Reviewerly/exHarmony huggingface dataset:
3-Dimensional Object Detection Using Deep Learning Techniques

Computer Vision is one of the branches of computer science. It will detect and understand the images and scenes. This work suggests a real-time, immediate motion tracking system for devices that follows an object's attitude in space as represented by its 3D bounding box. Computer Vision includes different features such as image recognition, image production, object detection, high-resolution image processing etc.… Object detection is frequently utilized in self-driving cars, security systems, facial recognition, pedestrian counts, and online photos. The most accurate acquisition algorithms and techniques are used in this research. This covers the precision of each identifying technique. Images can contain objects that can be automatically located and recognized. One of the core issues with computer vision is object detection. This work will show that the most cutting-edge approach to object detection at the moment is R-convolutional neural networks. This is the major objective is to examine and evaluate convolutional object identification techniques. The main idea behind such system is to classify various images and to classify object’s position approximately in all the images in order to give a full information about the images and videos. The system will be able to detect, localize and classify several objects using given image or videos. It is very difficult to classify images into different classes.
Organised vehicle crime in Europe: Six country case studies on organised vehicle crime and potential barriers to prevent the facilitation of online distribution of stolen vehicles and vehicle parts

Organized property crime is a pervasive issue across all EU Member States, demanding immediate and collective action. V-BAR specifically targets one aspect of organized property crime: motor vehicle crime, encompassing theft and the illicit trade of stolen vehicle parts. Despite a decline in vehicle theft, the overall prevalence of motor vehicle crime remains alarmingly high. This comprehensive report outlines V-BARâs objectives in enhancing operational cooperation between EU law enforcement authorities and private partners to combat the menace posed by mobile organized crime groups involved in vehicle theft. Additionally, the report delves into an extensive investigation of motor vehicle theft trends within six EU countries, namely Belgium, Bulgaria, Germany, France, and The Netherlands, providing valuable insights for combating this form of criminal activity.
Building Internationalized Applications with Laravel

In the previous chapter, we explored the critical aspects of advanced error handling and exception management in Laravel, ensuring that our applications can gracefully recover from errors and provide a seamless user experience. As our journey through Laravel's advanced features continues, we now focus on building internationalized applications—a key aspect of catering to a global audience with diverse language and cultural preferences.
Debugging Architecture Patterns

You now have enough background material that I can introduce debugging architecture patterns in this chapter. These are solutions to common problems of how to proceed with debugging. These patterns answer the most important questions of where, when, and how we debug and what we debug based on the debugging analysis patterns. The diagnostics and debugging “questions” pattern language thinking originated from memory and trace acquisition patterns. There are many ways to answer such questions and propose debugging strategies and methods; sometimes, you may even need to combine different approaches. However, there are the most important decisions to make before any debugging is done, and we name them debugging architecture patterns vs. debugging design patterns, which can be many for the particular architecture pattern. All debugging architecture patterns can be combined to form pattern sequences and pattern networks to solve complex problems of software behavior.
E-Commerce Supply Chain Optimization with the MOORA Method and Certainty Factor

This study analyzes supply chain optimization on e-commerce platforms by applying the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) and Certainty Factor methods. The aim of this research is to gain in-depth insights into the relative performance of e-commerce platforms in the context of predefined criteria and sub-criteria. The research methodology consists of six stages, including data collection, selection of criteria and sub-criteria, application of Certainty Factor, selection of case studies, relative analysis using MOORA, and certainty level analysis using Certainty Factor. The results of the analysis show that these two methods provide valuable insights regarding the performance of e-commerce platforms. The MOORA method provides a relatively strong rating, while the Certainty Factor provides an additional dimension by considering the level of certainty regarding the factors that affect performance. From a comparison of the results of the two methods, platforms such as Tokopedia.com and Shopee consistently rank well in both analyses. The implication of this research is that the e-commerce platform has greater development potential in supply chain optimization efforts. Overall, the integration of the MOORA and Certainty Factor methods has succeeded in providing more detailed and comprehensive insights into supply chain optimization on e-commerce platforms. This research provides guidance for stakeholders in making more informed and directed decisions regarding supply chain optimization strategies in e-commerce platforms
Presentation of the Book "Viktor Glushkov – Creator of New Worlds"

