Reviewerly/exHarmony huggingface dataset:
Research of methods based on neural networks for the analysis of the tonality of the corps of the texts

The object of the study is methods based on neural networks for analyzing the tonality of a corpus of texts. To achieve the goal set in the work, it is necessary to solve the following tasks: study the theoretical material for learning deep neural networks and their features in relation to natural language processing; study the documentation of the Tensorflow library; develop models of convolutional and recurrent neural networks; to develop the implementation of linear and non-linear classification methods on bag of words and Word2Vec models; to compare the accuracy and other quality indicators of implemented neural network models with classical methods. Tensorboard is used for learning visualization. The work shows the superiority of classifiers based on deep neural networks over classical classification methods, even if the Word2Vec model is used for vector representations of words. The model of recurrent neural network with LSTM blocks has the highest accuracy for this corpus of texts.
Leveraging Zero-Trust Architecture for Improving Transaction Quality of Financial Services Using Locational Data

Financial services are fundamentally essential to the economic services that the finance industry provides, which include a wide range of personal and commercial operations. Financial services include those services which are provided by banks, insurance companies, accounting firms, stock brokerages, and other financial institutions. Among all of these financial services, the banking system is a component of broader financial services and seeks to have a significant impact on how the money economy operates in every nation. This paper introduces the idea of zero-trust architecture security coupled with machine learning for a secure transactional environment in financial services with the highest accuracy of detecting fraud using locational data. Location data is geographic information about a particular device's location that is linked to a time identifier. A device identification serves as a proxy to keep the identity of the person away from the implications derived from the analysis, since it is considered that this device data is linked to a person. Along with this, we have integrated two-step authentication method for better enhancement. It will help an authorized individual from keeping his/her financials away from frauds and fake transactions to avert these worries.
Using Knowledge Management to Enhance Organizational Performance and Effectiveness

Beyond just system deployment efficiency and labor competency, a number of crucial aspects affect an organization's effectiveness. While the effectiveness of these factors is important, they are not the only factors that matter. The performance as a whole is greatly influenced by a number of other important factors. Recent observations have revealed a consistent upward trend in the metrics related to organizational performance, highlighting the importance of different elements cooperating. Therefore, the main goal of this research project is to carry out a thorough analysis into the complex and dynamic relationship between knowledge management inside an organization and its overall performance. The goal of this study is to delve deeply into the underlying mechanisms and examine how knowledge management techniques directly affect and contribute to an organization's overall performance. The approach used in this study is survey-based, providing for a detailed understanding of the respondents' viewpoints on a variety of topics, including knowledge dissemination, knowledge responsiveness, and organizational skills. By using this technique, the researchers were able to quantitatively and qualitatively evaluate the participants' points of view and acquire important insights into the intricate relationships that exist between knowledge management techniques and an organization's overall success. The survey's findings showed a strong positive relationship between increased organizational performance and effective knowledge management. This beneficial effect was apparent across a number of variables, illuminating the critical roles that organizational capacities, responsiveness to knowledge, and knowledge distribution play in boosting overall efficiency and production within the company. The results do, however, highlight the significance of developing a solid strategy or methodology that guarantees the successful deployment and seamless integration of knowledge management methods inside the business. Recognizing that a complete strategy involving careful planning, strategic alignment, proper resource allocation, and ongoing evaluation is required for the effective transformation of knowledge management within the organizational structure is crucial. Organizations can better harness their intellectual assets and maximize their total performance by concentrating on the implementation of a well-structured strategy, which will support sustained growth and competitive advantage in the dynamic business environment.
Design and implementation of Chinese musical system based on virtual reality technology

Research on the stage control system of musical dramas to quickly demonstrate the effectiveness of stage design, provide a basis for stage control, build a 3D rendering and control engine, establish a dynamic 3D simulation model related to spatiotemporal architecture, present comprehensive and nonlinear effects of musical stage creativity, and output data such as the state and position of dance beauty creativity during the simulation process for precise stage control. Through the behavior modeling of the three-dimensional digital stage, the system completes spatial analysis and calculation, realizes WYSIWYG stage design presentation algorithm, and carries out high credibility display and effect evaluation of stage creativity. Based on the mechanical characteristics of the three-dimensional digital stage, the system implements a video editing method based on projection decomposition, which segments and recombines video data in a parallel and multi-core fast calculation method, and generates video files that match the stage motion. Engineering practice has proven that the system can effectively improve the practicality, reliability, and efficiency of stage design.
Sample-balanced and IoU-guided anchor-free visual tracking

