Reviewerly/exHarmony huggingface dataset:
Copy and Paste method based on Pose for ReID

Re-identification(ReID) aims at matching objects in surveillance cameras with different viewpoints. It's developing very fast, but there is no processing method for the ReID task in multiple scenarios at this stage. However, this dose happen all the time in real life, such as the security scenarios. This paper explores a new scenario of Re-identification, which differs in perspective, background, and pose(walking or cycling). Obviously, ordinary ReID processing methods cannot handle this scenario well. As we all konw, the best way to deal with that it is to introduce image datasets in this scanario, But this one is very expensive. To solve this problem, this paper proposes a simple and effective way to generate images in some new scenario, which is named Copy and Paste method based on Pose(CPP). The CPP is a method based on key point detection, using copy and paste, to composite a new semantic image dataset in two different semantic image datasets. Such as, we can use pedestrians and bicycles to generate some images that shows the same person rides on different bicycles. The CPP is suitable for ReID tasks in new scenarios and it outperforms state-of-the-art on the original datasets in original ReID tasks. Specifically, it can also have better generalization performance for third-party public datasets. Code and Datasets which composited by the CPP will be available in the future.
Real Time Indian Sign Language Recognition Using SIFT by Depth Sensor

Indian sign language (ISL) provides interface to hard hearing community to communicate with the society. The hand plays a significant role in any sign language. ISL is composed of static and dynamic sign hand gestures. In proposed system, depth sensor is used to track and segment hand in clutter background and feature extracted by scale-invariant feature transform (SIFT). Finally, we recognize 10 hand gestures which represent as number in ISL using multi support vector machine with an accuracy of 94.9%.
Preferences in the quantified description of visual groups

Author(s): Briggs, Gordon; Harner, Hillary; Khemlani, Sangeet | Abstract: Research suggests that people minimize the amount of effort used to generate natural language descriptions of visual scenes. In the case of visual scenes with multiple groups, recent work has found that people tend to generate quantitative descriptions that mention the number and cardinality of groups (e.g., ``two groups of three limes''), but omit the total quantity (e.g., ``six limes''). This finding suggests that people groupitize, that is, they more quickly determine the number of grouped items by rapid enumeration of subgroups, rather than slower item-by-item counting. %This hypothesis predicts that during description, people exert less effort to encode and report only the number and cardinality of groups. A recent proposal predicts that during description, people exert less effort by encoding and reporting only information that is readily available to perception. In previously studied description tasks, people may have omitted the total quantity from their descriptions because of considerations of brevity and informativity. In this paper, we describe a study designed to test how individuals balance effort, brevity, and informativity when evaluating quantified descriptions. The experiment was designed to elicit more fine-grained preferences for descriptions using direct comparisons between two competing descriptive forms. The results suggest that perceptual effort plays a central role in how people describe grouped collections of items.
TRAINING OF FUTURE PHYSICS TEACHERS IN MASTER'S DEGREE FOR RESEARCH ACTIVITIES IN EDUCATION

M. Dammer 1, V. Kudinov 1, E. Leonova1, N. Zubova2 1South Ural State Humanitarian-Pedagogical University (RUSSIAN FEDERATION) 2Tryohkgorny Technological Institute – Branch of the National Research Nuclear University “Moscow Engineering Physics Institute” (RUSSIAN FEDERATION)
An Efficient Algorithm for Mining High Utility Rare Itemsets Over Uncertain Databases

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention. Association Rule Mining (ARM) is a well known and most popular technique of Data Mining. It identifies itemsets from the dataset which appears frequently and generates association rules. This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets. In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue. Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain. High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold. Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit. Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest. In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases. This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al. The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.
Towards Increasing Open Data Adoption Through Stream Data Integration and Imputation

Open data portals are used to make a growing number of government data resources public. However, difficulties in preprocessing and integrating multiple possibly incomplete open data resources hinder the potential of open data re-use for software development. While incomplete data sets can be imputed in an offline process, this is not the case for data streams expected to be published in an online manner. In this work, we propose a novel data stream preprocessing method aimed at simplifying open data stream re-use through the unification of time resolution and imputation of incomplete instances. The method relies on stream mining methods to predict categorical values to be imputed. A separate online learning model is built for every incomplete feature. The method we propose allows the model to benefit from both inter-feature similarities and temporal dependencies present in data streams. We validate the proposed method with public transport data streams.
IMMERSIVE TRAINING OF FIREFIGHTERS THROUGH AUGMENTED REALITY AIMED AT RESCUING FROM ELECTRIC CAR

