Reviewerly/exHarmony huggingface dataset:
NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning

Network selection plays a pivotal role in ensuring efficient handover management. Some existing approaches for network selection may use one criterion, such as RSSI (Received Signal Strength Indicator) or SINR (Signal to Interference Noise Ratio). However, these approaches are reactive and may lead to incorrect decisions due to the limited information. Other multi-criteria-based approaches use techniques, such as statistical mathematics, heuristics methods, and neural networks, to optimize the network selection. However, these approaches have shortcomings related to their computational complexity and the unnecessary and frequent handovers. This paper introduces NetSel-RF, a multi-criteria model, based on supervised learning, for network selection in WiFi networks. Here, we describe the created dataset, the data preparation and the evaluation of diverse supervised learning techniques (Random Forest, Support Vector Machine, Adaptive Random Forest, Hoeffding Adaptive Tree, and Hoedding Tree techniques). Our evaluation results show that Random Forest outperforms other algorithms in terms of its accuracy and Matthews correlation coefficient. Additionally, NetSel-RF performs better than the Signal Strong First approach and behaves similarly to the Analytic Hierarchy Process–Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS) approach in terms of the number of handovers and throughput drops. Unlike the latter, NetSel-RF is proactive and therefore is more efficient regarding Quality of Services (QoS) and Quality of Experience (QoE) since the end-devices perform the handover before the network link quality degrades.
Evaluation of student academic performance using e-learning with the association rules method and the importance of performance analysis

Abstract In managing the student study process, there is a pattern that occurs repeatedly every year. The recurring data will produce information in the form of student profiles, entry paths, student academic results, study period, average graduate-level and much other information as long as students take the process of teaching and learning activities. This research was conducted to predict student performance and e-learning satisfaction at the Computer Science department in Mulawarman University using the Association Rules method and Importance Performance Analysis. The sample in this study was 389 data of computer science graduate students. Based on the results of the research that has been done, it can be concluded that the graduation rate of computer science students of the Faculty of Computer Science and Information Technology, as follows; students who have graduated most have a GPA interval of 2.76 - 3.50 with male gender and take a study period of more than 6 years which has a support value of 0.321 and a confidence value of 0.628. Their perception of e-learning according to IPA coordinates community, collaboration, materials, social media, knowledge, synthesis, application, understanding, multimedia, evaluation, video, and news in quadrant II has a high level of importance with a relatively high level of performance and must be maintained.
Machine Translation System for the Industry Domain and Croatian Language

Machine translation is increasingly becoming a hot research topic in information and communication sciences, computer science and computational linguistics, due to the fact that it enables communication and transferring of meaning across different languages. As the Croatian language can be considered low-resourced in terms of available services and technology, development of new domain-specific machine translation systems is important, especially due to raised interest and needs of industry, academia and everyday users. Machine translation is not perfect, but it is crucial to assure acceptable quality, which is purpose-dependent. In this research, different statistical machine translation systems were built – but one system utilized domain adaptation in particular, with the intention of boosting the output of machine translation. Afterwards, extensive evaluation has been performed – in form of applying several automatic quality metrics and human evaluation with focus on various aspects. Evaluation is done in order to assess the quality of specific machine-translated text.
On Logic of Strictly-Deontic Modalities. A Semantic and Tableau Approach

Standard deontic logic (SDL) is defined on the basis of possible world semantics and is a logic of alethic-deontic modalities rather than deontic modalities alone. The interpretation of the concepts of obligation and permission comes down exclusively to the logical value that a sentence adopts for the accessible deontic alternatives. Here, we set forth a different approach, this being a logic which additionally takes into consideration whether sentences stand in relation to the normative system or to the system of values under which we predicate the deontic qualifications. By taking this aspect into account, we arrive at a logical system which preserves laws proper to a deontic logic but where the standard paradoxes of deontic logic do not arise. It is a logic of strictly-deontic modalities DR.
A model for describing phase-converted image intensity noise in digital fringe projection techniques

Digital fringe projection is a surface-profiling technique that is gaining popularity due to the increasing availability and quality of low-cost projection equipment and digital cameras. Noise in the pixel field of imaged targets induces error in the reconstructed phase and ultimately the surface profile measurement. In this paper, we present an approximate analytical probability density function for the estimated phase given an arbitrarily-correlated Gaussian pixel noise structure. This probability density function can be used to estimate the single point phase measurement uncertainty from easily obtainable pixel intensity noise statistics. We confirm the accuracy of the new model by comparing it to a Monte-Carlo simulation of the phase distribution. A complimentary graphics model is proposed which simulates the physical process of full-field phase measurement using a pin-hole camera model and three-dimensional point clouds of the measurement surface, allowing for another level of model verification.
Towards Intelligent Integration and Sharing of Stem Cell Research Data.

