Reviewerly/exHarmony huggingface dataset:
A Case Study Using Augmented Reality for Teaching Organic Compound Reactions

This research-to-practice full paper is elaborated from the next problem, 'Augmented Reality's technology can assist in understanding the contents of Chemistry in a more concise way? The Case Study was conducted in a small-town school. The absence of structures, such as teaching laboratories, limited Internet access, or an insufficient number of computers is common in many small cities such as the one chosen. There is also a lack of teachers trained in pedagogical methods that make the student take an interest in learning. On the other hand, most young people and teachers have cell phones and enjoy recreational activities. So, we combine these various aspects to promote learning in a playful, cooperative way and based on a relevant learning theory. This paper is aimed at analyzing learning supported by Augmented Reality (AR) for Chemistry teaching. It was a quantitative study using Augmented Reality with the intent to check the benefits that AR brings to education. We focused on Organic Reactions because many students have difficulties with this topic and we evaluate that this topic is appropriate for using Augmented Reality. First, a pedagogical approach had been discussed with teachers of the proposed discipline and from that, an app for the Android system was developed, to assist in teaching Organic Compound Reactions, uniting Vygotsky concepts on the Proximal Development Zone. The proposal is integrated to the movement based on learning by design in basic schools, STEM, the system promotes students' abilities to work with technology to understand the teaching of Chemistry. Evaluations were carried out with 54 high school students during the semester, and the results obtained on the technology used identified that 84% of students considered the contents of chemistry relevant. A significant part suggested that the approach using Augmented Reality could be used in teaching other subjects. It was possible to observe the learning with cooperative characteristics highlighting the importance of working with this perspective, uniting attractive technologies that can collaborate effectively for the construction of knowledge in school disciplines.
Reversible Data Hiding Based on Bidirectional Generalized Integer Transform

This paper proposes a high capacity reversible data hiding (RDH) scheme by extending the unidirectional generalized integer transform (UGIT) to bidirectional GIT (BGIT). For non-embeddable blocks in UGIT based scheme, we further divide into two sets: to-be-overflow blocks and overflow blocks. And then we embed data into to-be-overflow blocks by scaling back instead of scaling out, to avoid the overflow problem. The experimental results verify that the proposed scheme can achieve a higher capacity than UGIT based RDH scheme while maintaining a better image quality with a high payload.
Control limit for the quantum state preparation under stochastic control errors

We investigate the effect of stochastic control errors on the Hamiltonian that controls a closed quantum system. Quantum information technologies require careful control for preparing a desired state used as an information resource. However, because the stochastic control errors inevitably appear in realistic situation, it is difficult to completely implement the control Hamiltonian. Under this error, the actual performance of quantum control is far away from the ideal one, and thus it is of great importance to evaluate the effect of the control errors. In this paper, we derive a lower bound of the fidelity between two closed quantum systems obeying the dynamics with and without errors. This bound reveals a reachable and unreachable set of the controlled quantum system under stochastic noises. Also, it is easily computable without considering the stochastic process and needing the full dynamics of the states. We demonstrate the actual performance of this bound via a simple control example. Furthermore, based on this result, we quantitatively evaluate the probability of obtaining the target state in the presence of control errors.
Would You Be Stressed If Anthropomorphic Agents Were Handing Out POCKET TISSUES Around Town? : Evaluation by VR Study

Part-time workers are seen handing out pocket tissues containing advertisements to pedestrians in the streets and around train stations in Japan. However, there are cases in which pedestrians feel stressed by workers handing out tissues. This is when the tissues are unnecessary for pedestrians. Workers actively try to hand out tissues to pedestrians because if they do not hand out tissues, their work will be evaluated negatively. Most pedestrians understand that refusing to accept a tissue is a betrayal of the worker’s expectations. Therefore, refusing to accept a tissue is emotionally taxing for the pedestrian. The authors believe that anthropomorphic agents handing out tissues can alleviate the aforementioned mental burden for pedestrians. In this paper, we conducted an experiment in which pedestrians interacted with a tissue-distributing worker by reproducing a typical Japanese cityscape in VR. The results showed that the anthropomorphic agent handing out tissues may cause less stress to pedestrians than a human worker handing out tissues. The results of this paper suggest the usefulness of anthropomorphic agents for tasks such as distributing advertisements in the city and around train stations, and for fundraising activities.
Review of Adoption Theories in the Context of Blockchain

