Reviewerly/exHarmony huggingface dataset:
3D skeletal gesture recognition through discriminative coding: Human activity recognition using time warping invariant Riemannian trajectories

To recognize the human activity is considered very essential in human-to-human interaction and interpersonal relations due to its nature of providing information regarding the identity of a people, their individuality, and psychological state. The extraction of this information is very challenging. The extraction of this information is very challenging. The major subject of study of the scientific areas of computer vision and ML is the potential of human for identifying the activities of another person. A sequence of human body movements in which different body parts are engaged in a concurrent manner is known as action. According to computer vision perception, any kind of observation is matched with earlier defined patterns and then it is labeled while recognizing the action. In this research work, the 3 D Skelton-based technique is proposed for human activity reorganization. The proposed technique will improve performance for the human activity detection in terms of accuracy, precision and recall also the proposed technique will compared with the other techniques which authenticates reliability of the model.
Graph Representation for Learning the Traveling Salesman Problem

Training deep learning models for solving the Travelling Salesman Problem (TSP) directly on large instances is computationally challenging. An approach to tackle large-scale TSPs is through identifying elements in the model or training procedure that promotes out-of-distribution (OoD) generalization, i.e., generalization to samples larger than those seen in training. The state-of-the-art TSP solvers based on Graph Neural Networks (GNNs) follow different strategies to represent the TSP instances as input graphs. In this paper, we conduct experiments comparing different graph representations finding features that lead to a better OoD generalization.
Perancangan Dan Implementasi Sistem Akuntansi Pada Mama Kitchen

Mama Kitchen is a business in the form of food stand that sells various kinds of food. Until now, Mama Kitchen does not have an accounting system. Mama Kitchen are still using manual recording methods. The purpose of this work is to make accounting records easier for the owner.The research was done by interviewing the owner and employees, and direct survey to location in order to know clearly about their daily activities. System will be designed according to Mama Kitchen’s needs.The result of implementation of this research is an accounting system that can produce financial reports. By having a system, owner can easily see the business profits accurately and ease owners in making decision about their business.
Exploring beneath the surface using interactive data transects

Maps depicting different types of standardized data densities, general ratios/rates, and proportions/percentages are displayed as planimetric projections of continuous or discrete surfaces. However, these standardized data often have sub-layers that are used in compiling a final surface being mapped such as sub-layers stacked on top of one another, or sub-layers used to compute rates and percentages. In this study, we propose an interactive transect tool that allows the user to explore beneath the final surface to describe the patterns of these sub-layers. The tool was developed as a Python add-in for ArcGIS. This package provides two different representations of the cross-sections, a stacked profile tool and a line graph tool, for users to choose based on the type of surface and the purpose of the exploratory analysis. The illustrated applications of the transect tool include exploring the constituent layers of density surfaces, comparing different probability density surfaces, resolving the visual equalization issue for ratio surfaces, and interpreting the spatial patterns of areal classes. An empirical test finds that the transect tool is somewhat more time-efficient than when making visual comparison of values and more accurate in detecting the rank orderings of these values. Overall, it has potential as a visualization tool for multivariate spatial exploratory analysis.
Factors Affecting Dynamic Moving Detectability in VR Space

The use of VR technology is growing in a number of fields, but there are still some problems that need solving. One of said problems is the improvement of interaction within the VR space and how user interfaces are displayed. In this study, research has been conducted on how users feel using a simple, solid figure. In order to investigate better user interfaces in VR space and more effective ways of displaying information. Tasks were conducted to recognize the trajectory of an object’s movements in virtual space using VR contents. This was used and an investigation was conducted into how much recognition was affected by the speed of an object’s movement, the range of the object’s motion, and how far an object is away. As a result, it was found that the appropriate distance needed to recognize a dynamic body’s movement, proved that there is a tendency for the rate of accurate recognition to rise the wider the range of motion is, and learned that recognition is not affected if the speed of movement is within the range where ocular following responses are possible. In addition, it was proved that a combination of distance and speed heavily influences recognition of a dynamic body.
Smart Tourism Recommender System Using Semantic Matching

