesson for the student is associated with the personal experience (according to Stanislavsky) of its semantic context. The individuality of the tempo/rhythm of this process guarantees the emotionality of the student's mastering of the studied. To provide teachers with the ability to design such a “lesson direction” from the situational-business conjugations of theatrical, hermeneutic and pedagogical spheres of professional activity, drama hermeneutics arose in didactics. Switch super task of which is to provide teachers with a system of figurative prompts: which of the well-known methodological techniques it makes sense to involve in their next lessons, building out of them one of the modern versions of the “hermeneutic procedure”.
Comparison of Forward Chaining and Hill Climbing Methods in Blood Disease Diagnosis Expert Systems

Blood disease is a condition in which one or several parts of the blood can’t function normally. Blood disease itself is classified as quite mild but also some are classified as serious diseases and are usually genetic. Sometimes people underestimate the symptoms of mild illnes and lazy to see a doctor or laboratory because they think it troublesome and need to spend money. Therefore an early diagnosis of symptoms that may be related to blood disease is needed, so people can be alert and also prevent severe blodd disease. That’s why we need an expert system application that can help people diagnose and be aware of diseases that related to blood.
PENGARUH PEMBELAJARAN KONTEKSTUAL TERHADAP KETERAMPILAN MENULIS PUISI KELAS III SDN 66 PONTIANAK KOTA

AbstractThis study aims to see the presence and magnitude of the influence of contextual learning on poetry writing skills for class III SDN 66 Pontianak City. The research method used is an experimental method, in the form of Quasi Experimental Design, Non-Equivalent Control Group Design. In this study, the population was all third grade students consisting of 52 students. The sampling technique used nonprobability sampling technique, namely purposive sampling, so that the sample obtained was 26 students in class III A as the experimental class and 26 students in class III B as the control class. The data collection technique used is a measurement technique using a written test to complete/write a missing poem. The results of data analysis showed that the post-test average for the control class was 55.88 and the posttest average for the experimental class was 68.58. The results of the study were processed by t-test obtained t_count (3.676) and t_table (α = 5% and dk = 26 + 26 – 2 = 50) of 1.678. Because t_count (3.676) t_table (1.678) then the alternative hypothesis (Ha) is accepted. Based on the calculation of the effect size (ES) obtained by 0.73 (medium category).Keywords: Contextual Learning, Influence, Poetry Writing Skills
Automatic syllable stress detection under non-parallel label and data condition

Typically, automatic syllable stress detection is posed as a supervised classification problem, for which, a classifier is trained using manually annotated (existing) syllable data and stress labels. However, in real testing scenarios, syllable data is estimated since manual annotation is not possible. Further, the estimation process could result in a mismatch between the lengths of the estimated and the existing syllable data causing no one-to-one correspondence between the estimated syllable data and the existing labels. Hence, the existing labels and estimated syllable data together cannot be used to train the classifier. This can be avoided by manually labeling the estimated syllable data, which, however, is impractical. In contrast, we, in this work, propose a method to obtain labels for estimated syllable data without using manual annotation. The proposed method considers a weighted version of the well-known Wagner–Fisher algorithm (WFA) to assign the existing labels to the estimated syllable data, where the weights are computed based on a set of three constraints defined in the proposed algorithm. Experiments on ISLE corpus show that the performance obtained on the test set for four different types of estimated syllable data are higher when the assigned labels and estimated syllable data are used for training compared to those when existing labels and existing syllable data are used. Also, the label assignment accuracy using the proposed method is found to be higher than that using a baseline scheme based on WFA.
Machine Learning-Based Feature Importance Analysis of Seismic Attributes to Assist Fault Prediction

Abstract
Administrasi Keuangan Sekolah

Definition of finnancinal nadministrnation in the bronadest sense is na policy regnarding the nand use of orgnaniznationnal finnance to renalize the nactivities of the orgnaniznation, where the form of finnancinal mnannagement includes plnanning, regulnation, naccountnability, nand finnancinal supervision
Budget-Aware Scheduling for Hyperparameter Optimization Process in Cloud Environment