Siamese network-based visual tracking algorithms have achieved excellent performance in recent years, but challenges such as fast target motion, shape and scale variations have made the tracking extremely difficult. The regression of anchor-free tracking has low computational complexity, strong real-time performance, and is suitable for visual tracking. Based on the anchor-free siamese tracking framework, this paper firstly introduces balance factors and modulation coefficients into the cross-entropy loss function to solve the classification inaccuracy caused by the imbalance between positive and negative samples as well as the imbalance between hard and easy samples during the training process, so that the model focuses more on the positive samples and the hard samples that make the major contribution to the training. Secondly, the intersection over union (IoU) loss function of the regression branch is improved, not only focusing on the IoU between the predicted box and the ground truth box, but also considering the aspect ratios of the two boxes and the minimum bounding box area that accommodate the two, which guides the generation of more accurate regression offsets. The overall loss of classification and regression is iteratively minimized and improves the accuracy and robustness of visual tracking. Experiments on four public datasets, OTB2015, VOT2016, UAV123 and GOT-10k, show that the proposed algorithm achieves the state-of-the-art performance.
Virtual Environments’ Knowledge Base: A Bibliometric Analysis

The goal of this research is to explore the scientific literature on virtual environments by conducting a bibliometric analysis. This provides insight into the main topics, most contributing sources, most-cited articles, prolific authors, and countries that are involved in the research. Additionally, this analysis acts as a tool for understanding the intellectual foundation of the structure, worldwide research concentration on this domain, and social structure of the literature on the subject, as well as the knowledge base associated with the use and integration of virtual environments. The research was conducted by analyzing articles indexed in the Scopus database, which is believed to be the top-most database in scope regarding social sciences research. Using the PRISMA sampling technique and VOSviewer software, the data was analyzed to provide an overview of the virtual environment domain, and we reported tables, graphs, and maps to highlight the main performance indicators for the production of articles and their citation. The results show that terms such as "innovation management," "social media analysis," "business development," "digital technology," "customer engagement," "firm performance," "technology management," "virtual environment," "digital capability," and "market performance" have been used regularly. In addition, the density map reveals some of the newer topics of research, such as "industry 4.0," "sustainable development," "digital business strategies," "virtual environment," "artificial intelligence," and "big data technology." This study's results offer a deeper understanding of how the past, present, and future are connected and shed light on any topics that remain unexplored.
Software Managers' Guide to Operational Excellence

Software engineering managers (or any senior technical leaders) have many responsibilities: the care and feeding of the team, delivering on business outcomes, and keeping the product/system/application up and running and in good order. Each of these areas can benefit from a systematic approach. The one I present here is setting up checks and balances for the team's operational excellence.
FOSTERING LEARNING AUTONOMY AND CREATIVITY IN CROSS CULTURAL UNDERSTANDING THROUGH PROJECT-BASED LEARNING

Teachers require instructional resources to assist students in learning. This research met the material requirements of students and evaluated 21st-century learning and the analysis of popular literary works in various countries. Thirty students in English Education and Literature participated in this study. The research was primarily concerned with three topics: design of Booklet Journey to the World, the 21st century skills in project-based learning, and literary work analysis as one of the booklet's sections. Three instruments were used to gather data from the field. Document analysis, a Focused-Group Discussion (FGD) interview, and a questionnaire indicate students' perspectives of their writing process through the implementation of project-based learning. Three points disclosed: 1) The booklet displayed good creativity, idea courage, and language; 2). Concerning the compatibility of learning models and instructional materials, creativity; and self-directed learning, the three criteria were met by 53.34 percent, 63.34 percent, and 56.67 percent of respondents, respectively which means that the students' perception on the implementation of project-based learning was rated as excellent; 3) The majority of works included in the booklet were collected, paraphrased, and commented on, demonstrating sufficient, effective, and thorough literary comprehension.
Sosialisasi pemanfaatan sistem informasi keuangan toko sembako bu Riya di Kelurahan Banyu Urip Surabaya