Due to the widespread diffusion of information technologies and the number of social network users who are increasing day after day, today there is an explosion of data on the web, which requires a filtering mechanism to customize the data to be displayed to the users according to their needs. For this reason, the recommendation systems have been designed to facilitate and personalize data access, according to the needs of each user. But each system can recommend services linking to a specific area, which makes the system of recommendations point and more efficient, where we find systems for the field of e-commerce, tourism, etc. In this paper, our work focuses on designing an intelligent recommendation system for the tourism field, which is used to analyze images stored in mobile devices of users in order to identify the fields of interest of these users (keywords). Then, we base on these latter to recommend the tourist services and places suitable to each user according to their preferences.
KNOWLEDGE ECONOMY CONCEPTS AND ROLES IN PERSPECTIVES OF UNIVERSITY TEACHERS OF ASIAN AND EUROPEAN STATES

Purpose of the study: The study aimed to identify knowledge economy concepts and roles in perspectives of university teachers of Asian and European States and examine the relationship between knowledge economy concepts and roles of university teachers.&#x0D; Methodology: This study was quantitative correlation and survey type in nature. The study population was public sector universities teachers of three countries (Pakistan, United Kingdom, and India). Six public universities were included in the population. From population one hundred and eighty teachers selected as a sample of study conveniently.&#x0D; Main Findings: Teachers agree unanimously that human beings are knowledge capital because they generate new knowledge with innovative ideas and conducting researches in different fields. Humans are the source of producing and placement of knowledge excellently and professionally. There is a strong path relationship r = .8 between knowledge economy concepts of teachers and their roles regarding this. Findings showed that there was a significant difference in teachers’ conceptions of Pakistan and the United Kingdom.&#x0D; Applications of this study: The teachers may make clear conceptions about the knowledge economy and recognize their roles to produce innovative and skills-oriented human resources and data to strengthen the knowledge-based economy.&#x0D; Novelty/Originality of this study: The knowledge economy is a new concept itself in this context. University teachers’ of three different countries highlighted concepts and roles about the knowledge economy.
Campus-Navigation-System Design for Universities – A Method Approach for Wismar Business School

Digitization is influencing a variety of industries and services, including universities. In this research, mockups for a mobile, indoor navigation application are presented using Wismar University as an example. Therefore, we utilized the first step of the Digital Innovation and Transformation Process (DITP). In the run-up to this research, digitization-relevant requirements for universities were identified, with 62 students from Wismar Business School using the first process step of the DITP. The important requirements identified were: routing to classrooms, localizing points of interest, and booking of rooms within the university. Our research represents our insights from applying the second step of the DITP. The aim was to develop a digital prototype for indoor navigation as a mobile application that can be utilized by different universities for the same purpose. The results were verified based on user opinions through a usability test. This research is relevant for IT experts and UI designers, as well as enterprise architects, who are interested in developing a mobile indoor navigation application for universities.
Improving learning in neural networks through weight initializations

Feedforward neural networks are known to exhibit universal approximation property, i.e., these networks can effectively approximate any function arbitrary well, provided there is one hidden layer with sufficient number of hidden nodes, and some non-linearity at the hidden layer nodes. The property of universal approximation in these networks contributed to wide range of application, remarkably in the area of predictive modelling. However, these networks have a major drawback of slow convergence. In this work, we proposed a new weight initialization algorithm to improve the convergence speed in feedforward networks using statistical analysis of system, based on the set of predefined assumptions. The proposed algorithm is compared to other established weight initialization algorithm for predictive models, and is expected to perform better than other algorithms.
A Study of Blockchain for Secure Smart Contract

Blockchain is a novel and effective technology, which renders the implementation capability of various cases like public services, economic systems, Internet of Things, internet interaction systems, and smart contract applications. Smart contract works in such a way that the computer works as a third party, and the other two senders and receivers are those who digitally agree to the obligations and rights. Blockchain keeps the record of the contract which is executed by the network’s distributed nodes, where the third party requirement is eliminated as Blockchain decentralized in nature. There are still multiple security and privacy issues with the smart contract that imposes a huge threat to its continued effectiveness and growing esteem and recognition. In this article, the issues of transactions in smart contracts were discussed and different tools were analyzed to overcome these issues.
XTR Algorithm: Efficient and Compact Subgroup Trace Representation

In Crypto 2000, Lenstra et al. introduced the concept of XTR. The term XTR is used for efficient and compact subgroup trace representation. The security of the XTR cryptosystem is based on the discrete logarithm problem (DLP) and its variants, defined over a subgroup of multiplicative group of a finite field. It is considered as favorable alternative to RSA and elliptic curve cryptosystem. In this book chapter, we will thoroughly discuss about the mathematics behind XTR and its applications in cryptography.
On the Definition of Data Regulation Risk