Hyperparameter optimization, as a necessary step for majority machine learning models, is crucial to achieving optimal model performance. Unfortunately, the process of hyperparameter optimization is usually computation-intensive and time-consuming due to the large searching space. To date, with the popularity and maturity of cloud computing, many researchers leverage public cloud services (i.e. Amazon AWS) to train machine learning models. Time and monetary cost, two contradictory targets, are what cloud machine learning users are more concerned about. In this paper, we propose HyperWorkflow, a workflow engine service for hyperparameter optimization execution, that coordinates between hyperparameter optimization job and cloud service instances. HyperWorkflow orchestrates the hyperparameter optimization process in a parallel and cost-effective manner upon heterogeneous cloud resources, and schedules hyperparameter trials using bin packing approach to make the best use of cloud resources to speed up the tuning processing under budget constraint. The evaluations show that HyperWorkflow can speed up hyperparameter optimization execution across a range of different budgets.
Common acronyms

column Share on Common acronyms Author: Inroads Staff View Profile Authors Info & Claims ACM InroadsVolume 13Issue 1March 2022 pp 66https://doi.org/10.1145/3514215Online:14 February 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
On the representability degrees of linear orders

The existence of anti-isomorphic inﬁnite chains of m-degrees and degrees of negative representability of linear orders is established.
Exponential Fine-Tuning Harmony Search Algorithm

This paper gives one kind of new harmony search algorithm–exponential fine-tuning harmony search algorithm. The specific improvement is to use an exponential function to change the generation mode of fine-tuning probability, and thus gives a new harmony. The new algorithm is tested by using the standard test function of cec2005, and compared with other four existing harmony search algorithms. The numerical results show that the new algorithm is very competitive with the other harmony search algorithms.
Mining Frequency-Utility Patterns from a Big Data Environment

Both frequent itemset mining (FIM) and high utility itemset mining (HUIM), which have been widely discussed before, consider the frequency or utility of the project separately to find the information required by users. However, this information is not complete as merchants need to find the commodities that occur frequently and have higher profits. To tap into multiple user preferences, the concept of skyline was proposed, which considered both the frequency and utility of the item to better support user decision-making. Nevertheless, in real life, the generation of data from multiple sources leads to a large volume of data, and the previously proposed algorithm is not enough to handle large-scale data sets on a single machine. In this paper, a skyline mining framework based on MapReduce is proposed. The algorithm adopts distributed method to process large data sets in parallel, which solves the problem that a single machine cannot process a large number of distributed data. Experimental results show that the MR-Skyline algorithm is superior to the most advanced algorithms in large data sets.
Nanotechnology: Applications in Treatment of Industrial Wastewaters: A Review

Water pollution and freshwater contaminations is one of the global problems that directly affect lives of organism which directly rely on water for their livelihood (man, organisms, plants). To alleviate these challenges, various treatment methods such as membrane filtration, ion exchange, coagulation as well as adsorption have been adopted to reduce wastewater generated and improve qualities of effluent discharged from industries to meet specified minimum permissible limits. Among all, Adsorption is considered to be more efficient in terms of cost, ease of handling and re-use for water treatment. Furthermore, adsorption technology exhibits excellent removal efficiency using adsorbent prepared from raw or waste materials or nanoparticles synthesized from wastes. Nano-materials exhibit outstanding properties such as small particle size, catalytic potential, large surface area and great number of active sites, hence allowing high removal efficiency. The nano-material in this context referred to as “new generation nano-adsorbent” is capable of removing pollutants even at low concentration. Numerous pathways of synthesizing nanoparticles fall under two approaches namely: bottom-up and top-down approach. Different materials have been adopted for bulk and nano based adsorbent with their efficiency varying for different waste water sources, nature of the adsorbents, adsorbent dosage and experimental conditions. This investigation will review advancement of waste water treatment using waste and nano-based materials as an adsorbent from 2015 till 2021.
Oh the places media literacy can go! Developing media and data literacy resources and at an academic library during the "infodemic"

In order to view the entire gallery of posters, videos, and other materials, please click "Submissions" below. ATTENTION ATTENDEES: To log in and download the posters & ask questions, please sign in using the link here: [https://www.morressier.com/signup/CLC2020](https://www.morressier.com/signup/CLC2020) To find the Q&A for each poster category, please click "Sessions" below. Further instructions for how to ask a question are included in each section. A handy list of the submissions and their corresponding Q&A Session can be found here: [Submissions by Q&A Category](https://drive.google.com/file/d/1vurahYJIGA9IjA8v94IKfh36QhCFHTQi/view?usp=sharing)
A Solomonic Solution to Ownership Disputes: An Application to Blockchain Front-Running

Blockchain front-running involves multiple agents, other than the legitimate agent, claiming a payment from performing a contract. It arises because of the public nature of blockchain transactions and potential network congestion. This paper notes that disputes over payments are similar to classic ownership disputes (such as King Solomon's dilemma). We propose a simultaneous report mechanism that resolves Solomon's dilemma (using only ordinal preference information) and also eliminates blockchain front-running. In each case, the mechanism relies on threats to remove ownership from all claimants and preferences from the legitimate claimant over allocations to other agents.
Recipient Design by Gestures

