Reviewerly/exHarmony huggingface dataset:
Internet of Behavior: Types, models, principles

This article presents a general view and conceptualization of the Internet of Behavior from a social science perspective. The Internet of Behavior (IoB) is defined as a technology for machine monitoring, recognizing, and responding to human behavior (including emotion expression and biometrics). Based on the analysis of a typical case in higher education, the article forms the conceptual outline of social research on IoB by highlighting the types, possible models, and principles of IoB implementation. The study identifies three models of IoB: binary (based on checking the compliance of an individual’s behavior with the norm and his or her identification), medical (based on determining the physiological causes of certain human actions), and social (focused on identifying the features of human social behavior in addressing professional or other public problems). Monitoring, summarizing, and evaluating students’ social behavior potentially allows the implementation of new models of interaction between universities, employers, and students, based on the digitization and monetization of student engagement in learning and the responsibility of universities for the demand for graduates. The implementation of these models will lead to a profound transformation of higher education, including the digital servitization of education, the reduction of the established gap between knowledge and the social interactions associated with the generation, translation, and assimilation of this knowledge, and the inclusion of individual social behavior associated with professional knowledge in the list of those aspects of society that are subject to digitization and monetization. The basic principles of social IoB, without observance of which this technology can lead to anti-human consequences, are defined: privacy, multivalency of interpretation, continuity, relevance, and reflexivity.
Philosophical Analysis of the Prospects of Higher Education

The role of universities under the influence of the massification of higher education, the development of the educational market and globalization is rapidly changing. In foresight research, the ideas of the future of the university range from the traditional model, in which universities continue to develop a full and critically thinking personality, to the idea of complete oblivion of universities, thrown overboard of history by massive online courses. At present, the main ideas of universities are in sharp conflict with each other. The conflict can lead either to the synthesis of ideas and the development of an updated model, or to the complete degradation of universities and their disappearance from human life. What are the prospects for higher education we take as the main goal of our research. The main methods of this foresight research are a descriptive method of future scenarios for the development of universities, as well as a philosophical analysis of each of the presented ideas of universities. The results provide an overview of the research literature, a description of the main models of universities, and a philosophical analysis of these models. The red line in the works of modern researchers is the idea of whether the modern university can transform, continue to fulfill its high social mission, move away from the market and break with neoliberal ambitions. At the same time, specialists offer their recipes for reforming universities, most of which consist in returning the lost humanistic, fundamental, spiritual foundations to higher education. This study will complement ideas about the future of higher education, the transformation of universities under the influence of globalization, market capitalism, and other factors. Its results will be useful to teachers of higher educational institutions, philosophers of education and everyone who is interested in the problems of higher education.
Evaluation of Feature Extraction Techniques in Automatic Authorship Attribution

There are two main approaches to automatic text classification: content-based classification and style-based classification. With content-based text classification, the topic of a document (politics, sports, health) or fake news is detected. On the other hand, Style-based text classification is used to detect the gender or age of an author, author identification, and authorship attribution. In style-based classification, the set of words defines the author’s vocabulary, which contains several hundred words. In this work, the words are known as dimensions. Texts generate high-dimensional vectors. Multiple works have shown that a large number of dimensions decreases the performance of classifiers. To reduce dimensions there are selection and extraction techniques. This article discusses the use of extraction techniques, which create low-dimensional vectors from combinations of the high-dimensional vector. Due to the development of Deep Learning networks, the use of dimension reduction techniques has decreased because these networks perform dimension reduction automatically. However, in Machine Learning such techniques are still used intensively. Motivated by the above, in this paper, the Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) dimension reduction algorithms are proposed for the identification of texts written by 14 authors of the Corpus PAN 2012. The texts were divided into sequences of 10, 20, and 30 words called sentences. Likewise, blocks of texts made up of 100 sentences were created. The supervised classification was performed with the Nearest Neighbors (KNN), Support Vector Machines (SVM) and Logistic Regression (LR) algorithms using the accuracy metric. The results showed that the reduction of dimensions with PCA and the LR and SVM classifiers achieved better results than other similar works of the state of the art using the same corpus.
Leukocyte classification for acute lymphoblastic leukemia timely diagnosis by interpretable artificial neural network

