Reviewerly/exHarmony huggingface dataset:
Techno-Economic Analysis of Fixed and Single Axis Tracking Systems Using NPV and IRR Methods

A low power, low cost, high utility value photovoltaic power generating system has been designed and developed with an intention of catering to the needs of the rural population located in far flung villages or hilly regions where electricity supply is either unavailable or is erratic. The proposed solar photovoltaic system has been subjected to rigorous performance evaluation using the four widely used performance metrics namely, Energy Gain, Panel Efficiency, Capacity Utilization Factor (CUF) and Performance Ratio (PR). Performance wise, the proposed single axis tracking system has been proved to be quite advantageous when compared to the latitude-tilt fixed panel system. It now becomes mandatory to ensure that the single axis system is also economically viable. Since the target customers belong to economically weaker strata of the society, the system has to be financially attractive if it has to have widespread acceptance. Financial analysis of the proposed system was carried out using two most trusted methods viz. NPV and IRR methods to ensure the system will return better economic benefits. The net cost of the proposed single axis sun tracking system is INR. 4066.00 and that of the latitude-tilt fixed panel is INR. 3413.00. Financial analysis using NPV method has shown that both the latitude-tilt fixed panel system and the single axis sun tracking systems have positive values of NPV (INR. 3237.02 for fixed tilt system and INR. 4997.12 for single axis sun tracking system). Hence both the project proposals can be accepted since both will be economically feasible for the investor. The financial analysis of both the latitude-tilt fixed system and the single axis tracking system was carried out using IRR method. Results of this financial analysis, show that the latitude-tilt fixed system has returned a value of 13.124% which is less than the normally acceptable value, while the IRR value obtained for the single axis tracking system is 15.491 % which is more than the normally specified level. Thus, the proposed single axis sun tracking system is a better option than the latitude-tilt fixed panel in terms of power output and also in terms of financial analysis
Gradient Flow Analysis and Performance Comparison of CNN Models

CNNs(Convolutional Neural Networks)은 컴퓨터 시각 인식(Computer vision)과 자연어 처리(Natural language processing) 분야에서 뛰어난 성능을 보여 가장 널리 사용되고 있는 딥러닝 방법이다. CNNs은 입력데이터에 컨볼루션 레이어를 연속적으로 적용하는 구조를 통해 입력 데이터의 locality와 correlation을 효과적으로 추출하여 CNNs의 깊이가 깊어질수록 신경망의 성능이 향상되어왔다. 그러나 CNNs의 깊이가 깊어질수록 신경망의 정확도가 반드시 높아지는 것은 아니다. 그래디언트 소실 문제(Gradient vanishing problem)으로 인해 weighted layers의 가중치들이 수렴하지 않는 현상이 발생할 수 있기 때문이다. 이에 본 연구에서는 VGGNet 모델, ResNet 모델, DenseNet 모델의 gradient flow를 분석하고 비교함으로써 각 모델의 error rate 성능에 차이가 나는 근거를 도출하였다.
PENGGUNAAN INSTAGRAM SEBAGAI MEDIA PEMBELAJARAN DALAM MENINGKATKAN PRESTASI BELAJAR BAHASA ARAB MAHARAH KALAM MTs SABILUL MUTTAQIN MOJOKERTO

Arabic is a unique subject because it has many features and also has many problems. Arabic language is also a subject that must be implemented in every madrasa, starting from madrasah ibtidaiyah, tsanawiyah, aliyah and even universities. Because even though Arabic is not part of the official language of the State of Indonesia but Arabic is part of the Islamic religion. Teaching Arabic is not easy, the biggest problem that must be known by the speakers of Arabic subjects is the motivation to learn Arabic for students. One effort that can be done by a teacher in increasing students' motivation to learn Arabic is through developing the use of instructional media. This study aims to determine the use of Instagram as a medium of learning in improving the learning achievement of Arabic Maharah Kalam / MTS Sabilul Muttaqin Mojokerto speaking skills. In this study using descriptive qualitative research methods and data collection techniques using observation, interviews and documentation. The data sources are Mts Sabilul Muttaqin class XB students, and Arabic subject teachers. The results of this study stated that Instagram media can improve students' Arabic learning achievement, especially in the Maharah Kalam.
PERFORMANCE ANALYSIS OF C4.5 AND NAÏVE BAYES ALGORITHM ON CUSTOMER RELATIONSHIP MANAGEMENT (CRM) IN JATIMAS FURNITURE LTD.

