Reviewerly/exHarmony huggingface dataset:
Optimization Techniques of Deep Learning Models for Visual Quality Improvement

Video restoration is a widely studied task in the field of computer vision and image processing. The primary objective of video restoration is to improve the visual quality of degraded videos caused by various factors, such as noise, blur, compression artifacts, and other distortions. In this study, the integration of post-training quantization techniques was investigated to optimize deep learning models for super-resolution inference. The results indicate that reducing the precision of weights and activations in these models substantially decreases the computational complexity and memory requirements without compromising performance, rendering them more practical and cost-effective for real-world applications, where real-time inference is often required. When TensorRT was integrated with PyTorch, the efficiency of the model was further improved taking advantage of the INT8 computational capabilities of recent NVIDIA GPUs.
PENERAPAN ALGORITMA C.45 DALAM MENGANALISA KEPUASAN PELANGGAN PADA PENJUALAN FASHION TRIFT

Fashion thrift sales have become one of the fastest growing industries, especially in this digital era. This research was conducted at one of the thrift businesses in the city of Batam, while the problem with this thrift fashion shop is the lack of stock of goods that attracts customers, because the store buys sack items, likewise this store does not serve purchase orders because items in sacks are available in various types. This study aims to analyze customer satisfaction in data mining techniques to generate new knowledge using the C4.5 classification algorithm, this algorithm uses a data mining method that carries out the process of extracting information about previous decisions. This information is used as information to form a decision tree pattern. Based on all the results of the research stages that have been carried out on the Application of the Decision Tree Classification with the C4.5 Algorithm, it can be concluded that the decision tree analysis produced by the calculation of the C4.5 algorithm shows that the variables that have the highest gain or which are the main factors in determining customer satisfaction are discount variables, product quality and price
A LITERATURE REVIEW ON SOCIAL MEDIA IN FINANCE

Purpose- The aim of this study is to summarize the findings obtained from research on social media and finance, to analyze various perspectives, and to make some contributions to the researchers and shed light on future studies. Methodology- In the study, articles examining the relationship between social media and finance were examined in the national database, DergiPark. In this context, the study will provide information on the usage of social media, customer relationship management, marketing strategies of financial companies and contribute to a better understanding of the role of social media in the financial world. In the study, a literature review of the last five years is included. For this purpose, article was searched in DergiPark using the expression "social media and finance". Then, the selected studies were examined within the framework of titles such as content, keywords, method, findings, and suggestions. The study is important in terms of presenting information about the importance and effects of social media in the financial world and informing the parties about the studies. The literature review clearly reveals the impact of social media on the financial world for the last five years. Findings- Studies show that social media enables news to spread faster in financial markets, facilitates investors' access to information and is effective on investment decisions. In addition, it has been determined that social media platforms are important in marketing and customer relationship management for financial companies. On the other hand, analyses show that social media brings some difficulties in the financial world. Information pollution and false news raise the issue of reliability of social media. Therefore, more attention and precautions are needed to understand and manage the rising role of social media in the financial world. In the literature review, it is seen that social media has an increasing effect in the financial world and this effect is expected to grow even more. In this context, it is important that financial institutions use social media strategically and take precautions against reliability and accuracy problems. At the same time, social media should be used consciously for individuals and investors who are interested in financial issues. Conclusion- Social media platforms facilitate access to financial information, increase communication between investors and influence financial decisions. In addition to all this, some challenges arise regarding the rising role of social media in the financial world. Problems such as misinformation, misdirection and manipulation attempts raise concerns about reliability and accuracy. Therefore, appropriate measures should be taken, and social media should be used carefully to manage the impact of social media in the financial world. Keywords: Social media, customer satisfaction, finance, financial performance, banking JEL Codes: M40, M41
A statistical performance analysis of three double-layer homomorphic encryption schemes for cloud enterprise resource planning (ERP) data