This paper investigates how depictive gestures, i.e., hand movements that depict actions, scenes or objects, are configured and used for accomplishing instructions. By drawing on video recordings of second language interactions in cooking classes for newcomers in Finland, we focus on instructions that project a certain type of complying bodily action as the relevant next action. We demonstrate that the instructions are designed to be sensitive not only to the contingencies of the material ecology of the kitchen but also to the epistemic and linguistic asymmetries between the participants. The analysis shows how depictive gestures contribute to the forward-feeding function of cooking instructions by visualizing how the instructed action should be appropriately carried out. The findings contribute to the accumulating understanding of how embodied resources further intersubjectivity in second language interactions (Eskildsen &amp; Wagner, 2015; Greer, 2019; Lilja &amp; Piirainen-Marsh, 2019).
DESIGNING EXPERIENTIAL ACTIVITIES IN TEACHING THE MATHEMATICS EDUCATION MODULE FOR PRIMARY EDUCATION STUDENTS

The objective of this article is to propose a way to design the learning activities for primary education students in teaching the module Mathematics Education. The article focuses on theoretical research on experiential education; research on competency-based teacher training; research on the organization of experiential activities in teaching and learning Mathematical Education modules for students and applying it to the designing of experiential activities in teaching the module “Building Mathematics lesson plan to develop the qualities and competences of learner for primary students while studying this module.
Akhan raises funds for lab-grown diamonds

The lab-grown diamond start-up Akhan has raised $20 million from private investors. Akhan’s core technology is a set of methods to deposit nanocrystalline diamond films onto substrates. The firm says its flagship diamond coating can make cell phone screen glass 10 times as tough as untreated glass. It can also make diamond semiconductor wafers, and the synthetic diamond has high thermal conductivity that can help manage heat in electronics. Akhan says it will use the money to expand its staff and its R&D and manufacturing facility near Chicago.
Inside the Metaphysical Workshop

The paper begins by noting that a certain ontology (van Inwagen’s favored ontology) seems naturally to invite a formal semantic grounding (that is support from contemporary formal model theory). Using variations on classic and recent meta-semantic arguments, the paper goes on to show that such a grounding is not available. It suggests that the reason for this failure is that formal semantics is the wrong tool for the task of grounding particular ontologies. Building on that negative argument, the paper concludes by suggesting that formal model theory is well suited for a different metaphysical task.
Mining Challenger from Bulk Preprocessing Datasets

The success of a successful company is dependent on the ability to make an object attractive for its consumers rather than the competition. In the framework of this challenge, we have some questions: How can competition between two elements be formalized and quantified? Who are a given item’s key competitors? What are the most competitive characteristics of an item? While this topic has so many fields of influence and importance, only a small amount of work has been spent on an appropriate solution. In this paper, the competition between two products is seen formally, depending on the business categories they both represent. Our competitiveness assessment uses consumer feedback, a wide range of available knowledge in a variety of fields. In extensive datasets, we propose efficient methods to assess competition and resolve the natural problem of finishing the top-known competitors of a particular object. Finally, we assess the validity of our findings and the scalability with several datasets from various fields.
Efficient multi-step reasoning attention network for visual question answering

Visual question answering task requires utilizing the content of the question to locate the corresponding regions of the image. But the traditional attention-based VQA methods can not accurately match the regions of the image that is relevant to the question, which results in less satisfactory performance. In this paper, an Efficient Multi-step Reasoning Attention Network (EMRA), which is mainly composed of the multi-step reasoning attention module and the G-LReLU non-linear layers, is proposed to address this problem. Specifically, the multi-step reasoning attention module combines the initial visual features, question features and the jointed features to generate more effective attended features, which can precisely represent the regions information of an image related to question. Then, the attended visual features generated by multi-step reasoning attention model and the question features are fed into the G-LReLU non-linear layers executing non-linear transformation to better fusion for answer prediction. In addition, considering the relationship between the scaling and the reasoning steps, as the number of inference steps increases, increasing the model width will improve the accuracy of our model. Experimental results on the VQA v2.0 dataset demonstrate that our model significantly outperforms the Bottom up and Top-down Attention based methods, and can be competitive with state-of-the-art models.
Preface

