Reviewerly/exHarmony huggingface dataset:
Bitcoin Transaction Computational Efficiency and Network Node Power Consumption Prediction Using an Artificial Neural Network

This paper develops and discusses the predictability of power consumption of the Bitcoin networks using Artificial Neural Networks (ANN) machine learning algorithm and solving the computational problem of the Bitcoin mining process. It discussed its impacts on energy consumption in the crypto mining process. In this paper, we used data sets for Bitcoin historical information for the training and testing of the ANN algorithm. With the help of Python libraries, the data filtration process was done. Python has provided the best feature for data analysis and visualization. After understanding the data, we trim the data and use the characteristics or attributes best suited for the model. The implementation of the model is done, and the result is recorded and analyzed. The results as obtained demonstrated that the use of Artificial Neural Networks (ANN) machine learning algorithm could approximately predict the actual electricity consumption of Bitcoin with high accuracy.
Analysis of the Performance of NewSQL Databases Compared to RDBMS Based on Linux OS

Besides the well-known relational database management systems (RDBMS) a new kind of databases called NewSQL databases were developed. NewSQL databases aim to combine the advantages of RDBMS and NoSQL databases. In this article the performance of the most popular RDBMS and NewSQL databases is measured and compared to each other. The objective is to measure whether there is a improve of performance for NewSQL databases as promised. To get comparable results a data set was prepared as well as homogeneous test settings were defined. Furthermore KPIs for the measurement were specified. The outcome of the performance tests are displayed and explained. Conclusions as well as recommended actions are made.
Instance Segmentation with BoundaryNet

Instance segmentation is one of the key technology in many domains, such as medical image analysis, traffic and critical infrastructures monitoring, understanding of natural scenes. Recent methods for instance segmentation rely on bounding box regression, however the bounding boxes are not a natural representation for many domains. We address the limitations of the bounding boxes with a new approach called BoundaryNet, in which we train a fully convolutional neural network to draw the boundaries around each object of each class. The boundaries allow for an easy bounding box and mask inference while still providing detailed information about the shape of the object. BoundaryNet avoids the restrictions of YOLO such as the number of bounding boxes, while it is more computationally efficient than the R-CNN methods. The conducted experiments with the proposed neural network architecture BoundaryNet on the Common Object in Context (COCO) dataset show promising results in improving the instance segmentation process.
Machine Learning Based System for the Control and Evaluation of Programming Vulnerabilities

With the increasing role of technologies in our daily lives, software security has become an important issue to provide protection from malicious attacks and ultimately avoid irreversible damage to systems. The collection of data reflecting the quality of programming allows the application and training of machine learning algorithms to predict if potential software vulnerabilities are in a script, knowing some of its features. We examine if these algorithms can be used to predict some score that reflects the severity of vulnerabilities in a script prior to release. To this end, we develop a crucial preprocessing stage to define a metric that reflects the programmer evolution over time and be aware of future flaws in his code. To provide a comprehensive performance assessment, we use a private dataset containing labelled vulnerabilities of a programmer over time recorded by code snippet. Ultimately, an effective early diagnosis system is obtained.
Face Characterization Using Convex Surface Decomposition

In 2-dimensions, the analysis of face images has been done in a detailed way. The characterization of faces from 3D inputs may be interesting and discerning with respect to the uniqueness of a face. In this work, we have attempted to capture the surface curvature of a 3D face and thereof derive its characteristic features. The approach is based on the convex surface decomposition of the face models. Experimental results are encouraging and amenable to further treatise towards better realization of the characterization of a face.
Comparison of Web Pagebuilders and Their Use in Teaching Web Development

Web development and web design are one of the main aims of informatics. The technology age allows the creation of websites with many frameworks, languages, and utilities. One helpful utility is Pagebuilders. These editors or applications are useful for designing prototyping, building landing pages, learning how web design UI/UX works, or creating webs and e-shops for customers in record time. The article describes block programming and comparison with Pagebuilders. The following pages compare five popular Pagebuilders and their usage with their pros and cons, style of their main aim, and functions mainly aimed at teaching web development.
The Integrated Digital Image Correlation Technique Applied to Fracture Mechanics: An Improvement on Crack Detection and Localization