This study presented a statistical performance analysis of three double-layer homomorphic encryption schemes for cloud Enterprise Resource Planning (ERP) data. In this study, Abukari et al. (2021) proposed a hybrid of two homomorphic encryption scheme and compared with the state-of-the-art Usha (2018) and Bellafqira et al. (2017) double layer homomorphic encryption schemes using the combination of the RSA cryptographic algorithm and the modified Paillier cryptosystem in a hybrid approach. The Multivariate Analysis of Variance (MANOVA) was used to test whether the mean encryption times of the three schemes differ significantly across the data sizes as well as determine which encryption times among the three schemes have the largest difference for each of the data sizes. Preliminary analysis revealed that our new proposed scheme Abukari et al. (2021) has the lowest mean mean encryption times of 5.4150, 10.6570 and 39.9770 across the different data sizes of 7MB, 14MB and 200MB compared to the existing state-of-the-art Bellafqira et al. (2017) and Usha (2018). The MANOVA test revealed that there is a statistically significant difference in encryption times of the various schemes across the different data sizes, F (6, 52) = 8.827, p
UIPATH BOT FRAMEWORK: ACCELERATING RPA DEVELOPMENT AND INNOVATION

This research paper explores the significance of the “UiPath Bot Framework” and its pivotal role in streamlining UiPath bot development processes. In an era where businesses are constantly seeking ways to enhance efficiency and productivity, UiPath has emerged as a powerful tool for automating repetitive tasks and driving digital transformation. &#x0D; UiPath's intuitive interface, AI-powered intelligence, and extensive capabilities have made it a preferred choice for organizations worldwide. Its ability to automate tasks, boost efficiency, cut costs, integrate with existing systems, and facilitate rapid scalability has led to high demand in the industry. As UiPath's adoption continues to grow, the need for structured and systematic approaches to bot development becomes increasingly apparent.&#x0D; The central idea of this research paper revolves around the introduction and exploration of the "UiPath Bot Framework." This framework serves as a standardized foundation that guides developers in building bots, ensuring consistency, scalability, and success. It is particularly valuable when collaborating with citizen developers and establishing Centers of Excellence (COEs) for RPA within organizations.&#x0D; The paper delves into the fundamental elements of the UiPath Bot Framework, emphasizing key components such as context ID creation, working folder setup, log management, configuration file reading, error handling, standardized communication, and secure bot execution environments. These components collectively contribute to a structured and resilient approach to bot development.&#x0D; Furthermore, the UiPath Bot Framework significantly accelerates the development lifecycle by providing developers with a well-defined starting point. It allows developers to focus on crafting functionalities specific to their use cases without reinventing common practices for each bot. This streamlined approach not only saves time and resources but also empowers developers to innovate and create value-added features.&#x0D; To illustrate the practical application of the UiPath Bot Framework, the paper presents two comprehensive case studies: "Data Synchronization" and "HR Onboarding." These scenarios demonstrate how the framework's standardized components, streamlined practices, and reliable error management accelerate the development of distinct automation use cases.&#x0D; In conclusion, the UiPath Bot Framework represents a paradigm shift in UiPath bot development, emphasizing the importance of consistency, resilience, design patterns, and development acceleration. This research paper offers insights into the benefits of adopting the UiPath Bot Framework and its potential to revolutionize UiPath bot development practices, ultimately contributing to the realization of more efficient and effective automation solutions.
Improving Student’s English Vocabulary Mastery Using The Sing and Play Method in Class 4 MIS Baiturrahman Basilam BL 2022/2023 Academic Year

This study was structured with the aim of increasing students' vocabulary mastery, increasing motivation and learning outcomes of English for 4th grade students of MIS Baiturrahman Basilam BL 2022/2023 Academic Year using the Sing and Play method. This type of research is Classroom Action Research (CAR). In this study, researchers applied research in three cycles. Researchers used two data collection techniques, namely: observation and tests. Data is classified in the form of qualitative and quantitative. Qualitative data were obtained from observations while quantitative data were obtained from student learning outcomes. The subjects of this study were 4th grade students at MIS Baiturrahman Elementary School, totaling 16 students. Data were analyzed and presented in tabular form. These results also simultaneously show that the use of the Sing and Play method can improve English vocabulary mastery and learning activity for 4th grade students at MIS Baiturrahman Elementary School.&#x0D; &#x0D; Keywords : Methods, Vocabulary, Sing and Play, Learning Outcomes, Elementary School
Linear operators with vector masks in digital image processing problems

