Reviewerly/exHarmony huggingface dataset:
Layering Images with Convolution Neural Networks on Cloud Computing

Artificial neural networks combined with deep learning (DL) techniques are becoming a very powerful tool that gives the best performance for many difficult problems such as: the speech recognition, the image recognition, etc. the language processing. The training of the neural network models takes place in many different languages, different techniques, different sizes of organizations. However, previous studies only focused on the model training techniques, the datasets, currently there is no research that fully introduces running artificial neural network models, the network models are run in the cloud connecting directly from RStudio. In this article, we focus on creating models and applying deep learning models of the artificial neural networks based on the cloud computing, in order to create a separate research direction. The results of this study open up a new approach to cloud-based deep learning programming, providing an additional choice of the deep learning approaches for those wishing to enter the field.
A hybrid classification model for predicting breast cancer survivability using public dataset

Breast cancer has been one of the deadliest diseases and becomes the leading cause of cancer deaths in women worldwide. It becomes an issue to any countries as the number of victims developing breast cancer is increasing, and the survivability rate of cancerous women is still ambiguous. In this study, a hybrid classification model is developed for predicting breast cancer survivability. The hybrid classification model is a combination of three single classification models, namely Naive Bayes, J48 Decision Tree and RBF Network. Stacking method has been used to combine those models. This hybrid model has been tested using Breast Cancer Wisconsin dataset which gives result 96.14% of accuracy, 96.30% of precision, 96.10% of recall and 96.20% of f-measure. These results are outperformed each of the single classifiers.
Video structure extraction using shot boundary detection for authentication detection

Many recent cases of illegal activities have been reported regarding the video and all the unlawful activities designed to destroy the originality of the video data. A slight modification can alter the meaning and intent of the video. Nowadays, many devices have gained the power of tapping or capturing digital video, and all these videos can be approved over the internet and many other unsafe channels. Therefore, the detection o of video authentication proving is a widespread and critical problem in various areas such as criminal, forensic investigations, and court cases. The preceding study proposed numerous techniques to ensure the authentication of video data and structure. This study emphasized retrieving a video structure to detect an alteration made in the video using shot boundary detection to prove authentication. The detection of shot boundary is a complicated task and is usually utilized for structured video because these techniques can identify modifications made with a fundamental shot change frequency. This study focuses on retrieving a video structure to detect an alteration made in the video by detecting shot boundary to prove authentication. The detection of shot boundary is a complicated task and is usually utilized for structured video because these techniques can identify modifications made with a regular shot change frequency. Furthermore, this study aims to develop a new model for video authentication detection using the shot boundary detection method, which is considered the practical tool to track down the originality based on their fundamental structure.
Employee performance assessment system based on smart method (case study: Banda Aceh Military Court I-01)

Employee performance assessment is the evaluation of the employee's performances recorded at the end of every month. This approach is currently used at the Military Court Office I-01 Banda Aceh. The main aim of this assessment is to measure employee's performances in achieving their targets. The results will be used as the measuring key, where an employee is only eligible to receive the allowance, if the targets are successfully fulfilled. At this moment, the assessment form has to be filled in manually by each employee, and they will be later assessed manually by the assessors. This manual process undeniably has many disadvantages, especially in terms of accuracy and efficiency. To solve this problem, the Employee Performance Information System was designed using the SMART (Simple Multi Attribute Technique). The assessments were based on eight indicators, i.e; performance, attendance, the proportion of performance targets, behavioral scores, number of tasks, services, overtime, and monthly rewards earned. The weights for each variable were assessed, and keyed into the system. The values for each variable were compared, and results were generated. In SMART, the weighting is measured as a scale between 0 and 10, thus it is easier to make calculations and comparison. The obtained results (SI-TUKIN) indicated that the system is capable of evaluating and analysing the employee's performances, quickly and effectively.
Digital Competence and Teaching Practices of Language Teachers in Sweden in a COVID-19 World

