Reviewerly/exHarmony huggingface dataset:
Auditing an Agile Development Operations Ecosystem

In an enterprise software development, DevOps is a practice of integrating development and operations to deliver cost-efficient, improved quality solutions to the customer by automating the existing processes to achieve “continuous delivery.” In the current dynamic IT Ecosystem where there is a rising need to prove a competitive edge to maximize profitability, it is pivotal to drive business value with profound emphasis on quality. Agile enables us to take calculated risks during development whereas its affinity to adopting DevOps will promote continuous delivery with reduced friction to improve business efficiency. As this approach requires a change in people, process, technology, culture, usage of right tools and techniques, the early involvement of IT Auditors during the process of transformation could aid to build effective Risk Management strategies to handle organizational challenges. This article aims to present a risk-based audit approach to effectively use audit tools and techniques in an Agile-DevOps transformation environment to achieve maximum business value.
The Web Crawler Based on Python to Obtain Public Emotional data Analysis - Take The Weibo Hot Rearch data as An Example

With the development of the times continuously, the increasing popularity of the Internet and the maturity of its date base, the amount of date in human daliy study, life and work presents an enponentail growth, when the collected data is large and more complex, it is urgent to collect data from a large amount of data and extract valuable information. Taking Sina Weibo as an example, this paper uses web crawlers to crawl data, and analyzes the emotion of weibo hot search data through Bayesian network model, which is of great significance for controlling the direction of public opinion.
A Minimum and Maximum of Regional Information Method to Improve the Sobel Edge Detector

Edge detection is the most common approach for detecting meaningful discontinuities in grayscale images. In this paper we develop an improved edge detector based on Sobel method which produces promising results. We analyze the effect of considering the minimum and maximum of the regional information around a given pixel in the stage of the gradient computation. We treat each image as a set of dark or bright regions depending on a predetermined threshold. Then, we create a pixel set by linearly increasing or decreasing the range of the image intensities. To finish, the edge map and direction map are generated. We experimentally demonstrate that the method is competitive with some conventional methods such as Sobel, Prewitt and Canny.
Learning Tabular Embeddings at Web Scale

Contextual embeddings, such as ELMo and BERT [21], [49], assign each word a representation based on its context [44]. This research builds on an observation that the context for structured data can be encoded very differently from the traditional sentence and text-based context. This means that embeddings for the structured data can be constructed on different principles, and if properly optimized, they can be used to improve performance ML and AI-based tasks related to the structured data. Here we present several new types of tabular embeddings taking into consideration structure of columns, rows, and presence of metadata. We demonstrated that properly optimized embeddings in combination with ML and DL models show significant improvement on such important tasks as tabular column and tuple recognition.
Understanding the Creation of Abstract Concepts beyond the Intangible and Tangible Materials of Land Art

Background and objective Understanding abstract art as an art form requires depth of thought. Moreover, understanding land art as abstract art is challenging, given its focus on the minimalism and abstract concepts. Much focus, research, and work were actively conducted in the 1970s, as it represented an abstract expression of minimalism. The characteristics of minimalism connote abstract meanings in the use of materials. Nevertheless, the original research of works or artists has often been mentioned, but few studies have analyzed the abstract language of land art materials. The aim of this study is to thus determine the abstract meanings of materials in land art from the 1970s to the 2010s. Methods Art-based research was employed to address the aim. This study classified the land art materials into intangible and tangible materials, where intangible materials focused on lines, circles, and labyrinths, and tangible materials focused on the earth, stones, wood, and snow. Results Intangible and tangible materials of land art conveyed various abstract meanings. Intangible materials were reflective of connection and symbiosis with nature, delivering abstract languages of ‘take-nothing,’ ‘reflection’ and ‘opportunity.’ Tangible materials reflected the abstract concepts of ‘intervention,’ ‘resistance,’ ‘unliving,’ and ‘change,’ and conveyed caveats. In other words, taken together, intangible and tangible materials were presented in symbiosis–and with caveats–and delivered messages for the present and the future. Interestingly, intangible materials inherently reflect symbiosis and communicate caveats in works based on a non-contextualized present and future. Conclusion Interpretation of the abstract languages derived from intangible and tangible materials could imply a symbiosis between humans and nature, while conveying the message that caveats, to humans, are still ongoing. This relationship plays a significant role in an artist’s selection of a medium, which is reflective of abstract beliefs reflected in contemporary, nature-based works created on Earth.
Deep Neuroevolution: Training Neural Networks Using a Matrix-Free Evolution Strategy