Abstract We are honored to introduce the Proceedings of the 4 th International Conference on Informatics, Engineering, Science, and Technology (INCITEST 2021). With its main theme on “A Future Road Map, Encouraging Optimism and Technology to Face Post Covid-19 Era”, this event is hoped to serve as a platform of gathering for anyone interested in exploring optimism, potential solutions, and answering issues and challenges in the era Covid-19 Pandemic”. Unlike the 1st and 2 nd INCITEST, the 3 rd and 4 th INCITEST was carried out in the virtual conference due to the impact of COVID-19. Universitas Komputer Indonesia (UNIKOM) held this online conference on 10 March 2021 to gather experts and scholars worldwide to continue disseminating the latest advanced research in the field of Informatics, Engineering, Science, and Technology and developing the academic exchange among researchers. Following the success of INCITEST 2018, 2019, and 2020, the enthusiastic responses to the call-for-papers in the fourth INCITEST were increasing. More than 150 papers were submitted to the organizing committee from both local and foreign participants. A peer-review process has been conducted to check paper originality and quality, resulting in 120 accepted papers being presented. The papers have been presented via Zoom in 10 minutes for each paper. It is an honor for us to have four renowned experts as our keynote speakers : Prof. Minoru Terano and Prof. Hiroyuki Iida from Japan Advanced Institute of Science and Technology (JAIST), Dr. Siva Kumar Sivaneshan from Taylor’s University, and Dr. Salmon Priadji Martana from UNIKOM. The keynote speech is presented in the plenary session in online format (via Zoom) and lively broadcasted on UNIKOM Youtube official channel. Each keynote presented their address in 45 minutes duration, including the discussion session. After completing the plenary session, the schedule continued with a parallel session held in an online format. The participants were divided into several classes, which a moderator chaired. Each presenter should present their paper within 5 minutes via Zoom. Five minutes after the presentation will be given to each of them for the discussion session. List of Committees of Incitest 2021 are available in pdf.
Face Emotion Recognition by Machine Learning

Detection of Facial expressions and emotions is always an easy task for humans but to achieve the same task using different computer-based algorithms is a challenging task. It is possible to detect emotions from images using various machine learning algorithms as there is a huge advancement in computer vision and machine learning over the years. Programmed face appearance acknowledgment is an effectively arising research in Emotion Recognition. In this paper, the Convolutional Neural Network (CNN) which is a subset of AI is rehearsed as a way to deal with outward appearance acknowledgment tasks. Thus, the proposed method is found to be more effective than other methods and has an accuracy of 92. Face appearances are the vital qualities of non-verbal correspondence. Non-verbal explanations are imparted through outward appearances. Face looks are the delicate indications of the greater correspondence. Nonverbal correspondence implies correspondence among people and animals through the eye to eye association, signals, outward appearances, non-verbal correspondence, and paralanguage. Human facial expressions can be recognized by using deep learning.
Experimental Face Recognition System Using Deep Learning Approaches

The spike in challenges to security as well as information and resource management across the globe has equally borne the rising demand for a better system and technology to curb it. Despite the advancement in technology, the issues of security and information management have been lingering, due to the lack of a foolproof method of tackling it. Although widely adopted and preceding in existence, biometric systems such as fingerprint recognition have their shortcomings especially in regulating the security of public places. The use of security cameras has also been increasingly adopted especially in public places like banks, parks, airports, malls, and markets; however, it is also plagued by issues surrounding recognition and identification. A better approach might be combining the best features of the existing technologies such as foolproof verification and validation, mass identification, and instant recognition into a singular system. Although it can be considered as still in its maturing phase, face recognition technology is at the forefront of the race to tackle this global challenge. The combination of face recognition technology and artificial intelligence using deep learning might be just what the world needs to gain the leading hand and cement the challenges of security and information management in perpetuity.
Development of contextual teaching-based e-modules in grade V elementary school learning

The purpose of this study was to determine the development of e-modules based on Contextual Teaching in the fifth grade of elementary school learning. This research method uses research and development methods with the ADDIE approach. This research method uses research and development methods with the limits of expert judgment and practicality. Expert assessment is carried out on media, language, and material experts, while practicality with teachers and students. Data collection techniques use documentation and questionnaires, while data analysis techniques use percentages. Based on the results of the study showed that the E- Module Based on Contextual Teaching with an average of 3.70. Thus, it can be concluded that the development of Contextual Teaching-based E-modules in fifth grade elementary school learning is included in the valid or feasible category. From the results of the small group test and large group test, an average of 3.645 was obtained. Thus, it can be concluded that the feasibility of Contextual Teaching-based E-modules in class V elementary school learning is included in the practical or feasible category.