Customer relationship management (CRM) was a part of the marketing strategies to maintain customer satisfaction. Customer satisfaction was an important factor in a business to stay afloat or continued to grow so that business managers needed an evaluation from the customers to find out whether customers were satisfied or not satisfied with the services provided by the company under customer expectations. The objective of this study was to measure the customer satisfaction in the Jatimas Furniture Ltd. using C4.5 and Naive Bayes Algorithm. The variables of this study were service, price, promotion, product quality, and facilities. This study was expected to assist the company in increasing the level of customer satisfaction to defend the company from its competitors. After that, the performance analysis of the C4.5 and Naive Bayes Algorithm were done to find out the algorithm that had higher accuracy for evaluating customer satisfaction survey results. The tests were carried out through the Split Validation technique. The result of the tests 1 was that the test 1 used 6 variables of the Naive Bayes Algorithm by generating 81.48% accuracy and AUC (Area Under Curve) was 0.871 through 90% training data and 10% testing data; while, the test 2 used 4 variables of decision trees C4.5 Algorithm by generating 76.19 % accuracy and AUC (Area Under Curve) was 0.500 through 70% training data and 30% testing data. The conclusion of this study was that the accuracy and AUC (Area Under Curve) of the Naive Bayes Algorithm were higher than the decision tree C4.5 Algorithm. Keywords: Customer Relationship Management (CRM), Decision tree C.45 Algorithm, Naive Bayes Algorithm
Can Selfless Learning improve accuracy of a single classification task?

The human brain has billions of neurons. However, we perform tasks using only a few concurrently active neurons. Moreover, an activated neuron inhibits the activity of its neighbors. Selfless Learning exploits these neurobiological principles to solve the problem of catastrophic forgetting in continual learning. In this paper, we ask a basic question: can the selfless learning idea be used to improve the accuracy of deep convolutional networks on a single classification task? To achieve this goal, we introduce two regularizers and formulate a curriculum learning-esque strategy to effectively enforce these regularizers on a network. This has resulted in significant gains over vanilla cross-entropy training. Moreover, we have shown that our method can be used in conjunction with other popular learning paradigms like curriculum learning.
The Death of Victor Hugos

Production of digital literature minimally requires (1) an orthography and (2) a text input mechanism. Many talk about orthography, but few talk about how minority-language users experience their orthographies during the typing process. With a primary focus on Eastern Dan, this paper addresses one method for assessing the typing-difficulty for a language while at the same time comparing typing-difficulty across languages. The perception of difficulty is an important factor in language choice for every multilingual computer-user.
Complete &lt;i&gt;l&lt;/i&gt;-diversity Grouping Algorithm for Multiple Sensitive Attributes and Its Applications

For the first stage of the multi-sensitive bucketization (MSB) method, the l-diversity grouping for multiple sensitive attributes is incomplete, causing more information loss. To solve this problem, we give the definitions of the l-diversity avoidance set for multiple sensitive attributes and the avoiding of a multiple dimensional bucket, and propose a complete l-diversity grouping (CLDG) algorithm for multiple sensitive attributes. Then, we improve the first stages of the MSB algorithms by applying the CLDG algorithm to them. The experimental results show that the grouping ratio of the improved first stages of the MSB algorithms is significantly higher than that of the original first stages of the MSB algorithms, decreasing the information loss of the published microdata.
CONDUCTING DISTANCE E-LEARNING IN THE KINDERGARTEN DURING THE PANDEMIC 2020 USING FACEBOOK