The grocery store business is currently very promising for entrepreneurs who are currently involved in the business economy. This business has many enthusiasts due to its unique concept with customers being free to choose the type of product they sell themselves. If you look at the competitors, there are quite a lot and each shop has its own advantages over the others. One of the differences that can be seen is on the management side, various variations or methods are used to increase productivity from the management side, which include recording existing product stock, calculating expenditure and calculating input. This will be the key to the success of management in carrying out processing because when these things are not done well it will result in the emergence of new problems. Therefore, the service activities carried out at the Bu Riya grocery store are to help with these problems using an approach method that includes planning, implementation and evaluation stages. The stages that have been carried out in planning are able to create a plan to resolve problems in stall management such as recording and the flow of money in and out. After that, the implementation method itself is implemented, namely by implementing what has been identified by creating a management application solution. From the entire series of activities carried out, a new management process was obtained by digitizing several aspects such as recording product stock and managing the flow of cash in and out. The digitalization that has been implemented is based on Android and significant results have been obtained, namely that operational shop management is able to use it well.
Lessons Learnt from Online Teaching and Beyond: What now? Three Countries Speak

The COVID-19 pandemic brought unprecedented challenges and changes to higher education. More than 100 countries went into lockdown, severely affecting education systems worldwide. There was an expectation that higher education institutes would “find solutions” — and fast. Many questions arose concerning the role of online courses, technology-mediated learning resources, and the long-term effects of the pandemic on students, faculty, and institutions. Information systems educators from Southern Africa came together at the 51st Annual Conference of the Southern African Computer Lecturers’ Association (SACLA 2022). The panel reported on here was part of that conference and brought together information systems educators from three countries, South Africa, Mexico, and the United States to share their experiences and reflections regarding the effects of the COVID-19 transition and its aftermath. A wide-ranging discussion ensued, that ran the gamut from micro-level individual experiences to macro-level effects and challenges. These perspectives cover “big picture” challenges and observations, inclusion and exclusion considerations, threats and opportunities, and two reports of experiences and the resulting modifications in COVID-19 classrooms. This panel report also focused on the lessons learned from the conference attendees' discussion and suggested a possible future research agenda.
Scaling Private Deep Learning with Low-rank and Sparse Gradients

Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scale with model dimension, hindering the learning capability significantly. We propose a unified framework, LSG, that fully exploits the low-rank and sparse structure of neural networks to reduce the dimension of gradient updates, and hence alleviate the negative impacts of DPSGD. The gradient updates are first approximated with a pair of low-rank matrices. Then, a novel strategy is utilized to sparsify the gradients, resulting in low-dimensional, less noisy updates that are yet capable of retaining the performance of neural networks. Empirical evaluation on natural language processing and computer vision tasks shows that our method outperforms other state-of-the-art baselines.
Federated Optimization with Linear-Time Approximated Hessian Diagonal

Federated learning (FL) or federated optimization is a type of distributed optimization where multiple clients collaboratively train a global model without sharing local data. One of the key challenge in FL is the communication overhead due to slow convergence of the global model. In this paper, we propose a federated learning algorithm to handle this slow convergence by incorporating Hessian diagonal while training client’s models. To reduce the computational and memory complexity in local clients, we introduce a linear time Hessian diagonal approximation technique by using only the first row of the Hessian. Our extensive experiments show that our proposed method outperforms state-of-the-art FL algorithms, FedAvg, FedProx, SCAFFOLD and DONE in terms of training loss, test loss and test accuracy.
Unlocking the Black Box: Towards Interactive Explainable Automated Machine Learning

Automated machine learning (AutoML) has transformed the process of selecting optimal machine learning (ML) models by autonomously searching for the most appropriate ones and fine-tuning associated hyperparameters. This eliminates the burdensome task of trial-and-error selection and parametrization of ML algorithms. Nonetheless, the lack of transparency and explainability poses a significant challenge when using AutoML, as it hampers user trust in the system’s recommendations. Consequently, users often allocate more resources to the search process, resulting in reduced efficiency of the AutoML systems. To address this challenge, we propose an interactive and explainable AutoML framework that enables users to understand the reasoning behind the recommendations and diagnose any limitations of the suggested models using various explainable AI methods. Additionally, our framework provides the possibility of automated performance refinement. To operationalize the framework, we introduce AMLExplainer, an XAI system for interactive and interpretable AutoML that visualizes and performs all stages of the proposed pipeline(s) within the widely used Bootstrap Dash environment.
Students Difficulties in Reading Comprehension on Descriptive Text at Second Semester FKIP in Batanghari University Jambi

