Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Measurement Method for Tube’s Endpoints Based on Multi-view Stereovision

In order to achieve the accurate measurement of pipeline’s end position, a method based on centerline vector with the help of multi-vision is proposed. This method uses pictures taken by eight industrial cameras to extract the pipeline’s centerline. And location of the center of end plane is determined with the aid of two data, the centerline vector and the pipeline’s outer diameter. The deep learning method is used for obtaining the camera calibration matrix which is used to reconstruct the space coordinates of endpoints. The relative position of two endpoints is calculated. The measurement experiment shows that the maximum relative deviation between the measured value and the standard value of this method is 0.6%, and the method is simple and measurement efficiency is high.
Application of TTS Technology in Online Teaching of Chinese as a Foreign Language

With the rapid development of science and technology, information technology is widely used. Influenced by this, the international education of Chinese has gradually developed a variety of online teaching models. In the new era, how to use computer aided technology to carry out online teaching of Chinese as a foreign language is a topic worthy of further study. As a convenient and fast speech synthesis technology, TTS technology has brought convenience to People's Daily life, but it has not been widely used in the field of education. Through a brief overview of TTS technology, this paper discusses the great advantages of TTS voice application in online teaching of Chinese as a foreign language, which is conducive to improving the innovation of online teaching of Chinese as a foreign language.
Understanding of sentence based on researches on human cognitive system

This study attempted to clarify what sentences are based on an understanding of the human cognitive system. Until now, there has been a widespread understanding of whether sentences represent events in the real world or propositions that have truth-value. However, based on recent studies of cognitive psychology, brain science, and consciousness science, Sentence(utterance) is neither an event nor a proposition. An organism compares, analyzes and judges real-time sensory data input or declarative memory in the prefrontal lobe, the working memory area. Sentence is an output of such a process of speculation, inference, prediction and evaluation on the world. In order to derive these results, this paper theoretically reviewed and synthesized the achievements of brain science, consciousness science, and cognitive science so far to establish a cognitive model, and explained what sentences are based on this cognitive model.
How Regulation Sentiments Influence Cryptocurrency? Is Crypto a Wall Street Darling?

This paper analyses the prospect of cryptocurrency regulation, especially, on crypto prices, volatility and volume. We employ the Google Trend technique and introduce the Crypto Regulation Sentiment Index (CRSX) that reflects Crypto regulation fear at the US and Global levels. We utilize over 75% of the crypto financial market daily data, including Bitcoin and several Altcoins (Ethereum, Solana, etc.). Surprisingly we find that CRSX has small and not statistically significant long term impact on cryptocurrency prices, and thus we classify crypto as the “Wall street Darling,” as a reflection of the forgiveness that investors exhibit to crypto bad news. Yet, CRSX exhibit a large impact on cryptocurrency volatility and trading volume, especially in recent years, perhaps due to the fact that crypto becomes more as a main stream. Moreover; the CRSX impact on crypto varies by coin’s key blockchain characteristics, especially, the type of blockchain consensus mechanism (POF vs POS). Collectively, we demonstrate that the regulating sentiment impacts crypto volatility and volume and varies by the type of the employed consensus mechanism.
Combination of Auto-encoder architecture and super resolution for better segmentation of thinned and cursive handwritten documents

Abstract In neural networks an auto-encoder architecture has several applications such as image denoising, feature reduction, data compression, image colorization, dimensanality reduction, segmentation and so on. Super-resolution is used to upgrade the low resolution images into high resolution. In order to get a better result on segmentation of thinned hand written images, this paper proposes a method of combination of associative auto-encoder architecture and super resolution for pixel expansion. Experimental results show that the combination of proposed network and super resolution method accurately segments the thinned handwritten Arabic words.
Predicting elections over Twitter: a campaign strategies of political parties using machine learning algorithms

Twitter is a social networking web application built to find out what is happening around us and all over the world. For this study, we analysed one million tweets for applying sentiment analysis is helpful to analyse the information where opinions are either good or bad/negative, or neutral and highly structured, heterogeneous in some cases. In this article we used a few machine learning algorithms like naïve Bayes, support vector machine, decision tree and neural network for analysis of elections results. Twitter data is used to predict the outcome of election by collecting data and analysing sentiments about the candidates. The experiential results show that the winning party's electoral success is expressively associated with social media/Twitter. It is a main promotional tool for new and upcoming parties in the 2019 general elections. This article mainly focused on Karnataka state elections of 2018 month of May for examining the analytical power of Twitter.
Strings