Digital Image Correlation (DIC) is a well-known experimental technique allowing for the identification of 2D and 3D displacement fields. iDIC (Integrated DIC) is one of its most successful applications: differently from standard DIC, it uses shape functions that are proper of a specific problem, thus allowing for direct solution of inverse problems. Fracture mechanics is one of the fields in which the iDIC technique has been widely used. To this aim, the terms of Williams’ series are used as shape functions, thus allowing for the direct identification of KI, KII, and σT. However, proper estimation of fracture mechanics parameters requires identifying the crack tip location and its orientation. Usually, these parameters are defined a priori by the user. The authors propose an enhanced approach based on a hierarchical minimization capable of identifying the crack tip location and of estimating the crack axis orientation. The analysis is performed first on synthetic images to ensure the reliability of the algorithms and then on real data acquired during high-cycle fatigue test. To allow for in-process acquisition, an adaptive acquisition system is developed able to modify the sampling rate during the life test based on the damage evolution.
Application of the K-Means Method for Clustering Land and Building Tax Payments Based on Tax Types (Case Study: BPKPAD Binjai City)

Land and Building Tax or abbreviated as PBB is a fee that must be paid for the existence of land and buildings owned by the community or residents. The determination of PBB in Binjai City is based on the application of the Land Value Zone (ZNT) which is close to the market price, which will be able to create equitable development throughout Binjai City. BPKPAD (Regional Revenue and Assets Financial Management Agency) Binjai City is a government agency that receives PBB payments from the community. Data - data on PBB payments for the people of Binjai City have been stored in an existing system and every year it will continue to increase so that it will cause data accumulation in the land and building tax archives. A data processing system is needed to manage these data, one of which can be done with data mining which can process piles of data into useful information and can be utilized by grouping PBB data based on criteria. Clustering is a method in data mining that can be used to automatically detect clusters of adjacent records that have a certain definition in all variables. K-Means algorithm is a simple algorithm to classify or group a large number of objects with certain attributes into groups (clusters). So that this system can be used as input for the Binjai City BPKPAD in finding solutions to increase regional income from PBB payments.
Application of Clustering Method For Customer Royal Data Grouping at CV. Garuda Mas Motor Binjai

Communication is a very important activity. Communication is carried out with the aim of exchanging information between several individuals. Communication can be done in various ways. Those who wish to communicate with each other can meet in person or through intermediaries. There are several types of media that function as intermediaries of information. You can use several media such as print media, electronic media, and online media to get various information. Digital communication in the modern era is not far from the Internet because almost all digital communication tools use the Internet. Internet needs vary greatly from old to young, because Internet needs are different, connection management is needed (Management Bandwidth) so that client requests with high connections do not interfere with clients with relatively low connection requests. One of the tools commonly used for connection management is MikroTik with the Peer Connection Queue (PCQ) feature. The reason for choosing Mikrotik is because Mikrotik has complete features at an affordable price. The importance of connection management is to maximize the connection provided by the ISP so that clients can use the Internet without buffering.
Blockchain as an Object of Value Assessment

The article reveals the features of blockchain technology in contemporary situation of the Russian financial market. The problems of economic and value assessment of blockchain technology, which is the object of non-financial digital assets are discovered. The authors consider the typology of blockchain and their value for business. The types of effects are formulated and the structure of the financial model to determine the economic efficiency is specified. The results confirm the correct choice of methods that justify the cost estimation. The calculation of the cost of NFT-marketplace is carried out. Based on the analysis, proposals aimed at expanding the use of new technologies in business processes have been made.
Goal Modeling for Linked Data Exploitation of Municipalities Data Access in South Africa

Ontology validation has been an important part of measuring the quality and accuracy of knowledge encoded in that ontology. Since ontologies are machine-readable representations of knowledge, the use of formal languages, such as SPARQL can help query ontology. In this article, we apply Tropos as the goal modelling methodology to enable the definition of Competency Questions (CQs). We used a combination of qualitative and quantitative methods to collect data using a literature review, and online related resources from the local, district, and metropolitan municipalities. The data were analyzed with the Topos methodology and then translated into SPARQL queries using the Competency Query Translation Approach (CQT).
Sidcof - inspection and surveillance of penstocks

