Reviewerly/exHarmony huggingface dataset:
Blockchain Disruption on Management Accountant's Role: Systematic Literature Review

Blockchain considered as an emerging technology that potentially disrupts how management accountant work and his role. This research intends to understand what Blockchain’s capabilities will disrupt the profession by conducting a systematic literature review (SLR). A protocol of SLR is inspired by Kitchenham, Van Akthley, and Okoli in their works. The protocol consists of identifying purpose and outcome research, definition research question, preparing search and review protocol, title and abstract screening, paper quality assessment, data extraction, and data analysis. MAXQDA was used to conduct the SLR protocol. Seven academic journal databases were used in the searching stage. Kitchenham’s guidelines inspire data extraction and narrative synthesis. This study finds that Blockchain enables the user to do real-time accounting, gather data for supervising and monitoring function, and streamline the accounting practice process. With these findings, the management accountant can prepare to blockchain disruption by upgrading his analytics and computation skills.
A theory of ‘authorship transfer’ and its application to the context of Artificial Intelligence creations

This article aims to contribute to the copyright debate concerning Artificial Intelligence (‘AI’) creations. AI-created works could and should be protected by copyright law. However, existing answers to the issue of allocation of authorship remain somewhat unsatisfactory. A reasonable and practical solution to this issue, fortunately, could be established upon the doctrine of ‘authorship transfer’ (the initial transfer of authorship from the actual creator to a constructive author) in modern copyright law. The ‘control of the creative process’ theory can provide a reasonable and justifiable explanation of ‘authorship transfer’. The person, either a natural or a juridical one, who has exercised sufficient control over the creative process, should be constructed as an author of the outcome. This theory is quite flexible before the ever-changing AI technology that challenges copyright law. For AI-created works, the authorship is better transferred to a person behind the AI who had control over the creative process in order to safeguard the current copyright system and its founding principles.
Four-Directional Total Variation With Overlapping Group Sparsity for Image Denosing

In this paper, a new model combining four-directional total variation with overlapping group sparsity is proposed, which not only suppresses the staircase effects introduced by traditional total variation, but also fully utilizes the gradient neighborhood information on each pixel of the image. In order to decrease the computation time of image denoising, the alternating direction method of multipliers (ADMM) is adopted to divide the complex optimization problem into separate subproblems that are easy to solve. At the same time, two-dimensional Fast Fourier Transform (FFT) and majorization-minimization (MM) are used to solve the subproblems alternatively. Then, the proposed new model is compared with other state-of-the-art models. Experiments show that the new model is robust in denoising. The new model not only excavates the gradient information of the four directions on the image to remove the noise more effectively, but also better in preserving image features, further reducing staircase artifacts.
Fast Bayesian Deconvolution Using Simple Reversible Jump Moves

We propose a Markov chain Monte Carlo-based deconvolution method designed to estimate the number of peaks in spectral data, along with the optimal parameters of each radial basis function. Assuming cases where the number of peaks is unknown, and a sweep simulation on all candidate models is computationally unrealistic, the proposed method efficiently searches over the probable candidates via trans-dimensional moves assisted by annealing effects from replica exchange Monte Carlo moves. Through simulation using synthetic data, the proposed method demonstrates its advantages over conventional sweep simulations, particularly in model selection problems. Application to a set of olivine reflectance spectral data with varying forsterite and fayalite mixture ratios reproduced results obtained from previous mineralogical research, indicating that our method is applicable to deconvolution on real data sets.
Influence of Task-evoked Mental Workloads on Oculo-motor indices and their connections

The frequency of microsaccades is often used as a measurement of eye movement in order to estimate the level of effort required, because some indices of oculo-motors suggest the level of mental activity.In an experiment involving several task-manipulation levels, ocular information including microsaccades, saccades and pupil diameters were measured and compared in order to estimate workload levels during problem solving.While some oculo-motor metrics correlate with the estimated scores of the mental workload, these metrics mutually correlate with each other.A causal relationship model was created using all metrics, including subjective measurements.Metrics of microsaccades perform the function of intermediating behaviour between participant's subjective assessments and conventional ocular measurements, such as saccades and pupil responses.
Tree-Based Missing Value Imputation Using Feature Selection