Strings model character sequences and possess the type str, which offers a variety of functions. In this chapter, you will learn about this topic through various exercises.
Introduction

We show that empirical corpus-based research is prevalent across subdisciplines of (applied) linguistics, not just in “corpus linguistics” journals. We define a corpus as a large, principled sample of texts designed to represent a target domain of language use. Corpus representativeness is conceptualized as the extent to which a corpus permits accurate and meaningful generalizations about linguistic patterns that are typical in a domain. Corpus representativeness involves two main considerations, which are both relative to the linguistic research goal of interest: domain considerations (adequate representation of the text varieties in the domain), and distribution considerations (adequate representation of the distribution of linguistic features in the domain).
Driver Drowsiness Detection and Object Detection for Vehicles Using Yolov3

The objective is to detect objects using You Only Look Once (YOLO) approach. This method has several advantages as compared to other object detection algorithms. Far-off detecting information is a significant hotspot for making or refreshing geological data framework dataset and are utilized in various sorts of territories. Programmed division of distant detecting information into semantically classes has been a traditional issue from past many years and has numerous application zones like guides, route, checking of climate, arranging of metropolitan and country region, yet after such extraordinary advances, it actually needs more precision and effectiveness. The difficult assertion for this work is essentially playing out the scene division which can be an elevated picture of metropolitan and provincial region from far-off detecting information, so various constructions can be recognized like vehicles, streets, structures and so on This assignment is testing a result of the presence of profoundly various items or design or shapes which calls for earlier information apart from standard smoothness and co-occurrence assumptions. In this paper, we discuss a model that detects when the driver is getting into drowsiness and alerts him. It also allows him to visualize the lane on which vehicle is moving on and also deviations in the lane.
Enabling Reliable and Secure Data Query Services for the Power Utilities

Data confidentiality and reliable permission management have always been primary concerns that hinder enterprise customers from adopting data services of three-party professional providers (e.g., Amazon AWS). This situation is especially prominent in the State Grid Corporation of China (SGCC), in which the data is particularly sensitive. Accordingly, we hope only authorized users have access to the expected data, while unauthorized entities, including cloud service providers and unapproved internal staff, know nothing about the data. In SGCC, we utilize cloud facilities to maintain our data, and multifold efforts have been made to achieve these requirements. Specifically, for reliable authority management and access control, we devise an authority separation mechanism; the data is stored in encrypted form, and we design a set of mechanisms to enable search and query on encrypted data using searchable encryption and homomorphic encryption. In this paper, we present architectures, designs, and experiences in launching our systems.
GELibRec: Third-Party Libraries Recommendation Using Graph Neural Network

Third-party libraries have become an indispensable part of the software. The function provided by well-tested third-party libraries can be reused through their programming interfaces, significantly increasing developers’ software quality and productivity of developers. However, the vast number of third-party libraries and the complex dependencies are the main obstacles to efficiently exploiting the available resources. So, advanced methods are needed to explore the dependencies between projects and third-party libraries to make meaningful recommendations. This paper proposes GELibRec, which combines graph embedding and collaborative filtering to recommend libraries to developers. We extract a dataset from the open-source dataset libraries.io, and the experimental results show that GELibRec outperforms these methods concerning various quality metrics.
DIRECTIONS FOR IMPROVING THE METHODOLOGICAL TRAINING OF FUTURE COMPUTER SCIENCE TEACHERS FOR THE ORGANIZATION OF EDUCATIONAL AND RESEARCH ACTIVITIES OF STUDENTS

The article suggests the directions of methodological improvement of the organization of educational and research activities of future computer science teachers. In the process of organizing the educational and research activities of students, various aspects of the teacher's pedagogical activity are used. These are: organization, communication and specifics of computer science education with the organization of educational and research activities. Forms the knowledge and skills of future computer science teachers related to the organization of educational and research activities of students in the process of studying computer science at school. The organization of educational and research activities of students begins with the process of preparing the teacher for the lesson and includes the importance of areas of methodological improvement that improve the quality of students' knowledge.
Development of An O(n)-Round Strategy for the Magnus-Derek Game

The Magnus-Derek game, a two-player game played on a round table with n positions, is investigated further. The players jointly control the movement of a token. One player, Magnus, aims to maximize the number of positions visited while minimizing the number of rounds. The other player, Derek, attempts to minimize the number of visited positions. We provide a new Magnus approach that allows him to visit the most spots in 3(n - 1) rounds, which is the optimum number of rounds up to a constant factor.
Durian volume determination using short-range coded-light three- dimensional scanner

