Reviewerly/exHarmony huggingface dataset:
Feeding What You Need by Understanding What You Learned

Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it.Existing research works in MRC rely heavily on large-size models and corpus to improve the performance evaluated by metrics such as Exact Match (EM ) and F 1 .However, such a paradigm lacks sufficient interpretation to model capability and can not efficiently train a model with a large corpus.In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status.Specifically, we design an MRC capability assessment framework that assesses model capabilities in an explainable and multi-dimensional manner.Based on it, we further uncover and disentangle the connections between various data properties and model performance.Finally, to verify the effectiveness of the proposed MRC capability assessment framework, we incorporate it into a curriculum learning pipeline and devise a Capability Boundary Breakthrough Curriculum (CBBC) strategy, which performs a model capability-based training to maximize the data value and improve training efficiency.Extensive experiments demonstrate that our approach significantly improves performance, achieving up to an 11.22% / 8.71% improvement of EM / F 1 on MRC tasks.
Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics

Current practices in metric evaluation focus on one single dataset, e.g., Newstest dataset in each year's WMT Metrics Shared Task.However, in this paper, we qualitatively and quantitatively show that the performances of metrics are sensitive to data.The ranking of metrics varies when the evaluation is conducted on different datasets.Then this paper further investigates two potential hypotheses, i.e., insignificant data points and the deviation of Independent and Identically Distributed (i.i.d) assumption, which may take responsibility for the issue of data variance.In conclusion, our findings suggest that when evaluating automatic translation metrics, researchers should take data variance into account and be cautious to claim the result on a single dataset, because it may leads to inconsistent results with most of other datasets.
Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models

Realistic fine-grained multi-agent simulation of real-world complex systems is crucial for many downstream tasks such as reinforcement learning. Recent work has used generative models (GANs in particular) for providing high-fidelity simulation of real-world systems. However, such generative models are often monolithic and miss out on modeling the interaction in multi-agent systems. In this work, we take a first step towards building multiple interacting generative models (GANs) that reflects the interaction in real world. We build and analyze a hierarchical set-up where a higher-level GAN is conditioned on the output of multiple lower-level GANs. We present a technique of using feedback from the higher-level GAN to improve performance of lower-level GANs. We mathematically characterize the conditions under which our technique is impactful, including understanding the transfer learning nature of our set-up. We present three distinct experiments on synthetic data, time series data, and image domain, revealing the wide applicability of our technique.
Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video

We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. Given a video stream of point cloud observation of an articulated body under arbitrary motion, our approach discovers the unknown low-dimensional skeletal relationship that can effectively represent the movement. Then the discovered structure is utilized to encode the motion priors of dynamic sequences in a latent structure, which can be decoded to the relative joint rotations to represent the full skeletal motion. Our approach works without any prior knowledge of the underlying motion or skeletal structure, and we demonstrate that the discovered structure is even comparable to the hand-labeled ground truth skeleton in representing a 4D sequence of motion. The skeletal structure embeds the general semantics of possible motion space that can generate motions for diverse scenarios. We verify that the learned motion prior is generalizable to the multi-modal sequence generation, interpolation of two poses, and motion retargeting to a different skeletal structure.
Promoting Physical Activity for Elderly People with Immersive Virtual Reality (IVR)

Physical and social activities should be included in an efficient e-coaching for older adults and immersive virtual reality (IVR) is efficient if behavioral changes are transferred to the real physical world. In an experiment con-ducted with 42 older adults (63-85 years-old), participants were asked to interact with a specific immersive virtual reality (IVR) daily for 4 months in their homes, at least 15 minutes per day. Results demonstrate several positive impacts related to the use of the IVR (decrease of weight, decrease of waist circumference, number of steps outside) even if the time spent to use this IVR system decreases. So the transfer from the IVR to the real world is very encouraging.
Towards a Better Understanding of Tarajem: Creating Topological Networks for Arabic biographical Dictionaries

