Reviewerly/exHarmony huggingface dataset:
Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope

Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope sensors, Cameras, and GPS. The selection of the Support Vector Machine algorithm is due to its capabilities to minimize errors in training data sets and the Curse of dimensionality which can estimate parameters as well as its ability to find the best hyperplane that separates two classes. The SVM algorithm was originally developed for the classification of two classes. Problem raised if there are more than two classes. In addition, the performance will not optimal for the large-scale data. Therefore, modification the current design is needed. An ensemble technique can be used to combine the Support Vector Machine algorithm with the bagging algorithm. This study proposes the application of an ensemble SVM algorithm to classify human activities based on accelerometers and gyroscope sensors on smartphones. The total data is 13725 records with 4575 representatives of each class. From the results of the overall data partition carried out in the calcification process using the ensemble SVM algorithm, the best performance was generated when comparing datasets with 80% training data and 20% test data from a total of 13725 records because it succeeded in increasing accuracy, precision, and sensitivity.
Rancang Bangun Aplikasi Pendistribusian Sparepart Motor Berbasis Web

CV. Wahana Baru is engaged in the distribution of spare parts specifically for two-wheeled vehicles. Wahana Baru has been established since 18 years ago or more precisely on March 29, 2003 with a name that has never changed, namely Wahana Baru. The business world that is lived in this modern era is very dependent on computerization which is growing and becomes a supporter in various activities within the company or organization. In distribution activities, a motorcycle spare part distribution system application is needed to make it easier for customers to order spare parts on CV. Wahana Baru to support activities is becoming more developed. To realize a motorcycle spare part distribution information system, a web-based motorcycle spare parts distribution information system application was designed at CV. New Rides. In defining requirements and analyzing system development, data collection methods were carried out by means of observation, interviews, and literature review from various sources of information. For the method of analysis and design using Unifield Modeling language (UML). The Motor Parts Distribution Information System uses the Hypertext Preprocessor (PHP) programming language and MySQL as its database. With the Motorcycle Spare Parts Distribution Information System, the processes involved in the distribution of motorcycle spare parts are easier to use, accurate, and integrated.
A combined model of Hawkes-ICEEMDAN-Catboost-Holt for interval-valued carbon price forecasting

Accurate carbon price forecasting is crucial for efficiently operating both carbon trading and financial markets. This study proposes a Hawkes-ICEEMDAN-Catboost-Holt combined interval forecasting model to improve the accuracy of carbon price forecasts. First, the t-SNE-LLE second-order dimensionality reduction was performed on the collected search index data to quantify the attention of netizens. Subsequently, the attention series was converted into an interval series using the Hawkes exponential decay model, and further decomposed into radius and center series. In addition, the radius and center series were decomposed using ICEEMDAN and then reconstructed. Finally, the carbon price interval combined forecast was based on the Catboost–Holt model. The proposed model exhibits high accuracy and strong stability in comparison to other forecast models. The Hawkes exponential decay of netizens’ attention is creatively included in the influence mechanism of carbon price fluctuations, effectively improving forecast accuracy. The interval decomposition and reconstruction methods provide an innovative development path for interval forecasting research. The residual and trend series obtained from interval reconstruction can be forecasted using Catboost and Holt, respectively, thus significantly improving the accuracy and stability of interval forecasts and enhancing the decision-making process for carbon trading and financial markets.
A Proposed Model for Bahrain Firm’s: Study the Factors Empowering Employee Retention—A Literature Review

The study sets out to establish a proposed model for Bahrain firm’s: study the factors empowering employee retention—a literature review. The study is constructed entirely from a review of previous literature. A literature review indicates the variables empowering employee retention namely employee training, employee motivation, and leadership style as independent variables, and organizational justice as moderator variable along with their benefits suitability and impacts to improve the relationship between the variables. The study gave useful insights to human resources management managers, policymakers, decision-makers, and management inside businesses about employee retention, as well as recommendations for future studies from the human resources management specialty that human resource researchers should be aware of. This contributes to long-term performance and enhances employee motivation toward reaching firm goals.
Application research on table structure recognition and information extraction in sci-tech academic journals based on visual studio tools for Office technology

