Reviewerly/exHarmony huggingface dataset:
A Novel Design of Digital Circuits Using Reversible Logic Synthesis

In digital circuits, energy and power dissipation are the main concern. The loss of information is depicted in the form of heat that results in power dissipation. In digital system design, many gates are not reversible, like AND, OR, XOR, and so on. Logic that does not result in loss of information is called reversible. Reversible computing is enabled by a theorem of modern physics proposed by the von Neumann Landauer (VNL) principle, which explains that irreversible logic operations acquire a primary minimum energy cost. Reversible logic (RL) has received immense consideration in recent years due to its capability to reduce power dissipation. In this chapter, the authors have implemented different universal gates using reversible logic. There are six different types of reversible gates, based on simulation and evaluation of different parameters using Verilog and SPICE; the Toffoli gate shows remarkable results. Using a Toffoli gate, different combinational and sequential circuits are designed and simulated using both reversible and irreversible logic. It has been observed that using reversible logic shows exceptional results.
Returning to the Body: Somatic Sensing in Multi-Person Virtual Reality Technology

This article uses a dance-somatic standpoint to explore the complexities of body-technology relations across the virtuality and corporeality of bodies and environments using multi-person Virtual Reality technology (VR). Immersion into a virtual environment (VE) using VR can lead to a sense of presence, of ‘being there’. Dancers move attending to a field of sensation which is felt and tactile, undertaking somatic and sensory practices to de-centre vision so to foreground and thus activate non-visual and somatic senses. From this dancerly standpoint, entering into a VE brings into play the immediate effect of a perceptual tension or ‘gap’ between the visual, virtual environment and the physical, felt environment. Technologists and artists engaging with VR typically find ways to cover-over this perception gap&amp;nbsp;in order&amp;nbsp;to create a reality that is fluidly and synchronously experienced by the participant. This article introduces and discusses two participatory performance projects&amp;nbsp;Figuring&amp;nbsp;(2018) and&amp;nbsp;Soma&amp;nbsp;(2020) which challenge this approach. Drawing on participant responses to&amp;nbsp;Figuring, and the creative development of&amp;nbsp;Soma, the article presents and discusses six themes which unpack and challenge normative notions and expectations around VR technology and how bodies sensorially engage with the technology; and discusses an ‘ethics of care’ which calls for somatic activation and participatory agency in human encounters with technology. Throughout, the article offers a commentary on the tensions between a thematic research approach and an intuitive, practice-led approach in the analysis of participant testimonies and in the creative processes of performance-making.&amp;nbsp;
Quantitative analysis method for measuring dead zone of non-orthogonal shafting structure

Non-orthogonal shafting structure composed of rotary tables and collimated laser or camera has emerged as a cost-effective measurement technique. However, the non-orthogonal shafting structure leads to measuring dead zone. This paper presents a quantitative analysis method to calculate the measuring dead zone. The laser axis is rotated 360° around horizontal axis and the movement trajectory is an uniparted hyperboloid. The uniparted hyperboloid is rotated 360° around vertical axis to generate the measuring dead zone. Based on the relative position relationship of the three axes, the position information of measuring dead zone is calculated. The simulation and experimental results demonstrate that the proposed method is highly-accurate and practical for determining the measuring dead zone of non-orthogonal shafting structure.
Arrays

Arrays are data structures that store either values of a primitive data type or object references in a contiguous storage area. Array creation is shown as follows for numbers of type int and in two variants for names of type String. In the latter case, the shorthand notation and syntactic feature of direct initialization are used, where the size of the array is automatically determined by the compiler by the number of specified elements.
Impacts of the discipline plan of National Natural Science Foundation of China on discipline development: Analysis and implications based on the bibliometrics-method

