Reviewerly/exHarmony huggingface dataset:
Humanitarization of technical university education: effective strategies and practices

Last changes in the education system impose certain requirements for the training of technical specialties students, taking into account individual – personal characteristics, social orders of regions, countries, as well as international integration processes. Due to such trends, the humanitarization of technical education can become multidimensional and predictive. The article is aimed at identifying and analyzing the dominant strategies for the humanitarization of higher education of technical students through the prism of universal competencies. The set of methods was used to the analysis of the humanitarization’s strategies of higher education of technical students: the case-study method, the object – Ural Federal University (Ekaterinburg, Russia); method for obtaining and analyzing primary and secondary data. Based on the system and structural-functional approaches, not only strategies are considered, but also such forms of their implementation as purposeful efforts (in educational activities) and spontaneous practices (in extracurricular activities). Research results: the ratio of "4 C's" competencies, the group of universal competencies and universal competencies in accordance with the Federal State Educational Standard for Higher Education (version 3++) is found; taking into account the data from the Atlas of new professions, the connection of the competence group "4 C's" with soft skills required in the professions of technical industries is revealed; effective strategies and practices are identified, including the mandatory bachelor's degree "core" of humanities studies for technical specialties; the role of the center for the universal competencies development as a university structural unit for the promotion of humanitarian minors is justified; the resource of the institute of mentoring and supervision, student scientific, social, and creative associations in the formation of the humanitarian environment of the university is revealed.
Deep InterBoost networks for small-sample image classification

Deep neural networks have recently shown excellent performance on numerous image classification tasks. These networks often need to estimate a large number of parameters and require much training data. When the amount of training data is small, however, a network with high flexibility quickly overfits the training data, resulting in a large model variance and poor generalization. To address this problem, we propose a new, simple yet effective ensemble method called InterBoost for small-sample image classification. In the training phase, InterBoost first randomly generates two sets of complementary weights for training data, which are used for separately training two base networks of the same structure, and then the two sets of complementary weights are updated for refining the training of the networks through interaction between the two base networks previously trained. This interactive training process continues iteratively until a stop criterion is met. In the testing phase, the outputs of the two networks are combined to obtain one final score for classification. Experimental results on four small-sample datasets, UIUC-Sports, LabelMe, 15Scenes and Caltech101, demonstrate that the proposed ensemble method outperforms existing ones. Moreover, results from the Wilcoxon signed-rank tests show that our method is statistically significantly better than the methods compared. Detailed analysis is also provided for an in-depth understanding of the proposed method.
Rule-based Text Normalization for Malay Social Media Texts

Malay social media text is a text written on social media networks like Twitter.Commonly, this text comprises nonstandard words, filled with dialects, foreign languages, word abbreviations, grammatical neglect, spelling errors, and many more.It is well known that this type of text is difficult to process due to its high noise and distinct text structure.Such problems can be resolved using rigorous text normalization, which is critical before any technique can be implemented and evaluated on social media text.In this paper, an improved normalization method towards Malay social media text was proposed by converting non-standard Malay words using a rule-based model.The method normalizes common language words often used by Malaysian users, such as non-standard Malay (like dialect and slangs), Romanized Arabic, and English words.Thus, a Malay text normalizer was proposed using a set of rules that extend across different domains of natural language processing (NLP) and is expected to address the challenges of processing Malay social media text.This study implements the proposed Malay text normalizer in a Part-of-Speech (POS) tagging application to evaluate the normalizer's performance.The implementation demonstrates a substantial improvement in the POS tagging efficiency over several pre-processing stages, with an improvement of accuracy up to 31.8%.The increase of accuracy in the POS tagging indicates two main points.First, the Malay text normalizer's rules improve the performance of a Malay text normalizer on social media text.Second, our proposed Malay text normalizer has successfully improved the POS tagging percentage and demonstrates the importance of normalized preprocessing in any NLP application.
Multi-UAV Path Planning using Modified Dijkstra's Algorithm