Blockchain is an emerging technology, showing possibilities in many fields. It has provided its mark in finance with cryptocurrency. Its security, robustness, interoperability and reliability have promised application in various areas, but the demonstrated use of blockchain technology is rare. The stakeholders are in the process of decision-making about the adoption of blockchain and assimilating with this new technology. This article discusses blockchain technology adoption with theories that were formed on adoption, infusions and assimilation of technologies by firms and individuals. Blockchain technology will be adopted by firms, but most of the theories of adoption have been developed around individuals. Therefore, an assumption has been made that the premise for the firm will be the same as the premise for individuals, in the context of blockchain. Various firms, and the Indian government, are in the process of decision-making regarding blockchain. This article delivers explanations of constructs of different theories in the context of blockchain technology. This explanation will help practitioners to understand and analyse the adoption of blockchain technology in the context of their industry practice, and for academicians, it will act as the base to develop measurement tools for different assessments in the blockchain.
How international financial institutions help deliver the sustainable development goals

International financial institutions have a long history of assisting developing economies through financial and policy support. Simultaneously, they observe high ethical standards pertaining to environmental and social impact, procurement, gender equality, inclusion, governance, and anti-corruption, and apply those standards to their operations and clients. Amid the COVID-19 pandemic, they continue to contribute to the Sustainable Development Goals (SDGs) including through their emergency response facilities which specifically address the socioeconomic challenges posed by the pandemic.
The Construction of the Semantic Collocation Database of Verb-Complement Structure in Modern Chinese based on a Large-scale Chinese Chunkbank

In natural language understanding, the marked verb-complement structure can be identified by the complement markers such as $\unicode{x5F97}$(an auxiliary word). However, it is difficult to correctly identify the unmarked verb-complement structure and label the semantic categories expressed by it. The current researches lack a semantic collocation database for the full picture of the verb-complement structure. This paper builds a semantic collocation database based on a large-scale Chinese chunkbank for the verb-complement structure. Firstly, we summarized a comprehensive semantic classification system of the verb-complement structure; moreover, we subclassified the major categories and added the extensional meaning of complement, including seven major categories and twenty-five subcategories. Secondly, we formalized the verb-complement structure and wrote twenty-seven types of formal expressions. Then, we extracted the verb-complement collocation from the large-scale Chinese chunkbank according to the formal expressions, disambiguated, and semantically subclassified the extraction results. Finally, a semantic collocation database containing about 180,000 verb-complement collocations is constructed and statistically analyzed. This paper provides a relevant research paradigm for constructing the semantic collocation database and the proper structure and rich semantic knowledge for natural language understanding.
POST-CONSOLIDATION MACROECONOMIC VOLATILITY AND FINANCIAL PERFORMANCE OF DEPOSIT MONEY BANKS IN NIGERIA (2006 – 2020)

This study aims to examine the effect of volatility of macroeconomic variables on the financial performance of Deposit Money Banks in Nigeria. Secondary annual time series data from 2006 to 2020 on variables for this study were obtained from the Central Bank of Nigeria Statistical Bulletin 2021. A GARCH model analysis of annual time-series data on liquidity ratio, foreign exchange rate, interest rate, Real GDP, monetary policy rate, credit reserve ratio, inflation rate and aggregate bank profit as a measure of banks’ profitability from 2006 to 2020 was undertaken. The empirical findings showed that there exists volatility in these variables with LIQR, INTR, MPR, EXCR, and RGDP positively affecting banks’ financial performance; while INFL and CRR negatively affect banks’ profitability within the period under consideration. The study recommends that monetary policies should be introduced by Central Bank of Nigeria towards increasing MPR, RGDP and reduce CRR, INFL to stimulate the financial performance of deposit money banks and the general stability of the financial system in Nigeria
Secret Cinema, 2020–2021: pivoting, pipelines and poaching

In this concluding chapter, we re-contextualise SC within the wider immersive experience industry of 2021 through the themes of pivoting, pipelines and poaching – reaffirming and building on the ‘immersive experience industry ecosystem’ model established in the first chapter. Here we present the rich experiences and collaborations within the immersive experience industry of 2021 against the backdrop of the major transitions being wrought by the convergence of the creative production of film, theatre, games and screen media. We examine how the use of digital technologies in the production of creative outputs accelerated significantly and how innovative new applications were developed in response to the challenges brought by the pandemic from 2020. When physical and hybridised productions returned, creative advances continued apace as the same technologies, tools and software used to produce screen-based media were brought together with established stagecraft and emerging immersive performance talents and techniques. Our ecosystem model of 2021 is characterised by the emergence of super-hybrid experiences that are increasingly influenced by gaming and new technologies. We show how the picture that we painted of 2019, with mass-immersive events at their zenith, looked very different in 2021, following the rapid change, adaptation and evolution engendered by the global pandemic.
Knowledge Discovery and Intelligent Data Mining