Leukemia is a blood cancer characterized by leukocyte overproduction. Clinically, the reference for acute lymphoblastic leukemia diagnosis is a blood biopsy that allows obtain microscopic images of leukocytes, whose early-stage classification into leukemic (LEU) and healthy (HEA) may be disease predictor. Thus, the aim of this study is to propose an interpretable artificial neural network (ANN) for leukocyte classification to timely diagnose acute lymphoblastic leukemia. The “ALL_IDB2” dataset was used. It contains 260 microscopic images showing leukocytes acquired from 130 LEU and 130 HEA subjects. Each microscopic image shows a single leukocyte that was characterized by 8 morphological and 4 statistical features. An ANN was developed to distinguish microscopic images acquired from LEU and HEA subjects, considering 12 features as inputs and the local-interpretable model-agnostic explanatory (LIME) algorithm as an interpretable post-processing algorithm. The ANN was evaluated by the leave-one-out cross-validation procedure. The performance of our ANN is promising, presenting a testing area under the curve of the receiver operating characteristic equal to 87%. Being implemented using standard features and having LIME as a post-processing algorithm, it is clinically interpretable. Therefore, our ANN seems to be a reliable instrument for leukocyte classification to timely diagnose acute lymphoblastic leukemia, guaranteeing a high clinical interpretability level.
Bitcoin Price Forecasting Using LSTM

The volatility and complexity of Bitcoin make it a challenging task to accurately predict its price.While past research has implemented machine learning to enhance the precision of Bitcoin price prediction, limited attention has been given to examining the viability of employing diverse modeling techniques to datasets with varying data structures and dimensional attributes.In order to forecast Bitcoin prices using machine learning techniques at different intervals, this study initiates by categorizing Bitcoin prices into daily prices and highfrequency prices.This project aims to predict the price of Bitcoin using machine learning techniques, specifically the Random Forest Classifier algorithm.A useful tool for monitoring Bitcoin's price movement, spotting trends, gauging volatility, examining correlations, developing prediction models, performing risk analysis, and examining market sentiment is the historical Bitcoin price dataset, which covers the period from July 2010 to May 2023.The data was preprocessed and feature engineering was performed to extract relevant features for the model.The Random Forest Classifier algorithm was used to predict the price of Bitcoin based on the input features.The algorithm was trained on the preprocessed data and hyperparameter tuning was performed to optimize the model performance.Moreover, as an extension to our work, we have created a simulation dashboard utilizing Flask, a popular web framework model.The dashboard enables users to assess and contrast machine learning forecasting models.The 99% accuracy rate of the Random Forest Classifier method shows how well machine learning predicts Bitcoin values.When evaluating and using these predictions, it's imperative to use caution and take into account the intricacies and uncertainties of the cryptocurrency markets.
Preface

Laterite soil products are vital in coastal region construction. Conventional identification methods are slow and require significant labour. Remote sensing expedites large-scale identification, making it essential in the process. Soil samples were randomly collected from the study area for laboratory analysis to determine soil iron content. Additionally, spectral signatures were obtained from a spectroradiometer to assess iron content variability. The considerable variability in material composition underscores its significance as an indicator of soil characteristics. The iron content serves as a crucial indicator of soil development and can be utilized as an index for assessing material composition in soil. The iron content in soil samples is analysed using Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer, which operates within the spectral wavelength range of 350–2500 nm. The Vis-NIR reflectance spectroscopy is an innovative and efficient method for predicting soil iron content, requiring less effort compared to conventional techniques. The study uses Partial Least Squares Regression (PLSR), a statistical regression method employed to predict iron content in soil from spectral reflectance data. The application of PLSR resulted in moderate prediction accuracy, with coefficient of determination R2 value for calibration and validation are 0.74 and 0.73, respectively. The analysis revealed that the spectroradiometer in combination with PLSR can accurately predict the material composition in lateritic soil with an efficiency of 70%.
X-Transformer: A Green Self-attention Based Machine Translation Model