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 10970-10976ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2276Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
MQDS for PKE through CCA in cloud environment

In this paper, a mutual query data sharing protocol (MQDS) to provide security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-Strong Diffie-Hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.
RANCANG BANGUN AUGMENTED REALITY APLIKASI PENGENALAN KAIN ULOS TRADISIONAL SUMATERA UTARA MENGGUNAKAN METODE MARKERLESS

Wearing the Toba Batak Traditional Ulos is one of the values ​​that is strongly attached to the Toba Batak Tribe custom. Where the ulos has various types and different functions. The problem faced by school teachers in presenting and introducing these types of ulos is the lack of teaching aids. In the world of education, especially in elementary schools, it will feel boring if the delivery of the material is still conventional, still focusing on only using books. To keep up with the times, don’t forget that scienceand technology will also experience developments. With this development, an application was made which is a learning media using technology to help the learning and teaching process. To make it easier to recognize Ulos, it was made based on Android and utilized augmented reality technology in 3D. In designing this application there are also data collection methods such as direct interviews to schools because this will greatly assist teachers in learning to introduce Ulos Batak Toba. The researcher also provides 2D objects made in the bookmark book as a reference to display Ulos with 12 types of Ulos in 3D and along with the functions of the types of Ulos
IMPROVING STUDENT LEARNING OUTCOMES USING KNOWLEDGE DISCOVERY

Early student risk detection and amelioration continue to challenge the management education community especially in light of COVID-19. . The goal of identifying students at academic risk early in the matriculation process is not new. Specifically, Seidman proposed in the late 1990s that both early detection and continuous interventions were keys to student retention. Recent data suggest that machine learning-based predictive models can provide automatic and timely student assessments, which allow for both the detection of students at risk and the implementation of appropriate intervention initiatives. The empirical results from the current study, which included both student demographic and performance data, suggest that machine learning-based knowledge discovery can be used to design customized intervention strategies. Actionable knowledge discovery decision trees have seen increased use in the customer relationship management field to identify specific actions that would transform a fickle customer to one loyal to the organization. The approach, as applied to students, is to develop a decision tree that reports the risk dropout levels, i.e., percentages, for the various relevant student categories, e.g., gender, quizzes, work experience. These trees can be used to identify a set of interventions that maximizes the chances of transforming a student at risk into the mainstream student body. The primary purpose of this presentation is to highlight how knowledge discovery can be used to design specific intervention strategies with the goal of improving both student retention and overall learning outcomes throughout the management education community of practice.
세종학당 한국어 교원 운영 현황 분석 연구

The present study suggests ways to improve faculty management at the King Sejong Institute through an analysis of the data of the past three years. The analysis reveals the following: 1) the total number of Korean teachers has increased over the past three years; 2) the number of dispatch teachers has also increased, especially at the lowest level, and 3) the number of locally hired teachers also increased; however, the ratio of the experienced teachers and the teachers holding Korean language teachers’ certificates remained largely unchanged. In the light of this analysis, this study proposes the following as ways to improve faculty management at the King Sejong Institute: 1) a clear division of responsibilities should be provided through job analysis, 2) the current faculty rank system should be subdivided and teacher education should be redesigned in accordance to the system, and 3) it is necessary to strengthen teacher education for locally hired teachers. (Hansung University Chonnam National University King Sejong Institute Foundation Harvard University Korea University)
READING HABIT AMONG THE STUDENTS IN SEKOLAH KEBANGSAAN SERI PAKA, PAKA, DUNGUN,TERENGGANU.