Researchers and practitioners of many areas of knowledge frequently struggle with missing data.Missing data is a problem because almost all standard statistical methods assume that the information is complete.Consequently, missing value imputation offers a solution to this problem.The main contribution of this paper lies on the development of a random forest-based imputation method (TI-FS) that can handle any type of data, including high-dimensional data with nonlinear complex interactions.The premise behind the proposed scheme is that a variable can be imputed considering only those variables that are related to it using feature selection.This work compares the performance of the proposed scheme with other two imputation methods commonly used in literature: KNN and missForest.The results suggest that the proposed method can be useful in complex scenarios with categorical variables and a high volume of missing values, while reducing the amount of variables used and their corresponding preliminary imputations.
Cross-Modal Self-Attention Distillation for Prostate Cancer Segmentation

The automatic segmentation of prostate cancer (PCa) from the multi-modal magnetic resonance imaging (MRI) is of prime importance for the initial staging and prognosis of patients. Nevertheless, the challenge of how to utilize multi-modal image features more efficiently still needs resolving, especially in the segmentation scenario. In this paper, we propose a crossmodal self-attention distillation network that can fully exploit the encoded information of the intermediate layers from different modalities, and the learned attention maps of different modalities are then transferred among modalities to provide significant spatial information with more details incorporated. Furthermore, we propose a novel spatial correlated feature fusion module that is able to learn more complementary correlation and nonlinear information from different modality images. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on the PCa MRI dataset, and the experiment results demonstrate that our proposed approach could achieve state-of-the-art performance.
A Survey of Network Traffic Classification

This survey is dedicated to the task of network traffic classification, particularly to the use of machine learning algorithms in this task. The survey begins with the description of the task, its variations and possible uses in real-world problems. It then proceeds to the description of the methods used historically to solve this task, their limitations and evolution of traffic making machine learning the main way to solve the problem. Then the most popular machine learning algorithms used in this task are described, with the examples of research papers, providing the insight into their advantages and disadvantages in relation to this field. The task of feature selection is discussed, followed by the more global problem of acquiring the suitable dataset to use in the research; some examples of such popular datasets and their descriptions are provided. The paper concludes with the outline of the current problems in this research area to be solved.
Machine-learning predictions of redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, south-central United States

First posted February 10, 2021 For additional information, contact: Director, Lower Mississippi Gulf Water Science Center U.S. Geological Survey 640 Grassmere Park, Suite 100 Nashville, TN 37211 Machine-learning models developed by the U.S. Geological Survey were used to predict iron concentrations and the probability of dissolved oxygen (DO) concentrations exceeding a threshold of 1 milligram per liter (mg/L) in groundwater in aquifers of the Mississippi embayment physiographic region. DO and iron concentrations are driven by and reflect the oxidation-reduction (redox) conditions in groundwater. Predictions from boosted regression trees, a type of machine-learning model, of iron concentration and DO threshold probability were used to categorize redox zones in the Mississippi River Valley alluvial aquifer (MRVA), middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Model predictions indicated that DO concentrations greater than 1 mg/L are uncommon across the MRVA. DO events (where the predicted probability was greater than 0.5) tended to occur on the margins of the MRVA and in upland areas where MCAQ and LCAQ units crop out at the surface or are at shallow depth. Predicted iron concentrations were higher in the MRVA than in the MCAQ and LCAQ. Uncer­tainty in predicted iron concentrations tended to be high in areas where measured concentrations were also high, result­ing in small areas (encompassing less than 1.5 percent of the areal extent of the MRVA) of predicted iron concentrations that exceeded 100,000 micrograms per liter. Despite the large magnitude of overpredicted iron concentrations, the general proportion and spatial distribution of predicted iron concen­trations reflected observed concentrations in groundwater wells. Where the probability of exceeding a DO concentration of 1 mg/L was 0.8 or more and the iron concentration was less than 1,000 micrograms per liter, aquifers were catego­rized as oxic. Oxic conditions were mostly in the uplands where MCAQ and LCAQ units crop out at the margins of the modeled area. The MRVA was mostly anoxic, which was controlled by DO threshold probabilities less than 0.1. The predictions and redox zones support conceptual models of redox conditions in the Mississippi embayment. The MRVA is predominantly anoxic with high iron concentrations. In the Claiborne aquifers (including the MCAQ and LCAQ), groundwater flows along regional flow paths toward the axis of the Mississippi embayment (the approximate location of the Mississippi River), the residence time in the aquifer increases, DO is consumed, and iron concentrations generally increase. Elevated concentrations of trace elements, such as manganese and arsenic, are often associated with reducing conditions in anoxic and mixed anoxic zones, but other factors such as sediment mineralogy affect the occurrence and distribution of these constituents.
PENGEMBANGAN MULTIMEDIA INTERAKTIF AKSARA JAWA BERBASIS ADOBE FLASH UNTUK SISWA KELAS IV