Advancements in regenerative medicine have brought to the fore the need for increased standardization and sharing of stem cell product characterization to help drive these innovative interventions toward public availability. Although numerous attempts have been made to store this data, there is still a lack of a platform that incorporates heterogeneous stem cell information into a harmonized project-based framework. The aim of this project was to introduce and pilot-test an intelligent informatics solution which integrates diverse stem cell product characteristics with study subject and omics information. In the resulting platform, heterogeneous data is validated using predefined ontologies and stored in a NoSQL repository. Pilot-testing was performed on nine sponsored stem cell projects conducting preclinical and intervention evaluations. The pilot-testing demonstrated the robustness of the proposed platform, by seamlessly harmonizing diverse common data elements, and the potential of this platform for driving knowledge generation from the aggregation of this shared data.
Psychological Profile of Unsuccessful University Students

Nowadays higher education institutions train competitive, competent, and mobile professionals.One of the factors that influence the success of higher education students' educational activities is the degree of development of their volitional qualities.The aim of our work is to determine the general psychological profile of an unsuccessful student concerning volitional qualities.In order to achieve this goal, we used a specially developed questionnaire "Volitional and Involuntary Qualities".The questionnaire consists of 25 volitional qualities (of goal-oriented, encouraging, self-regulating, selforganizing and morally-directed groups) and involuntary qualities (of not oriented on a goal, braking, not regulating, not organizing and antimoral groups).To identify gender differences, we conducted a survey of an equal number of males and females.Thus, 476 unsuccessful students (238 males and 238 females) participated in the scientific study.Unsuccessful students were determined according to the results of educational activity.The research was conducted in 10 Ukrainian higher educational institutions of humanitarian direction.It was determined that the volitional qualities of unsuccessful students mainly include the following indicators: a) a high degree of development of decisiveness, courage, and bravery; b) the average degree of development of valor and boldness; c) a low level of development of purposefulness, persistence, initiative, self-control, self-mastery, patience, organization, diligence and heroism.
A Graph-Based Tag Recommendation for Just Abstracted Scientific Articles Tagging

Tags, when properly assigned to limited access papers, help users to estimate their relevance. This paper introduces a new approach for the selection of relevant tags as well as a recommendation for scientific papers tagging. The approach defines the relatedness between the tags attributed by users and the concepts extracted from the available sections of scientific papers based on statistical, structural and semantic aspects. Two different term-based graphs ([Formula: see text]-graph and [Formula: see text]-graph) were generated whose vertices indicate the terms and the edges represent the relatedness score between these terms. In addition, two algorithms were implemented to select and recommend the relevant tags: the neighbor-algorithm and the best-path-algorithm. The results of the experiments performed on a CiteULike collection of tagged papers show significant improvements only for the tagging of abstracted scientific articles. The approach was evaluated by referring to the full text of the papers with expert evaluation and comparing the tags generated by CiteULike users. Using the neighbor-algorithm, 80% of the top 10 recommended tags based on [Formula: see text]-graph and 76% of the top 10 recommended tags based on the [Formula: see text]-graph were relevant. While only 62% of those recommended by CiteULike users were relevant. The best-path-algorithm gave the best results in the top 20 and top 30 recommended tags and this in comparison with the tags recommended by the neighbor-algorithm and the tags assigned by CiteULike users.
Automated Essay Scoring using Word2vec and Support Vector Machine

Essay scoring is one of the most important tools for evaluating and assessing the level of achievement of educational goals.It aims to innovate performance, arrange, integrate ideas, and connect them by using the vocabulary of the particular subjects.Human essay scoring consumes a lot of time and effort, this leads to mistakes.Automated Essay Scoring (AES) solve to great extent problems.A new approach for AES is presented.It is based on Natural Language Processing (NLP) which is used to unify linguistic answers, word2vec model which converts words into features and synonyms in semantic space, Support Vector Machine(SVM) is used to classify students answers and estimate score levels.The system stages consist of preprocessing, feature extraction, classification and similarity algorithm.The results of proposed method reaches high precision (94%) relative to human resident scores.
Implementation of the distance learning by Moodle platforms in the process of future philologists training