The impression of the author on participation in the presentation of the book “Victor Glushkov – creator of the new worlds” is described in the message. The book is issued by publishing house “Summit-book” for 100th anniversary of the birth of academician V. Glushkov. The presentation is held on 15th of November 2023 in the National Library named after Yaroslav the Wise. The author of the book, the editors of the publishing house and their numeral guests shared the memories about the prominent scientist, remembered his creative achievements in cybernetics, and analyzed the impact of Glushkov’s ideas on the modern directions of information technologies development and digitalization of economic, social, educational and defense activities in the country. The purpose of the message is to familiarize the readers of the journal with new book about outstanding personality, the founder of information technologies in Ukraine, the genius, who by far outranged his time. The author of the book narrated in the interesting, accessible and delighted way about the complex inventions and works in the field of IT technologies. This became the deserving contribution in popularization not only the scientific legacy of Viktor Glushkov, but the modern works of his successors in cybernetics and IT technologies as well. Publication of the book became possible thanks to the program of the Ukrainian Institute of Book with the funds of the Ministry of Culture and Information policy of Ukraine. Just similar programs give the opportunity to make popular the information about the prominent personalities, who’s activity determined the establishment and the development of scientific ideas of world level. Keywords: Viktor Glushkov, cybernetics, Institute of Cybernetics, information technology.
The Role of E-Commerce in Increasing Sales Using Unified Modeling Language

The advent of contemporary technology advancements and the proliferation of the internet have presented firms and retail establishments with significant prospects for augmenting their sales outreach via electronic commerce. E-commerce enables stores to engage in the sale of their products across Indonesia and international markets without being constrained by limitations of physical location or temporal constraints. E-commerce has the potential to offer consumers a more convenient, expedited, and efficient shopping experience. Consumers can conveniently look for and make choices on the things they intend to purchase, expediting and enhancing the efficiency of the transactional process. Furthermore, e-commerce can offer consumer satisfaction through many means, such as efficient delivery services, flexible return and refund policies, product exchanges, and responsive and amiable customer support. In contrast, e-commerce can enhance cost-effectiveness and improve operational efficiency within the sales process. E-commerce enables businesses to mitigate operating expenses, including expenditures related to physical store rentals, marketing initiatives, and inventory management. Furthermore, implementing e-commerce can facilitate inventory management and sales tracking for retail establishments. Hence, the significance of e-commerce in promoting the augmentation of store sales is noteworthy. Despite the significant profitability of e-commerce, retailers encounter other obstacles, including intensifying rivalry and the looming risk of cybercrime. Hence, retailers must develop a robust e-commerce strategy to compete effectively in the market and ensure secure and dependable services to their customers. This study uses the Unified Modeling Language, a precious tool for the design and modeling of software to be developed.
Analyzing Customers in E-Commerce Using Dempster-Shafer Method

This research explores the analysis of consumer sentiment in the context of e-commerce by applying the sophisticated Dempster-Shafer method. We started with the collection of more than 20,000 consumer reviews from various leading e-commerce platforms and continued with a detailed data pre-processing stage to obtain a clean and structured dataset. Next, we leverage the Dempster-Shafer method to represent and combine information from multiple sources, addressing uncertainty in diverse consumer opinions. The results of the sentiment analysis show that the Dempster-Shafer method achieves an accuracy of around 85%, with good evaluation metrics. Additionally, this research provides insight into the factors that influence consumers' views of products or services in the growing e-commerce context. The literature review also reveals the potential application of the Dempster-Shafer method in other aspects of e-commerce business, such as risk management and consumer trust. This research highlights the contribution of the Dempster-Shafer method in addressing uncertainty and complexity in consumer sentiment analysis, yielding a deep understanding of consumer perceptions, and enabling more accurate decision making in a dynamic e-commerce context. This research also provides a foundation for further development in consumer sentiment analysis and the application of the Dempster-Shafer method in e-commerce.
Development of A QR Code-Based Attendance System for Factory Employees