In this paper, we discuss an evolutionary method for training deep neural networks. The proposed solution is based on the Differential Evolution Strategy (DES) – an algorithm that is a crossover between Differential Evolution (DE) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We combine this approach with Xavier's coefficient-based population initialization, batch processing, and gradient-based mutations—the resulting weight optimizer is called neural Differential Evolution Strategy (nDES). Our algorithm yields results comparable to Adaptive Moment Estimation ADAM for a convolutional network training task (50K parameters) on the FashionMNIST dataset. We show that combining both methods results in better models than those obtained after training by either of these algorithms alone. Furthermore, nDES significantly outperforms ADAM on three classic toy recurrent neural network problems. The proposed solution is scalable in an embarrassingly parallel way. For reproducibility purposes, we provide a reference implementation written in Python.
Hardware-Accelerated ZYNQ-NET Convolutional Neural Networks on Virtex-7 FPGA

Convolutional neural network is a class of deep neural networks that has made a great breakthrough in image recognition. CNNs are commonly used to detect and classify visual applications so that they are frequently embedded in image classification tasks. The common trend nowadays is to accelerate the processing of CNNs in order to use them in real-time applications such as image classification and object recognition. This paper presents the implementation of ZynqNet CNN architecture on FPGA. The full ZynqNet CNN layers are implemented on FPGA to reach the max acceleration and make full use of all DSP units. Several optimizations techniques are used in different design phases to improve processing speed, utilized area, and power consumption. In addition, the proposed hardware accelerator achieves 15.6 fps for ZynqNet CNN at maximum frequency. The proposed architecture runs at two different frequencies of 100MHz and 125MHz, and is implemented on Virtex-7 FPGA.
CFR-GAN: A Generative Model for Craniofacial Reconstruction

Craniofacial reconstruction is to reconstruct the face from the skull based on the relationship between the skull and the face to help recognition. This paper proposes a deep generative model for craniofacial reconstruction: CFR-GAN, which avoids the disadvantages of traditional methods of insufficient deep information learning ability of craniofacial data and insufficient ability to express specific features of the dataset. The model is divided into two steps: rough reconstruction and refinement reconstruction. Rough reconstruction rebuilds the overall structural content of the corresponding human head through the skull, and refinement reconstruction restorate facial feature contours. This paper constructs a dataset of 2210 two-dimensional images with craniofacial depth information, which is used to train a CFRGAN model to realize facial reconstruction of skull images. Experiments are conducted from the perspectives of qualitative analysis and quantitative analysis. The results show that CFRGAN generated image retains more identity information, and the similarity between the reconstructed face image and the real face image reaches 94%, which is better than the existing methods. In summary, CFR-GAN proposed in this paper has ability to generate high-fidelity images and is efficient at craniofacial reconstructing.
Looking Beyond the Haze: A Pyramid Fusion Approach

Haze is a natural phenomenon caused mainly due to wide spread of dust particles from construction activities, smoke, water vapour causing poor visibility in the surrounding. Traditional methods normally involve enhancing the contrast of the image which fails in most of the cases except in light and uniform haze. Many neural network based algorithms have been developed which use hazy images and their corresponding non-hazy images i.e. ground truth, to create their model. However, such models are generally data specific and do not yield good results in every case as any specific benchmark data set with wide level of non-uniform haze is not available in public domain. In this paper, a simple and effective dehazing technique to increase the visual perception of a hazy image without any prior knowledge of the outdoor scene, has been proposed. Its most significant contribution is the formulation of an unique approach for extracting haze relevant features followed by an image fusion technique to finally obtain the resultant unhazed image with minimizing resource requirement.
A Comparative Analysis of Image Compression using PCM and DPCM

The quality of the signal is essential in digital communication and signal processing. The transmission channel is also important. Modulation is used for effectively transmission of signal. There exist several types of modulation techniques. One of such is the pulse code modulation (PCM). The performance of PCM is however affected by quantization error and noise in the transmission channel, which affects the quality of the output. Against this backdrop, this paper presents the use of differential pulse code modulation (DPCM) so as to address the limitation of pulse code modulation. The simulation environment is MATLAB 2018a. The MATLAB Simulink is used to design the PCM and DPCM systems using appropriate digital processing blocks. The DPCM system shows a significant improvement in terms of error reduction and quality of output.
Model and Pedagogical Conditions for Preparing a Future Teacher for the Implementation of Psychological and Pedagogical Support for Intellectually Gifted Schoolchildren

In this article, the authors consider the phenomenon of the future teacher's readiness to provide psychological and pedagogical support for intellectually gifted schoolchildren. The authors define the structural characteristics of the future teacher's readiness to provide psychological and pedagogical support for gifted schoolchildren.
Detailed Study of Cloud Storage Services on Aws Cloud (Amazon Web Services)