<p indent=0mm>The science departments of National Natural Science Foundation of China (NSFC) carry out strategic research on discipline development every five years, and formulate discipline plans and several priority funding areas for the next five years. Then they successively release guidelines for Key Program and Major Program (K&M) projects to promote the development of priority funding areas. In order to understand the impacts of the discipline plan on discipline development, this study takes the discipline of management science and engineering during the 13th Five-Year Plan period as an example, and uses the approved K&M projects to represent the discipline plan, the General Programs, Young Scientists Fund, and Fund for Less Developed Regions (GY&L) projects to represent the individual thinking of general scholars, and the academic literature from representative international journals to represent the international frontiers and hot spots. This study uses the Latent Dirichlet Allocation (LDA) method, as well as high-frequency word analysis, and introduce a concept of fitting rate to analyze the relationship between NSFC projects during 2016 and 2020 and academic literatures during 2011 and 2020. The results show that NSFC projects have higher fitting rates with academic literature in the academic areas with more K&M projects funded than in those with fewer K&M projects funded, and the academic areas with higher fitting rates between K&M projects and academic literature usually have higher fitting rates between GY&L projects and academic literature. Furthermore, academic areas with higher fitting rates between NSFC projects and academic literature tend to have more projects of National Science Fund for Distinguished Young Scholars and Excellent Young Scientists funded. Therefore, NSFC’s discipline plan has a profound, extensive and long-term impact on the discipline development. The impact goes from top to bottom, from points spreading to areas. It does not only determine the current distribution of priority areas, but also affects the potential distribution of young scientists in the future. Because the development paths of sciences have similarities, the conclusions of this study could also be useful to other disciplines. We should fully understand and make use of these advantages of the discipline plan to promote the development of academic areas to meet national requirements, and aim at key fundamental fields to accumulate young talents in advance.
The Memory of a Goldfish

So far, we’ve covered how to create multiplayer games in Unity using Mirror Networking, but that’s only a part of the battle when creating large, scalable multiplayer products. In the following chapters, I’ll discuss how we can implement data persistence for our users and deploy our games in a scalable manner.
Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data

Intermittent dataset is a unique data that will be challenging to forecast. Because the data is containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both sometimes no data recorded in a certain period. In this research, the model is created to overcome the problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are rainfall and sales data. So, our approach is creating the base model from the time series regression with Negative Binomial based, and then we augmented the base model with a tree-based model which is random forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by 1.79 and 7.18.
Decentralized Learning-based Scenario Identification Method for Intelligent Vehicular Communications

Scenario identification (SCI) is one of key techniques for intelligent vehicular communications (IVC) to maintain an effective and reliable operating state. Based on the deep learning (DL), it is a hotspot to identify scenarios of wireless communication using the characteristic quantity inherent in wireless channels. This paper proposes a decentralized learning-based SCI (DecentSCI) for IVC, relying on the algorithm of lightweight and model aggregation. By improving training efficiency and meanwhile reducing model complexity, the proposed method achieves low computing and communication, which is applicable for vehicular devices. Simulation results show that the training efficiency is upgraded by 97.15% and the model complexity is decreased by 90.25% at the cost of slight performance loss, i.e., 0.15%.
Understanding the Paralinguistic Features Disclosure Depicted in the Lecturer’s Visual Modes of Writing Class Instruction

Today’s writing instruction performance is greatly aided by oral and nonverbal communication. This study intends to contextualize the lecturer’s visual modes regarding the paralinguistic features in writing class. Data were gathered from the PSETs’ self-rated questionnaire and the lecturer’s visual modes that were durably video recorded. Data were analysed using the IBM SPSS version 20 for descriptive and factor analysis, as well as the ELAN’s software to contextualize the non-verbal linguistic matters. The findings showed that the paralinguistic features, namely gesture, articulation, and loudness were strongly visible, whereas sonority and facial expression and lips setting were visible relating to the lecturer’s visual modes (r = .833, n = 243, p&lt;.05). Meanwhile, Eigenvalue and factorial analysis affirmed two rooted paralinguistic features components of 83.803% (68.128% for component 1 and 15.675% for component 2). Further, ELAN analysis qualitatively contextualized the lecturer’s visual modes performance corresponding with the meta-discourse taxonomy through the portrait of the quoted material, dysfluencies, and discourse organization on introducing, delimiting, adding, and concluding the topic through the normative and pre-planned types of spoken data in the writing class activities. This study synchronizes the verbal and non-verbal viewpoints regarding both the PSETs and the lecturer’s understanding of the paralinguistic features in the writing class. Keywords: ELAN analysis; Meta-discourse taxonomy; paralinguistic features; writing instruction
Streaming Multi-layer Ensemble Selection using Dynamic Genetic Algorithm