With the exponential growth of data and the increasing demand for computational resources, it is crucial to develop energy-efficient and environmentally friendly deep learning models in AI research. This paper proposes a novel machine translation model designed to address the growing concern on energy consumption. The proposed model named X-Transformer, which refined from the state-of-the-art Transformer model in three aspects. First, the model parameter of the encoder is compressed. Second, the encoder structure is modified by adopting two layers of the self-attention mechanism consecutively and reducing the point-wise feed forward layer to help the model understand the semantic structure of sentences precisely. Third, we streamline the decoder model size, while maintaining the accuracy. Through experiments, we demonstrate the effectiveness of the green X-Transformer in achieving significant training time saving and performance upgrading. The X-Transformer reaches the state-of-the-art result of 46.63 and 55.63 points in the BiLingual Evaluation Understudy (BLEU) metric of the World Machine Translation (WMT), from 2014, using the English–German and English–French translation corpora, thus outperforming the Transformer model with 19 and 18 BLEU points, respectively. The heat maps of the X-Transformer reach token-level precision (i.e., token-to-token attention), while the Transformer model remains at the sentence level (i.e., token-to-sentence attention). In addition, the X-Transformer demonstrates significantly shorter training time, requiring only one-third of that of the original transformer.
Contrastive self-supervised representation learning without negative samples for multimodal human action recognition

Action recognition is an important component of human-computer interaction, and multimodal feature representation and learning methods can be used to improve recognition performance due to the interrelation and complementarity between different modalities. However, due to the lack of large-scale labeled samples, the performance of existing ConvNets-based methods are severely constrained. In this paper, a novel and effective multi-modal feature representation and contrastive self-supervised learning framework is proposed to improve the action recognition performance of models and the generalization ability of application scenarios. The proposed recognition framework employs weight sharing between two branches and does not require negative samples, which could effectively learn useful feature representations by using multimodal unlabeled data, e.g., skeleton sequence and inertial measurement unit signal (IMU). The extensive experiments are conducted on two benchmarks: UTD-MHAD and MMAct, and the results show that our proposed recognition framework outperforms both unimodal and multimodal baselines in action retrieval, semi-supervised learning, and zero-shot learning scenarios.
Common best proximity points of some graph-theoretical notions of dominating pairs

This article deals with a metric space endowed with a directed graph.In order to obtain common best proximity point results, we introduce a concept of Geraghty's type contraction satisfying certain properties on the graph.An example and some consequences are also provided.
Investigating The Efficiency Of Machine Learning Methods In Authorship Detection For Low-Resourced Languages: The Case Of Kurdish Authors

Textual data continues to multiply with time, Alongside the exponential growth of textual information, an increase in anonymous material has also been seen.Authorship detection has significant potential for usage in numerous applications of authorship analysis, such as history and literary science, Forensic examination, or Plagiarism detection.We manually collected 2798 documents by 150 authors for this study in order to investigate how effectively existing machine learning algorithms can differentiate Kurdish authors from unidentified writings.The approach that has been developed uses a TF-IDF technique to calculate the weight of each token and extracts the token frequency of each token, ranging from 1 to 5 grams, as a feature to find a pattern in each author's text.We train SVM, CNB, MNB, and K-NN classifiers with a collection of available documents because an unknown document's essential tokens are similar to a known document's crucial tokens.Then we give it a mysterious document so it may assess how closely it resembles the known document.We achieved an accuracy of 80% by SVM with both O-V-O and O-V-R approaches for the token 1-gram, also a promising results in precision, recall, and F1-score measures.Furthermore, to our knowledge, this is the first study to investigate authorship detection for the Kurdish language.
Data Industrialization: Between Datafication Commodification, and Digital Infrastructure

Data is a new oil because in the development of industry and communication technology, digital data can be an important thing that can be produced, distributed, and consumed, or commodified.Every digital data can be obtained from every side of everyday human life so that datafication becomes something commonplace today.On the other hand, the role of digital infrastructure both physical and non-physical as supporting the operation of the internet and the development of industry and communication technology and Data Industrialization cannot be denied.Even the DC/server is used to gain profit for the company itself.This article tries to link datafication, commodification, and the role of digital infrastructure in the process.This article uses a qualitative approach with a systematic review and meta-analysis methods (Crowther, Lim, & Crowther, 2010) which was accompanied by interviews with several relevant informants (Alshenqeeti, 2014) to elaborate related concepts.In this article, I argue that in the digital age, every side of human life can become a digital data which then becomes a digital commodity so that the process of datafication and commodification always appears in every digital data.However, the important role of digital infrastructure both physical non-physical is very important.
Development of Child Safety Management Class Model Using PBL in the Child Care Teacher Training Course