Digital competence is seen as one of the important skills for lifelong learning, and schools have begun to focus on the effect of digitalisation on society so that pupils can use and understand digital tools and media. We have mapped teaching practices among Swedish high school language teachers, focusing on how the COVID-19 pandemic has affected their use of digital tools. While teachers are quite digitally competent privately, they felt they did not have the time to properly investigate new tools for their teaching. Chinese and Spanish teachers in particular felt that English teachers were at an advantage, with more tools and tutorials being available in English. Digital tools were positive for learning; however, it was important to focus on language learning. Thus, the pandemic has led to a positive change in language teaching, but more time needs to be made available for finding and learning new tools.
IR-Eclat: A new algorithm of incremental R-Eclat for infrequent itemset mining

The key challenge of association rule mining is to discover and extract a valuable information from databases. However, mining association rule may require repetitious scanning of large databases that leads to the high memory usage and affects the running time. Rare Equivalence Class Transformation (R-Eclat) algorithm is specifically design for infrequent itemset mining. In response to the promising results of mining in speedy processing time and taking consideration of dynamic transaction of data in a database, a new incremental approach is introduced called Incremental R-Eclat (IR-Eclat). This model adopted via relational database engine management system; My Structured Query Language (MySQL) serves as a database engine for testing benchmark datasets. The experimental results on several benchmark datasets indicate that Incremental R-Eclat outperforms the R-Eclat by reducing its running time to process especially in dynamic database as the data is increasing in volume from time to time.
Introduction

As a branch of machine learning, deep learning has developed rapidly in the past two decades. The reason is that a series of neural networks with large parameters and complex structures have been established, which can be used to widely extract the characteristics of input information, thereby dividing the decision boundary more accurately. In the neural network, the convolutional neural network (CNN) is a classical and efficient network hierarchy. CNN was first proposed by Yann Lecun and applied to handwritten number recognition (Lecun et al., Proc. IEEE 86(11):2278–2324, 1998). Its essence is a multi-layer perceptron. The reason for its success lies in the way of local connection and weight sharing, enabling effective feature extraction on images at different scales.
CNN for White Blood Cell Classification

As the blood circulates through the body, blood cells, which are present in the blood, can move about the body. Red blood cells, white blood cells, and platelets make up the majority of blood cells in humans. The primary job of red blood cells is to carry oxygen. White blood cells primarily provide an immunological function. When germs infiltrate the body, white blood cells can penetrate the capillary wall, focus on the invasion site, encircle the bacteria, and swallow them. In the process of hemostasis, the process to stop bleeding, platelets are crucial.
Supervised Machine Learning in a Nutshell

This chapter introduces the fundamental to supervised machine learning algorithms, namely the classification and regression problems. We explain each technique using an inspiring example and discuss how the corresponding algorithms work together with the data engineering pipelines. They provide some guidelines for implementing a classification or regression task for other problems and required materials to evaluate the supervised learning being used.
Document-Level Neural Machine Translation With Recurrent Context States

Integrating contextual information into sentence-level neural machine translation (NMT) systems has been proven to be effective in generating fluent and coherent translations.However, taking too much context into account slows down these systems, especially when context-aware models are applied to the decoder side.To improve efficiency, we propose a simple and fast method to encode all sentences in an arbitrary large context window.It makes contextual representations in the process of translating each sentence so that the overhead introduced by the context model is almost negligible.We experiment with our method on three widely used English-German document-level translation datasets, which obtain substantial improvements over the sentence-level baseline with almost no loss in efficiency.Moreover, our method also achieves comparable performance with previous strong context-aware baselines and speeds up the inference by 1.53×.The speed-up is even larger when more contexts are taken into account.On the ContraPro pronoun translation dataset, it significantly outperforms the strong baseline.
Understanding Foundation of Neural Network: Reasoning-Based Approach

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Enhancing Deaf and Dumb Collaboration by Video Annotations

Online annotations, which enable information to be added to existing data without altering it, have emerged as a crucial technique for engaging with web pages and user queries. Annotations become a tool for targeted collaboration between users (Annotators) with similar interests when the visibility of annotations is restricted to groups of users. Many strategies have been developed to improve online user conversations and collaboration. Websites are suitable media for that since they enable users to engage in online discussions by adding comments (also known as annotations) to page elements like texts, photos, and videos. There are numerous annotations, including written, spoken, visual, and graphic. Although textual annotations are well-known, users are increasingly adding their own video comments to the HTML web document components to have conversations and exchange ideas. This enables deaf and dumb people to have the chance to participate in online discussions. At the end of the work, we conducted a comprehensive experimental test in order to compute the collaboration percentages between users in which promising results were achieved.
Dynamic analysis of quantum annealing programs

