Reviewerly/exHarmony huggingface dataset:
Realization of Discovery for Burst Topic Transition Using the Topic Change Point Detection Method for Time-Series Text Data

In this paper, we present a realization method of discovery for burst topic transition using the topic change point detection method for time-series text data. In our method, we focus on the topic change point detection method for time-series text data. By similarity measure using the topic change point detection method for time-series text data, we can discover for burst topic transition. In general, when we would like to understand the outline or main points of an event, we often read articles written by people who know information about the event or ask others who are aware of the event to tell us about it. However, the information obtained by these means is hearsay from others and subject to third-party bias, it is difficult to comprehend the events objectively. In our paper, we focus on the topic change and extract the topic change point detection It enables us to discover burst topic transitions. In this paper, we describe an evaluation experiment of a prototype system using our discovery for burst topic transition to verify the effectiveness of our method. We also implement an application by the user interface that provides some crews of a trendy word.
Extraction of Choreography for Service robots from a Ningyo Joruri Acting script by associative model

What we should do with service robots to attract people’s attention and communicate more smoothly? We are proceeding with the robot OSONO project, which is referring to Japanese Joruri puppets, with the theme of utilizing expressions that incorporate the "Performing arts and technology" that has been passed down through the ages. In this paper, we propose the associative model that clarifies the correspondence between Ningyo Joruri’s acting script of the performance, and the choreography, and its meaning. And using this method, we discover a number of choreographies that can be used for service robots from the performance record video. Then we will prototype robot OSONO2 and implement this choreography on it so that we confirm that it is possible to express typical poses and typical choreography comfortably by mainly static evaluation. As a result, a series of flows for extracting the choreography for service robots from the acting script / acting video will be established.
Using Label Information in a Genetic Programming Based Method for Acquiring Tag Tree Patterns with Vertex Labels and Wildcards

Machine learning and data mining from tree structured data are studied intensively. In this paper, as tree structured patterns we use tag tree patterns with vertex and edge labels and wildcards in order to represent label connecting relation of vertices and edges in tree structured data. We propose an evolutionary learning method based on Genetic Programming for acquiring characteristic tag tree patterns with vertex and edge labels and wildcards from positive and negative tree structured data. By using label information, that is, label connecting relation of positive examples, as inappropriate individuals we can exclude tag tree patterns that do not satisfy label connecting relation of positive examples. We report experimental results on our evolutionary learning method and show the effectiveness of using label connecting relation of positive examples.
Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

Along with the increasing prominence of energy and environmental issues, solar energy has received more and more extensive attention from countries around the world, and the installed capacity of photovoltaic power generation, as one of the main forms of solar energy development, has developed rapidly. Solar energy is by far the largest available source of energy on Earth, the use of solar power photovoltaic system has the advantages of flexible installation, simple maintenance, environmentally friendly, etc., by the world's attention, especially the grid-connected photovoltaic power generation system has been rapid development. However, photovoltaic power generation itself is intermittent, affected by irradiance and other meteorological factors very drastically, and its own randomness and uncertainty are very large, and its grid connection affects the stability of the entire power grid. Therefore, the short-term prediction of photovoltaic power generation has important practical significance and guiding meaning. Multi-input deep convolutional neural networks belong to deep learning architectures, which use local connectivity, weight sharing, and subpolling operations, making it possible to reduce the number of weight parameters that need to be trained so that convolutional neural networks can perform well even with a large number of layers. In this paper, we propose a multi-input deep convolutional neural network model for PV short-term power prediction, which provides a short-term accurate prediction of PV power system output power, which is beneficial for the power system dispatching department to coordinate the cooperation between conventional power sources and PV power generation and reasonably adjust the dispatching plan, thus effectively mitigating the adverse effects of PV power system access on the power grid. Therefore, the accurate and reasonable prediction of PV power generation power is of great significance for the safe dispatch of power grid, maintaining the stable operation of power grid, and improving the utilization rate of PV power plants.
A Possibilistic Fuzzy Approach for Novelty Detection with Automatically Adjusted Number of Clusters