Employees are the most asset for any company. Achieving the best results from employees should be accompanied by a comfortable working environment. Whether working in an office or in the field, attendance is of utmost importance. Attendance is a crucial factor that can support and motivate every work activity. In this advanced era, many companies still rely on manual attendance systems. The use of manual systems often leads to errors caused by both administrators and employees, such as inefficient paper usage, failure to report absences, delayed submission of monthly reports, or loss of paper records. These issues can lead to employee carelessness, as there is a risk of attendance manipulation with manual (paper-based) attendance systems, resulting in attendance data falsification. By implementing QR codes, employees only need to scan the code using their smartphones. This research employs Unified Modeling Language (UML) for modeling. The application is then developed using Kodular and Firebase. The purpose of this application is to simplify attendance reporting for business owners or company managers, eliminating the need for manual attendance data recording, and making the attendance system of the company or factory more efficient.
Deploying SMS Gateway in the Design and Development of Web and Mobile Academic Information Systems using the Waterfall Method

: The integration of technology in various sectors, beyond its common application in the gaming industry, has extended to the field of education. This evolution mandates parallel progress in academic institutions to keep pace with technological developments. A significant challenge identified was the reliance on manual accounting methods at SMAN 1 Kejobong, which includes teacher management, staff, and assistant administration, as well as processing student learning data. Outdated practices often lead to inaccuracies and inefficiencies, resulting in time-consuming data collection and potential losses for all stakeholders. To overcome this problem, it is proposed to implement a comprehensive information system or e-learning platform. However, the implementation of e-learning in educational institutions often encounters obstacles due to user discomfort or incompatibility with the institution's needs. This study validated all system features through Black box Testing and user satisfaction assessments, resulting in an average satisfaction level of 73.7%. The expected outcome is increased efficiency and accuracy in academic processes and student management, thereby facilitating a more effective learning and teaching environment.
Prediction of deformation and damage of foundation support system based on BP neural network

Abstract The pit in the ground that needs to be excavated during the construction of a building is known as a foundation pit. In this paper, a MATLAB neural network toolbox is proposed to establish a three-layer BP neural network to predict building settlement. The relative error of the predicted values obtained after training is between -2% and 1%, and the maximum absolute error is 0.049 mm. this error is acceptable for rock-on-rock engineering problems. It shows that the building settlement prediction using artificial neural network method is feasible and the prediction results are credible. It automatically identifies the complex nonlinear relationships between the dependent and independent variables through self-learning of the optimal operation strategy, and considers the interactions among the factors in a comprehensive and integrated manner, which has good application prospects for solving nonlinear problems in the field of geotechnical engineering.
Analyzing and Applying Big Data Technologies Based on Industrial Internet Data

Abstract With the development of the industrial internet, data has gradually become the key engine driving the digital transformation of enterprises. The progress of the industrial internet is inseparable from the application of industrial data. To more effectively utilize data to empower businesses, many enterprises have put forward higher requirements for the real-time and reliability of industrial data collection. In the actual process of industrial data collection, issues such as large data volume, high real-time data processing requirements, and inconsistent data protocol standards are often encountered. In response to these problems, this paper, combined with practical engineering applications, proposes a big data construction technology solution based on industrial internet data processing. This solution aims to meet the high-concurrency data access needs of industrial equipment, using distributed messaging systems, high-throughput real-time stream processing frameworks, and distributed storage databases as the technical support for industrial data processing. This will help to complete the processing of massive industrial equipment data, thereby meeting the application requirements of various industrial big data processing business systems.
Research on Temperature Control of Plastic Laser Welding Based on Improved Snake Optimization Algorithm

Abstract Aiming at the nonlinear and time-varying characteristics of plastic laser welding temperature control system, and the problems of low accuracy and large overshoot when using the traditional proportional-integral-derivative (PID) controller, an improved snake optimization (ISO) is proposed to optimize the PID parameters. Firstly, the elite reverse learning strategy is introduced into the snake optimization (SO) to generate the reverse solution, and new individuals are generated through comparison to improve the convergence speed of the algorithm; Simultaneously introducing the water wave dynamic adaptive factor to enhance the algorithm’s iterative optimization ability. By iterating the algorithm, a set of suitable PID parameters are obtained to control the temperature control system. The simulation results show that compared to traditional PID controllers, ISO-PID reduces the adjustment time by approximately 0.42 seconds and does not cause overshoot.
Data from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology

&lt;div&gt;Abstract&lt;p&gt;Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at &lt;a href="http://www.pathml.com" target="_blank"&gt;www.pathml.com&lt;/a&gt;.&lt;/p&gt;&lt;/div&gt;
Shared prosperity characterized by four development goals: pro-poor growth, pro-poor development, inclusive growth, and inclusive development

This paper is on shared prosperity and its measurement. Economic growth enhances total prosperity, increasing the economic pie in society, but the pie distribution determines how the population shares the pie. Based on a social welfare framework, we have developed an integrated methodology to evaluate growth and distribution simultaneously. Linking the two phenomena gives rise to four development goals: (i) pro-poor growth, (ii) inclusive growth, (iii) pro-poor development, and (iv) inclusive development. These four goals provide an alternative characterization of shared prosperity. The paper defines the four goals, providing a methodology to operationalize them using real-world data. The empirically measured goals inform at what rate the shared prosperity is enhancing in any country or the world. The methodology is applied globally to determine whether the growth and development have been pro-poor and inclusive in 173 countries over the two decades in the new millennium.
Revolutionizing Automotive Parts Classification Using InceptionV3 Transfer Learning

This study presents a novel methodology for classifying automotive parts by implementing the Transfer Learning technique, utilizing the InceptionV3 architecture. We use a proprietary dataset encompassing diverse categories of automotive components for training and evaluating the model. The experimental findings demonstrate that this approach attains a performance accuracy level of 93.78% and a loss rate of 0.2938. The efficacy of InceptionV3 Transfer Learning in addressing the intricacies associated with automotive parts classification is demonstrated through its utilization of pre-existing knowledge from diverse domains. The resultant model reflects a capacity to accurately discern spare parts, thereby enhancing the efficiency of the automotive inventory management process. Utilizing InceptionV3 Transfer Learning in this scenario yields a notable and favorable outcome, thereby revolutionizing the traditional framework of automotive parts categorization. The model's efficacy in enhancing the efficiency and accuracy of automotive inventory management is evidenced by its achievement of a notable precision level and a minimal loss rate. The implications of these findings are significant in addressing intricate classification challenges within the automotive industry. They pave the way for utilizing intelligent technologies to optimize parts identification and management processes. This study establishes a foundation for a novel approach to comprehending and applying categorization systems for automotive components. This is achieved by harnessing the capabilities of Transfer Learning using the InceptionV3 model.
Thrifty Shadow Estimation: Reusing Quantum Circuits and Bounding Tails

Shadow estimation is a recent protocol that allows estimating exponentially many expectation values of a quantum state from ``classical shadows,'' obtained by applying random quantum circuits and computational basis measurements. In this Letter we study the statistical efficiency of this approach in light of near-term quantum computing. We propose a more practical variant of the protocol, thrifty shadow estimation, in which quantum circuits are reused many times instead of having to be freshly generated for each measurement. We show that reuse is maximally effective when sampling Haar random unitaries, and maximally ineffective when sampling from the Clifford group, i.e., one should not reuse circuits when performing shadow estimation with the Clifford group. We provide an efficiently simulable family of quantum circuits that interpolates between these extremes, which we believe should be used instead of the Clifford group. Finally, we consider tail bounds for shadow estimation and discuss when median-of-means estimation can be replaced with standard mean estimation.
Implementation of The Citizen Services Information System RW 016 Kapuk West Jakarta City

The advent of e-government in Indonesia, initiated by the Presidential Instruction in 2003, has paved the way for the digital transformation of governmental services at both national and regional levels. This research focuses on the utilization of this transformation by Rukun Warga (RW) 16, Kapuk Village, West Jakarta, demonstrating a shift towards digital-based administration in local governance. The study aims to facilitate more efficient and effective communication and information dissemination between the RW.16 administrative body and its residents. The deployment of the proposed application is expected to streamline public service processes by providing residents with comprehensive access to various public services information specific to RW.16. Furthermore, the application empowers residents to submit letters, express their aspirations, and report environmental issues directly to the RW.16 management, thereby enabling prompt resolution of these concerns. The application's development adhered to the System Development Life Cycle (SDLC) using the waterfall methodology, ensuring a structured and sequential approach. The backend database is built on MySQL, while the front-end interfaces are developed using HTML and PHP, supported by the Laravel framework. This study highlights the potential of e-government initiatives in enhancing public service delivery and community engagement.