The rapid development of Information and Communication Technologies (ICT) has led to firms embracing data processing. Scholars and professionals have developed a range of assessments and management methodologies to better answer the needs for trust and privacy in ICT. With the ambition of establishing trust by reinforcing the protection of individuals’ rights and privacy, economic interests and national security, policy makers attempt to regulate data processing through enactment of laws and regulations. Non-compliance with these norms may harm companies which in turn need to incorporate it in their risk assessment. We propose to define this new class of risk: “Data Regulation Risk” (DRR) as “a risk originating from the possibility of a penalty from a regulatory agency following evidence of non-compliance with regulated data processing and/or ICT governances and processes and/or information technologies and services”. Our definition clarifies the meaning of the defined terms in a given context and adds a specific scope to facilitate and optimize decision-making.
Niche, Media Displacement, and Multimodal Relationships

This chapter introduces theories of the niche and media displacement to address the idea that although there is an ever-expanding array of personal media options, the displacement of one type of media for another is slow and gradual. This chapter starts by exploring text-based communication in the context of both interpersonal communication and classic CMC literature. The emergence of email and then-new phenomena such as chat rooms, message boards, and the listserv will be examined, followed by the emergence of SMS. This chapter also examines the rise of social media. Finally, this chapter documents the frequency of social interactions on various modes of communication, and concludes by focusing on the coexistence, rather than total displacement, of various communication options. The chapter finishes by suggesting a set of modalities that are likely to endure as technology changes.
Vehicle Flow Detection Based on Improved Deep Structure and Deep Sort

Real-time vehicle detection based traffic monitoring is a hot research topic within the area of computer vision. In view of the problem of low detection accuracy and low processing speed, a vehicle detection method based on Improved Deep Structure is proposed in this study. Due to the characteristics of highway vehicles with a fixed aspect ratio, k-means ++ clustering method is used to select new anchor boxes to eliminate false targets at an early stage followed by improved depth structure with deep sort. Experimental results demonstrated that our proposed method on standard data set KITTI-UA achieved higher precision and faster speed than the existing algorithms.
Mask Detection Algorithm for Public Places Entering Management During COVID-19 Epidemic Situation

COVID-19 now is spreading fast all over the world. Wearing masks has proven to be an effective way to prevent COVID-19 to some extent. This paper studies a mask detection algorithm for public places entering management during COVID-19 epidemic situation. Only people wearing masks are allowed to enter. Cameras are fixed at the entrances of public places and take photos of people who are coming in. Then, a series of pre-processing are performed, including face detection, normalization, etc. Residual network is used as the classifier. Simulation results show that the average recognition accuracy can reach 90%.
A Comparative Analysis of Machine Learning Techniques for Spam Detection

Internet provides a medium to connect with individuals of similar or different interests creating a hub. Since a huge hub participates on these platforms, the user can receive a high volume of messages from different individuals creating a chaos and unwanted messages. These messages sometimes contain a true information and sometimes false, which leads to a state of confusion in the minds of the users and leads to first step towards spam messaging. Spam messages means an irrelevant and unsolicited message sent by a known/unknown user which may lead to a sense of insecurity among users. In this paper, the different machine learning algorithms were trained and tested with natural language processing (NLP) to classify whether the messages are spam or ham.
Effect of d-Dimensional Re-orderings on Lossless Compression of Radio-Astronomy and Digital Elevation Data

For multidimensional data, Space-Filling Curves (SFCs) have been used to improve the execution time of spatial data queries. However, their effect on compression, when used to reorder the uncompressed values, is known to a lesser extent. We investigate the impact of three SFCs on Shuttle Radar Topographic Mission (SRTM) elevation data and Square-Kilometre Array telescope (SKA) radio-astronomy data: two types of datasets to which SFCs have not been extensively applied, within a compression context. This work contributes to the understanding of how such reorderings impact compression performance and affect different compression schemes and preprocessing techniques through their use. We show empirical results from combining eight common compression schemes, the Z-Order, Gray-Code, and Hilbert space-filling curves, and the bitwise preprocessing technique BitShuffle. The Hilbert Curve consistently outperforms the other orderings for the SRTM dataset though the mapping implementation incurs a significant speed penalty. However, the Z-Order and Gray-Code Curves are best for the SKA dataset. Through an analysis of the dataset autocorrelations, file-entropies, and block-entropies; we show that the SKA dataset's dimensional bias is not exploited as much by the Hilbert Curve compared to the Z-Order and Gray-Code Curves. However, the Hilbert Curve is the most appropriate for the SRTM dataset as it can be modelled as isotropic and has a significantly higher level of local autocorrelation. BitShuffle is necessary to practically compress the SKA data, but does contribute to the compression performance of the SRTM dataset. These curves and BitShuffle are advantageous in reducing block-entropy values for such datasets.
The link between Bitcoin and Google Trends attention