The aim of this research is to identify the educational abilities and achievements of the students. challenges or hardships In the field of reading comprehension, Paraphrased: regarding texts that provide detailed descriptions. This kind of investigation is Quantitative data refers to information that can be measured and expressed numerically. The population sizes The focus of this research investigation is on this particular study. According to the research, the difficulty level of answering main idea questions is determined to be 0.3%. The difficulty for students in answering inference questions is 0.14. Additionally, the percentage of challenging questions that involve intricate information is 0.12% out of four total question items. The rate of students who have trouble comprehending vocabulary is 0.14%. The overall average score for the exam is 84.6. Students therefore fulfill the requirements for achieving a high score. Researchers have arrived at the inference that students possess a strong comprehension of descriptive material.
Simulating Dynamic Vehicle Routing Problems with Athos

Complex routing problems, such as vehicle routing problems with additional constraints, are both hard to solve and hard to express in a form that is accessible to the human expert and at the same time processible by a computer system that is supposed to produce a solution of suf�?cient quality. The formulation must be formal enough to avoid ambiguities and also comprehensible enough to be created, discussed and shared by domain experts. In this paper, we present the domain speci�?c language Athos in which complex routing problems can be expressed in a computationally independent, human-readable form. Athos is then transformed into code that can be run in an adequate target platform. Suitable methods for solving problems are available and applied to the given problem. We present a case study in which we use a genetic algorithm to solve instances of a vehicle routing problem with time windows and demonstrate the end to end process to produce a solution in the Athos environment. Moreover, we show how the Athos system goes beyond optimisation of static routes and can be used as a tool to simulate the impact of traf�?c and congestion on the tours. We call this extended problem a dynamic vehicle routing problem with time windows.
SDIM: A Subtly Designed Invertible Matrix for Enhanced Privacy-Preserving Outsourcing Matrix Multiplication and Related Tasks

Matrix multiplication computation (MMC) is one of the most important basic operations with a variety of applications in the scientific and engineering community, including linear regression, k-nearest neighbor classification and biometric identification. However, performing these tasks with large-scale datasets can result in significant computation beyond the capabilities of resource-constrained clients. As outsourcing intensive tasks to cloud server has become a promising method, many matrix-transformation-based privacy-protected schemes have been presented for certain outsourcing tasks, such as Lei et al's scheme for the outsourcing MMC task and Zhao et al's scheme for matrix determinant computation. Nevertheless, Lei et al's scheme suffers from inherent security flaws that reveal the statistical information of zero elements in the original data. Additionally, Zhao et al's scheme can only be applied to specific outsourced tasks and is not suitable for more universal situations, such as MMC, where the client needs to compute the inverse matrix of the secret key. Therefore, designing an invertible matrix is a difficult task that affects privacy security, efficiency, and universality of the matrix-transformation-based privacy-protected outsourcing computing scheme. To address this challenge, we propose a subtly designed invertible matrix (SDIM) and a privacy-protected outsourcing MMC scheme based on the SDIM to remedy the inherent security flaws of Lei et al's scheme. We also propose an optimized matrix-chain multiplication method to maintain high efficiency of the SDIM-based privacy-protected scheme. This optimization also allows the SDIM to be universally applied not only to MMC tasks but also to other related outsourced tasks such as linear regression. Theoretical analyses and experiments show that our methods are more secure in terms of data privacy, with comparable efficiency to the state-of-the-art scheme based on matrix transformation. This SDIM-based scheme has achieved a well-balanced trade-off between security, efficiency and universality.
Provable Secure Anonymous Device Authentication Protocol in IoT Environment

The inherent massive heterogeneous devices and open channels in the Internet of Things (IoT) present significant challenges for identity authentication between devices and cloud servers. For this issue, reliable protocols ensure the legality of participants and act as a crucial method to provide security for authentication. In previous research, schemes devised by researchers exhibit certain security vulnerabilities, making it challenging to withstand comprehensive network attacks, e.g., stolen device attacks, replay attacks, impersonation, etc. Additionally, some protocols have complex interaction processes, which incur significant computational redundancy and resource loss. Motivated by this, this paper proposes an anonymous and certificateless lightweight authentication protocol (ACLAP) for device-to-server and device-to-device based on elliptic curve cryptography. It improves the communication quality between devices and cloud servers and solves the security risks in authentication. In the scheme, we utilize device users’ passwords and biometric features as verification credentials without storing any trusted proofs on the cloud server. We address the issue of resource consumption caused by numerous devices in the IoT environment. From formal security analysis and comparisons with other works, our protocol has preferable security performance, and effectively saves communication resources for authentication. Simulation results demonstrate the feasibility and practical significance of the scheme.