Reading is the bridge of knowledge. Reading is a tool needed by learners to understand all other subjects. Children begin to develop lifelong attitudes toward reading in the home. Parents are the first person being identified as the agents that can enrich the language environment of a community. Anyhow, teachers play important role to cultivate good reading habits among the students. Nowadays, the children are exposed to varieties of funky gadgets which slowly taking a steady control over individual lives and gradually vanish the reading habits among them. This phenomenon will affect the quality and achievement of education in the country. The study examined the students’ reading habits, the purpose of reading and the hindrances to reading. Two hundred and twenty primary school pupils were drawn to answer the questionnaires. Participants were mainly drawn from standard four, five and six. Their ages ranged from 10 – 12 years old. Results further showed that pupils spent most of the time watching television and playing games rather than reading. The availability and accessibility for reading such as convenient facilities and reading materials were provided by parents and school library. Variety of reading materials and furnishing school libraries could not attract them towards love and choose reading as habit. Yet, they refused to read during their free time. They read just to pass the examination and not for being well-informed person. Recommendations based on the results of the study for parents, teachers and schools are highlighted
청소년 자원봉사의 정책담론에 대한 비판적 논의

This paper address volunteering of youth as an important socio-cultural trend which have been generally taken for granted and not interrogated. The volunteering had been regarded as a charity which came from altruism and intimacy in community. But in the neoliberal post-welfare society, the policy discourse of youth volunteering have been changed by the state. Now, Young people have to participate voluntary activity in order to enter university or to get a job in intense competition. The field and scope of volunteering of youth have been expanded and the policy of volunteering have influenced on the life of young people. To understand the changing of volunteering policy discourse, researcher have investigated the historical and sociological contexts of policy discourses in the case of USA, British, China and Korea and have tried to interpretate it’s meanings. The main points that have constructed the policy discourses of youth volunteering, ‘service learning’, ‘citizenship’, and ‘service work’ have been discussed. Lastly, it has been discussed the possibility of alternative perspectives to learning to become active citizenship through youth volunteering.
Map of Earthquake Prone Areas Using Geographic Information Systems(Peta Daerah Rawan Gempa Menggunakan Sistem Informasi Geografis)

This study aims to map the earthquake prone areas, and identify the nearest alternative route for evacuation of earthquake victims in Singkarak District, Solok Regency, West Sumatra. This research method was conducted with descriptive methods that analyzed by GIS (Geographic Information System) based on intensity scale (MMI). The results of this study stated that 90% of villages in Singkarak District had a high earthquake intensity, with an acceleration of the land surface of more than 0.34 g. For evacuation areas can be directed away from areas with steep cliffs and areas close to high buildings and should be directed in the direction close to Lake Singkarak, because the area around this has a low earthquake risk with earthquake acceleration of only 0.1 g.
UPAYA MENINGKATKAN HASIL BELAJAR EKONOMI MELALUI MODEL PEMBELAJARAN PROBING PROMPTING PADA PESERTA DIDIK KELAS XI.IPS 1

The problems in this study are 1) the low economic learning outcomes of students, 2) teaching and learning activities do not actively involve students, 3) only a small portion of students are active during the learning process and 4) the variation of learning models that have not used to make students become bored easily and bored. In this study the authors used a probing prompting learning model with the aim of increasing economic activity and learning outcomes of class XI students. IPS 1 in economic subjects in SMA Negeri 1 Baradatu in the academic year 2019/2020. This research is a Classroom Action Research (CAR) conducted in two cycles which in its application uses the probing prompting learning model. The subjects in this study were students of class XI.IPS 1 SMA Negeri 1 Baradatu, amounting to 33 students. The results showed the percentage of students' activities increased from 50% in cycle 1 to 75.55% in cycles 2. The percentage of student learning outcomes also increased from 60.61% in cycle 1 and 84.85% in cycle 2. So it can be concluded that the learning activities by applying the probing prompting learning model can improve the economic learning activities and outcomes of students in class XI. IPS 1 SMA Negeri 1 Baradatu in the academic year 2019/2020.
Fine-tuning GPT-3 for Russian Text Summarization.

