Reviewerly/exHarmony huggingface dataset:
KISAH KHOIRI

Covid-19 pandemic have quite significant impact on the Indonesia education especially for Early Chilhood Education (ECE). The Indonesian government made a new policy on education including encouraging online learning for students. This model which is strongly influenced by current technological trends as part of the 4.0 industrial revolution. For example: the using of sosial media channel in teaching learning activity. Based on design thinking process, this paper proposes a practical animation video “Kisah Khoiri” to suppoert teaching learning activity. This research used Design Thinking method through experiments and direct trials with data collected qualitatively through deep interviews with key community figures and direct observation of the sample community. The Semantic Differential method used to understand the response captured by users of the “Kisah Khoiri” animation video. This research is very important to help teachers to deliver the knowledge through animation video. Hopefully, this research can be used as a reference to published animation to support teaching learning activity relatively productive during the pandemic.
Surface Material Classification Based On Tapping Sound Characteristics

Target material recognition is of great significance for robot grasping and manipulation. Humans can easily recognize objects through a combination of multiple senses. However, it is difficult to distinguish objects with the same shape but completely different internal materials for a vision-only robot. This paper proposes, for the first time a surface material classification based on tapping sound features to make up for the lack of machine vision. The algorithm includes audio data collection, data preprocessing and category judgment, etc. To evaluate the performance of the algorithm, we performed two tasks: distinguishing the category of objects based on the audio and judging whether the two audios are from the same category of objects. Experimental results show that the accuracy of the two tasks reaches 87% and 98%, respectively. This is enough to prove the feasibility of the algorithm in this paper.
Quantum Conformance Test

We introduce a protocol addressing the conformance test problem. We demonstrate theoretically and experimentally that a simple quantum strategy, using available re-sources and measurement schemes (two-mode squeezed vacuum, photon-counting) can outperform classical strategies.
Non-trigonometric Waveform and Iterated KMD

In this chapter we will consider the mode recovery Problem 1 generalized to the case where the base waveform of each mode is the same known, possibly non-trigonometric, square-integrable 2π-periodic function t → y(t), and develop an iterated micro-local KMD algorithm for its solution.
Modelling the Inhibitors of Online Learning Over 4G Networks: ISM-MICMAC and FMICMAC Analysis

Online learning is a well-proven application in all walks of the teaching–learning process that depends on wireless mobile technology and Internet connectivity. It is accomplished through mobile handheld devices and wireless networks. It has proved its importance in this COVID pandemic era for distant learning to facilitate online classes for all university students. The first evolution of remote Internet-based learning was termed electronic learning (e-learning) with wired net-connected desktop computers. Mobile learning or m-learning is accomplished with wireless Internet connectivity-enabled laptops or mobile handheld devices. Now, this technology of the teaching–learning process is widely accepted as the education system of pandemic-affected world and is termed online learning. Online learning is a subset of mobile learning over wireless networks using portable electronic devices. Inhibitors caused due to the existing 4G wireless networks, mobile handheld devices, and the outlook of end users are the main factors selected for this particular study. Inhibitors of online learning over 4G networks were identified through an intensive study conducted by discussions with experts, end users, designers and learners. The methodology adopted for this research work is the fuzzy matrix of cross-impact multiplications applied to classification (MICMAC). The fuzzy-MICMAC method is used to analyse the selected inhibitors of online learning over wireless networks. A digraph with all possible interconnections, correlation study and the comparative analysis of fuzzy logic approach to the binary logic ISM-MICMAC method were implemented. More outlined results were achieved with the fuzzy analysis. The driving power-dependence abilities of these inhibitors were recorded, which will resolve many undue variables in the future implementation of the system.
Deep Multi-task Learning for Shadow Detection and Removal

Shadows actually play an important role in image understanding. But even for the same object, the intensity and shape of the shadows can vary with the environment. Thus it is a quite changeling issue to detect and remove shadows from images. Recent studies have been trying to solve these two tasks independently, but they are closely related to each other actually. Therefore, we propose a multi-task adversarial generative networks (mtGAN) that can detect and remove shadows simultaneously. For the proposed mtGAN, the cross-stitch unit is applied to learn the optimal ways to share features between multi-tasks, which is not set empirically as usual. Also, the combination weight of multi-task loss functions are trained according to the uncertainty distribution of each task. Based on these multi-task learning strategies, the proposed mtGAN can achieve shadow detection and removal tasks better than existing methods. In experiments, the effectiveness of the proposed mtGAN is shown.
Biometric products star at first online CES show