The aim of this paper is to share practices on how to achieve effective distance e-learning during the 2020 pandemic through facebook resources .Below we offer one of the many solutions for teachers to conduct online training with children during flu and special holidays. This year the training in electronic environment was carried out under the Law on Measures and Actions during a state of emergency. Experience has shown that this type of training can be used in any extraordinary circumstances – flu holidays, climate or other factors that do not allow face-to-face training.
PHONEMS IN TOULOUR A CONSTRUCTION TO THE POSSIBILITY OF LEARNING REGIONAL LANGUAGE IN NORTH SULAWESI

Abstract This research was conducted with the aim of knowing how the phonemes of the Toulour Language were seen from their form, function and meaning. The objectives of this study were: 1) to identify the phonemes contained in the Toulour language, 2) to describe the distribution of phonemes in the Toulour language. The method used is a qualitative descriptive method. The data source of this research is informants. To collect data, the techniques used were listening techniques and proficient techniques. The data analysis techniques are: 1) recording sounds that are phonetically similar, 2) recording the rest of the sounds that are similar, 3) contrasting the same or similar environment (using work hypothesis A), and 4) with a complementary environmental basis (working hypothesis B). Keywords: Phoneme, toulour languege
사실적 이해 능력 평가를 위한 한국어능력시험 읽기 문항의 환언 적절성 연구

The purpose of this research is to set appropriate standards for paraphrasing when writing multiple-choice reading questions for testing literal comprehension ability and to make suggestions for appropriate paraphrasing based on the results obtained through test-item analysis. Three main criteria were set for evaluating the level of adequacy in paraphrasing, which includes the adequacy of the paraphrased unit, the consistency of meaning between the original and the paraphrased texts, and the linguistic level of paraphrased text. The unit of the paraphrasing should be done at the syntactic level rather than at the lexical level, and the meanings of the original and the paraphrased texts must exactly match. In addition, the language level of the paraphrased text ought to be equal to or lower than that of the original text. Based on these criteria, the items measuring literal comprehension ability in the TOPIK(Test of Proficiency in Korean) were analysed.First, observing the adequacy of the paraphrased unit, it was found that although paraphrasing was done mostly at the syntactic level, there were more lexical-level paraphrasing in the beginner level than the intermediate and advanced level.Next, the results showed that there were more discrepancies of meaning in the intermediate and advanced level than the beginner level, and more cases of inconsistencies in syntactic-level paraphrasing than thelexical-level paraphrasing.Lastly, it revealed that the ratio of the linguistic level of the paraphrased text being higher than that of the original text were somewhat higher in the beginner level than the intermediate and advanced level.Thus, when creating multiple-choice items for assessing literal comprehension ability, it is necessary to construct the questions based upon these criteria of the adequacy and to check the items against the criteria in the editing stage.
Virtual reality

This chapter examines the 21st-century phenomenon that is manipulated reality experiences. We look at a brief history of VR/AR/MR (see definitions below) and explore how manipulated reality technology is disrupting storytelling and entertainment as we know it. We scrutinize how this medium is dependent on all the past technologies covered yet is radically different from everything that preceded it. We end the chapter with a look into the future of VR/AR/MR and artificial intelligence, and what issues await us going forward to the middle of the century. Could this be the Brave New World foretold and forewarned by visionaries?
An optical flow direction-based full reference video quality assessment algorithm

Modelling of functions of visual cortex and optical flow field has made it possible to develop many sophisticated human visual system-based visual quality assessment algorithms. There exist very few video quality assessment algorithms which consider spatio-temporal distortions effectively. In this article, based on the direction of optical flow field, we propose a full reference video quality algorithm. We claim that some temporal distortions, which are present in other directions than the majority of optical flow field directions, can not be easily perceivable by the human observer; hence it results in reduced video quality scores. Our proposed method achieved high video quality score and higher correlation with formally existed algorithm and near to the objective scores. Our experimental results demonstrate that the proposed algorithm improves the state of art.
인공지능 챗봇의 중국어 교육 활용 방안 탐색