Quantum software engineering is emerging as a relevant field, as it deals with the challenges of producing the new quantum software, whose adoption is steadily increasing. Quantum annealing software has gained a certain market penetration, demonstrating a good performance for optimization problems. However, there are no reverse engineering techniques with which to discover the underlying optimization problem definitions (the Hamiltonian functions to be minimized). Problem definitions are, in turn, dynamically defined using classical software, and can evolve over time, which make their accurate comprehension and abstract representation difficult. This paper, therefore, presents a dynamic analysis technique for D-Wave (Python) programs with which to reverse Hamiltonian expressions, and which are additionally represented according to the Knowledge Discovery Metamodel. The usage of this standard makes it possible to represent the reversed Hamiltonians in combination with other parts of classical–quantum software systems. In order to facilitate its adoption, the proposed technique has been empirically validated through a case study with 27 D-Wave programs that demonstrates its effectiveness and efficiency. The main implication of this research is that the proposed technique helps modernize quantum annealing software alongside hybrid software systems.
A new deterministic PSO algorithm for real-time systems implemented on low-power devices

The paper presents a novel, deterministic particle swarm optimization (PSO) algorithm, facilitating its implementation in low-power application specific integrated circuits (ASIC) realized in CMOS technology. The PSO algorithms are commonly used in a variety of optimization problems. They allow to search for a global extreme in a selected fitness function, which is used for numerical modeling of specific optimized problems. The conventional version of the algorithm is relatively difficult to implement directly in hardware, especially when each particle is represented by a separate circuit working in parallel with other particles. In such a situation, each particle has to be equipped with its own copy of each function. One of the problems is, for example, an efficient realization of the random function block that provides for each particle some factors used in computing a cognitive and a social velocity components. In this paper we shows a novel way to work around this problem. In the proposed approach the random function does not need to be used. It has been replaced by a simple computationally deterministic mechanism, which at the level of the overall swarm allows for effective emulation of the former random mechanism. We proposed such simplifications that reduce the formulas describing the algorithm to only basic arithmetic and logic operations. Comprehensive investigations have shown that performance of the proposed approach is comparable with its original version or in may situations even better. The proposed algorithm was tested with numerous test functions (50 runs for each tested function), to verify its flexibility and adaptation abilities to different problems.
A novel robust edge detection method for full-field modal parameter estimation using an image-based tracking continuously scanning laser Doppler vibrometer system

A novel robust edge detection method is developed for an image-based tracking continuously scanning laser Doppler vibrometer (CSLDV) system to track a rotating structure without attaching any encoder or mark to it and scan its whole surface. The robust edge detection method can extract edges of the rotating structure from its complex background by processing grayscale images of the rotating structure. Once edges of the rotating structure are clearly shown in the processed image, their positions can be easily determined. To track a rotating structure like a rotating blade of a wind turbine with multiple blades, a distance condition is used to determine the position of an edge of the structure that needs to be tracked and estimate its real-time rotation speed. Once the position of the edge of the rotating structure is determined, the image-based tracking CSLDV system can scan the whole surface of the rotating structure via a two-dimensional scan scheme. An improved demodulation method is used to process measured data of response of the rotating structure subject to random excitation and estimate its modal parameters including damped natural frequencies and full-field undamped mode shapes. The robust edge detection method is investigated by tracking and scanning a rotating fan blade subject to random excitation with a complex background using the image-based tracking CSLDV system, and estimating modal parameters of the rotating fan blade with different constant speeds.
Deep texture representation analysis for histopathological images

Deep texture representations (DTRs) produced from a bilinear convolutional neural network allow objective quantification of tumor histopathology images effectively. They can be used for various analyses, including visualization of morphological correlation between histology images, content-based image retrieval (CBIR), and supervised learning. This protocol describes the simplified workflow to analyze DTRs from data preparation, visualization of the histological profile, and CBIR analysis, to supervised learning model development to predict the profile from histological images. For complete details on the use and execution of this protocol, please refer to Komura et al. (2022).1.
Bat4RCT: A suite of benchmark data and baseline methods for text classification of randomized controlled trials