In this study, we introduce a novel framework for non-stationary data stream classification problems by modifying the Genetic Algorithm to search for the optimal configuration of a streaming multi-layer ensemble. We aim to connect the two sub-fields of non-stationary stream classification and evolutionary dynamic optimization. First, we present Streaming Multi-layer Ensemble (SMiLE) - a novel classification algorithm for nonstationary data streams which comprises multiple layers of different classifiers. Second, we develop an ensemble selection method to obtain an optimal subset of classifiers for each layer of SMiLE. We formulate the selection process as a dynamic optimization problem and then solve it by adapting the Genetic Algorithm to the stream setting, generating a new classification framework called SMiLE_GA. Finally, we apply the proposed framework to address a real-world problem of insect stream classification, which relates to the automatic recognition of insects through optical sensors in real-time. The experiments showed that the proposed method achieves better prediction accuracy than several state-of-the-art benchmark algorithms for non-stationary data stream classification.
Book Review

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SELECTION CRITERIA FOR CONTEMPORARY ART WORKS AS A SUBSTANTIVE BASIS OF SPIRITUAL AND MORAL EDUCATION OF PUPILS

The article presents the results of the empirical study, which makes it possible to identify the criteria that teachers of general and additional education schools are guided when assessing the possibilities of contemporary art works in solving the problems of students’ spiritual and moral education. The empirical research data are supplemented by the results of theoretical analysis carried out from the humanitarian pedagogical paradigm, which makes it possible to correlate these criteria with the value-semantic nature of spiritual and moral education. There are identified such criteria as the moral norms reflection in contemporary art works, the presence of value content in it, spiritual or ethical conflict that is relevant for students, the form of values representing, problems, ideas corresponding to the students age. As a result, the scientific knowledge about the requirements for the content of students’ spiritual and moral education and the possibilities of contemporary art in solving the problems of spiritual and moral education has been clarified.
Analysis of promising areas of research in the field of new materials and nanotechnologies within the framework of the scholarships of the President of the Russian Federation for young scientists and graduate students .

The article provides an analysis of promising areas of research carried out by young scientists and graduate students of Russia. Statistical methods show the dynamics of development of certain branches of knowledge, their thematic and problem headings in comparison with other sciences according to the classification of the State heading list of scientific and technological information.
Application and Practice of Big Data Analysis in Enterprise Brand Marketing Management

Data information, further analysis and clear objectives, extract and output effective information, and provide an important reference for marketing. With the support of large-scale data analysis technology, the design of marketing strategies can better meet the needs of consumers, and more operable and accurate market trends and trends. In order to study the application and practice of enterprise brand marketing management under big data technology, we select four enterprises with similar brand values from this city and divide them equally into two groups, two enterprises in group A use traditional methods for brand marketing management and two enterprises in group B use big data technology for brand marketing management. Over time and with the progress of the experiment, we found that there were significant differences between the two groups of enterprises. The experimental results showed that brand awareness of group A increased by 20% after one month, 8% after the third month, and finally 40% at the end of the experiment. At the same time, brand awareness in Group B increased gradually to 45% from 22% a month later, which is significantly greater than Group A. Therefore, using big data technology can not only improve consumer satisfaction, but also enhance the brand awareness of enterprises. Big data can help enterprises better adapt to market needs and achieve long-term development.
The Implementation of Unsupervised Learning Techniques as a Data Sharing Model in the Back-propagation for the Classification of Student Graduation