Permasalahan di SDN Banyuputih 01 yaitu rendahnya hasil belajar materi Aksara Jawa dan tidak adanya media pembelajaran. Tujuan penelitian yaitu mengembangkan multimedia materi Aksara Jawa. Metode penelitian ini Research and Development (R&amp;D). Subjek penelitian siswa kelas IV SDN Banyuputih 01 dan SDN Banyuputih 04 yang berjumlah 49. Teknik pengumpulan data menggunakan tes dan non tes. Instrumen non tes dengan wawancara, observasi, angket, dan dokumentasi, sedangkan instrumen tes yaitu soal uji coba, soal tes awal, dan tes akhir. Hasil penelitian menunjukkan multimedia layak digunakan berdasarkan ahli materi, bahasa, dan ahli media, hasil perhitungan analisis data akhir uji pemakaian produk di kelas yaitu uji z didapatkan Zhitung &gt; Ztabel yaitu 2,319 &gt; 1,64, hasil uji-t didapatkan thitung ≤ - ttabel yaitu -5,31 ≤ -2,04, dan uji n-gain 0,701 dengan kriteria tinggi. Simpulan penelitian adalah multimedia layak digunakan dan efektif meningkatkan hasil belajar Bahasa Jawa siswa kelas IV SDN Banyuputih 01.&#x0D; The problems in Banyuputih State Elementary School 01 were based on the low cognitive learning outcomes in Javanese subjects precisely on (Javanese Alphabet) material and the absence of Javanese learning media. This research aimed to develop multimedia (Javanese alphabet) material. This metod of research was Research and Development (R&amp;D). The research subject was the students of grade IV Elementary School in Banyuputih 01 and Elementary School in Banyuputih 04 which amounted to 49 students. Data collection techniques are test and non-test. Non-test instruments used were interviews, observation, poll, and documentation, while the test instruments used are questions of trials, preliminary tests, and final tests. Eligibility assesment results demonstrate could be used based on material expert assessment, language amounted, and the media assessment. The results of the calculation of final data analysis in classes of the z test obtained Zcount&gt; Ztable is 2.319&gt; 1.64, the results of the t-test obtained tcount ≤ - ttable is -5.31 ≤ -2.04 while the test results n -gain 0.701 with high criteria. The conclusion of this research was that adobe flash-based (Javanese alphabet) multimedia material was feasible to used and was effective in improving the learning outcomes of Javanese students in grade IV Banyuputih State Elementary School 01.
The Effects of Integrated Physical Education Class on Self-directed Learning Ability and Physical Education Class Attitude of Middle School Students

The purpose of this study was to examine the effect of STEAM-based PE lessons on selfdirected learning abilities, a core competency of the 21st century, and on attitudes towards PE classes related to physical education alienation and avoidance.To achieve this purpose, 6 classes out of 8 at J Middle School, J City, Jeollabukdo, South Korea were selected.The experimental and control groups, consisting of 87 and 88 students, respectively, were chosen from among the 238 first grade students by means of convenience sampling.The experimental group had PE classes based on STEAM for 14 weeks, whereas the control group were subjected to traditionally teacher-centered PE classes.The results of the study show that the experimental group displayed a significant difference in all the sub factors of attitude towards physical education classes and in all the sub factors of self-directed learning abilities as compared to the control group.In conclusion, PE classes based on STEAM have a positive effect on the students' attitude towards physical education classes and self-directed learning abilities.Based on the results, it suggests that efforts to combine STEAM education and physical education are needed.
"LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.
UJA Human Activity Recognition multi-occupancy dataset

Activity Recognition Systems -ARS are proposed to improve the quality of human life.An ARS uses predictive models to identify the activities that individuals are performing in different environments.Under data-driven approaches, these models are trained and tested in experimental environments from datasets that contain data collected from heterogeneous information sources.When several people interact (multi-occupation) in the environment from which data are collected, identifying the activities performed by each individual in a time window is not a trivial task.In addition, there is a lack of datasets generated from different data sources, which allow systems to be evaluated both from an individual and collective perspective.This paper presents the SaMO -UJA dataset, which contains Single and Multi-Occupancy activities collected in the UJAmI (University of Jaén Ambient Intelligence, Spain) Smart Lab.The main contribution of this work is the presentation of a dataset that includes a new generation of sensors as a source of information (acceleration of the inhabitant, intelligent floor for location, proximity and binary-sensors) to provide an excellent tool for addressing multioccupancy in smart environments.
Efficient Turkish Text Classification Approach for Crisis Management Systems