The SIDCOF (Innovative Forced Pipe Diagnostic System) project, carried out within the framework of FUI 22, aims to develop innovative NDT methods and tools in three batches allowing:  In situ monitoring of penstocks  A robotic inspection of the inside of the pipes to avoid human intervention in a complex environment  A software platform to process data and help the operator to make decisions This paper will focus on the 1st batch. The work has enabled the development of sensor networks permanently installed directly on the pipe for monitoring installations and monitoring in real time the damage caused by rockfall, landslides and water leaks. Two dedicated systems have been developed and validated under real operating conditions. The first is based on a network of connected autonomous extensometers, each module of which operates wirelessly and is energy independent. It is able to follow the deformations of pipes in risk areas following landslides. This tool greatly simplifies the implementation of sensors and automatically routes the data produced by these sensors to the cloud. A totally IIOT product. The second uses a network of acoustic emission sensors to detect and locate (with an accuracy of the order of a few meters) in real time falling rocks, and the appearance of leaks on the pipe. The tool developed is a dedicated multi-channel system for on-board electronics and processing. The data processed is transferred automatically to the client cloud via the 4G network. The SIDCOF project is led by SETEC with CETIM, SOREA, ETE and Sub C Marine as partners, funded by BPI France, TENERRDIS, INDURA, Région Occitanie, Région Auvergne Rhône Alpes.
Technology Acceptance Model to Determine the Impact of Faculty Member Experiences on Utilizing LMS During Covid19

This study aimed to measure the differences between the attitudes of King Saud University academics based on their expectations before they use the learning management system since the outbreak of the COVID-19 pandemic and after attaining their experience through practice.The sample individuals are 86 as a pre-sample and 85 as a post-sample.TAM2 model staff were used to build the data collection tool and link the impact of variables (sex, specialization, scientific rank, training duration and use).The results showed that cumulative experiences play a crucial role in creating a meaning of orientation and practice in connection with the actual use of techniques.It also shows that practice, according to long periods of experience in the uses of technology in the educational process, provides a more realistic measurement that is different from the opinion of any sample at the beginning of its use of technology in its initial stages.There were statistically significant differences in favour of the presample about their high acceptance of the system, and they had positive attitudes towards expecting their use.Different attitudes were monitored for the post sample (after experience practicing the system) because of the barriers and difficulties they faced during their activation and use of the system, which negatively affected their attitudes.The study recommended raising cultural awareness and promoting the proper activation of technical tools by conducting more training courses in this field because it impacts the formation of positive accumulative orientation affecting adoption during use.
Developer Observability

Production is the one thing that matters. Everything else we do is just preparation for production and fixing issues related to production. Despite the tremendously important nature of production, we have relatively limited tools to track issues there. When I say production bug, people always assume the worst – a crash. But a vast majority of production bugs are your typical day-to-day bugs.
Bug Strategies

In the 1990s, I worked for a gaming company that built flight simulators for Electronic Arts. The founder of the company was a former air force pilot, so were over a dozen on staff. One of their core principles was that of a debrief. Not meetings, we had fewer meetings than any other company. A debrief is what pilots do after a mission is done, successful or not. It makes no difference. They get as much information as it’s fresh in the pilot’s mind.
Open Source Software Computed Risk Framework

The increased dissemination of open source software to a broader audience has led to a proportional increase in the dissemination of vulnerabilities. These vulnerabilities are introduced by developers, some intentionally or negligently. In this paper, we work to quantity the relative risk that a given developer represents to a software project. We propose using empirical software engineering based analysis on the vast data made available by GitHub to create a Developer Risk Score (DRS) for prolific contributors on GitHub. The DRS can then be aggregated across a project as a derived vulnerability assessment, we call this the Computational Vulnerability Assessment Score (CVAS). The CVAS represents the correlation between the Developer Risk score across projects and vulnerabilities attributed to those projects. We believe this to be a contribution in trying to quantity risk introduced by specific developers across open source projects. Both of the risk scores, those for contributors and projects, are derived from an amalgamation of data, both from GitHub and outside GitHub. We seek to provide this risk metric as a force multiplier for the project maintainers that are responsible for reviewing code contributions. We hope this will lead to a reduction in the number of introduced vulnerabilities for projects in the Open Source ecosystem.
Gender Balance Ensuring in IT field: Ukrainian Study Case

The paper considers gender balance ensuring in the Ukrainian IT field. Learning outcomes in STEM courses (math and physics) are analyzed as a background for the IT sector formation. The results of External Independent Assessment demonstrate 3% more successful results for girls than boys in STEM courses. In all cases, the distribution has a positive skewness. The dependence of the gender parity index on the IT-specialties and higher education level (positive ratio) is shown. The gender profile of IT field is made. Some brief recommendations are given to overcome the social stereotypes and gender gap.
Message from the General Chair: ICEDCS 2022