One of the requirements to increase the accreditation of higher education is the percentage of students who graduate on time. Responding to this issue, it is necessary to discover the factors that affect students in completing the Final Project. One of the algorithms that can be used to determine the classification of student graduation is the Backpropagation Neural Network (BPNN). The factors that have the most effect on student graduation, it includes procrastination, total credits and the number of repeat courses. To gain the best accuracy results on the classification technique, it was carried out by experiment of training and testing data sharing by applying the clustering technique. The cluster division consisted of the K-Means and K-Medoid algorithms, had the best cluster validity Davies-bouldin Index (DBI) 0.063 on the K-Means algorithm using 101 training data and 44 testing data. Based on BPPN, data sharing using K-Means had a big impact on the BPNN classification process with an accuracy value of 98% from a learning rate of 0.005 with 1000 iterations.
A Mobile Learning Model to Improve the Learning Environment in Iraqi Secondary Schools

Nowadays the use of mobile devices has increased dramatically as they have been integrated into different learning facilities. In this paper, the opinions of high school students and their teachers will be evaluated in order to get a better understanding of how mobile devices are used in the learning environment. A qualitative and quantitative method was used in this study. Multiple cases for the purpose of understanding the level of students' use of these devices in schools. Through the results of this study, it can be determined whether spending on textbooks and supplies is necessary compared to replacing it with technology. This model can be divided into five categories. (MLIS) mobile phone by developing a mobile learning model in Iraqi secondary schools (MLIS). This model can be divided into five categories, including mobile learning, drivers, process, community, and influencing factors. Each of the categories is related to each other, as well as related to planning and goals. However, both students and teachers believe that using mobile devices in an educational setting can help increase overall achievement, improve student motivation, and create a positive learning environment in schools. This study also helps enrich the existing literature on mobile technology in schools, where knowledge is lacking in the Iraqi educational system.
MovieEmotion-IMG: An Emotion Distribution Dataset of Movie Scene Images

One of the biggest challenges in sentiment analysis is to predict the complex feelings that humans evoke when viewing images. Due to the limitation of the quality and quantity of the dataset, the current learning-based algorithm mainly analyzes a single dominant emotion. Based on the above, this paper introduces an emotion distribution dataset of movie scene images with rich emotional semantics and professional aesthetic design, called MovieEmotion-IMG. Firstly nine labels, including eight emotional words and a “neutrality” label, were identified, and a set of emotional evaluation rules were designed. Then, the emotion distribution annotation experiment was carried out on 17140 movie images, and an emotion annotation system was developed to assist the annotation work. Finally, two reliability test methods were designed based on the external reliability test method to verify the reliability of the results and ensure the availability of the dataset. This is the first large-scale emotion dataset of movie scene images and annotated distributed emotions to our knowledge.
Speckle Methods

Speckle methods are high-sensitivity non-contact optical methods for measuring displacements. They are based on the speckle effect. Speckles are granular dots that result from the illumination of a diffusively reflecting rough surface with coherent lightLightcoherent. The reflected or scattered wavelets interfere to create a random speckle pattern with statistical properties. The laser speckle is due to the coherenceCoherence of the light and the roughness of the surface of the order of the wavelengthWavelength of light.
Enum Types

In this chapter, you will learn: what enum types are in Java, how to declare and use enum types, how to use enums in switch, how to associate data and methods to enum constants, how to perform a reverse lookup for enums, and how to use EnumSet to work with ranges of enum constants.
Impact of the External Environment on Organizational Culture in the Higher Education Institutions

Since the very beginning of management as a science people were the key element for studying and analysis. Hundreds of the theories were introduced and thousands of papers were published on topics like motivation, human resources management and leadership. Yet, still some companies are more successful than others, some companies remain on the top for decades and some disappear after a short time. The article is devoted to one of the most important areas of management in the modern conditions –the corporate or organizational culture. Currently, each company, no matter commercial or non-profit, that wants to be successful in the long-run sets values and principles which should be accepted by its employees. However, the external factors may have a significant impact on the way employees act themselves at the working places. The article describes Hofstede 6-D model which helps to understand the specific way of the internal quality management at Kazakhstani companies in general and at HEIs in particular. The article highlights the importance of the organizational culture in the process of the internal quality management at the company as it is becomes clear that involvement of every employee may benefit the company as a whole.
Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.