Automatic summarization techniques aim to shorten and generalize information given in the text while preserving its core message and the most relevant ideas. This task can be approached and treated with a variety of methods, however, not many attempts have been made to produce solutions specifically for the Russian language despite existing localizations of the state-of-the-art models. In this paper, we aim to showcase ruGPT3 ability to summarize texts, fine-tuning it on the corpora of Russian news with their corresponding human-generated summaries. Additionally, we employ hyperparameter tuning so that the model's output becomes less random and more tied to the original text. We evaluate the resulting texts with a set of metrics, showing that our solution can surpass the state-of-the-art model's performance without additional changes in architecture or loss function. Despite being able to produce sensible summaries, our model still suffers from a number of flaws, namely, it is prone to altering Named Entities present in the original text (such as surnames, places, dates), deviating from facts stated in the given document, and repeating the information in the summary.
The Result of a Square Root Transformation on the Error Part of the Additive Time Series Model

The square root transformation of the error component of the additive time series model was studied. The distribution of the transformed error component was established and shown that it is a proper probability density function. The properties of the distribution were investigated. It was shown that the mean of the transformed series is equal to one while the mean of the original series = 0 for . Also, the variance of the transformed was established and given as and is four times that of the original series for to one decimal place. The transformed series is normally distributed for .
Ontological analysis of the models of scientific conceptuality bordering on metaphysics

The article analyzes some conceptual models of modern science, which border on metaphysics, that is, when working with them, they require an assessment of their ontological and cognitive status. The concepts of “dark” or “black” matter are considered in connection with the so-called problem of “mass defect”, and negative quantities in mathematics. Questions are raised about the nature and cognitive meaning of understanding the notion of matter within the framework of conceptual scientific models, which allows us to identify the cognitive grounds for using these concepts and their relationship with metaphysics. As a result of their analysis, the authors come to the conclusion that the system of the Universe has more than one direction of development, the movement of being does not go in a straight line, it is not one-dimensional, but is described by multidimensionality. Since this movement of being is volumetric and multidirectional, it is not possible to build a substantiated theoretical and cognitive model of objects with a completely opposite direction of development in complex, multidimensional systems with nonlinear development. The phenomena studied by science have a single ontological basis, which not only provides a close relationship between the conceptual apparatus of science and philosophy, but also allows us to assert that numerous concepts that do not have a fundamental cognitive status in science can and should be interpreted within the framework of metaphysical conceptuality.
Review Pemetaan Domain Pengetahuan Tentang Navigasi Dalam Ruangan Melalui Pendekatan Bibliomatrik

Indoor navigation has been recognized as a technological innovation in the field of navigation. Due to the limitations of GPS in indoor areas, indoor navigation has become a growing area of research in recent years. Due to the rapid development of research in the field of indoor navigation, various stakeholders require an up-to-date review of the research and implementation of indoor navigation. The purpose of this study is to provide an objective and accurate summary of the domain in indoor navigation research using 1618 papers related to indoor navigation taken from the SCOPUS database from 2016 to 2021 using bibliometric analysis. The results are useful for identifying clusters and research topics in this community, such as mobile robots, indoor navigation systems, 3d pedestrian indoor navigation and other clusters. More importantly, these results can help highlight how research related to indoor navigation has evolved, thereby greatly contributing to understanding the underlying structure of the field. This study offers useful and new insights to encapsulate the status quo of Indoor Navigation knowledge and can be used as a dynamic platform to integrate the future development of Indoor Navigation
Multi-channel quantum communication receiver made from waveguide-integrated superconducting nanowire single-photon detectors

We present a fiber-coupled multi-channel waveguide-integrated superconducting single-photon detector array for fast, efficient and highly parallelized counting with below 65 ps timing accuracy for high-rate quantum key distribution implementations at telecommunication wavelengths.
Automatic Age and Gender Estimation using Deep Learning and Extreme Learning Machine

Age and gender classification has become applicable to an extending measure of applications, particularly resultingto the ascent of social platforms and social media. Regardless, execution of existing strategies on real-world images is stillfundamentally missing, especially when considered the immense bounced in execution starting late reported for the related taskof face acknowledgment. In this paper we exhibit that by learning representations through the use of significant Convolutiona lNeural Network (CNN) and Extreme Learning Machine (ELM). CNN is used to extract the features from the input imageswhile ELM classifies the intermediate results. We experiment our architecture on the recent Adience benchmark for age andgender estimation and demonstrate it to radically outflank current state-of-the-art methods. Experimental results show that ourarchitecture outperforms other studies by exhibiting significant performance improvement in terms of accuracy and efficiency.