The Consumer Electronics Show (CES) – which usually draws 200,000 visitors to Las Vegas annually – took place online-only this year, but biometric products still featured strongly.
Probabilistic Traffic Tweet Generation Model

In this paper, we proposed a model which tries to generate natural language tweets. Sometime it becomes difficult for us to share information about certain incidents instantly. We have analyzed the Kolkata Police Traffic Twitter handle for day to day traffic-related tweets and tried to generate a new tweet based on certain Natural Language Generation (NLG) techniques. In the classical Rule-based text generation domain some rigid rules, templates, and heuristics are employed. Due to the lack of human touch and low scalability, these generated sentences become monotonous. In the modern era, we generally use neural networks-based models in this domain, but it needs a powerful computing framework. We provide an effective probabilistic solution to generating tweets and the significance of this is it does not require a powerful computing framework and also escapes the monotonous tweets of the classical approach. Exhaustive experimentation has been done, considering various use cases. The proposed model outperforms (improvement of on an average 8–38%) comparing the other state of the art models.
Experimental Detection of BPSK Signals with a Qubit Sensor

We experimentally execute recently developed quantum signal detection protocols and show that they remain effective when applied to real-world communications signals that do not meet the assumptions of the original formulation of the protocol.
Towards Body Language Investigation: A Contrastive Survey of Uncovering Areas of Convergences and Divergences between English and Arabic Facial Expressions

Nonverbal communication is of various realizations across the world's languages where body language (henceforth BL) is one of its manifestations.The present study is concerned with investigating BL in standard English and standard Arabic to uncover the areas of convergences and divergences of facial expressions.The study is based on three hypotheses.First, some facial expressions tend to have the same forms and meanings in both languages.Second, due to cultural differences, some English facial expressions are probably absent in Arabic and vice versa.Third, when English-speaking people communicate, they are likely to use facial expressions more than Arabs are.However, the data collected from both languages depend on a variety of sources that belong to different fields of knowledge (e.g.sociology, psychology, anthropology, linguistics and communication).After the data analysis carried out in the light of an eclectic model based on Van Els et al. (1984) and R. Lado (1957), the following conclusions were drawn.First, it has been remarked that some BL aspects have the same form in both languages, but they convey different meanings.Further, some have the same meaning in these two languages, but their forms are different.Second, because of the cultures and languages interaction, some facial expressions transfer from one language into another.Third, facial expressions are considered to have the crucial role in communication because people look at each others' faces when they communicate.Fourth, it has been found out that one of the similarities between both languages is that some aspects of BL have been noticed to be similar in form and meaning alike.Accordingly, the first hypothesis has been accepted.Fifth, the theoretical investigation has indicated that owing to cultural differences between both languages, some BL aspects have remained absent in Arabic or in English.This has verified hypothesis No. 2. Sixth, depending on the statistics of using BL by English-speaking people and Arabic-speaking people in the present study, one can conclude that English people use bodily movements (especially facial expressions) more than Arabs do because they believe that their language enjoys sanctity due to the fact that the Glorious Qur'an is written down in it.Notwithstanding the evidence, they stick to use it verbally and tend to minimize the nonverbal use of language.This has given rise to the higher frequency use of the spoken form by Arabic native speakers.This leads to verify the third hypothesis.Seventh, one cannot figure out the BL aspects in his native language unless s/he investigates them.Therefore, some forms and meanings of Arabic BL aspects seem strange to Arabic-speaking people, but in fact they are Arabic BL aspects.
Cybersecurity Risk

We develop a novel firm-level measure of cybersecurity risk using textual analysis of cybersecurity-risk disclosures in corporate filings. The measure successfully identifies firms extensively discussing cybersecurity risk in their 10-K, displays intuitive relations with quantitative measures of cybersecurity risk disclosure language, exhibits a positive trend over time, is more prevalent among industries relying more on information technology systems, correlates with several characteristics linked to firms hit by cyber attacks and, importantly, predicts future cyber attacks. Stocks with high exposure to cybersecurity risk exhibit high expected returns on average, but they perform poorly in periods of increasing attention to cybersecurity risk.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Development of higher education in the regions of Russia: pedagogical staff number