Importance of the work:The quality of a durian fruit can be inferred from its specific gravity, whose determination requires its volume.Traditionally, the volume of a durian is determined by the water displacement method; However, this is a slow process that runs the risk of fungal growth due to residual high humidity.Objectives: The present study evaluated the viability of three-dimensional (3D) scanning as an alternative approach to water displacement for the determination of the volume of durian fruit.Materials & Methods: Durian fruit samples at different maturity levels and harvested from two different growing areas were determined for volume using the traditional specific gravity method and compared with volumes measured using the 3D scanning method.Each scan took about 60 s.Results: The analysis showed that there was no significant (p > 0.05) difference between the traditional and proposed methods for durian fruit of different maturity levels.The average relative percentage difference was 1.06% (sample size of 116), which was less than that obtained from other reported studies that tested other fruits with reasonable axial symmetry. Main finding:The proposed alternative technique has potential for the continuous and automatic measurement of the volumes of durian fruit on a moving conveyor belt, as a part of the maturity determination process.
Construction of a prediction model for individual investors psychology and behaviour based on cognitive neuroscience

Traditional forecasting models cannot extract the trend information of retail investors' multi-scale psychological and behavioural data, and the predictions are not accurate. To solve this problem, a Markov-based individual investor psychology and behaviour prediction model is proposed. Using the wavelet multi-scale analysis method, the multi-scale data of individual investor's psychology and behaviour are extracted. A long-term-memory analysis is performed on multi-scale data of individual investors' psychology and behaviour using the correlation analysis method, and the trend information is extracted. On this basis, a Markov prediction model is established, and a modified investment preference model is introduced to improve the accuracy of the prediction. Using the individual similarity degree, the nearest neighbour set of the target individual is established, and a multi-order predictive Markov fusion model for multiple individuals is formed to achieve accurate prediction. The experimental results show that the proposed model achieves better nonlinear fitting and higher prediction accuracy.
Police Brutality and the #EndSARS# Protests in Nigeria: Appraisal of the Legal Issues

The Police are state officials charged with the duties of law enforcement and other responsibilities.They are empowered to use reasonable force in the discharge of their duties, which is regulated by international human rights norms, national constitutions and other legislations.However, despite the restraints on police use of force, the Police often over step their boundary and use force beyond the limits permissible by law.In Nigeria, the indiscriminate use of force by the Special Anti-Robbery Squad (SARS) led to the popular #EndSARS# uprising, which took over Nigeria like a whirlwind.In the wake of the Protests, this paper examined the legal issues that arose from the protests involving the legal limits of police use of force, the factors that determine when and how the police deploy the power to use force and the impediments to police use of force in Nigeria.The paper found that there are constitutional and other legal provisions that encourage the police to use excessive force in performing their duties of securing the country.It was recommended that both sections 33(2)(b) and 214(1) of the constitution and section 271 of the Criminal Code must be amended to redefine the limits of police powers to use force in Nigeria.
Pandemic Analysis: An ANN based Approach

Machine Learning has seen prominent application in healthcare domain.In this paper, an attempt has been made to predict the spread of a disease using machine learning algorithms.In this research, a model is built to predict the casualties created by a particular virus.A pandemic spread analyzer is designed to estimate the cases across the globe.Hyper Parameter Tuned Artificial Neural Network is utilized to implement this research.AI based cognitive modeling simulates properties of biological neural networks.This research implements this model for better preparation to fight pandemic in future.
Plasticity After Cognitive Training Reflected in Prefrontal Local Field Potentials

Learning to perform a new cognitive task induces plasticity of responses in the prefrontal cortex and other brain areas. The predominant effect of training in new tasks is activation of more neurons as well as increases in firing rate, however its effects on single neurons are diverse and complex. We sought to understand how training in a cognitive task affects global measures of neural activity by recording and analyzing local field potentials (LFPs) in monkeys before and after they learned to perform working memory tasks. We examined power during the stimulus presentation and delay periods of the task, relative to the baseline period. Training primarily decreased power in the alpha and beta frequency range during the task intervals. Both evoked power, synchronized to task events, and induced power exhibited this decrease relative to baseline after training. The effect was consistent across variants of the task, requiring working memory of spatial location and of stimulus shape. Our results reveal signatures of cortical plasticity in local field potentials associated with learning to perform novel cognitive tasks.
Views of medieval eastern scientists on education

