Reviewerly/exHarmony huggingface dataset:
Research on Semantic Based Metadata Method of SWIM Information Service

Semantic metadata is an important means to promote the integration of information and services and improve the level of search and discovery automation. Aiming at the problems that machine is difficult to handle service metadata description and lack of information metadata description in current SWIM information services, this paper analyzes the methods of metadata sematic empowerment and mainstream semantic metadata standards related to air traffic control system, constructs the SWIM information, and service sematic metadata model based on semantic expansion. The method of semantic metadata model mapping is given from two aspects of service and data, which can be used to improve the level of information sharing and intelligent processing.
Applying coupling and cohesion concepts in object-oriented software: a controlled experiment

Context: Coupling and cohesion concepts support the object-oriented (OO) design activity. A better design implies less coupled and more cohesive classes. Thus, it is expected studies on OO design investigate issues impacting the good application of these concepts. However, most studies in the area have focused on high-level metaphors, such as code smells and design patterns; or software metrics. Objective: This study aims to build empirical evidence reinforcing that basic concepts deserve more attention towards better OO design. The study investigates the application of coupling and cohesion concepts during programming activity. Two topics are addressed: i) how developer characteristics (“previous knowledge on the concepts” and “developers experience”) impact on software design during programming activity; and ii) the differences in applying the concepts, independently. Method: A quasi-experiment with 17 participants. The participants were graduate and undergraduate students, but all have professional experience in software development. The software they programmed was compared with the experiment’s oracle in terms of the application of coupling and cohesion concepts. Result: One finding is previous knowledge on the concepts impacts on design quality, in some way; while developers experience doesn’t. Another finding is: application of cohesion is significantly easier than application of coupling. Conclusion: A conjecture based on these findings is training on basic OO principles (including formal education) might be more relevant than developer experience towards better software design. At last, the topic needs more investigation. We suggest this approach brings a complementary perspective of analysis to discuss the topic.
The Transformers for Polystores - The Next Frontier for Polystore Research

What if we could solve one of the most complex challenges of polystore research by applying a technique originating in a completely different domain, and originally developed to solve a completely different set of problems? What if we could replace many of the components that make today’s polystore with components that only understand query languages and data in terms of matrices and vectors? This is the vision that we propose as the next frontier for polystore research, and as the opportunity to explore attention-based transformer deep learning architecture as the means for automated source-target query and data translation, with no or minimal hand-coding required, and only through training and transfer learning.
영등굿의 영감놀이를 통한 리듬융합교육 프로그램개발

The research has developed an original culture and art educational program for primary education using Younggamnori, cultural heritage of South Korea. The program consists of three-hour lessons and took place at the Ggumdarak Saturday Culture School with three lessons for three weeks. The first lesson is developed based on the nested model of Fogaty`s integrated curriculum and focused on Younggam theme. The lesson is gradually expanded by making connection among dance, arts, and music subjects and allows students to acquire various skills simultaneously. The second lesson utilizes a threaded model, focusing on Younggam theme and reflecting the diversity of each subject. The third lesson is an integrated model that combines team teaching and sub-topic learning with Younggamnori therapy. The implementation of Younggamnori into school context reveals that it is more effective to utilize integrated lessons of various subjects instead of teaching individual subjects respectively. In addition, the lessons combine pop culture in order to intrigue students to understand the traditional culture better, and as a result, students accepted the lessons without much rejection although Younggamnori is a type of Gut(Korean exorcism) and even showed higher interests in art culture. Thus, it is more required to develop education program that can lower the rejection in art culture.
Video Manipulation Detection and Localization Using Deep Learning

Nowadays, several low-cost yet productive video editing software tools have been developed due to which it is essential to check the genuineness of digital videos, before they can be used as legal evidences. Thus, detecting and localizing forgery in videos has become an important research field. In this paper we have proposed a solution to detect and localize spatio-temporal manipulation in videos using a deep learning approach involving an architecture based on LSTM or Long short-term memory. Our proposed model is able to perform temporal as well as spatial localization of forgery. The experiments are carried out on the REWIND dataset having 10 original and 10 forged videos. 16 videos are used for training and 4 are used for testing the model. The evaluation of model performance is done at pixel-level, frame-level and video-level for computing the spatial localization, temporal localization and forgery detection accuracy values respectively.
Report on Solvency and Operating Capability Analysis of ZLCs in 2019