Unmanned Aerial Vehicles (UAVs), informally referred to as drones, are ubiquitous in various fields today.The widespread applications and popularity of drones arise from the fact that these automatic devices can cover large distances efficiently and without the need of an operator on board.However, to cover these large distances in an efficient manner, an efficacious algorithm is also necessary.In order to optimize not just the distance between the source and the destination but also satisfy various other constraints such as covering the maximum area possible using the least number of drones and visiting the closest charging stations to complete the journey within the battery life, a modified version of Dijkstra's Algorithm has been used.There already exist algorithms to optimize the path of a drone but very few algorithms also take constraints such as energy cost and area maximization into account.This was the inspiration to take up this project; to devise an algorithm that satisfies the aforementioned constraints while also remaining pertinent to the main objective of multiple UAVs path optimization algorithmsdistance and cost minimization.
INVESTMENT DECISION ANALYSIS OF INTERNATIONAL MEGAPROJECTS BASED ON COGNITIVE LINGUISTIC CLOUD MODELS

The investment decision analysis of international megaprojects is a major area of interest. The choice of international megaprojects usually depends on the multi-discipline knowledge from experts. Besides, experts may not be able to provide accurate or crisp evaluations such as deterministic numbers on each criterion because of the complexity of the decision problem. In this case, natural evaluation language, either single linguistic variable or multiple linguistic variables, is a good expression tool for experts to sharing their opinions freely and flexibly. To this end, this paper introduces a cognitive linguistic cloud model for the investment decision analysis of international megaprojects as a decision support system and provides a survey of the cloud model. Afterwards, the technique to tackle multi-granularity of cognitive linguistic information is proposed to capture personalized semantics. In addition, operators of the cognitive linguistic model are proposed to aggregate natural language. The proposed approach has the advantages of more accurate utilization of experts’ knowledge, reducing uncertainties, and more effective operations of cognitive clouds for decision analysis in comparing with the state of the art. Finally, a case study about the investment of international megaprojects is given to show the flexibility and understandability of the cognitive linguistic model.
Diagnosis of Breast Cancer from Mammogram Images Based on CNN

Breast cancer has become the most common malignant tumor with the highest incidence of death in women. The MIBCAD (Medical Image Based Computer-Aided Diagnosis) system currently in use has a low diagnostic accuracy rate of only 85%. Furthermore, this system has major limitations for image processing of mammogram. To address these issues, this paper proposed a breast cancer diagnosis method based on an improved CNN (Convolutional Neural Networks). To avoid the image overfitting problem, transfer learning and data augmentation methods were used. The image classification accuracy was improved by using different CNN structures and changing the classifier type. Our results showed that the classification accuracy of the model reached 91.4%, which was significantly improved compared with the existing MIBCAD system. &nbsp
Privacy Preservation in Edge Consumer Electronics by Combining Anomaly Detection with Dynamic Attribute-Based Re-Encryption

The expanding utilization of edge consumer electronic (ECE) components and other innovations allows medical devices to communicate with one another to distribute sensitive clinical information. This information is used by health care authorities, specialists and emergency clinics to offer enhanced medication and help. The security of client data is a major concern, since modification of data by hackers can be life-threatening. Therefore, we have developed a privacy preservation approach to protect the wearable sensor data gathered from wearable medical devices by means of an anomaly detection strategy using artificial intelligence combined with a novel dynamic attribute-based re-encryption (DABRE) method. Anomaly detection is accomplished through a modified artificial neural network (MANN) based on a gray wolf optimization (GWO) technique, where the training speed and classification accuracy are improved. Once the anomaly data are removed, the data are stored in the cloud, secured through the proposed DABRE approach for future use by doctors. Furthermore, in the proposed DABRE method, the biometric attributes, chosen dynamically, are considered for encryption. Moreover, if the user wishes, the data can be modified to be unrecoverable by re-encryption with the true attributes in the cloud. A detailed experimental analysis takes place to verify the superior performance of the proposed method. From the experimental results, it is evident that the proposed GWO–MANN model attained a maximum average detection rate (DR) of 95.818% and an accuracy of 95.092%. In addition, the DABRE method required a minimum average encryption time of 95.63 s and a decryption time of 108.7 s, respectively.
Environmental Education: Nurturing of the Humanistic Orientation of a Personality

The way to a high ecological culture of a society lies through effective environmental education.Training of citizens with a high level of environmental knowledge, consciousness, and culture based on new criteria for assessing the relationship between human society and nature (not violence but harmonious coexistence) should become one of the main levers in addressing environmental and socioeconomic problems of modern Russia.The research is aimed at analysis of possibilities of environmental education in the upbringing of a humanistic orientation of the personality.Based on an expert survey, the article defines the principles of environmental education, the observance of which is necessary for the correct organization of environmental education in universities, increasing its efficiency in the formation of a humanistic orientation of the personality, and also highlights the structural components of the humanistically oriented personality of a specialist when implementing environmental education.The conclusions drawn can serve as the basis for the development of guidelines for the upbringing of a humanistic orientation of the personality in the process of environmental education.The materials and conclusions of the research can also be used in the practice of teaching pedagogical disciplines at the university.The scientific novelty of the research lies in the identification of the structural components of a humanistically oriented personality of a specialist and the directions of their formation when implementing the environmental education.
Influence of Dynamic Field of View Restrictions on Rotation Gain Perception in Virtual Environments