Randomized controlled trials (RCTs) play a major role in aiding biomedical research and practices. To inform this research, the demand for highly accurate retrieval of scientific articles on RCT research has grown in recent decades. However, correctly identifying all published RCTs in a given domain is a non-trivial task, which has motivated computer scientists to develop methods for identifying papers involving RCTs. Although existing studies have provided invaluable insights into how RCT tags can be predicted for biomedicine research articles, they used datasets from different sources in varying sizes and timeframes and their models and findings cannot be compared across studies. In addition, as datasets and code are rarely shared, researchers who conduct RCT classification have to write code from scratch, reinventing the wheel. In this paper, we present Bat4RCT, a suite of data and an integrated method to serve as a strong baseline for RCT classification, which includes the use of BERT-based models in comparison with conventional machine learning techniques. To validate our approach, all models are applied on 500,000 paper records in MEDLINE. The BERT-based models showed consistently higher recall scores than conventional machine learning and CNN models while producing slightly better or similar precision scores. The best performance was achieved by the BioBERT model when trained on both title and abstract texts, with the F1 score of 90.85%. This infrastructure of dataset and code will provide a competitive baseline for the evaluation and comparison of new methods and the convenience of future benchmarking. To our best knowledge, our study is the first work to apply BERT-based language modeling techniques to RCT classification tasks and to share dataset and code in order to promote reproducibility and improvement in text classification in biomedicine research.
EMPOWERING ACCOUNTING EDUCATION: A STUDY ON MICROSOFT EXCEL KNOWLEDGE AND SKILLS

Purpose- The aim of this research is to know the level of Microsoft (MS) excel knowledge and skills in the broad field of accounting education. &#x0D; Design/methodology/approach- The survey questionnaire is designed as a research instrument and distributed to graduate students enrolled in an accounting course from Oman universities. 142 questionnaires were gathered using Google Forms and analyzed using the partial least-squares structural equations modeling (PLS-SEM) technique.&#x0D; Findings- The findings show that both MS Excel knowledge and skills are most important and developing MS excel skills in accounting education suggest that the Omani accounting education system might do more to give opportunity for students to thrive in their future careers.&#x0D; Research limitations/implications- The findings provide MS knowledge and skills of accounting graduates in Oman's higher educational institutions (HEIs). In particular, this study sheds light on the skills that accounting students want and how relevant these skills for Omani graduates.&#x0D; Practical Implications- Some modifications to accounting education are proposed to enhance accounting students' MS skills.&#x0D; Originality/value- Because there is a lack of specific research on MS excel knowledge and skills in accounting education in Gulf Cooperation Council (GCC) countries, this study contributes to the body of expertise on MS skills in GCC countries, especially in Oman.&#x0D;
The southern IOCG and hydrothermal nickel mineralization trend of the Carajás Mineral Province: Airborne geophysical and remote sensing evidences for structural controls and hydrothermal signature

The iron oxide‑gold‑copper deposit (IOCG) and hydrothermal nickel deposit are globally important targets for mineral exploration due to their notable role in the clean energy transition. The Carajás Mineral Province stands out for having the highest known concentration of world-class IOCG deposits, especially within the regional Canaã shear system, which exerts a critical control in polymetallic deposit distribution. The dense regolith cover and rainforest vegetation hinder the structural mapping and recognition of host rocks and metasomatic facies. In this context, we integrated remote sensing and geological data to evaluate the influence of the structural framework on the IOCG and hydrothermal nickel mineralization location and the metasomatic footprints that envelope these mineral deposits. These deposits are spatially correlated to anomalies that have values greater than the average and standard deviation (⩾ x̄ + sx) of the gamma-spectrometry and geomorphological features (K/eTh, eU/eTh, F-parameter, K and eU deviations, and morpholineament density). The regional control is exerted by NW–SE- and N–S-trending structures, whereas NE–SW- and E–W-oriented shallow structures present the most significant mineralization control at the local scale. Structure intersections express the Carajás deep to the shallow crustal architecture of the region and the potential pathways for the hydrothermal and ore-forming fluids in multiple metallogenetic events.