This chapter analyzes the asset structure and quality of listed companies in Zhongguancun in detail, and measures their overall solvency by indicators such as asset-liability ratio, cash flow-debt ratio and current ratio. Its operational capability is presented through average collection period, inventory turnover days, total assets turnover days and other indicators. Through the corresponding research, it is found that the overall debt ratio of Zhongguancun listed companies is low and the solvency is good; All kinds of assets have high turnover efficiency and strong operational capability.
A Virtualized Target Queuing Balanced Model Using Vmax/M/G-Load(Min)/Cache-P<sub>2</sub>λM for Reducing Queuing Time Expectation of Customer Delay Tolerance

Queuing theory is a formal mathematical model of late response arrival of customer waiting in various access services, accepting processes management as a whole field. Often multiple server sorting can be used to estimate the number of servers and service rates be high due too lacking of time, with an average delay sequence in the analysis. Using these sequence models to read more than one sequence during execution approximates system sorting performance. To resolve this problem we modulated the distributed process into Virtualized Target Queuing Balanced Model (VTQBM) using Vmax/M/G-Load (Min)/Cache-P 2 λM for reducing queuing time expectation of customer delay tolerance. The Virtual Resource queuing System is optimized with fork-join queue by creating single to multimodal service performance using target request load balancer in a shared distribution mode. Sorting the request buffer cache is created based on Customer Request Arrival Rate (CRAR) and resource output preferred to check with target rule waiting in the Load balancer. To improve the use of model complex virtual resource sharing shared with customer arrival to Cache Target Load Allocator (CTLA) on queuing distributed resources, This fork joiner target the service performance to reduce the customer waiting time in queuing and produce outputs can achieve a different parameter input transformation on queuing theory.
CompareWords: Measuring semantic change in word usage in different corpora

We present CompareWords; A software package developed for measuring semantic change of particular words between two corpora. We have used it for measuring changes in meaning between two time periods, but it could also be used to measure changes in meaning between different topic areas or literary genres.Our technique uses word-embeddings for each corpus, and cross-lingual transformations. Thus it requires the corpora to be large enough to train good word-embeddings.
Meanings of Screen Culture as Pedagogical Phenomenon

The purpose of the study is to determine theoretically (including from the standpoints of sciences related to pedagogy) and empirically (through perceptions of pedagogical students) the interpretative characteristics of screen culture as a personal phenomenon generated by the information society. The article uses a comparative analysis of various scientific approaches to screen culture to form its semantic concepts, which are subsequently subjected to experimental verification. Scientific novelty of the study lies in determining essential statuses-meanings of screen culture through the lens of pedagogy, taking into account interdisciplinary positions of its understanding, and in documenting corroborative empirical facts. As a result, basing on culturological, communicative, competency-based approaches, the researchers have identified the primary meanings of the studied phenomenon in pedagogical science: formation of an individual image of the world in students; a way of transmitting and assimilating the content of education; development of communicative and information competencies through the use of screens; integrative quality of a personality in the structure of its basic culture.
Development and Application of Integrated Meteorological Observation Online Monitoring in “One Diagram”

Abstract The efficient automatic and intelligent real-time monitoring means have not been fully established after the reform of unmanned ground meteorological observation automation, and the monitoring systems of various observation device are isolated from each other, which makes it impossible to efficiently take an overall view for the running and data transmission state of the ground meteorological observation stations. The integrated meteorological observation online monitoring in “one diagram” for nearly 3000 meteorological devices in 15 categories is designed. The status of software or hardware, network faults of the meteorological devices is displayed by the color code in “one diagram” and the alarm of 3D virtual model components is updated simultaneously, and displayed automatically in the “one diagram” according to the fault level. It is interactively associated with a 3D virtual visual monitoring system based on C/S with one diagram B/S page being embedded, the faults tips of automatic positioning in the 3D virtual model are directly associated and displayed from the 3D virtual visual online monitoring system, with one click on the color block of the fault station in “one diagram”. So “macro-level” monitoring from “one diagram” device layout, category, fault number, and alarm status to “micro-level” correlation of virtual model fault details is established, by which what you think is what you see, meeting the demand of on-line monitoring of integrated meteorological observation stations from “one diagram” to “the whole system”.
Pixel-RRT*: A Novel Skeleton Trajectory Search Algorithm for Hepatic Vessels