Knowledge Discovery in Databases (KDD) is a programmed, exploratory examination and demonstrating of enormous information storehouses. KDD is the coordinated course of recognizing legitimate, novel, valuable, and justifiable examples from enormous and complex informational collections. Data Mining (DM) is the core of the KDD interaction. The model is utilized for figuring out peculiarities from the information, investigation and expectation. The bringing together objective of the KDD cycle is to extricate information from information with regards to huge data sets. KDD is a fantastic tool for keeping organizations and sectors up to date on consumer demands, behaviours, and actions. There are several obvious benefits to employing the KDD technique, as well as some drawbacks. The Intelligent Data Mining and Analysis is a trend setting innovation in data handling to remove rules and information from huge data sets deliberately and break down the nonlinear connection among info and result factors in complex issues or peculiarities. Data mining is a full-grown application in different regions like marketing. The proposed model comprises of extricating Twitter client information utilizing programmable bookkeeping sheet apparatuses like Google Docs. This content purposes the Twitter API alongside passing a bunch of boundaries, similar to client handle or hashtags to bring client metadata. In any case, straightforwardly utilizing Twitter API is additionally conceivable yet it builds the intricacy of the system.
Data Visualization Techniques

Data visualisation is the representation of the information using standard images like charts, plots, infographics, and even animations. These data visualisations convey complicated data linkages and data-driven insights in an easy-to-understand manner. It aids in the explanation of facts and the selection of courses of action. It will assist any field of research that demands novel approaches to presenting massive amounts of complicated data. Modern visualisation has been shaped by the introduction of computer graphics. A taxonomy of visualisation approaches is also offered, based on the number of variables that may be shown. Novel trends in user interface design are examined, as well as a range of new visualisation approaches and their applicability. In the topic of software visualisation, there are several novel visualisation approaches and tools for studying the datasets. However, finding the correct technology to meet user needs for displaying huge datasets remains a challenge. It gives a quick rundown of a few of the most popular visualisation tools and examines their suitability for supporting research with big volumes of environmental data and also provides significant opportunities for technical communication researchers to expand the field’s knowledge of environmental data visualizations and their function in environmental communication. Here, we imported the dataset in data mining tool i.e., Rapid Miner and by using the dataset, we designed various types of data visualization chart.
Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition

Human Activity Recognition (HAR) using on-body devices identifies specific human actions in unconstrained environments. HAR is challenging due to the inter and intra-variance of human movements; moreover, annotated datasets from on-body devices are scarce. This problem is mainly due to the difficulty of data creation, i.e., recording, expensive annotation, and lack of standard definitions of human activities. Previous works demonstrated that transfer learning is a good strategy for addressing scenarios with scarce data. However, the scarcity of annotated on-body device datasets remains. This paper proposes using datasets intended for human-pose estimation as a source for transfer learning; specifically, it deploys sequences of annotated pixel coordinates of human joints from video datasets for HAR and human pose estimation. We pre-train a deep architecture on four benchmark video-based source datasets. Finally, an evaluation is carried out on three on-body device datasets improving HAR performance.
Data Mining Application: Rainfall Predictions

Abstract: Data mining is method or process of extracting ( Implicit previously unknown and potentially useful) pattern or information from large amount of Data. It is used to extract relevance knowledge from raw data. Some data mining methods and algorithms or some organizations used this because to enhance their businesses and they found required result. In 1936 Alan Turing introduced this idea first time. And other name of Data Mining is Knowledge Discovery in Database (KDD), because of unknown and potentially important data stored in database. Rainfall is the prime input for wedding seasons or any occasion in India . It is designed for information about the rainfall season on occasion or by taking the previous 10 years data. This is very helpful for primary sector workers for crop planning .The Daily rainfall data for a period of 10 years is used to understand usual rainfall, deficit rainfall, Excess rainfall and Seasonal rainfall This analysis will provide useful facts for water resources planner and formula available is used to evaluate return period of monthly , seasonally and annual rainfall.
The Proposal of a Verbal Interaction Framework for Elementary Science Teachers

This study is purposed to specifically present and suggest two of the five aspects of the existing framework using data recorded by science class teachers. Mortimer and Scott (2003) viewed teaching purposes, content, communicative approach, patterns of discourse, and teacher intervention as five aspects of classroom discourse analysis, analyzed for secondary science classes. In this study, four episodes were extracted from elementary science classes, the entire analysis was conducted, and the analysis frame developed in this study was applied. The results of the study showed that elementary science classes were analyzed differently according to class flow and the score of the Reformed Teaching Observation Protocol (RTOP). In addition, the analysis framework presented in this study and the score of the RTOP were slightly consistent.
Distributed PyTorch Modelling, Model Optimization, and Deployment