The premise of intelligent table processing in Word is to extract the table structure and text information. By using visual studio tools for Office (VSTO) to obtain the extensible markup language (XML) information of the table, the structural relationship of the table and the text format of each cell can be further recognized. Compared with Visual Basic for Applications (VBA) technology, VSTO technology is slower in handling Word, but it has better extensibility and efficiency than VBA. VSTO technology can effectively recognize the structure of the table and extract information, providing possibilities for subsequent intelligent processing.
Completely entangled subspaces from Moore-like matrices

Abstract Construction of completely entangled subspaces (CES) has gained a considerable attention, recently. These subspaces which contain only entangled states are of great importance for entanglement theory and also provide a valuable resource for quantum information processing tasks. The results of [ Proc. Math. Sci. , 114 , 365 (2004)] and in particular using the properties of certain matrix, namely Vandermonde matrix, to build CES motivated us to search for new distinct CES's. Mainly, the stimulating question of whether there are other matrices that can lead to building CESs emerged. In the current paper we give an affirmative answer to this question by providing a method for constructing CESs using the properties of Moore-like matrices. In addition, we give few examples for the proposed subspaces in case of 3-qubit and 2-qutrit systems. Then a comparison between the resulted subspaces and those constructed from Vandermonde matrix has been given for the systems understudy. The results shows that the two methods give identically the same subspaces in case of multiqubit systems. However, for multipartite systems with local dimensions d ≥ 3 the two methods gave unequivalent CES subspaces. Interestingly, the properties of the proposed Moore-like matrices provided a far rich way for constructing CES subspaces. It leads to generating as many distinct CES's as we want for each multipartite quantum system. This is in contrary to Vandermonde-based method which can give only one CES per system. In addition, the basis for each of the given examples has been obtained in a simple form. Moreover, we evaluated the entanglement of uniformly mixed states over the obtained subspaces in terms of concurrence and geometric measure of entanglement. Since different parameters of a Moore-like matrix lead to distinct CESs for the same system, the realized results can open the door for more investigations and/or applications.
Prevention of VM Timing side-channel attack in a cloud environment using randomized timing approach in AES – 128

The term "cloud computing" refers to the delivery of various computer services to users via the Internet. These services include servers, storage, databases, networking, software, and analytics. The ability for businesses to swiftly and easily access computing resources as needed is one of the primary benefits of cloud computing, along with scalability, flexibility, and cost savings. To protect themselves from data breaches, distributed denial of service attacks, and insider threats, cloud providers and consumers alike need to deploy adequate security measures. Shared resources and timing inconsistencies within the hypervisor can make it possible for attackers to deduce sensitive information from other Virtual Machines (VMs). In this research, a software-based solution to the problem of VM timing side-channel assaults (SCAs) in CC (Cloud Computing) is proposed. Following an analysis of the process's empirical complexity, the solution uses a randomized timing method, which is compatible with all of the AES–128 sub-steps.
Epistemic Logics with Quantification Over Epistemic Operators: Decidability and Expressiveness

The optimal balance between decidability and expressiveness is a big problem of logical systems, in particular, of quantified epistemic logics (QELs). On the one hand, decidability is a very significant characteristic of logics that allows us to use such logics in the framework of artificial intelligence. On the other hand, QELs have important expressive capabilities that should not be lost when we construct decidable fragments of these logics. QELs are known to be much more expressive than first-order logics. One important example of their extra expressive power is that they allow us to distinguish between de dicto and de re readings of epistemic sentences. It is clear that such capabilities should be preserved as much as possible in decidable fragments. In this paper, we consider extensions of QELs that include quantification over modalities. Denote this extensions by QELs. QELs allows us to make more subtle distinctions between de dicto and de re readings of epistemic sentences, and we also should keep these new features as much as possible in decidable fragments. It is known that there are not much interesting decidable QELs. The situation with QELs is the same. But in recent years (after 2018), we have obtained a variety of decidable QELs constructed in different ways. We distinguish between (1) the approach in which for every undecidable QEL and for every variant of its decidable fragment, a specific proof is constructed, and (2) the approach in which a class of decidable QELs is obtained using general tools and a uniform method for all QELs of this class. In this paper, we compare the results of these approaches.
Neural Network – based Sensitivity Analysis of the Factors affecting the Solar Photovoltaic Power Output