Biographical writing is one of the earliest and most extensive forms of Arabic literature. Some scholars tend to assume that classical Arabic biographies, widely known as Tarāǧim, arose in conjunction with the study of the reliability of the Hadith transmitters (the reciters of the Prophet Mohammad's sayings) which lead to a proliferation of biographical material collected and used to assess the transmitter's trustworthiness . However, a scrutiny of the well-known classical Arabic biographical dictionaries such as Siyaru 'A`lāmi an-Nubalā' `The Lives of the Noble Figures' for Adh-Dhahabī shows that they extend their entries to other classes of persons important to the development of particular fields such as Islamic jurisprudents, rulers, poets, philosophers or physicians. The main contribution of Arabic biographical dictionaries is the cumulative value of the thousands of life histories which construct a picture of the Islamic society in different eras. An Arabic biographical dictionary, therefore, is predominantly used by scholars to look up an eminent person's achievements and historical background. In this project, however, we explore Arabic biographies as a prosopography, rather than a biography in the strict sense. We introduce a novel method for a better understanding of Arabic biographical dictionaries by creating a network of relations among different persons. We utilise Natural Language Processing (NLP) tools to create a topological network from the unstructured data of 45,500 biographical entries collected from different dictionaries. We aim to illustrate how network analysis leveraged by NLP tools can provide scholars with innovative methods for discovering complex constellation of relations between prominent and non-prominent figures spanning over several eras and from different fields of knowledge. We also use graph visualisation as a means to effectively communicate and explore such complex constellations. Each network visualisation is purposefully designed to be as simple and robust as possible to offer scholars a way to move relatively fluidly between the large scale of biographical entries and to easily interpret the minute ties between persons of different walks of life. We make both our data and code publicly available for researchers to replicate the experiment. It can be found at:https://github.com/sadanyh/Relational-Network-for-Arabic-Tarajem
Pelatihan Pembuatan Video Ajar Pada Guru MIN 1 Klaten

Since the COVID-19 pandemic has occurred throughout the world, all activities and activities have been disrupted, including educational activities. All educational activities that were initially carried out offline must be online or online to control the spread of COVID-19. One way to facilitate teaching and learning activities and delivery of material to students needs an easy and exciting way for students and teachers. Teachers are required to immediately adapt to technology so that the educational process in schools is not disrupted. One of the efforts to upgrade skills is to hold training to develop exciting learning videos that can be tracked at any time, wherever they are. Open Broadcast Software or OBS is one of the open-source tools that can be used to make learning videos easily and not connected to the internet. So that it is more efficient and does not incur additional costs when building, all teachers held this training at MIN 1 Klaten; the participants were very enthusiastic. In general, this training aims to improve the quality and ability in information technology in teaching and learning activities for teachers at MIN 1 Klaten in the 5.0 era. This community service activity is by direct delivery of material or material oration and implementation. At the end of the activity, an evaluation was held in the form of a questionnaire to measure the success of the absorption of the material presented. The activity evaluation results showed that it was satisfactory for all activity participants, easy to understand the material presented and easy to use.
Ability Write Reading Summary

The data collection tool used was a written test, namely making a summary of the story of the legend of Malin Kundang, with assessment criteria, namely point 2 length of the summary, main idea, explanatory idea, use of words and sentences according to the contents of the summary with the reading. before the indicator is determined as a means of collecting research data, it is first validated by the three Indonesian language study teachers in a triangulation manner until it is declared fit for use. After the legend manuscript was administered to the respondent, it was processed statistically; with the results as the interval 71-85. Second, from the results of observations of the level of ability obtained information: 1) a description of student learning outcomes in learning obtained data, namely: students who have a high level of ability are 16 people or 40.504%, students who have a moderate level of ability are 9 people or 24.32%, students who have a low level of ability are 13 people or 34.14%. 2) a description of student learning outcomes for each item of question is obtained: for item 1 (ability level 36 people, and medium ability level 1 person), for item 2 (high ability level 22 people, medium ability level 7 people, low ability level 8 people ), for icon 3 (high ability level 24 people, medium ability level 5 people, low ability level 8 people), for item 4 (high ability level 25 people, medium ability level 6 people, medium ability level 7 people, low ability level 28 people) based on the description above, it can be concluded that the students' ability to write a narrative paragraph framework is classified as moderate. This statement is stated as moderate because of the variation in the abilities of students where there are good performers and some are low. So that to determine it, take the middle way, namely moderate.
Digital Twin of a Data Center at an Educational Institution

Digital twins are among the most important trends of the fourth industrial revolution. They present a crucial tool for protecting critical mission systems and the development of new services, products, and processes. This paper presents the first digital twin for a data center. The rapid growth of the Internet of things and the areas of modeling and simulation results in high demand for the development of data center digital twins (DCDT) to ensure the safety/protection of critical and costly mission infrastructure and guarantee business continuity, enhance efficiency, and sustain development. This paper presents the design and implementation of a digital twin for a university data center. Different sensory data like temperature, humidity, smoke, and water leakage are analyzed using an intelligent event detection approach, which detects abnormalities using an Extreme Learning Machine (ELM) fed with the minimum ratio between successive real-time data streams. The performance of ELM has outperformed that of both Learning Vector Quantization and Radial Basis Function-based neural network classifiers and proved much faster in abnormal event detection.
Pengelolaan Dokumen Digital untuk Meningkatkan Efektivitas Pembelajaran PAUD