The paper shows that it is expedient to use vector masks for solving some types of digital image processing problems. The main advantage of vector masks compared to matrix masks is that they reduce the computational complexity of algorithms while maintaining, and in some problems even improving, quality indicators. The article demonstrates examples of the use of vector masks in the problem of estimating the level of discrete white noise in an image, forming a basis for constructing a correctly working sigma filter, which are used for obtaining smoothed partial derivative estimates in the problem of edge detection and detecting straight lines in a contour image. The work uses results obtained by the authors in their earlier publications.
Leveraging physics-informed neural computing for real-world optimization tasks: From swinging up a pendulum to swingby of a spacecraft

Abstract Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development. Recently, machine learning applications, including deep reinforcement learning (RL) and genetic algorithms (GA), have emerged in these real-world optimization tasks. We introduce a new machine learning-based optimization scheme that incorporates physics with the operational objectives. This physics-informed neural network (PINN) could find the optimal path in well-defined systems with less exploration and also was capable of obtaining narrow and unstable solutions that have been challenging with shooting approaches like RL or GA. Through an objective function that integrates governing laws, constraints, and goals, PINN enables top-down searches for optimal solutions. In this study, we showcase the PINN applications to various optimization tasks, ranging from inverting a pendulum, determining the shortest-time path, to finding the swingby trajectory. Through this, we discuss how PINN can be applied in the tasks with different characteristics.
Decentralization in Blockchain: Reconsidering Change Management Theories

This paper explores the implications of blockchain technology's decentralization for traditional change management theories. As digital technologies continue to disrupt organizational structures, blockchain stands out with its unique feature of decentralization, challenging the centralized premise inherent in many existing change management theories. While influential and widely applied, these theories are largely predicated on certain fundamental assumptions about organizations and the nature of change, often assuming a centralized structure where authority and decision-making are concentrated at the top. However, with its inherent decentralization, blockchain technology democratizes decision-making and authority, disrupting traditional power dynamics and posing significant challenges to these assumptions. The present paper aims to address the gap in the current literature by exploring how blockchain's decentralization challenges and expands our current theories of change management wherein we propose theoretical adjustments and potential new constructs for change management. By doing so, we contribute to the evolving discourse on change management in the digital age and illuminate the transformative potential of blockchain’s decentralization and its implications for our understanding and practice of managing organizational change.
COVID-19 and the Digital Transformation that Followed in the Kingdom of Saudi Arabia

This study examines the impact of the COVID-19 pandemic on the digital transformation landscape in the Kingdom of Saudi Arabia. Its main goal is to find out to what extent the pandemic has spurred digital technology adoption. During the pandemic, Saudi Arabia has seen a rapid transition to digital solutions in various areas such as remote working, online education and e-commerce. And telemedicine. This study examines the factors that contribute to this transition and assesses its long-term consequences. A quantitative, inductive and explanatory study design was selected for this study to analyze primary data collected from 247 respondents working in official UK organisations, ministries and agencies. Saudi Arabia through a survey questionnaire page. Statistical analysis tools were used to evaluate the conclusions of the results. Key findings highlight the central role of government investments in digital infrastructure and highlight the challenges of digital literacy and data security. Ultimately, this study acknowledges that the pandemic has acted as a catalyst, reshaping traditional business models and service delivery. To build on this momentum, the recommendations call for sustainable investment in digital infrastructure, increasing digital literacy, and encouraging innovation through collaborative and responsive partnerships. Actively on network security issues.
IMPLEMENTASI DATA MINING DENGAN ALGORITMA NAIVE BAYES UNTUK KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO

The percentage of the population receiving assistance was 5.19%, this figure increased compared to March 2022, while the line of aid recipients was recorded in 2022 of 783,730 people per capita per month. The number of poor households in Batu Aji District is Bukit Tempayang Village with 15,857 inhabitants, 300 poor households, 37,531 inhabitants in Buliang Village, 289 poor households, 28,693 The beneficiary of assistance is an inability to meet basic needs including food, clothing, education and housing. Thus it is necessary to carry out a strategy in dealing with the level of beneficiaries, namely by providing accurate and targeted data on beneficiaries. Naive Bayes is an algorithm that exists in data mining and is part of the data mining classification technique by using probability and statistical techniques to estimate or predict opportunities that will occur based on previous opportunities, namely there are two feasible and inappropriate classes. The results of the classification that will be carried out later will help in the processing of assistance to help make decisions regarding the classification of determining basic food recipients. And by testing calculations manually and using rapid minner software, you get an accuracy value of 80%.
Mutual Modality Learning for Video Action Classification