This research aimed to assess the indicators that characterize the number of teaching staff at universities and other higher education institutions (HEIs) in the regions of Russia. These indicators were: (1) the number of teachers per thousand working-age residents of each of the regions, (2) the average number of teachers per HEI in the region, (3) the proportion of teachers who have a Doctor of Science (DSc) and a Doctor of Philosophy (PhD) degree in the number of teachers working in HEIs in the region, and (4) the number of students per teacher working in HEIs. We used official statistical information of 82 regions of Russia. We found that, on average, there are slightly more than two teachers working in HIEs per thousand working-age residents. The average number of teachers per HIE in Russia is 158. In universities, the average number of students per teacher is more than 20. The proposed methodological approach and the obtained results are a scientific novelty, since the assessment of regional characteristics of the number of teaching staff in the regions of Russia has not been carried out before.
Noninformative Vision of Body Movements can Enhance Tactile Discrimination

Vision of the body without task cues enhances tactile discrimination performance. This effect has been investigated only with static visual information, although our body usually moves, and dynamic visual and bodily information provides ownership (SoO) and agency (SoA) sensations to body parts. We investigated whether vision of body movements could enhance tactile discrimination performance. Participants observed white dots without any textural information showing lateral hand movements (dynamic condition) or static hands (static condition). For participants experiencing the dynamic condition first, it induced a lower tactile discrimination threshold, as well as a stronger SoO and SoA, compared to the static condition. For participants observing the static condition first, the magnitudes of the enhancement effect in the dynamic condition were positively correlated between the tactile discrimination and SoO/SoA. The enhancement of the dynamic visual information was not observed when the hand shape was not maintained in the scrambled white dot images. Our results suggest that dynamic visual information without task cues can enhance tactile discrimination performance by feeling SoO and SoA only when it maintains bodily information.
On the Relation between Memory and Language from a Cultural-historical Perspective in Neurolinguistics

Abstract: In this essay, we reflect about the relationship between memory and language, conceiving both as complex functional systems, developed along the human history and strongly influenced by culture. We give special emphasis to the mediating role of signs, mainly based on the (neuro)psychological principles postulated by Vygotsky and Luria, but also in dialogue with several authors from the fields of Linguistics, Philosophy of Language and Semiotics, among which Mikhail Bakhtin, Aleida Assmann, Augusto Ponzio and Susan Petrilli. Two data are presented in order to contribute to our reflection – the first is extracted of a dialogical episode with a subject diagnosed with Alzheimer’s disease and the second episode with an aphasic individual. In sum, we argue that the cultural-historical approach may provide a better understanding of the interdependent and constitutive nature of the relationship between language and memory. Key-words: Memory, Language, Historical-Cultural perspective, Neurolinguistics, Semiotics
READERS’ RESPONSE TO MAMAN SUHERMAN’S RE

The topic of the research is Readers' Reactions to Maman Suherman's Re: The study's goals were to look at the readers' reactions to the plot components in Maman Suherman's Re: and the reasons for their reactions to Maman Suherman's Re: The descriptive qualitative method was used in this study's investigation. The readers' responses to the novel's questionnaire served as the study's data. The information was gathered from readers who had completed the novel in its entirety. The outcome revealed how readers reacted to Maman Suherman's Re: Beach, and Marshall's theory was used to examine the data. The reader liked the story elements in Maman Suherman's Re: the reader liked the story elements in Maman Suherman's Re: the reader liked the story elements in Maman Suherman's Re: the reader liked the story elements in Maman Suherman's Re: the reader liked the story elements in Maman Suherman's Re: the reader liked the story elements in Maman Suherman's Re
О необходимости проведения в Республике Корея исследования фестивалей этнических(национальных) меньшинств российского Заполярья как феномена «синей культуры»

This article provides the rationale for the need to conduct a study of ethnic (national) minority festivals in the Russian Arctic as a phenomenon of blue culture in the Republic of Korea. If earlier the Arctic was difficult to develop due to natural and environmental factors, now it is becoming a space for economic growth. South Korea also reiterated the importance of the North Pole for its economic development and put forward various proposals under the new policy of the Northern Government. Among them, we recognize the importance of the Arctic in projects such as Blue Growth and Blue Economy that focus on the economic use of the oceans. However, the development of resources in the Russian Arctic is causing the destruction of indigenous people s urbanization, the destruction of their living spaces, and the disappearance of traditional culture due to the influx of new populations. Since cultural diversity and preservation of ethnic minority cultures are also important to humanity, it can be said that it is very important to study the archetypes and religious systems of ethnic minorities living in the Russian Arctic Circle and to study festivals. In particular, the study of cultural festivals that support the ethnic identity of the region may be related to the tourism industry along the Arctic route, and its value may be high.
Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis

Big data's infrastructure is a technology that provides the ability to store, process, analyze, and visualize large data. The tools and applications used are one of the challenges when building big data's infrastructure. In the study, we offered a new strategy to optimize big data infrastructure design that was an essential part of big data processing by performing performance analysis applications used at each stage of big data processing. The process started from collecting data sourcing online news using web crawler methods using Scrapyand Apache Nutch. Next, implement Hadoop technologies to facilitate the distribution of big data storage and computing. No-sql databases Mongo DB and HBase made it easier to query data, after which they built search engines using Elasticsearch and Apache Solr. Data saved later in analysis using hive apache, pig, and spark. The data has been analyzed was shown on the website using Zeppelins, Metabolase, Kibana, and Tableau. The test scenario consisted of the number of servers and files used. Testing parameters started from process speed, memory usage, CPU usage, throughput, etc. The performance testing results of each application were compared to and analyzed to see the merits and defaults of the application as a reference to building optimal infrastructure design to meet the needs of the user. This research has produced two big data infrastructure design alternatives. The suggested infrastructure has been implemented on computer nodes in the big data pens for processing big data from online media and proving to be running well.
Detecting Payment Fraud Using Automatic Feature Engineering with Harris Grey Wolf Deep Neural Network

The disruption triggered by the coronavirus pandemic is tremendous, and the banking and financial services industry must support the lasting impact of the outbreak. Banking and financial institutions' crime experts have planned, designed, and directed on how best to digitally transform the financial services industry. The coronavirus pandemic created a massive paradigm shift to digital banking. In many investigations, it was reported that lockdown accelerated the move to a cashless society, citing a 65% drop in the number of cash withdrawals at ATMs and an increase in card payments for online shopping, particularly for groceries. In the interim detecting, fraudsters pose a challenging problem in these scenarios. Because of the intrinsic properties of the transactional data and imbalanced nature, it is quite challenging. The existing strategies and mechanisms are not fit and result in unproductive performance. Therefore, an effective fraud detection model named the Harris Grey Wolf–based automatic feature engineering with deep stacked auto encoder is proposed. The proposed model provides an effective and optimal solution for identifying fraudulent transactions. The Hidden Markov Model (HMM)-based feature engineering mechanism enhanced the accuracy of the detection rate of the proposed model. The features, which are selected based on feature engineering, makes the detection process more accurate and effective in real-time. The performance of the model is evaluated using the metrics for sensitivity, specificity, and accuracy values, at 0.7923, 0.9102, and 0.9845, respectively.
Leonardo da Vinci: Lean Educator or Lean Sensei?

The inspiration behind the research conducted in this conference paper roots back to the 6th European Lean Educator Conference (ELEC 2019) held in Milan. Named “The Lean Educator and Practitioner Mashup”, the conference was meant to explore the latest academics and industrial contribution to lean education, embracing the figure of Leonardo da Vinci as a conceptual leitmotif.Both formal and informal events were paramount in the inspirational process leading to this research, highlighting some relations between lean thinking principles and the character of Leonardo da Vinci.In this regard, analogies have been analysed to study if and how Leonardo’s tools, theories and techniques could describe him as one of the contemporary figures of the Lean Educator or Lean Sensei.The focus of the research was on Leonardo’s tendency to outline the method and process through which his genius developed and manifested, to point out similarities and differences with the contemporary lean thinking and practice and to compare his figure with the one of a Lean Educator first and of the Lean Sensei afterwards.
Parallel Computing in Problems of Classification of Teenagers Based on Analysis of Digital Traces

This paper considers a model for classifying high school students by digital traces obtained from the VKontakte social network. The classification is based on the belonging of social network users to communities, the number of which is about hundreds of thousands, which leads to the emergence of big data in the process of analysis. The problem of working with big data is solved by parallelizing computations. The classification model was developed with the aim of recovering information from digital traces of users of social networks. On the basis of the trained model, the identification of users of the VKontakte social network was carried out by place of residence (village or city of the Altai Territory) and age (9 or 11 grade) among teenagers with incomplete information on the grade and place of study in the digital traces. The best prediction accuracy for the trained model was of the order of 0.9. In the future, it is planned to build an extended classification model by including in the data sample of users of social networks of other age groups and to develop a support system for making managerial decisions for the university's admissions campaign.
The Internet of Value and Internet of Things

This chapter focuses on discussing new business models that can emerge from the Internet of Value (IoV) when Internet of Things (IoT) devices are used. Using the Business Model Canvas, different types of emerging business models powered by IoT and the IoV are discussed, including customer to customer, customer-to-machine and machine-to-machine. Some use cases of IoT and blockchain follow. Finally, the chapter concludes with a brief analysis of how IoT is intertwining with other emerging technologies, including artificial intelligence and 5G.