This research revolves around understanding the Cloud Storage Services offered by world's most famous Cloud Provider Amazon Web Services (AWS). We will be covering major Cloud Storage Services like EBS, S3 and EFS. But first let’s understand more about AWS. We should use these end-of-life services as a per-project and keep in mind the key benefits of these end-to-end services. Amazon EBS brings the highest end-to-end prices available with block for level of Amazon Elastic Compute Cloud (EC2) instances. Saves data to file system stored after EC2 status closure. Amazon EFS provides portable file storage, also designed for EC2. It can be used as a standard data source for any application or load that works in most cases. Using the EFS file system, you can configure file system installation settings. The main difference between EBS and EFS is that EBS is only accessible from a single EC2 state in your specific AWS region, while EFS allows you to mount a file system in multiple regions and scenarios.
The Effect of Researcher Characteristics and Cooperation System Suitability on Relationship Quality and Success Possibility in Industry-Public Research Institutes Cooperation

Purpose This study aims to analyze the net effect and interaction effect of researcher characteristics, cooperation system suitability, and relationship quality on the success possibility of I-PRIs cooperation in Industry-Public Research Institutes (I-PRIs) cooperation. Design/Methodology/Approach Based on the existing literature, we explored researcher characteristics that are expected to affect the success possibility of I-PRIs cooperation. A relationship that affects the success possibility of I-PRIs cooperation by combining researcher characteristics, cooperation system suitability, and relationship quality was established as a conceptual research model. To analyze this, we conducted a survey targeting researchers with experience in I-PRIs cooperation in Daedeok Innopolis. PLS and fsQCA were used for data analysis. Findings From the results of PLS analysis, we found that technology commercialization capability and relationship quality enhance the success possibility of I-PRIs cooperation. In the fsQCA results, Solution 1 showed that the success possibility of I-PRIs cooperation was high if high technology commercialization orientation, high innovation orientation, and high relationship quality exist. Solution 2 showed that the success possibility of I-PRIs cooperation was high if high technology commercialization capability, high technology commercialization orientation, high cooperation system suitability, and high relationship quality exist. Research Implications This study contributed academically in that it revealed the relationship that influenced the success possibility of I-PRIs cooperation by combining researcher characteristics, cooperation system suitability, and relationship quality.
The GaaS Paradigm: How Fortnite’s Cloud Computing Infrastructure and Tencent Partnership May Be Ethically Compromised

With a constantly growing technological landscape where younger generations are addicted to social media and video games, especially in a time of lockdowns and self-quarantine, Fortnite has remained to grow prominence throughout the years since its release in 2017. With over 350 million registered users (2021) and 80.4 million active users (2020), Fortnite has proven to be a successful game with 5.1 billion dollars in profit alone in 2021. Predicated on the games-as-a-service (GaaS) paradigm, this paper addresses the privacy and security concerns regarding its backend of the cloud computing infrastructure and the ethical implications behind its partnership with Tencent, a Chinese tech giant that owns 40 percent of Fortnite's stake.
Enhance Enterprise Security through Implementing ISO/IEC 27001 Standard

The ISO/IEC 27001 standard has been adopted by worldwide organizations and companies. The main contribution of this paper is to systematically address all 21 requirements consisted of 7 mandatories and 14 categories in ISO/IEC 27001 standard when designing and developing information security management system (ISMS) policies. The PDCA model is adopted, and the statement of applicability is assessed. The 13 policies of ISMS are designed to address the individual requirement of ISO/IEC 27001 standard respectively and effectively.
Domestic and foreign literature analysis on the types and effectiveness of Edu-Tech based smart learning for students subject to learning support: focusing on experimental research

이 연구는 에듀테크를 기반으로 한 스마트러닝 교수를 학생들에게 직접 적용한 국내외선행연구들을 고찰해 보고, 연구 동향 및 스마트러닝 활용 유형 특성, 그리고 그 효과성을알아봄으로써 후속 연구를 위한 방향 제시 및 이론적 기반을 마련하는 것을 연구목적으로하였다. 분석 대상 논문은 2001년~2021년 5월까지 한국연구재단 등재지 또는 등재후보지에 발표된 논문으로 한정하였다. 연구결과, 학습장애 학생들을 대상으로 한 논문이 가장많았고, 초등학생 대상 연구가 가장 많았으며, 단일대상연구 유형이 가장 많았다. 중재영역별로 살펴보면 수학 중재 논문이 가장 많았다. 스마트러닝 활용 유형은 교육용 프로그램, 인터넷 네트워킹, 가상ㆍ증강현실, 혼합 유형 총 4가지로 분류하여 알아보았는데, 웹기반소프트웨어 활용 논문이 가장 많았고, 모바일 및 애플리케이션 활용 및 ARㆍVR 활용 논문이 그 뒤를 이었다. 스마트러닝 중재 효과성을 랜덤효과모형으로 분석한 결과, 전체 효과크기(0.75)를 비롯하여 모든 중재 영역에 걸쳐 비교적 높은 중간 효과크기~큰 효과크기를나타내고 있음을 알 수 있었다. 본 연구의 후속 작업으로 포스트코로나 시대에 필수적인다양한 에듀테크 기반 스마트러닝 프로그램을 개발하고, 그 효과성을 검증할 수 있는 다방면의 노력이 뒷받침되어야 할 것이다.This study examines domestic and foreign previous studies that directly applied smart learning professors based on Edu-Tech to students, and suggests directions for follow-up research and theoretical The purpose of this study was to lay the foundation. Papers to be a nalyzed were l imited t o papers p ublished i n j ournals registered o r candidates f or registration by the National Research Foundation of Korea from 2001 to May 2021. As a result of the study, the largest number of papers were on students with learning disabilities, the largest number of studies on elementary school students, and the largest number of single-subject research types. Mathematics intervention papers were the most common by intervention area. Smart learning application types were classified into four categories: educational programs, internet networking, virtual/augmented reality, and mixed types. Papers using web-based software accounted for the most, followed by papers using mobile and application and AR/VR. As a result of analyzing the effectiveness of the smart learning intervention with a random effect model, it was found that a relatively high medium to large effect size was shown across all intervention areas including the overall effect size(0.75). As a follow-up of this study, various efforts to develop various Edu-Tech-based smart learning programs essential in the post-corona era and verify their effectiveness should be supported.
Skelibras: A Large 2D Skeleton Dataset of Dynamic Brazilian Signs