This study aimed to investigate the possibility of using artificial intelligence (AI)chatbot in Chinese class curriculum of university education in Korea. A chatbot using mobile phone and SNS service might be the one of theattractive and useful tools to foreign language learners. This study is to provide basic data for developing Chinese language education programs using chatbots. First, we looked at artificial intelligence technologies that can be used in foreign language education, and looked at the development status of chatbots for foreign language education, which are rapidly developing in recent years, and their use cases in the educational field. Based on this result, we are going to use a chatbot service using a messenger interface for Chinese education in Korea. The aims of this, propose a plan to design a chatbot for learning Chinese in Korea, also propose related educational programs using this and teaching-learning methods applicable in the field of Chinese language education.
Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies

When repairing a device, humans employ a series of tools that corresponds to the arrangement of the device components. Such sequences of tool usage can be learned from repair manuals, so that at each step, having observed the previously applied tools, a sequential model can predict the next required tool. In this paper, we improve the tool prediction performance of such methods by additionally taking the hierarchical relationships among the tools into account. To this aim, we build a taxonomy of tools with hyponymy and hypernymy relations from the data by decomposing all multi-word expressions of tool names. We then develop a sequential model that performs a binary prediction for each node in the taxonomy. The evaluation of the method on a dataset of repair manuals shows that encoding the tools with the constructed taxonomy and using a top-down beam search for decoding increases the prediction accuracy and yields an interpretable taxonomy as a potentially valuable byproduct.
The Effect of Team Game Tournament and Board Game on Students’ Mastery of Simple Past Tense and Past Continuous Tense at SMK Muhammadiyah Karanganyar

The aim of this study was to find out the significant difference on students’ grammar mastery with the application of team game tournament and grammar board game and know the responses of tenth grade students of SMK Muhammadiyah Karanganyar in the Academic Year 2018/2019. This research was a quasiexperimental research. In conducting the research, I used pre-test post-test control group design. The population in this study were the students of grade X SMK Muhammadiyah Karanganyar in the Academic Year 2018/2019. Two classes were selected as a sample of the research taken by simple random sampling technique. They were 30 students from grade X Pharmacy I as the experimental group, taught by using team game tournament and board game. While 30 other students from class X Culinary as the control group were taught by using conventional`s method. The data analysis was calculated by using the t-test formula. Based on the calculation of the t-test, there was a significant increase in students' grammar mastery in the experimental class after being given a treatment that was equal to 12.08.While in the control class only increased as much 3.38. I made conclusion that the use of team game tournament and board game gave better result of students’ mastery of past tense and past continuous tense in grade X Pharmacy 1 in Academic Year 2018/2019. The method and media could raise the students’ interest in learning. Based on the results of the research, the teachers are suggested to use cooperative learning team game tournament and board game on the learning process to improve students’ grammar mastery. Keywords: Team game tournament, board game, and grammar
GDPR and Training: How to organize GDPR compliant online events

Take-aways from the workshop organised by the OpenAIRE Community of Practice of training coordinators
맞벌이 가정 어머니의 직무만족도가 양육행동에 미치는 영향에서 양육 스트레스의 매개효과: 만 2세 자녀를 둔 어머니를 중심으로

The purpose of this study is to analyze the effect of job satisfaction of working women with two-year-old children on the parenting behavior via parenting stress. The objects of this study are 455 working mothers who raise two-year-old children in the third materials(2010) from the Panel Study on Korean Children. The method of research was based on Pearson's correlation analysis to check the correlation between each variable with data by measuring job satisfaction, parenting behavior, and parenting stress. Then, we verified the mediating effect of parenting stress on the effect of job satisfaction on parenting behavior. The results of the study show three conclusions. First, all factors of job satisfaction, except job stability, showed static correlation with parenting behavior. However, in relation to parenting stress, all factors of job satisfaction showed malice correlation. Second, all variable factors on parenting stress and job satisfaction showed malice correlation. Third, job satisfaction had a direct impact on parenting behavior, and an indirect influence with parenting stress as a partial medium. These findings are meaningful as reference materials for parent education for working mothers and as basis for the development of systems and policies on the compatibility of working-family of working mothers.
Memory constraint parallelised resource allocation and optimal scheduling using oppositional GWO for handling big data in cloud environment