We are honored to welcome all experts, researchers, professionals, innovators from academia and industry for the conference. The aim of this conference is to provide a forum for exchanges of research results, ideas and experience of application among researchers and practitioners involved with all aspects of electronics and devices, computational science. The participants can develop the ideas and catch the tendency, and accelerate the theoretical research, technology development, application and innovation in the field of electronics and devices, computational science.
An Analysis and Implications of the AI lifelong learning system in Singapore

This study aims to explore Singapore's AI lifelong learning system associated with AISG initiatives, and to examine the implications for establishing Korea's AI national strategy.To this end, researchers analyzed the features of Singapore AI lifelong learning system's current states, operating strategies, and curriculum.As a result, first, Singapore's AI lifelong learning system seeks to resolve the AI social gap through an inclusive operation method.Second, it emphasizes the practicality of AI capabilities, and the program continues to evolve.Third, based on cooperation among government-academia-industry, it effectively supports securing flexible teaching resources and reinforcing teacher expertise by reflecting on-site demand.Those characteristics bring a virtuous cycle of improving AI national competitiveness through strengthening AI capabilities of the entire nation, fostering AI professional talent and activation of the AI ecosystem.The implications for Korea are as follows.First, a nationwide integrated AI lifelong learning system and platform are needed.Second, various learner-customized courses should be expanded for each life cycle.
Digital Curation and Contemporary Documentary Filmmaking

Abstract Documentary films have evolved considerably since 1922s Nanook of the North. Fans of nonfiction now stream multi-episode documentaries on platforms like Netflix or catch a feature at one of many documentary-centered film festivals around the world. Inexpensive video cameras and internet distribution have expanded the documentary film universe exponentially. From 1-min films to feature-length theater releases, moviegoers around the world have embraced this diverse and growing genre. To the benefit of aspiring filmmakers, documentaries can now be filmed on a wide array of digital video devices, including smartphones, and edited inexpensively. Given this abundance, it may seem counterintuitive that, from a preservation perspective, the documentary film genre faces substantial risks. Research indicates that independent filmmakers lack access to resources that would ensure the long-term stewardship of their works (Academy of Motion Picture Arts and Sciences 2012). This research project examines documentary film production through the lens of digital curation. It describes filmmakers’ data practices and proposes a data curation model designed to guide filmmakers and film archives in developing data management plans similar to those currently used by researchers in the sciences. The proposed data curation model reflects the influence of the growing research data management field and integrates components related to digital storage, copyright, publishing, context, and file organization.
Once again about collaboration: do preschool education pedagogues work in collaborating teams?

In order to remain competitive in this dynamic environment, it is important to look for new forms of work that would enable public and private sector companies and ins titutions to perform their work more efficiently and with higher quality. In order to achieve efficiency and quality, there is a tendency in most organizations to delegate specific work and activities not to individual employees, but to their teams. The many-sided education of a child can be realized only then when there are positive strong relations among the members of a pedagogical team. Teamwork is more effective if team members collaborate with each other. Sometimes it happens that the team of educators does not form. There are several reasons for the non-formation of the pedagogical team, such as unfavorable working conditions, unwillingness to communicate with other members working in the preschool group, burning out, etc. The absence of collaborative team provokes the problematic questions, the answer to which may reveal the importance of collaboration in teamwork. The questions are: how does the teamwork of teachers working in the class of a pre-school educational institution take place? How do educators value their collaboration in the team? After the analysis of the study findings, it was possible to conclude that teamwork is one of the essential components that help achieve the positive educational results. The research findings showed that collaboration is directly related to the quality of teamwork, team building and maintaining a good psychological climate.
Locally Weighted Enhanced DTW Based Online Signature Verification

In this paper, we present enhance Dynamic Time Warping (DTW) based method on the online Signature Verification (SV) by employing the code-vectors generated from Vector Quantization (VQ) to match the aligned pairs in the warping path. The DTW based method is used to compute the distance score between the test signature and the genuine enrolled signatures, for the decision making. In order to improve the results, we conducted the evaluation using MCYT-100 database and obtained an Equal Error Rate (EER) of 1.55% and SVC-2004 database provides the EER of 2.73%. In this work, we exploit the characteristics of the warping path for online SV and obtained enhanced efficacy of the system.