Dynamic sign language recognition is a challenging and active research topic over the last years. Due to the lack of high quality annotated datasets, it is currently unfeasible to train neural networks to perform neural machine translation of large dictionaries of dynamic signs from RGB videos. In this work, we present Skelibras: a large annotated skeleton dataset of dynamic Libras signs comprised of unsegmented and segmented conversations. Skelibras was built by exploiting an existing large and public RGB video dataset. We adopted an automatic approach to properly extract poses from each recorded individual, and then manually filter inconsistent representations and/or subtitles. We also present results of experiments performed on variants of recurrent neural networks to provide baseline benchmarks for future research on dynamic Libras signs recognition.
The Retail Industry’s Impact of Artiﬁcial Intelligence Chatbots and Augmented-Reality Applications: An examination of trust and satisfaction of the Youth Vietnamese online shopping

As competition in the retail industry heats up, businesses are increasingly resorting to kinds of artiﬁcial intelligence (AI) to differentiate themselves. E-commerce ﬁrms are combining technologies such as AI chatbots and augmented reality applications (ARA), which have established themselves as prominent customer service solutions in the practitioner area. However, little is known about consumers’ views and participation with developing technologies when they are implemented in a retail setting. A theory-based study model was developed to elucidate the motivational factors required for effective decision-making in this environment. The proposed model was supported by empirical testing conducted as a ﬁeld study.
Evolutionary multi-objective optimisation: a bibliometric study

Evolutionary algorithms are among the most popular methods for solving optimisation problems. The use of metaheuristics emulating the natural evolution of living organisms dates back to the 1970s. In this study, a particular focus is put to use these evolutionary techniques to solve multi-objective optimisation problems. Based on a bibliometric and network analysis, the paper provides a review of 5,549 scientific publications (journal articles and book chapters) on the field before June 2019. The top contributing authors, journals, organisations, and countries are identified, as well as the most used key-words related to the topic. Then, the most influential papers are compared on the basis of citations and PageRank. The established research clusters and emergent research directions are also discussed.
Features of Self-Actualization of Specialists with Various Professional Orientations

Introduction. The article analyzes the problem of self-actualization and professional orientation of specialists in various fields. A theoretical study of the problem of self-actualization has shown that it is a specific form of cognitive creative activity, involving the identification, study and assessment of one’s capabilities and implementation in the profession. Materials and Methods. The article presents the results of an empirical study of self-actualization and professional orientation of specialists of various profiles using a set of methods: the questionnaire “Diagnostics of self-actualization of the personality of A. V. Lazukin” (adapted by N. F. Kalin); test to determine the professional orientation of the personality of J. Holland; methods of diagnostics of socio-psychological attitudes of a person in the motivational-need-sphere of O. F. Potemkina; methods of statistical data processing: Mann – Whitney U-test, correlation analysis. Research Results. Statistically significant differences were revealed between the indicators of self-actualization, motivation and professional orientation among specialists from different professional groups. The correlation relationships between the types of professional orientation and various scales of self-actualization and attitudes in the motivational-need-sphere of representatives of various professional groups have been revealed. It was found that the features of self-actualization, values, motives and needs of specialists are largely due to the specifics of their professional activities and the type of professional orientation. The desire for self-actualization, creativity, spontaneity, self-understanding, contact and flexibility in communication is most pronounced among specialists with a social and entrepreneurial type of orientation, whose activities provide opportunities for creative self-expression. Discussion and Conclusion. The results obtained confirm the assumption that the desire for self-actualization of specialists is largely due to their professional orientation and the specifics of their professional activities.