Vigorous implementation of information and communication technologies in the higher education activity and the orientation of education towards the personal growth of a future specialist led to the development of distance learning.The article is devoted to the analysis of capabilities of Moodle system of distance learning of future philologists training.It is emphasized that the emergence of electronic educational resources and services for distance learning has expanded opportunities for basic and secondary education, lifelong learning, etc.It has been revealed that distance learning is characterized by qualitative features such as technological ability, innovation, flexibility, adaptability, economic efficiency, modularity, the ability to combine different forms of learning, social equality, change of the teacher's role, combination of traditional and remote monitoring.It is noted that the effectiveness of distance learning depends on the structure of the course, the quality of educational content, the readiness of teachers and students to use remote technologies, the level of their information training, the professionalism of technical and auxiliary staff, which provides the work of the system of distance learning at a higher education institution.It is found that the Moodle system is one of the common learning management systems that provides distance education for a wide range of students.It has the following benefits: the electronic training standard E-learning 2.0 support, effective teachers-students online interaction, powerful tools for displaying student learning outcomes, the ability of efficient renovation of the substantive content of the course.The article analyzes the structure and operating principles of the distance courses in the Moodle system during the training of students of philology, which is used at the Bogdan Khmelnitsky Melitopol State Pedagogical University.The purpose of the main units of the distance course is highlighted: the structure of the course; lecture block; block of seminars; unit of individual work; control unit.It is noted that distance courses for the professional training of students-philologists contain works of art and a list of literature for compulsory reading.Recommendations for students and teachers regarding the use of the Moodle system are provided.
Privacy Protection for Medical Images based on DenseNet and Coverless Steganography

With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user. The user can extract the privacy information successfully with a similar method of feature analysis and index construction. The simulation results show good performance of robustness. And the hiding success rate also shows good feasibility and practicability for application. Since the medical images are kept original without embedding and modification, the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.
An Interactive Visualization Platform for Deep Symbolic Regression

Discovering tractable mathematical expressions that best explain a dataset is a long-standing challenge in artificial intelligence. This problem, known as symbolic regression, is relevant when one seeks to generate new physical knowledge and insights. Since practitioners are primarily interested in knowledge generation, the ability to interact with a symbolic regression algorithm would be highly valuable. Thus, we present an interactive symbolic regression framework that allows users not only to configure runs, but also to control the system during training. The interface provides real-time visualization and diagnostics to help guide the user as they control the algorithm on the fly.
Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR Data, and Using Deep Learning for Early Sepsis Prediction

On a yearly basis, sepsis costs US hospitals more than any other health condition.A majority of patients who suffer from sepsis are not diagnosed at the time of admission.Early detection and antibiotic treatment of sepsis are vital to improve outcomes for these patients, as each hour of delayed treatment is associated with increased mortality.In this study our goal is to predict sepsis 12 hours before its diagnosis using vitals and blood tests routinely taken in the ICU.We have investigated the performance of several machine learning algorithms including XGBoost, CNN, CNN-LSTM and CNN-XGBoost.Contrary to our expectations, XGBoost outperforms all of the sequential models and yields the best hour-by-hour prediction, perhaps due to the way we imputed missing values, losing signal that relates to the time-series nature of the EHR data.We added feature engineering to detect change points in tests and vitals, resulting in 5% improvement in XGBoost.Our team, USF-Sepsis-Phys, achieved a utility score of 0.22 (untuned threshold) and an average of the three reported AUCs (test sets A, B, C) of 0.82.As expected with this AUC, the same model with tuned threshold (not run in the PhysioNet challenge) performed significantly better, as evaluated with 3-fold cross-validation of the entire PhyisoNet training set.
SELF-ADAPTIVE MODEL BASED ON GOAL-ORIENTED REQUIREMENTS ENGINEERING FOR HANDLING SERVICE VARIABILITY