This paper shows that Bitcoin is not correlated to a general uncertainty index as measured by the Google Trends data of Castelnuovo and Tran (2017). Instead, Bitcoin is linked to a Google Trends attention measure specific for the cryptocurrency market. First, we find a bidirectional relationship between Google Trends attention and Bitcoin returns up to six days. Second, information flows from Bitcoin volatility to Google Trends attention seem to be larger than information flows in the other direction. These relations hold across different sub-periods and different compositions of the proposed Google Trends Cryptocurrency index.
Opportunities of Electronic Learning Platforms for the Russian System of Higher Education

The habitual training routine of today’s students, who are the representatives of digital generation, is closely related to electronic educational programmes and courses, textbooks, assignments, and projects. The information support of the educational process is the prerequisite of obtaining the respective professional competencies, as well as of a successful professional career and its progression. The rapid development of online technologies has served as a catalyst for the emergence and spread of information educational technologies, particularly, of online education. The use of modern educational information and communication technologies gives the teacher many opportunities to simultaneously communicate the learning material to different groups of learners. The learners in such groups may have different levels of initial training, different perception speed, and sometimes even different attitudes towards the university education. These technologies dramatically reduce the number of slow learners, motivating them to educational activities and making the learning process more effective. Thus, the learning process rises to the proper level, and the university rankings rise accordingly. Although the World Wide Web as we know it is a relatively recent phenomenon, it is evident that the electronic distance learning has existed for a long time, and the level of its technological development was not always as high as it is now. The authors suggest that the standard structure and content of a media platform should be considered for a better understanding of the nature of modern digital learning technologies and the opportunities they offer.
Applying hierarchical clustering to homophonic substitution ciphers using historical corpora

This study examines whether hierarchical clustering can be used efficiently on homophonic substitution ciphers from the early modern age. The methodology was first tested on artificial homophonic ciphers based on historical corpora in 4 different languages. This investigation established that the decryptability of homophonic substitution letters does not depend on the plain text language, and it is effective if the text length goes beyond the unicity point. Finally the examination of original homophonic substitution cipher – cipher-key pairs followed in 4 languages. It determined that the method is applicable to original historical encrypted documents as well.
Application of computer technology in Aerobics Teaching and training

Abstract We know that aerobics exercise can have a good effect on our body function. Aerobics can improve the depth of our breathing. It can make the cells in every part of our body more active. Many experts believe that aerobics has gradually become people’s health dance. Many countries also hold corresponding aerobics competitions[1]. With the rapid progress of computer technology, the application of Aerobics Teaching and training based on computer technology has become a hot topic. This paper describes the current situation of aerobics teaching. This paper finally puts forward the application of computer technology in aerobics teaching.
NHC_MDynamics: High-Throughput Tools for Simulations of Complex Fluids Using Nosé-Hoover Chains and Big Data Analytics

One of the most promising alternatives to acquire knowledge of the behavior of biological systems are the methods of molecular simulation such as Molecular Dynamics (MD). MD has become one of the most powerful numerical tools to study thermodynamic properties of macromolecules such as proteins and other biomolecules. However, MD has two major limitations when it comes to study large systems such as proteins at the molecular level, these are: the size of the system (the number of atoms to study) and the integration time used to evaluate the equations of motion. Therefore, this work is focused on the implementation of the NVT algorithm of molecular dynamics based on the Nosé-Hoover thermostat chains with the high performance computing technology such as Graphical Processing Units (GPUs) and Big Data analytics for the generation of knowledge to help understand the functioning of thermodynamic properties in simulated systems of Lennard-Jones fluids, as well as the study of the behavior of proteins, such as diabetes, to refine their structures to formulate and integrate them into the improvement of food in an improved diet for people with this condition.