Technological advancements and modernization of different industries and disciplines contributed to more consumption of oil and electricity which powers these industries. Aligned with the United Nations (UN) Sustainable Development Goals (SDG), the use of alternative and renewable energy (RE) sources is encouraged as it allows the utilization of clean energy resources and access of populations in developing countries to electricity and energy. Forecasting and maximizing the harvest for renewable energy requires an understanding of the mechanics behind the variables that impact solar photovoltaic production. 755 datasets were created from 150 days of recorded data and used in the model building and sensitivity analysis. The approach used in this study to identify the variable importance of each meteorological variable to the solar photovoltaic (PV) production was the Garson’s algorithm (GA). In this study, an artificial neural network (ANN)-based sensitivity analysis (SA) using Garson’s algorithm (GA) was implemented to identify the relative importance (RI) of the factors influencing the solar PV output including the solar irradiance (SI), rainfall, maximum temperature (MaT), minimum temperature (MiT), relative humidity (RH), and wind speed (WS). The model also considers the relative significance of these parameters to the solar PV output. Results indicate that, with a relative value of 29.48% and 5.01%, respectively, solar irradiance and wind speed are the most and least important factors.
A Hybrid Algorithm Based on Artificial Bee Colony and Artificial Rabbits Optimization for Solving Economic Dispatch Problem

The Artificial Bee Colony (ABC) algorithm has gained widespread attention and has been applied in various fields due to its ability to achieve excellent global optimization results and ease of implementation. Despite these advantages, the basic ABC algorithm has some drawbacks such as slow convergence, poor exploitation, and difficulty in finding the best solution among all feasible solutions in some cases. Hence, a hybrid optimization algorithm called Artificial Bee Rabbit Optimization (ABRO) is proposed in this paper. This algorithm synergizes the ABC algorithm and Artificial Rabbits Optimization (ARO) algorithm. The original ABC algorithm has a better exploration approach while the ARO algorithm has a better exploitation strategy when approaching the optimum value. The new hybrid algorithm integrates the good features of both standard optimization strategies, thus producing better possible solutions. Four types of benchmark functions are applied to test the performances of the proposed algorithm. Furthermore, the proposed algorithm is applied in the IEEE-26 bus system for tackling the economic dispatch problem. The results show that the ABRO algorithm outperforms the original ABC algorithm and ARO algorithm in all benchmark functions and successfully reduces the cost of the power generation for the IEEE- 26 bus system.
Teaching and Research Practice of Secondary School Mathematics

The teaching and research of secondary school mathematics is a complex activity process. Teachers need not only to master the basic knowledge and methods of teaching and research well, but also to have good teaching and research abilities, which can only be improved through continuous and repeated practice. This chapter introduces the teaching and research practice.
Study and Empirical Analysis of Sentiment Analysis Approaches

Sentiment analysis is a useful tool for social media and customer analysis allowing one to glean a summary of the views of a large population regarding a particular topic. This work conducts an empirical survey of different techniques for sentiment analysis; they cover the implementation, advantages, and limitations of each of these methods and conduct an experiment to find out which of these methods is best suited for sentiment analysis in today’s scenario. Experimentation conducted on benchmark datasets and the results obtained highlight the fact that supervised learning algorithms like Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), and Deep-Learning-based algorithms like Convolutional Neural Network (CNN) and Bidirectional Recursive Neural Network (RNN) show improved performances over the lexicon-based methods.
Design and Implementation of 360-Degree Video Vignettes in Immersive Virtual Reality: A Quality Management in Higher Education Case

Immersive virtual reality (IVR) in higher education has gained traction as a way to engage learners in immersive and authentic learning experiences. However, there is a need for guidance on how to design and implement IVR solutions. We used a design-based research methodology to answer the following overarching research question and sub-questions: (1) How can 360-degree video vignettes presented within an IVR environment be designed and implemented to facilitate quality management competencies? (1a) How do learners experience this technology? (1b) What is the process for creating and implementing this technology? (1c) How can we best design the learning experience? We designed, developed, and formatively evaluated a use case within a master’s level quality management course in a college of business and collected quantitative and qualitative data from project stakeholders. This report focuses on our qualitative data collection and analysis. Results are presented in themes and sub-themes as follows: IVR experience (sub-themes: physical discomfort, emotional sensations, and attitudes); IVR technical integration (sub-themes: resources, process flow, and stakeholders) and IVR learning integration (sub-themes: teaching and learning affordances, drawbacks, and learning design). Findings can be useful in guiding the design and implementation of IVR applications for learning in higher education.
Snippet Comment Generation Based on Code Context Expansion