In the clinical treatment of liver disease such as tumor, the acquisition of vascular skeleton trajectory is of great worth to untangle the basin and venation of hepatic vessels, because tumor and vessels are closely intertwined. In most cases, skeletonization based on the results of vascular segmentation will be prone to fracture due to the discontinuous segmenting results of vessels. As the overall tree-like system of hepatic vessels is a thin tubular tissue, we expect to start the analysis of vessels from vascular skeleton to vascular boundary, not the contrary, which can more effectively implement the image computing of hepatic vessels and interpret the tree-like expansion. To this issue, in this paper, we propose an innovative approach Pixel-RRT* inspired by Marray's Law and the growing rule of biological vasculature. It can be applied to the skeleton trajectory search for the intricate hepatic vessels. In Pixel-RRT*, we introduce the novel pixel-based cost function, the design of pixel-distributed random sampling, and a multi-goal strategy in the shared graph of random tree based on the general algorithmic framework of RRT* and RRT. Without any prior segmentation of the vessels, the proposed Pixel-RRT* can rapidly return the rationally bifurcated vascular trajectories satisfying the principle of minimal energy and topological continuity. In addition, we put forward an adaptively interpolated variational method as the postprocessing technique to make the vascular trajectory smoother by the means of energy minimization. The simulation experiments and examples of hepatic vessels demonstrate our method is efficient and utilisable. The codes will be made available at https://github.com/JeffJFZ/Pixel-RRTStar.
WITHDRAWN: ADP efficient 4-level DWPT using approximation

In this paper, presented an efficient 4-level discrete wavelet packet transform (DWPT) using approximation where area and delay are considerably reduced. It gives lesser number of hardware arithmetic computations and lesser delay required at each computation level. Compared with the existing design, the presented design gives 16.34% less area for block size 2. The Area Delay Product (ADP) of proposed shift add register approximated design have saving of 21.7% and CPD of 4.4% lesser for block size 2 over existing design.
Abstract: Probabilistic Dense Displacement Networks for Medical Image Registration

Medical image registration plays a vital role in various clinical workflows, diagnosis, research studies and computer-assisted interventions. Currently, deep learning based registration methods are starting to show promising improvements that could advance the accuracy, robustness and computation speed of conventional algorithms. However, until recently there was no commonly used benchmark dataset available to compare learning approaches with each other and their conventional (not trained) counterparts.
Cloud Computing Economic Development Model from the Perspective of Business Ecosystem

Over the years, with the popularity of the Internet and the development of information technology, a lot of data have appeared social information beyond the range that individuals or systems can accept and use effectively, and the phenomenon of failure is more and more, data mining is particularly important, it is also a hot research direction in the information industry. Cluster analysis is a representative core technology of data mining. However, with the further study of clustering algorithm, it is found that the fuzzy classification problem is not suitable for the past classification methods. Based on the FCM fuzzy clustering algorithm, this paper uses six macroscopic methods As the basis of division, the economic index has obtained very good clustering results and has very strong practical reference value.
COVID-19 Spread Model in Hubei Province

Abstract This article provides prognosis of COVID-19 spread in Chinese province Hubei from January 22 2020 using SIR model. To compare the results with real data, we use statistics obtained by Johns Hopkins University.
Graph Database using Data Crawling

Data on the internet is growing nonstop. Every platform has millions of active users, searching for something specific. LinkedIn is one such platform, known for career opportunities, company and employee information and more. In this paper, we make a graph database system, collecting data from LinkedIn, IBM new feed, DNB. Using data crawling the data is gathered followed by data cleaning and, building APIs for graph database. A graph database is made of nodes and relationships, Cypher query language is used to store and retrieve the data from graph database. Neo4j and cypher query language are used for visual representation, with Neovis library. The system shows company details, employee details such as skills, experience, education background, contact information, certifications, licenses and more. The system is resourceful for companies and employees, provides easy and quick relevant information about the company and a person, such as a company's employees, it's blogs and articles, and further about the employee's details. The project holds great future scope, with bigger, multiple sources.
Reconstruction of 2D Images Into 3D

3D reconstruction is a long-standing complication when comes to testing happening from decades from machine learning, computer graphics, and computer perspective environments. Using CNN for the reconstruction of the 3D image has enchanted growing attentiveness and shown spectacular execution. Emerging in the new era of abrupt development, this chapter lays out an in-depth study of the latest developments in the field. Its focuses on activities that use in-depth learning strategies for measuring the 3D status of common things from one or more RGB images. It organizes based on literature in the layout presentations, network structures, and training methods they use. As the survey was conducted for methods of reconstructing common objects, this chapter also evaluates some of the latest efforts that emphasize particular categories of an object such as the shape of the human body and face. This provides an examination and correspondence of the execution of some important papers, summarizing some open-ended issues in the field, and exploring encouraging indications for subsequent research.
Affect of Artificial Intelligence Technologies and Digitalisation on Jurisprudence and Education