This paper introduces a new modification of the Possibilistic Fuzzy multiclass Novelty Detector for data streams (PFuzzND). Mentioned modification is based on the implementation of the automated adjustment of the number of clusters for each class (determined beforehand or during the novelty detection procedure) to improve algorithm’s ability to divide objects into small groups. As result, the proposed approach generates models with flexible class boundaries, which are capable to identify new classes or extensions of the ones that are already known as well as the outliers. Proposed possibilistic fuzzy algorithm for novelty detection was used to solve various benchmark problems with synthetically generated datasets. In order to show the workability and efficiency of the introduced modification its results were also compared with the results obtained by the original PFuzzND algorithm. Thus, it was established that the PFuzzND technique with automatically adjusted number of clusters allows achieving better results in regards to the accuracy, the Macro F-score metric and the unknown rate measure. Comparison to the original algorithm showed that the proposed modification outperforms it but is sensitive to the parameter settings, which can be also said about the PFuzzND method. Therefore, the MPFuzzND approach can be used instead of the original PFuzzND algorithm for other classification problems.
Automatic extractive summarization for Japanese documents by LDA

The demand for automatic summarization of newspaper headlines and article sum- maries has increasing with various studies on automatic summarization being currently conducted. However, there are only a few studies on Japanese documents as compared English documents. In this paper, wheter existing summarization methods can be effective for academic pa- pers written in Japanese is verified. First, we demonstrate the effectiveness of topic-based extractive summarization methods Latent Semantic Analysis (LSA). Then, a more effec- tive topic-based extractive summarization is possible by using Latent Dirichlet Allocation (LDA) is demonstrated.
The Survey of Data Stealing on Virtual School Based on NFC

In this Digital Era, we are facing a lot of threats to protect our data. One of the latest technology on the short raneg communication is Near-Field Communication (NFC) with so many implications of technology that ranging from Non-physical access control to digital payments. These applications are frequently proclaimed as being more secure, as they require near physical vicinity and don't include Wi-Fi or versatile systems. In any case, these frameworks are still defenseless to security assaults at the time of the exchange, as they require small to no extra confirmation from the user’s end. The problem that is the main concern of our paper is about NFC Data Stealing. Not everyone realized that their data get stolen by the Cyber Criminal. A lot of people complacent by NFC Technology that makes data transmission a lot easier, but they only see the good side and close their eyes on the bad side. The problem with NFC is that the data becomes a lot more accessible, and this opens wide the windows for the cyber criminals to steal any data as they like. This opens a new window for them to steal any personal data in no time. Our research method is a survey paper that is based on a real-life case, this method can help us identify a lot of case types. These findings may help all of us to understand more about the problem that we don't even realize.
Forced Selective Information Reduction for Interpreting Multi-Layered Neural Networks

The present paper aims to reduce unnecessary information obtained through inputs, supposed to be inappropriately encoded, for producing easily interpretable networks with better generalization. The proposed method lies mainly in forced reduction of selective information even at the expense of a larger cost to eliminate unnecessary information coming from the inputs in the initial stage of learning. Then, in the later stage of learning, selective information is increased to produce a small number of really important connection weights for learning. The method was preliminarily applied to two business data sets: the bankruptcy and the mission statement data sets, in which the interpretation is considered as important as generalization performance. The results show that selective information could be decreased, though the cost to realize this reduction became larger. However, the accompa- nying selective information increase could be used to compensate for the expensive cost to produce simpler and interpretable internal representations with better generalization performance.
Social Media Spamming on the Class Room Electronic

In the current state of the world, the issue of identifying spammers has received escalating attention due to its influence towards social network security. The popularity of social networking sites made them unsurprisingly easy target for most spammers due to the ease of information sharing they provide. While having important information shared easily is a good thing, the extra bits of harmful objects including viruses or malwares are not. Not to mention the irrelevant information found across almost everywhere in the social media shared by said spammers. Spamming does not only affect social media but could also affect most websites and e-mails. Thus, an extensive action is needed to detect and counter the act of spamming, and this study attempts to review them.
An Implementation of Expilicit Instruction Strategy in Teaching Recount Text on the Students Writing Skill