In this paper, an effective tweet classification system that fully supports the Turkish language has been developed. The proposed system can be used for mining (classifying) the recently published and publicly available tweets to find the crisis’s most related and useful tweets to gain situational awareness, which can help in taking the correct responses in order to prevent or at least decrease the effect of such situations. A deep study was carried out to improve and optimize the proposed system. In more detail, some intensive experiments were performed to investigate the performance of some well-known machine learning algorithms, i.e., K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) when used for text (tweets) classification. Then, the performances of the ensemble systems of the studied algorithms and the Random Forest (RF), AdaBoost Classifier (AdaBoost), GradientBoosting Classifier (GBC) ensemble systems have also been observed. As shown in the experimental evaluation and analysis, the proposed approach has stability, robustness, and can achieve quite good performance when processing the Turkish language. The performance of the proposed classifier was also compared with two state-of-the-art text classification approaches, i.e., "Empirical" and “Turkish Deep ".
STEM Education in Ukraine in the Context of Sustainable Development

The conceptual and generalizing experience of STEM education implementation presented in Ukraine reflects the realization of the sustainable development goals through educational innovations. The study is based on the premise that STEM is a component of education for sustainable development. This educational trend focuses on the goals of Education for All (EFA), conforms to the ideas of the education-related Millennium Development Goals (MDGs) and relates to solving societal challenges in the long run. It is argued that the concept of STEM seeks to offer non-standard solutions to global issues in the field of sustainable development, and STEM education should be seen as a mechanism to accelerate the achievement of all sustainable development goals (SDG) and a strategy to obtain each of them. The specifics of STEM key ideas as global educational trends in the national Ukrainian public space are demonstrated. The consideration of STEM education as a component of the strategy for sustainable development through the detailing of the structure and implementation principles as well as STEM competencies, which are defined as the expected result and key for the person of the XXI century, is proved. The experience of Ukrainian research initiatives in the field of STEM education in the context of sustainable development is summarized. A full-fledged programme complex for comprehensive, equitable and high-quality education is presented, which combines the following links: research and experimental work of different levels, teacher’s professional development, museums and science centres work, implementation of various educational programmes, festivals and projects to attract young people to STEM, ensuring equal access for girls and boys.
Duration Estimation Models for Open Source Software Projects

For software organizations that rely on Open Source Software (OSS) to develop customer solutions and products, it is essential to accurately estimate how long it will take to deliver the expected functionalities. While OSS is supported by government policies around the world, most of the research on software project estimation has focused on conventional projects with commercial licenses. OSS effort estimation is challenging since OSS participants do not record effort data in OSS repositories. However, OSS data repositories contain dates of the participants’ contributions and these can be used for duration estimation. This study analyses historical data on WordPress and Swift projects to estimate OSS project duration using either commits or lines of code (LOC) as the independent variable. This study proposes first an improved classification of contributors based on the number of active days for each contributor in the development period of a release. For the WordPress and Swift OSS projects environments the results indicate that duration estimation models using the number of commits as the independent variable perform better than those using LOC. The estimation model for full-time contributors gives an estimate of the total duration, while the models with part-time and occasional contributors lead to better estimates of projects duration with both for the commits data and the lines of data.
Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

Model predictive control (MPC) has been successfully applied in practical wind tunnel systems. As a critical component of MPC, the accurate prediction of Mach number becomes hence very significant. Real measurement data records often contain outliers or noisy data because of the harsh working conditions of practical systems. Such datasets are detrimental to most data-driven Mach number predictors. This paper focuses on improving Mach number prediction via a regression-based outlier detection framework. The development of a regression-based outlier detector for enhancing Mach number prediction is natural since only regression analysis needs to be implemented for both prediction and outlier detection. In contrast to existing regression-based detectors, we have taken into account three problems that may greatly impact the detecting performance. Specifically, we use Bagging technique to reduce the negative influence of unknown outliers during the training phase. Then, the notion of extreme value analysis is used to determine an appropriate threshold for the calculated outlier scores. A robust scaling method based on Hampel identifier is also used to alleviate the influence of outliers on data scaling. Several datasets stemming from a real-world wind tunnel are used in experiments to verify the effectiveness of our detector. The experimental results show that regression-based outlier detectors often perform better if the prior knowledge about the dependent variable is used. In addition, the proposed detector has outperformed other regression-based detectors.