The construction of models for video action classification progresses rapidly. However, the performance of those models can still be easily improved by ensembling with the same models trained on different modalities (e.g. Optical flow). Unfortunately, it is computationally expensive to use several modalities during inference. Recent works examine the ways to integrate advantages of multi-modality into a single RGB-model. Yet, there is still a room for improvement. In this paper, we explore the various methods to embed the ensemble power into a single model. We show that proper initialization, as well as mutual modality learning, enhances single-modality models. As a result, we achieve state-of-the-art results in the Something-Something-v2 benchmark.
Steganography: The Art of Encrypting and Decrypting Information with Different Hiding Tools and Command Prompt

The concealing information within other non-secret data to hide its existence. It involves embedding secret messages or data within innocuous carriers, like images, audio files, or text. This hidden information is often encrypted to enhance security. Steganography focuses on maintaining the secrecy of the communication. The efficiency of two steganography tools Shusssh and SteganographX Plus freely available on internet. Different file format of images (JPEG, JPG and BMP) were analysed by Windows-based Command Prompt Utility and two different steganography tools (Shusssh! and SteganographX Plus).
CRIL: A Concurrent Reversible Intermediate Language

We present a reversible intermediate language with concurrency for translating a high-level concurrent programming language to another lower-level concurrent programming language, keeping reversibility.Intermediate languages are commonly used in compiling a source program to an object code program closer to the machine code, where an intermediate language enables behavioral analysis and optimization to be decomposed in steps.We propose CRIL (Concurrent Reversible Intermediate Language) as an extension of RIL used by Mogensen for a functional reversible language, incorporating a multi-thread process invocation and the synchronization primitives based on the P-V operations.We show that the operational semantics of CRIL enjoy the properties of reversibility, including the causal safety and causal liveness proposed by Lanese et al., checking the axiomatic properties.The operational semantics is defined by composing the bidirectional control flow with the dependency information on updating the memory, called annotation DAG.We show a simple example of 'airline ticketing' to illustrate how CRIL preserves the causality for reversibility in imperative programs with concurrency.
Extracting software requirements-related information from online news using DomText-WMDS

Currently, there are not many studies that assess software requirements extraction from non-software artifacts. Most of the research in these related areas are focuses on software artifacts such as project descriptions or user reviews as a source of requirements extraction. This research aims to identify relevant information to the software requirements from online news using the vector space model. This software requirements-related information can assist systems analysts in discovering the problem domain based on the lesson learned presented by stakeholders in online news. This research proposes DomText-WMDS to extract requirements-related information from online news. We used online news and public software requirements specification dataset to develop software-specific vocabulary using domain specificity technique. Then we expanded the specific vocabulary software to obtain more comprehensive results by building vector space model from online news documents. This updated version of software-specific vocabulary can be used for basic filtering of software requirements-related information that previously extracted using the part-of-speech (POS) chunking. This study improved the performance for extracting software requirements-related information, with precision and recall 61.09% and 60.66% compared to domain specificity approach that only manages to obtain 43.34% and 40.78%.
H4MER: Human 4D Modeling by Learning Neural Compositional Representation with Transformer

Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel neural compositional representation for Human 4D Modeling with transformER (H4MER). Specifically, our H4MER is a compact and compositional representation for dynamic human by exploiting the human body prior from the widely used SMPL parametric model. Thus, H4MER can represent a dynamic 3D human over a temporal span with the codes of shape, initial pose, motion and auxiliaries. A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary codes. We present a novel Transformer-based feature extractor and conditional GRU decoder to facilitate learning and improve the representation capability. Extensive experiments demonstrate our method is not only effective in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks, including monocular video fitting, motion retargeting, 4D completion, and future prediction. The video demos are in the project homepage: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://boyanjiang.github.io/H4MER/</uri> .