The perception of rotation gain, defined as a modification of the virtual rotation with respect to the real rotation, has been widely studied to determine detection thresholds and widely applied to redirected navigation techniques. In contrast, Field of View (FoV) restrictions have been explored in virtual reality as a mitigation strategy for motion sickness, although they can alter user's perception and navigation performance in virtual environments. This paper explores whether the use of dynamic FoV manipulations, referred also as vignetting, could alter the perception of rotation gains during virtual rotations in virtual environments (VEs). We conducted a study to estimate and compare perceptual thresholds of rotation gains while varying the vignetting type (no vignetting, horizontal and global vignetting) and the vignetting effect (luminance or blur). 24 Participants performed 60 or 90 $$^\circ $$ virtual rotations in a virtual forest, with different rotation gains applied. Participants have to choose whether or not the virtual rotation was greater than the physical one. Results showed that the point of subjective equality was different across the vignetting types, but not across the vignetting effect or the turns. Subjective questionnaires indicated that vignetting seems less comfortable than the baseline condition to perform the task. We discuss the applications of such results to improve the design of vignetting for redirection techniques.
Determinants of Human Capital Development in Africa

Purpose: The study had two main objectives, namely (1) to investigate the determinants of human capital development and (2) to explore if the complementarity between FDI and information and communication technology-enhanced human capital development in Africa, are in line with theoretical predictions. Design/Approach/Methodology: The study used panel data analysis (fixed effects, random effects, pooled ordinary least squares, dynamic generalized methods of moments) with African panel data ranging from 2001 to 2015. Findings: The dynamic GMM method noted that human capital development was positively affected by its own lag in a significant manner. Under all the four econometric estimation methods, FDI had no significant influence on human capital development, yet information and communication technology significantly impacted human capital development across all the methods used. The study also revealed that the complementarity between FDI and information and communication technology significantly influenced human capital development under fixed effects, random effects, and pooled OLS only. Practical Implications: Africa is urged to implement policies to strengthen ICT implementation to help the inflow of FDI improve human capital development. The study implies that African nations must implement policies to develop their financial sectors if they want to enhance human capital development. Therefore, African countries are urged to increase their expenditure in revamping and strengthening their infrastructure if they wish to enhance the development of human capital. Originality/Value: This paper is unique because it is the first of its kind to investigate the impact of the complementarity between FDI and information and communication technology on human capital development and to consider the dynamic features of human capital development data.
Optimization of Neural Network using Nelder Mead in Breast Cancer Classification

Classification is one of the data mining techniques which considered as supervised learning. Classification technique such as Backpropagation Neural Network (BPNN) has been utilized in several fields to increase human productivity. BPNN can give better results (more natural) compared with other statistical techniques. However, the learning process of BPNN could give an inefficient synapse weight of each hidden layer. This ineffective weight can affect the performance of the network. In this research, BPNN optimization using Nelder Mead to identifying the appearance of breast cancer is proposed. The datasets used are Breast Cancer Coimbra Dataset (BCCD), and Wisconsin Breast Cancer Dataset (WBCD). The testing result using accuracy and k-fold validation presents better performance compared with the original BPNN. Best average performance can be seen in the fifth fold of BCCD with 76.5217% of accuracy. Moreover, the highest average result of WBCD presented in the fourth fold with 91.1765% of average accuracy.
Free Definite Description Theory – Sequent Calculi and Cut Elimination

We provide an application of a sequent calculus framework to the formalization of definite descriptions. It is a continuation of research undertaken in [20, 22]. In the present paper a so-called free description theory is examined in the context of different kinds of free logic, including systems applied in computer science and constructive mathematics for dealing with partial functions. It is shown that the same theory in different logics may be formalised by means of different rules and gives results of varying strength. For all presented calculi a constructive cut elimination is provided.
Multi-task Peer-Review Score Prediction