In English learning, there are four skills that need to be mastered by English students, they are listening, speaking, reading and writing. As four skills writing is one of the most important skill for students. Writing becomes one of the most important aspect of the language skills because the students are helped to expand the other language skill through the teaching of writing skill by teacher. The purpose of this research was to find out whether any significant effects of using Explicit Strategy in teaching recount text on students writing skill at the first grade of SMPN 26 Kerinci. The population of this research was the first grade of SMPN 26 Kerinci, there were 28 students from two classes. The sample used totaling sampling. Based on the result of this research, there is significant effect Explicit Strategy in teaching recount text on students writing skill at the first grade of SMPN 26 Kerinci. Therefore, the researcher suggested for English teacher to use Explicit Instruction Strategy in teaching writing, especially in teaching of writing recount text.
Drawing as reflection strategy for immersive virtual reality learning to enhance students’ science outcome

Immersive virtual reality (IVR) technology has a great potentiality in providing high presence and in-time interactions to simulate real learning situations by presenting 3D visualization to enhance the effectiveness of learning about biology, especially genetics topics which on a submicroscopic level including genes, chromosomes, and DNAs are abstract and cannot be directly perceived or touched. In this study, different types of media learning environments, including traditional PC-slides and IVR, such as VR-game, which were employed for experiential learning of science courses with 109 students in the middle school. The purpose of this study was to investigate the effects of reflection strategy and type of media on participants’ cognitive outcomes. Significant interaction between media and methods illustrated the reflection strategy enhanced learners' cognitive performance through immersive VR learning environments (IVREs), but not traditional PC-slides. By using the drawing method, the VR-game made students the most engaged in learning and focus on the key points of important concepts; non-reflection learners focused on games and their learning was easily disturbed. It was concluded that the immersive virtual reality with the specific reflection method was effective in increasing learning performance and the drawing as a generative learning strategy indeed reduced the external cognitive load of learning and promoted the effectiveness of cognitive processing.
Sarcasm Detection with External Entity Information

Sarcasm is generally characterized as ironic or satirical that is intended to blame, mock, or amuse in an implied way. Recently, pre-trained language models, such as BERT, have achieved remarkable success in sarcasm detection. However, there are many problems that cannot be solved by using such state-of-the-art models. One problem is attribute infor- mation of entities in sentences. This work investigates the potential of external knowledge about entities in knowledge bases to improve BERT for sarcasm detection. We apply em- bedded knowledge graph from Wikipedia to the task. We generate vector representations from entities of knowledge graph. Then we incorporate them with BERT by a mechanism based on self-attention. Experimental results indicate that our approach improves the accuracy as compared with the BERT model without external knowledge.
A Preliminary Study on the Medium-term Plan of Public Universities Transferred from Private Universities

This preliminary study visualizes the characteristics of the medium-term plan of ten universities that were transferred from private to public ones. The analysis tool is KH Coder version 3. Drawing 2-D maps in three ways: Co-occurrence network, Correspondence analysis, Self-organizing map make it to interpret the whole picture.
Garment Inventory Management Systems

One of the difficulties to create stock control is the way to deal with the stockroom and organize stock to satisfy both on the web and offline (retailer) orders. Mostly, the current improvement of stock administration by the garment industry is yet running physically utilizing paper and notes. It causes miscalculation and loss of stock by the piece of clothing industry happened when recording the stock information. Additionally, it influences shrinkage and quality fall flat reduces which prompts the inconsistency between the recorded stock and the actual stock. The fundamental motivation behind this theory is to assemble an answer for stock administration for Garment Industry and encourages the piece of clothing industry with the Buka Gudang – Inventory Management System. Advantage from Buka Gudang – Inventory Management System for an article of the clothing industry is to improve for eliminating bottlenecks and disposal of repetitive advances can without much of a stretch be accomplished, encourage Garment Industry for a mechanized report of stock by checking stock rundown that will be refreshed electronically each time a deal is made and print out the report featuring the stock to be restocked, and Help business for keeping up the stock rundown naturally and give an exactness to each refreshed information, for example, a precise numerical estimation on Buka Gudang – Inventory Management System. Investigation measure for this postulation is finished by Data Gathering through Interview with a few dealers around Tanah Abang Jakarta who possessed a centerpiece of the clothing industry, at that point doing some perception on the stock framework and current piece of clothing industry circumstance that requirements stock administration to have the option to control the stockroom of an article of the clothing industry. Inventory, Control, Garment, Stock, Online
Lived Experiences of Science Major Students in the Absence of Laboratory Activities