This article provides information on the views of medieval Eastern scholars on education and the spiritual maturity of the individual. A parent who is able to correct his or her own shortcomings can be an educator. One of the most important tools in moral education is to encourage the child to have a one-on-one conversation without touching his or her feelings or pride. Therefore, the history of the origin of our national instruments connected with the written works on the source of medieval music. During the years of independence, as in all spheres, special attention has been paid to the preservation of national masterpieces and the restoration of our values. Attitudes toward our people's history, past, customs and cultural heritage have changed dramatically.
The Law and Macroeconomics of Custody and Asset Segregation Rules: Defining the Perimeters of Crypto-Banking

Custody - simply defined as holding securities or funds on behalf of third parties – is one of the key institutions that defines and distinguishes major financial institutions in the financial system. However, custody rules in financial law have traditionally been studied as a microprudential tool for investor protection purposes, while their macroeconomic impact has largely been overlooked. Inspired by the literature on asset custody and its impact on the institutional design of the traditional financial markets, institutions, and infrastructures, this paper studies the potential impact of defining custody rules in the cryptoasset markets on the future developments of the cryptoasset ecosystem.In traditional finance, a survey of relevant regulations applicable to financial institutions shows that the custody rules and client asset (segregation) rules apply to all financial institutions, other than commercial banks’ core business activity (i.e., deposit-taking). The most salient impact of exempting deposit contracts from custody and client asset rules has been the emergence of a business model for banks that treat their clients’ funds as their own and use them for their own accounts. Comingling clients’ funds with that of the bank is a critical defining feature of the banking industry that differentiates it from non-bank financial institutions as well as non-financial firms, and positions banks at the heart of monetary systems.The custody and asset segregation rules can play the same important role in the future developments of the crypto-asset industry. To delineate the scope of crypto-banking and differentiate it from other types of cryptoasset services, such as exchange and custodial services, it is crucial to start from the custody and asset segregation rules. This paper advocates for a presumption of custody when a client does not self-custody his cryptoassets, giving (or sharing) the control of the assets to a third party. It argues that such a presumption not only would serve the objectives of investor protection but also could prevent excessive credit creation in the cryptoasset ecosystem and the potential risk spillovers to the conventional financial markets and the real economy.
Entropy Weight Allocation: Positive-unlabeled Learning via Optimal Transport

Positive-unlabeled learning (PU learning) aims to deal with the problem that only a fraction of positive instances are known. Due to the absence of negative instances, ordinary learning models cannot be directly applied. Existing PU learning methods either explicitly choose some unlabeled instances as negative instances in advance or reformulate the task as a weighted learning problem. Since working in such an ad-hoc fashion, these methods often suffer a bad performance and only have limited usage. This paper proposes a novel instance-dependent weighting method entropy weight allocation (EWA) for PU learning by optimal transport (OT). More specifically, we allocate each unlabeled instance an elaborate weight indicating the possibility that it is an underlying negative instance. Then any ordinary weighted learning models can be used to obtain a PU classifier. By concatenating EWA with four celebrated classification models, we show that EWA is a broad-spectrum weighting method that can boost almost all the mainstream machine learning models for PU learning.
Importance of Virtual Reality (VR) Tools in the Processes of University Orientation for Technological Degrees

This chapter analyses the use of the virtual reality (VR) digital tool in the processes of university orientation, especially for those degrees that have important training on technologies in their curricula. To do so, a study on pre-college students through an experience with digital tools, like the use of VR headsets, was done. After that, the students completed a questionnaire to assess this activity. A total of 3,680 satisfaction surveys were taken, before and during the COVID-19 pandemic period. The obtained data demonstrate that when the satisfaction degree increases with the activity, the rate of students who eventually choose technological degrees like Digital Business improves.
Blur and Noise Removal from the Degraded Face Images for Identifying the Faces Using Deep Learning Networks

Face recognition is one of the active research areas from past few years resulting in decent performance with deep neural networks. Identifying faces from poor quality images or video frames, degraded with noise, blur or illumination is extremely challenging work and needs more efficient methods. In the proposed work, to address the noise, an improved noise removal technique is introduced. The motion blur is addressed using a deep recurrent neural network by reconstructing the super-resolution image of the blurred image. The problem from the illumination variation is minimized using 4-Patch-local binary pattern features. In this work, we use a convolutional neural network with 4-Patch-local binary pattern feature to identify the faces from the restored images. The result of the experiments shows the proposed algorithms achieved a good performance.
On Writing Every Song

This speculative chapter explores where authorship might be heading given current developments in technology, and sets the task that is to be worked out in the following chapter in the mathematics of writing every song.