Code commenting plays an important role in program comprehension. Automatic comment generation helps improve software maintenance efficiency. The code comments to annotate a method mainly include header comments and snippet comments. The header comment aims to describe the functionality of the entire method, thereby providing a general comment at the beginning of the method. The snippet comment appears at multiple code segments in the body of a method, where a code segment is called a code snippet. Both of them help developers quickly understand code semantics, thereby improving code readability and code maintainability. However, existing automatic comment generation models mainly focus more on header comments, because there are public datasets to validate the performance. By contrast, it is challenging to collect datasets for snippet comments, because it is difficult to determine their scope. Even worse, code snippets are often too short to capture complete syntax and semantic information. To address this challenge, we propose a novel S nippet C omment Gen eration approach called SCGen . First, we utilize the context of the code snippet to expand the syntax and semantic information. Specifically, 600,243 snippet code-comment pairs are collected from 959 Java projects. Then, we capture variables from code snippets and extract variable-related statements from the context. After that, we devise an algorithm to parse and traverse abstract syntax tree (AST) information of code snippets and corresponding context. Finally, SCGen generates snippet comments after inputting the source code snippet and corresponding AST information into a sequence-to-sequence-based model. We conducted extensive experiments on the dataset we collected to evaluate our SCGen . Our approach obtains 18.23 in BLEU-4 metrics, 18.83 in METEOR, and 23.65 in ROUGE-L, which outperforms state-of-the-art comment generation models.
Impact of Statistics on Data Science

In this, we argue that statistics is one of the fields of greatest significance for developing tools and techniques to find patterns in data and to give a greater understanding of them, as well as the most crucial for analyzing and calculating uncertainty. We provide a summary of several data science architectures that have been developed and talk about how statistics affect certain phases such data collection, enrichment, exploration, modelling, validation, and reporting are all steps in the data-processing pipeline. We also highlight errors made when statistical reasoning is disregarded.
Elliptically polarized light photoelasticity based on LCD

We propose a three-wavelength elliptically polarized light photoelasticity method for high efficiency and low-cost stress measurement. By illuminating the sample with two different forms of elliptically polarized light for each wavelength sources, twelve images are acquired. From these images, phase delay and the principal internal stress difference are precisely computed using developed algorithms. Our proposed method based on an LCD panel has the unrivalled advantage that elliptically polarized light can be automatically adjusted, which reduces the mechanical rotation of the system, in contrast to the traditional six-step phase-shifting photoelasticity method, which requires manual rotation for circularly polarized light. In addition, the system has the potential to theoretically expand the area of illumination infinitely, thereby expanding the measurement area. The viability of the suggested methods is confirmed with numerical simulation and stress measurement.
Arabic Words Extraction and Character Recognition from Picturesque Image Macros with Enhanced VGG-16 based Model Functionality Using Neural Networks

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel.Formal signboards are placed with marketing objectives and are enriched with text for attracting people.Extracting and recognition of the text from natural images is an emerging research issue and needs consideration.When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult.Arabic language text scene extraction and recognition adds a number of complications and difficulties.The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations.The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition.This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images.For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase.The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences.This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts.Our research after experimentations shows that the Ayed et al.: Arabic Words Extraction and Character Recognition from Picturesque Image Macros with Enhanced VGG-16 based Model Functionality Using Neural Networks system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy.We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.
Research on the ecological balance and construction of English teaching mode under computer information technology

With the development of information technology, the ecological environment of English teaching has undergone significant changes. Under the influence of new technologies such as big data, VR/AR, and cloud computing, English teaching has changed all aspects of English teaching, which has brought considerable challenges to the traditional English teaching model. This paper constructs an ecological balance system of computer information technology based on English teaching. The computer system innovatively uses the ecological fitness function algorithm and combines computer information technology with English teaching through extensive computer data collection. Under the application of data, the system realizes the application of the ecological balance practice operation mode of the English teaching mode. In this paper, the research and development of this computer system have played an essential role in the informatization upgrade of the English teaching model.