The importance of laboratory activities in science education cannot be overstated. Scientific laboratory activities and experiments have helped students absorb science lessons more easily in recent years. Science cannot be more valuable to students if they do not get laboratory experience in school. As a result, the goal of this research is to learn about science major students’ lived experiences in learning science with the lack of laboratory activities while also identifying the obstacles they face. In an in-depth semi-structured online interview, ten science major students were asked to describe their learning experiences in science with and without laboratory activities. The students’ shared experiences described learning situations that did not involve laboratory activities as “finding light in the dark”. To put it another way, the students find the learning materials difficult to understand because of the unfamiliar terms they have encountered. Moreover, the lack of learning resources and home-based experimentation equipment and materials to perform the required laboratory activities made it even more difficult for them to gain new knowledge and formulate specific scientific explanations.
Information And Communication Technology Involvement For Social Development

One of India's most vibrant industries, the IT sector has the potential to produce ground-breaking outcomes for the country's economy as well as for its general growth if it is managed properly.India is still in the process of evolving, thus there is still plenty that may be done to further growth and progress.The social development of the nation is one such area where ICT can be of the utmost benefit because it is one where we all fall short and lag behind first-world countries.By putting more effort into social development, we can close the digital divide that currently exists in our nation.The poor continue to lack access to resources that the wealthy have in plenty.This research aims to investigate how information computer technology can promote social advancement in thirdworld emerging nations.It also emphasises how information technology is being introduced to outlying regions in an effort to spur growth and bring about the crucial digitization.By highlighting all the feasible ways to connect with the general public, who is still unaware of the existence of the digital world, the study seeks to close the gap between what is and what might be.ICT for social development will only strengthen goodwill and advance our nation, which is already praised for its growing IT sector on a global scale.This study paper intends to raise awareness, establish strategies for achieving social development through the technical sector, and make things simpler and more accessible.Researchers hope to integrate ICT into the social development sector and attract attention to the under-discussed issues by identifying the difficulties and also offering solutions.
Preservation and Presentation of Roland Eötvös' Scientific Heritage

In association with UNESCO, the world’s scientific community commemorated in 2019 the 100th anniversary of the death of Roland Eötvös, a worldfamous Hungarian polyhistor. This paper presents various multimedia tools and services applied in the commemorative year to preserve and present his multifold scientific and societal heritage on Web and mobile platforms.
Trends in the Evolution of the Digital Financial Assets Market in the Context of the Digital Transformation of the Global Economy

The study focuses on identifying the driving forces behind the digital transformation of the economy in the financial sector and the development of the digital financial assets (DFA) market. The subject of the research is the factors of digital transformation and the DFA market. The relevance is due to the transformation of the world economy, associated, among other things, with the active development of the DFA market, the expansion of the possibilities of using distributed ledger technologies (DLT) and blockchain against the background of high growth rates of the cryptoasset market. The aim of the paper is to summarize the main trends in the development of the global cryptoasset market, determine the main factors of investment attractiveness of cryptocurrencies and explore the conditions for the successful implementation of various models of digital currencies of central banks (CBDC). In the course of the work, the method s of systematization and classification of information, multivariate statistical analysis were used. As a result of the study, 5 clusters of cryptocurrencies were identified, depending on the dominance in the market and the dynamics of price changes. The resulting functions can be used to predict the attribution of cryptocurrencies to the corresponding clusters. Among the factors that have a significant impact on the development of CBDC projects in general and with the wholesale model in particular, one can single out “capital”. At the same time, CBDC projects with a retail model are actively and successfully implemented in countries with a high level of technical knowledge and entrepreneurial talent, ahead of the capital factor in their importance. Taking into account the review of global trends, the development of fintech technologies, it was concluded that the processes of digitalization of financial assets are inevitable, the emergence of new forms of digital assets that dictate the need for the advanced development of their legal regulation.