In this chapter, you will use PyTorch to implement the steps that are most commonly used in installation, training, and setting up distributed PyTorch for model training. The architecture followed for distributed data parallel training and distributed model parallel training can be explained using the following figures. The model optimization process reduces the model parameter’s size so that the model object becomes lighter. The bigger the model object, the slower the inference generation. If you reduce the number of layers in the deep learning model, the parameters that are getting trained also lessen, but this may impact the model accuracy. Hence, one technique used to reduce the model size is called quantization. There are different types of model quantization that need to be applied in order to put the model into production. Otherwise, bigger model objects are not compatible for deployment.
Introduction to Neural Networks Using PyTorch

Deep neural network–based models are gradually becoming the backbone for artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural network–based advanced modeling techniques. One obvious question is why neural networks are only now gaining so much importance, because they were invented in 1950s.
Fine-Tuning Deep Learning Models Using PyTorch

Deep learning models are becoming very popular. They have very deep roots in the way biological neurons are connected and the way they transmit information from one node to another node in a network model.
Information System for Public Complaints at the Industrial Department of Samarinda City

In the reporting system at the Department of Industry of Samarinda City, reports on problems from the community are distributed to related parties collectively and manually, so the submission process tends to be long. Submission of long reports has an impact on the slow response from the government to the community. In addition, the process of distributing reports manually means that the public does not know how the government will follow up on the report. Therefore, this research aims to help the community in terms by submitting complaints or suggestions to the Samarinda City Industry Office and to assist the Samarinda City Industry Office to serve the community in terms of answering public complaints. The results of this study can facilitate the work of the Samarinda City Industry Office in viewing complaints from Samarinda City residents via the Web and can be monitored online by the Admin. In addition, residents of Samarinda City can voice their aspirations freely and easily without having to go to the Samarinda City Industry Office
Changes of English education after the 2007 revised curriculum in South Korea: Focusing on Dong-A Ilbo newspaper articles

This study investigated the transition of English education after the 2007 revised curriculum in South Korea. This research focused on Dong-A Ilbo newspaper articles across three major political regimes: Presidents Lee Myung-bak, Park Geun-hye, and Moon Jae-in from March 2007 to June 2022. The research questions are 1) what are the prominent themes in English education in the three political regimes? and 2) what are the implications of these changes to the current English education in South Korea? A total of 522 newspaper articles were categorized into five major themes: 1) Experience of English education, 2) English education policy and its perception, 3) English instructors in and out of school, 4) English education curricula, and 5) English for the entrance exams in South Korea. Regarding political regimes, Lee Myung-bak administration emphasized English education by introducing English village, Jeju Global Education City, and the National English Ability Test (NEAT). Park Geun-hye administration applied criterion-referenced English test in the College Scholastic Ability Test (CSAT). Due to the COVID-19 pandemic, significant changes in English education during Moon Jae-in administration are not identified except for AI-appled online distance learning. This paper concludes with educational implications for future research.
Issues

The third step of the GRIDALL process is to manage issues. An issue is an event that disrupts the plan prevents the team from achieving the project goal. An issue can result from a risk being realized or from “out of the blue”. Either way, issues need to be effectively managed to get the project back on track. This chapter outlines a three-step process for identifying, assessing, and resolving an issue either via an in-team decision or escalation to higher levels of the organization. It also explains the importance of correctly framing the issue, using a case study to illustrate the principle.
Fritz, A. (2021). <i>Sustainable enterprise strategies for optimizing digital stewardship</i>

"Fritz, A. (2021). Sustainable enterprise strategies for optimizing digital stewardship." Journal of Web Librarianship, 16(4), pp. 227–228
SISTEM PAKAR DETEKSI KERUSAKAN JARINGAN LOCAL AREA NETWORK (LAN) MENGGUNAKAN METODE BECKWARD CHAINING BERBASIS WEB

The form of computer network connection can be via cable or wireless such as fiber optic, microwave, wireless, or satellite. One type of computer network that is often used to connect personal computers and workstations in an office or an organization, company or factory for the use of shared resources is a local area network. The purpose of this research is to analyze, design and create an application that can detect damage to Local Area Network (LAN) networks. The research method used is backward chaining. The results of this study are applications that can detect damage to local area networks using the web-based backward chaining method. With this expert system application, it can speed up and make it easier to detect damage to Local Area Network networks.