The chapter investigates the issues of artificial intelligence technologies /AIT/ and innovations in digital technologies affecting jurisprudence and education. Technologies associated with artificial intelligence like advanced computing, artificial neural networks, big data, machine learning, unmanned systems and robotics give new impetus to the development of universities and educational institutions around the world. The goal of this research is to demonstrate that Masters of Arts studying at RUDN University are trained for the English-language law activities affected by digitalisation: cyber security and culture, preventions of sexual offences against children facilitated through the use of information and communication technologies (criminal law), smart contracts and arbitration (civil law), e-filing—filing documents electronically with the courts, etc. The survey of students having legal practice simultaneously with the studies proved that they are leveraging AIT and digitalisation as a key strategic asset. The problems of AIT implementation in contemporary lawyers’ activities like ousting legal professionals from law services market by AIT are analysed. The chapter presents some recommendations on making legal practices more efficient by speeding up and facilitating the work in legal activities based on digital technologies. This research results can be applied by scientists and practitioners involved in AIT, educational and legal processes.
Online Teaching and Learning: Going Beyond the Information Given

In this chapter three basic features of online teaching and learning representing a paradigmatic change in the field are explored regardless the tensions/resistance generated on academics: (1) the shared construction of knowledge through digital instructional interaction; (2) the growing curriculum flexibility facilitated by an open and boundless Internet, and (3) the progress of multimodal digital formats, which demand adequate communication skills from teachers and students. The first aspect entails the possibility of collaborative knowledge creation through online communication, which requires dialogic scaffolding grounded on discourse comprehension and production strategies, going beyond the mere transmission and exchange of information. The second issue reflects the expansion and openness of the knowledge available on the Web, turning closed curricula based on limited resources into obsolete and reductive options. Third, multimodality involves an expressive leap, a change in meaning design, given the linguistic components are no longer the primary vehicle of meaning, which rather arises from a synergic mix of elements such as image, sound, and motion. These issues are approached and exemplified concerning the complex Latin American context, where the changes in the academic profession (scholarship) brought about by digitality (digital scholarship) have had a varying impact. Furthermore, some of the biggest challenges that higher education presents in LA nowadays are related, first, to the sociopolitical demands for higher education for all citizens, in a massive form that does not match the instructional models of online education on which the personal relationship between teachers and students is essential. Second, the need for empowerment of the Latin American faculty team in general, and not just some elite of professors in some elite universities. The conclusions can be summed up by stating that the paradigmatic triad (i.e., shared knowledge construction, Web as curriculum and multimodal communication) allows us to go “beyond the information given”, re-signifying Bruner’s powerful expression.
A non-Symmetric Key Dependent for Reverse Encryption Algorithm (REA)

Cryptography is used essentially for protecting information by using the encryption algorithms. It is assumed that the encryption algorithm must have minimal requirements for securing any such information. Therefore, A poor structure of any encryption algorithm can cause insecure product cipher and then puts the highly sensitive information or infrastructure at risk. This paper analyzes the performance of one algorithm called with Reverse Encryption Algorithm (REA). According to Shannon confusion and diffusion concept, we found this algorithm is not able to support a symmetric key encryption algorithm in the critical analysis. In addition, our experiments proved that the decryption of this algorithm is also nonsymmetric key dependent. Reverse engineering attack was exploited these weakness and vulnerabilities to reverse transformation of the ciphertext to obtain the plaintext. Therefore, any application supports this type of encryption is vulnerable for the primitive cryptanalysis. This paper purpose is to analyze the encryption algorithm functionality and structure and to identify the issues that can be exploited to attack this algorithm.
Hybrid Approach for Image Enhancement of Long Range Electro-optical Surveillance Systems

Electro-optical surveillance systems are capable of performing autonomous search, detection and tracking. The system range performance is primarily dependent upon image quality of imaging sensors, which is largely affected by the prevailing atmospheric conditions. Image quality improvement can be achieved by using suitable sensor and digital image enhancement technique with target/background signature knowledge; however their effectiveness varies with the ambient conditions. Besides, there are optical methods to mitigate the degradation caused by atmosphere. A novel hybrid approach for image enhancement has been presented in the proposed work, which uses an optimal combination of optical and digital means to enhance image quality.