Automatic prediction of the peer-review aspect scores of academic papers can be a useful assistant tool for both reviewers and authors.To handle the small size of published datasets on the target aspect of scores, we propose a multi-task approach to leverage additional information from other aspects of scores for improving the performance of the target aspect.Because one of the problems of building multi-task models is how to select the proper resources of auxiliary tasks and how to select the proper shared structures, we thus propose a multi-task shared structure encoding approach that automatically selects good shared network structures as well as good auxiliary resources.The experiments based on peer-review datasets show that our approach is effective and has better performance on the target scores than the single-task method and naïve multi-task methods.
Tunable coupling between a superconducting resonator and an artificial atom

Coherent manipulation of a quantum system is one of the main themes in current physics researches. In this work, we design a circuit QED system with a tunable coupling between an artificial atom and a superconducting resonator while keeping the cavity frequency and the atomic frequency invariant. By controlling the time dependence of the external magnetic flux, we show that it is possible to tune the interaction from the extremely weak coupling regime to the ultrastrong coupling one. Using the quantum perturbation theory, we obtain the coupling strength as a function of the external magnetic flux. We also find that our system is remarkably insensitive to the main noises, which shows its reliability in the fields of quantum simulation and quantum computing. In particular, it is worth pointing out that the best performance of our qubit both in terms of tunable coupling and coherence is achieved when the penetrating fluxes through two SQUIDs are in a restricted range near Φ0/2.
Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research

As social distancing, self-quarantines, and travel restrictions have shifted a lot of pandemic conversations to social media so does the spread of hate speech.While recent machine learning solutions for automated hate and offensive speech identification are available on Twitter, there are issues with their interpretability.We propose a novel use of learned feature importance which improves upon the performance of prior state-of-the-art text classification techniques, while producing more easily interpretable decisions.We also discuss both technical and practical challenges that remain for this task.
Health App Recommendation System using Ensemble Multimodel Deep Learning

Nowadays, mobile devices and apps are meant to fulfill the needs of various people in society. But, mobile app Stores are facing major challenges in recommending proper apps for users. Recommending mobile apps for users according to personal preference and various mobile device limitations is therefore important. In this scenario, there is a huge need for developing recommender systems (RS) for the user’s community in enabling critical mobile apps such as Health based Apps. Recommendation Systems perform an extensive survey on the collection of user reviews, preferences and opinions to discover recommendations of suitable applications to the users' community. In this paper, we have designed an aspect-based recommendation framework by performing three tasks: such as identifying the mentions associated with item aspects in user reviews, extracting the sentiment related opinions using Latent Semantic Analysis of such aspects in the reviews, and perform the opinion mining from all of the aspects to generate enhanced recommendations with Ensemble Multimodel Deep Learning (EMDL). EMDL comprises of two state-of- the-art classifiers such as Deep Neural Networks (DNN) and Long Short Term Memory (LSTM). In contrast to the prior work, we conducted a series of experiments with several state-of-art deep learning models to extract useful recommendations. The achieved results show that classification with outperforms in all the aspects based on various evaluation metrics when compared to the rest of the models.
DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers

We present DeepPaperComposer, a simple solution for preparing highly accurate (100%) training data without manual labeling to extract content from scholarly articles using convolutional neural networks (CNNs).We used our approach to generate data and trained CNNs to extract eight categories of both textual (titles, abstracts, authors, headers, figure and table captions, and body texts) and nontextual content (figures and tables) from 30 years of 2916 IEEE VIS conference papers, of which a third were scanned bitmap PDFs.We curated this dataset and named it VISpaper-3K.We then showed our initial benchmark performance using VISpaper-3K over CS-150 using YOLOv3 and Faster-RCNN.We have opensourced DeepPaperComposer for training data generation 1 and have released the resulting annotation data VISpaper-3K 2 to promote reproducible research.
A Data-Driven Framework for Automated Requirements Elicitation from Heterogeneous Digital Sources

Increased digitalization and the pervasiveness of Big Data, along with vastly improved data processing capabilities, have led to the consideration of digital data as additional sources of system requirements, complementing conventional stakeholder-driven approaches. The volume, velocity and variety of these digital sources present numerous challenges which existing system development methods are unable to manage in a systematic and efficient manner. We propose a holistic and data-driven framework for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources for the purposes of requirements elicitation and management. The proposed framework includes a conceptualization in the form of a meta-model and a high-level process for its use; the framework is illustrated in a real case of an enterprise software.