Early Childhood Education (PAUD) Anggrek in Parangtritis Village, in responding to the covid-19 pandemic, has adapted learning for students by carrying out an assignment learning program from home, which is regularly given assignments every week on Mondays. The problem that arises from this learning system is the accumulation of files resulting from student assignments that require storage and management, which is quite inconvenient for teachers. This community service offers and implements a learning document digitization program to address existing problems. This service program is carried out using an institutional approach method, through three main activities, namely objectification in the preparation of digital document management steps; externalization in the form of focus group discussions for managers and related stakeholders; and internalization in the form of coaching for PAUD management trainers and several hamlet cadres. The evaluation results showed a significant increase in the effectiveness of managing learning documents, namely the reduction of files that had to be physically stored by 90% from the amount usually held in the previous PAUD office.
A Survey on Sign Language Translation Systems

Abstract: Sign language is a way of communicating using hand gestures, movements and facial expressions, instead of spoken words. It is the medium of communication used by people who are deaf or have hearing impairments to exchange information between their own community and with normal people. In order to bridge the communication gap between people with hearing and speaking disabilities and people who do not use sign language, a lot of research work using machine learning algorithms has been done. Hence, Sign language translator came into picture. Sign Language Translators are generally used to interpret signs and gestures from deaf and hard hearing people and convert them into text. Keywords: Sign language, translator, gesture recognition, HamNoSys, Camshift, SIFT, SiGML
Construction risk evaluation of power mass entrepreneurship and innovation demonstration park under collaborative innovation

Abstract The power mass entrepreneurship and innovation demonstration park (PMEIDP) provides an incubation and transformation platform and the environment by converging resources such as technology, capital, and demand to promote regional economic development. This paper evaluates the effectiveness of further promoting systematic innovation and efficient use of resources from the perspective of collaborative innovation. Firstly, we build a risk indicators system for the construction risk evaluation of the PMEIDP. Secondly, we adopt the mutual information coefficient (MIC) to simplify the construction risk indicators. Thirdly, we train the fuzzy neural network (FNN) for the risk evaluation of the PMEIDP. Finally, we conduct a case analysis of 5 PMEIDP and provide support for the management of the future development and construction of the PMEIDP.
ANALYSIS OF HANGMAN GAME IMPLEMENTATION FOR HSK TEST PARTICIPANTS USING FLUTTER

Mandarin Chinese, more commonly known as Modern Chinese, is one of the worlds’ many languages. It is the mainofficial language of the People’s Republic of China, and it is also one of the most anticipated languages across the world. In year2020, the Mandarin language is the second most spoken language in the world at 1.12 billion speakers. To facilitate the growinguse of Mandarin in the workplace, more and more private schools are teaching Mandarin as a second language. This leads to a needto assess the expertise of the speakers, which leads to the creation of certain Chinese proficiency tests such as Hanyu ShuipingKaoshi (HSK). To support HSK test takers, the author intends to provide a new kind of media to help the test takers expand theirvocabulary. With the help of Flutter and Firebase Cloud Firestore, the result is an e-learning mobile application, served as ahangman game. Survey results show people are interested in the application and believe that it can help the players expandtheir vocabularies.
Improved Ability to Convert News Texts into Dialogue Forms with the Cooperative Integrated Reading and Composition (CIRC) Learning Model for Class IX Students of Berastagi Methodist Middle School in the 2020/2021 Academic Year

This study discusses the problems faced by grade IX students at SMP Methodist Berastagi.This study aims to improve students' ability to convert news texts into dialogue with the Cooperative Integrated Reading and Composition (CIRC) learning model in class IX students of SMP Methodist Berastagi in the 2020/2021 academic year.This research is a Classroom Action Research (PTK).The subjects of this study were 35 students of grade IX at SMP Methodist Berastagi in the odd semester of 2020/2021 academic year.From the results of the analysis, it appears that using the Cooperative Integrated Reading and Composition (CIRC) learning modelTo improve students' ability to convert news texts into dialogues, there was a significant change compared to before using the model.Based on these results it can be concluded that the use of the Cooperative Integrated Reading and Composition (CIRC) learning model in solving the problems faced by these students is an effective way to improve student abilities.
The problem of determining the features of understanding the concept of «spiritual culture» as a component of self-realization of personality