The purpose of this study is to develop a child safety management class model using PBL in the process of fostering childcare teachers. To present a conclusion for each research problem, first, the composition procedure of the child safety management course applying PBL was conducted in five stages. The instructional model(draft) was devised by analyzing previous studies and literature, and the instrucitonal model(draft) was revised and supplemented by reflecting the results of the first expert interview. Through the second expert interview, it was confirm that it was a model that could effectively achieve the goals of the child safety management course and confirm. Second, the instruction model of the child safety management subject applying PBL presented the instructional goals, concepts, and principles. The learners activities were developed in nine stages. In other words, it consisted of team building, problem identification, group discussion(1st), data collection(self-directed learning), group discussion(2nd), problem solution preparation, overall discussion, comprehensive organization and evaluation, and reflection. In the final stage, process evaluations and performance evaluation were conducted. If you make suggestions for follow-up research based on the results of this study, first, it is necessary to verify the effectiveness by applying the developed instructional model to the class. Second, research related to problem development, which is the starting point for problem-oriented learning, also needs to be conducted.
Virtual Environments in Higher Education: A Systematic Review

Virtual environments are technological tools that facilitate teaching and learning in various academic contexts. This study aims to systematize scientific evidence on the different digital tools and platforms used by university faculty in their teaching processes. A search was conducted in databases such as Scopus, SciELO, Eric, and Dialnet using the terms "digital tools and platforms" and Boolean operators in Spanish and English. The study analyzed 19 scientific articles published between 2011 and 2021 on higher education. The PRISMA flow chart was used to analyze the data. The results showed a variety of concepts related to tools and platforms, which can be confusing for readers. The study concludes that there is a need for more comprehensive research on the conceptualization and differentiation between tools and platforms.&#x0D; &#x0D; Received: 18 January 2023 / Accepted: 10 June 2023 / Published: 5 July 2023
Supplemental Material: Watershed-scale provenance heterogeneity within Eocene nonmarine basin fill: Southern Greater Green River Basin, western USA

&lt;p&gt;Table S1: Petrographic point counting data was gathered using a modified Gazzi-Dickinson point counting methodology. Table S2: U-Pb data for all samples from this study. Table S3: Paleocurrent data. Figures S1 and S2: Shepard plots for MDS analysis. Table S4: Sample results for DZmix modeling. Supplemental Text S1: Pertinent detrital zircon provenance populations.&lt;/p&gt;
Image-Point Cloud Bidirectional Fusion Network towards 3D Object Detection in Self-Driving

With the rise of self-driving, the fusion technology of image and point cloud is widely used in the perception system of unmanned vehicles to overcome the limitation of single modal data. As a task in the field of multimodal data fusion, image and point cloud fusion is challenged by dimension gap and data density difference. Research efforts in recent years have focused on ROI-based fusion or fine-grained pixel-point fusion approaches that have not achieved significant performance gains. This paper considers that the most important factor limiting the fusion effect is that 2D image features are difficult to align with the point cloud features in 3D space. In order to solve this problem, this paper proposes to use the depth completion model to convert 2D pixels into pseudo point clouds in 3D space, and designs a bidirectional fusion mechanism based on 3D sparse convolutional network to realize the exchange of two modal information between image and point cloud branches. In this paper, the proposed fusion network is applied to the 3D object detection framework, and the experimental comparison is carried out on KITTI dataset. The experimental results show that our network performs better than most existing fusion works.
Improving the operational forecasts of outdoor UTCI with post-processing

Universal Thermal Climate Index (UTCI ) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature, and it takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated together with the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, on these stations, global solar radiation measurements are performed, making estimating the true value of UTCI more reliable. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. The verification showed that the operational ALADIN model tends to overestimate the UTCI values. The overestimation is most pronounced in the morning when the mean error is about 8 &amp;#176;C. Also, a small percentage of cases have a very large error (up to 40 &amp;#176;C). We used two machine-learning methods to try to improve the forecasts of UTCI: linear regression and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both reduced the daily mean error from about 2.6 &amp;#176;C to almost zero, while the daily mean absolute error decreased from 5 &amp;#176;C to 3 &amp;#176;C for the neural network and 3.5 &amp;#176;C for linear regression.