Service system is currently facing environmental complexity problems, such as the need of a distributed, heterogeneous, decentralized, and interdependent system which operatesdynamically and unpredictably. This condition requires the service system to have an ability to adapt in order to realize sustainable functions. The success of service adaptation is determined by its ability to handle variability at runtime. The purpose of this research is to realize service flexibility through variability modeling, which is an extension of previous work to enrich the adaptability view. The methodology was developed through the monitor-analyse-plan-execute-knowledge control loops approach integrated into the adaptive service (service level) element within the adaptive enterprise service system metamodel based on goal-oriented requirements engineering. Service adaptation scenario was prepared through proactive and reactive adaptation mechanisms. For evaluation, the model was applied to the case of a configuration management system. The experimental results showed that the model is able to adapt to runtime variability and accomodates the growth of the service component items shown by the description of the system scalability. The proposed model has a better alternative design in analyzing variability with a total response that can be applicable in normal operations and overload. It also meets the expected level (level-5: adapting) of the adaptive capability maturity model as a standard for assessment of a service system adaptation.
A Feature Points Extraction Algorithm Based on Adaptive Information Entropy

Feature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Information Entropy Feature (AIEF) algorithm is proposed. According to the information entropy, the image blocks with less information are removed and those with more texture image information and larger gradient are selected. Then an adaptive algorithm is used to automatically calculate the optimal threshold of the image information entropy. The image blocks are homogenized to avoid that the extracted feature points are too dense and getting stuck is prevented, which makes the algorithm more robust. Finaly validation is performed using the Oxford standard data set and the performances of the AIEF algorithm are compared with those of the SIFT, SURF, and ORB-SLAM2 algorithms. Experimental results on the Oxford standard data set demonstrate that the AIEF algorithm outperforms the traditional counterparts in terms of processing time, number of feature points, correct matching number and correct matching rate.
Early Warning for Internet Finance Industry Risk: An Empirical Investigation of the P2P Companies in the Coastal Regions of China

Ma, L.; Wang, Y.; Ren, C.; Li, H., and Li, Y., 2020. Early warning for internet finance industry risk: An empirical investigation of the P2P companies in the coastal regions of China. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 295–299. Coconut Creek (Florida), ISSN 0749-0208.Recent years have seen the rapid development of Internet finance in China, and various peer-to-peer (P2P) lending platforms have been released. However, the emergence of diverse problems, such as absconding with the money and fraudulent platforms, has exposed the lack of supervision of P2P lending platforms. This paper collected 26 dimensions of data from 1292 P2P companies in the coastal regions of China from third-party websites. Risk warning and machine learning were incorporated to construct a risk warning model for P2P lending platforms. This model consists of a decision tree model, naive Bayes model, and support vector machine model. Correlation analysis was used to analyze the data source, which is the data predicted by the above models, and the target variables. Type I error rate was utilized to determine the optimal model. The conclusions are as follows: The indicators of registered capital, changes of business scope, and platform background have a great impact on the accuracy of P2P risk prediction; the decision tree model is capable of predicting problematic platforms; and the decision tree model, support vector machine model, and naive Bayes model have a descending order in accuracy. Hence, the decision tree model is the best model for risk warning.
LAND USE/ COVER (LULC) MAPPING IN BRAZILIAN CERRADO USING NEURAL NETWORK WITH SENTINEL-2 DATA

The Sentinel-2a and 2B satellites form a multispectral imaging mission for Earth observation. They have promising characteristics for the study of soils and vegetation cover, and their data can be applied for land use/cover (LULC) mapping. To this end, neural networks have shown good results in pattern recognition tasks in orbital images. In this sense, the study aimed to evaluate the use of Sentinel 2 (ESA) image for LULC mapping in the Cerrado Biome, through the application of artificial neural network methodology. Among the classes of use and occupation examined, 8 classes were selected, 4 of which were natural (water bodies, savanna, forest and field formation) and 4 anthropic (Pasture, Urban areas, Silviculture and Seasonal Crop). The classification system by artificial neural network (ANN) was considered successful, with thematic accuracy (Kappa coefficient) of 0.77. Although there are still some thematic confusions during the classification process, the classification results were considered superior when compared to the MaxVer classifier. The Sentinel-2 image, together with the use of a neural network, was shown a good input for carrying out this type of mapping. Key words: Orbital Remote Sensing System, Supervised Classification Techniques, LULC classes.