The article examines the impact of national and cultural values on socialization of a personality and its spirtual development. Main attention is related to the problem of education of value orientation, such as spiritual, human, national with have a significant influence on human socialization.&#x0D; Purpose. Spiritual development of the person includes a system of ethical values and national cultural landmarks, which are implemented in the subjective selection of ideals, objective rating system and specific human behavior. One of the most important is a problem of education and preservation of spiritual values of the younger generation, cultural and spiritual values, which are the basis of human existence.&#x0D; Methods The article analyzes the views of scientists to determine the spiritual values of national culture. They are formed during the historical development of the nation, development of its material and spiritual culture.&#x0D; Results. The current period of society development requires new approaches to education and future generations. A measure of social development has always been a measure of cultural and spiritual nature of human.&#x0D; Originality. Significant attention is given to nurturing influence of family in the formation of spiritual potential of a personality.&#x0D; Conclusion. The biggest impact of all processes contradictions that occur suffers from, primarily, the family as the most important center of society. Changes, occurring in our country, promote the growth of the family role in the morality formation of the younger generation. It is saying about the impact of national and cultural values, which change in the formation of spiritual culture
Arduino Based Distancing Wristband Design

Covid disease is a very dangerous disease because this disease is very easily transmitted to everyone, from the young to the elderly. With the conditions that are maintained and in this pandemic situation, many people underestimate how material the Covid 19 virus is. In designing this system simulation model, the devices used are in the form of a mini Arduino, pear sensor, battery, and buzzer. The method used to maintain a safe distance from people affected by Covid 19. In the form of research that can test how important it is for us to keep our distance from other people. With Arduino technology and pear sensors used to remind us that the distance between humans. So we have to be a bit oblivious when there is an output from the buzzer which has been warned by the pir sensor. With this tool, we can at least keep a distance from people around us
Study on Optimization of Irrigation and Drainage Systems in Jombatan Village

The development and management of irrigation infrastructure from a historical perspective is closely related to national food security. In infrastructure management, irrigation must meet the needs of farmers. Every plant needs sufficient water to increase agricultural productivity and farmer's welfare. Meeting water needs requires adequate irrigation and drainage channels in each area. One of them is Jombatan Village, which is a village located in Kesamben District, Jombang Regency. Optimising the performance of irrigation and drainage channels in Jombatan Village requires a concrete step. This irrigation and drainage literature review discuss optimising the performance of irrigation and drainage channels.&#x0D; All irrigation and drainage canal buildings have the same conditions; namely, the walls of the buildings are overgrown by plants and moss, some of the walls of the canal buildings are damaged due to the lack of strength of the building structure. The effectiveness of irrigation and drainage channels in Jombatan Village is still not optimal because infrastructure maintenance is still lacking, and community knowledge is still lacking.
BITCOIN INVESTMENT DURING COVID-19: THE CRITICAL FACTORS INFLUENCING ITS ADOPTION

Bitcoin was the first virtual currency to be created in 2009 as a decentralized digital currency that could be transferred from one user to another over the peer-to-peer bitcoin network without the use of intermediaries such as banks and other financial institutions. In terms of finance, governments and other experts are concerned that Bitcoin can be used to avoid currency restrictions, for money manipulation, or criminal activities, and that it may be risky for investors due to the absence of regulations. The purpose of this study is to investigate the factors influencing Bitcoin investment adoption in Covid-19, examine the mediating factors influencing Bitcoin investment adoption in Covid-19, and propose a model of influencing Bitcoin investment adoption in Covid-19. Therefore, a methodological method was employed to accomplish these aims. The theory adopted is the theory of planned behaviour. Purposive sampling was used as the sampling method for the study. A total of 101 full responses were obtained to the web-based questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM 3) is utilized to analyze the study's findings. Perceived Behavior Control and Perceived Lack of Alternatives were found to have a favourable association with Intention to Invest in Bitcoin. Meanwhile, Perceived External Pressure has the reverse result, having a marginal influence on the Decision to Invest in Bitcoin. Surprisingly, Perceived Lack of Alternative completely mediates the interaction between Perceived External Pressure and Desire to Invest in Bitcoin. It is anticipated that this analysis would have a clearer picture of Investors' and Consumers' Action on Investing in Bitcoin During Covid-19.