In cloud computing, task scheduling is one of the challenging troubles, especially when deadline and cost are conceived. On the other hand, the key issue of task scheduling is to reach optimal allocation of user's tasks in clouds. Besides, in terms of memory space and time complexities, the processing of a huge number of tasks with sequential algorithm results in greater computational cost. Therefore, we propose an efficient memory constraint parallelised resource allocation and optimal scheduling method based on oppositional GWO for resolving the scheduling problem of big data in the cloud environment in this paper. In parallel over distributed systems, the suggested scheduling approach applies the MapReduce framework to perform scheduling. The MapReduce framework is consisted of two main processes; particularly, the task prioritisation stage (with fuzzy C-means clustering method based on memory constraint) in map phase and optimal scheduling (using oppositional grey wolf optimisation algorithm) in reduce phase. The performance of proposed methodology is analysed in terms of makespan, cost and system utilisation.
On a Chatbot Navigating a User through a Concept-Based Knowledge Model

Information retrieval chatbots are widely used as assistants, to help users formulate their requirements about the products they want to purchase, and navigate to the set of items that satisfies their requirements in the best way. The work of the modern chatbots is based mostly on the deep learning theory behind the knowledge model that can improve the performance of the system. In our work, we are developing a concept-based knowledge model that encapsulates objects and their common descriptions. The leveraging of the concept-based knowledge model allows the system to refine the initial users’ requests and lead them to the set of objects with the maximal variability of parameters that matters less to them. Introducing the additional textual characteristics allows users to formulate their initial query as a phrase in natural language, rather than as some standard request in the form of, “Attribute - value”.
User-centred design for multi-user virtual reality art therapy (VRAT) system

With the development of virtual reality (VR) hardware in recent years, a number of industries have led a renewed interest in adopting VR in their fields. Virtual reality therapy (VRT) is one of the domains expecting the effective use of VR technologies because of their presence and vividness. In terms of art therapy, VR has the potential to facilitate creative artwork, not only for individuals but also for groups. However, in art therapy, we have limited knowledge of how to support user experiences, particularly in multi-user VR environments. This study aims to examine user-centred design requirements for successful multi-user VRAT system development. The research collected user-centred data from the perspectives of participants and therapists. The objective of the proposed system is to improve the social skills of teenagers through group VRAT. For data collection, therapists and teenagers were involved in a three-week VRAT session as a group by adopting a variety of research methods: video recording, subjective questionnaire based on five-point Likert scales, System Usability Scale (SUS) and in-depth interviews. As a result, it was found that a multi-user VRAT system can encourage individual creativity and provide new experiences through social interactions. On the other hand, the study points out that solely adding multi-user features to an existing VR drawing/painting application cannot achieve therapeutic goals.
Single-view 3D Reconstruction Algorithm Based on View-aware

While projecting 3D shapes to 2D images is irreversible due to the abandoned dimension amid the projection process, there are rapidly growing interests across various vertical industries for 3D reconstruction techniques, from visualization purposes to computer aided geometric design. The traditional 3D reconstruction approaches based on depth map or RGB image can synthesize visually satisfactory 3D objects, while they generally suffer from several problems: (1)The 2D to 3D learning strategy is brutal-force; (2)Unable to solve the effects of differences in appearance from different viewpoints of objects; (3)Multiple images from distinctly different viewpoints are required. In this paper, an end-to-end View-Aware 3D (VA3D) reconstruction network is proposed to address the above problems. In particular, the VA3D includes a multi-neighbor-view synthesis sub-network and a 3D reconstruction sub-network. The multi-neighbor-view synthesis sub-network generates multiple neighboring viewpoint images based on the object source view, while the adaptive fusional module is added to resolve the blurry and distortion issues in viewpoint translation. The 3D reconstruction sub-network introduces a recurrent neural network to recover the object 3D shape from multi-view sequence. Extensive qualitative and quantitative experiments on the ShapeNet dataset show that the VA3D effectively improves the 3D reconstruction results based on single-view.