Reviewerly/exHarmony huggingface dataset:
Problems of Higher School Didactics: Uncut Pages

Higher education today is not going through the best of times: decisions taken in the educational process at various levels (Federal Laws “On Education in the Russian Federation” of 29.12.2012 No 273-FZ and “On Amendments to the Federal Law “On Education in the Russian Federation” on the education of students” of 31.07.2020 No 304-FZ, Federal State Educational Standards of Higher Education, etc.), related to global changes taking place in society and in the world in recent decades, were made without careful didactic study. This cannot help to improve the quality of specialist training. Teachers are forced to work “by trial and error”, producing countless didactic documents. At present, identification of didactic problems in the educational process of higher educational institutions is extremely relevant due to the expansion of the field of didactic research in higher school pedagogy. The aim of the authors was to analyze some of the most relevant didactic phenomena in the digital reality that have not yet received proper understanding. As a result of the analysis, some important didactic problems of higher education are identified, concerning goal-setting, compliance of teaching with classical didactic principles, organization of distance learning in electronic informational and educational environment, mixed and project-based learning. The article also dwells on psychological problems of Internet content assimilation and “non-contact” interaction between the subjects of the educational process, etc. The most promising areas of didactic research in higher education, including the development of interdisciplinary contacts, are highlighted.
A Comparative Study to analyze crime threats using data mining and machine learning approach

In the last decade, with the development of new technologies crime rate is increasing rapidly. Nowadays, the crime rate analysis and identification is one of the systematic approaches to reduce the illegal activities in all around the world. Thus, this is the biggest challenge to the IT field to solve these critical issues by applying some tools and technologies. Data mining along with the machine learning approach is one of the efficient as well as best approaches to solve this critical issue. It provides the method to identify region and criminal in an appropriate time. In paper is basically emphasis on various machine learning algorithms such as K-means, SVM, A priori Algorithm, CART algorithms, Fuzzy-C and FP tree and machine learning is used to learn the pattern and matches it with actual data. This process is used to reduce the normalized the data and to delete all anomalies and provide better result.
Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

Privacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing.Using machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility.All data were normalized to a range between -1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data.The misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 102 and 103, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10-1 and 104, respectively.We applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations.
AviaDron : Development of Rescue Mission Drone

Drones are capable of vertical takeoff and landing, does not need runaway and can hover in place making it a preferable technology to be used in disaster management. This paper addresses the development of a rescue mission drone capable of carrying payload system that can be deployed to deliver items to person in distress or emergency. Commercial drones available in the market that can lift significant payload are very expensive and some have parts that are proprietary, limiting the options for developer to use off shelf parts. This framework highlights the components used in the development of a drone that can be deployed during a rescue mission. There is a trade-off that need to be made between weight and flight time contributed by choice of battery used. However, the completed drone was able to fly and completed the task of delivering floatation device to person in distress in the water. Further improvement of performance can be made by changing to bigger frame and higher capacity of motors.
Digital humanities and development of the biographical method

The article deals with the methodological foundations of the biographical method in the context of digital humanities. The bidirectional character of biography leads us to the problem’s methodological level: social predicament of the individual world and reciprocal determinability of the social world by the individual world. The biographical method is closely linked to thematization of subjectivity. The biographical method’ s methodological problems are connected with context, reference, authenticity, and reflexivity. The authors link the biographical method with memory studies and emphasize that the turn to memory is associated with many theoretical questions and controversies related to the status of history as a science and its relation to the field of human memory. The multidirectional biography studies reveal the lack of a conceptual apparatus, common for the interdisciplinary space of humanities, which allows to develop the criteria for comparability of scientific research results. The authors associate the further development of the biographical method with the development of the modern information society. They note the prospects of using the methods of digital humanities associated with the search, selection and analysis of texts presented in digital form. The rationale for this thesis is that at present there is a constant increase of electronic texts that can be used both for the development of the biographical method and serve as its empirical base. The article demonstrates the possibility of applying the biographical method using the tools of digital humanities on the examples of research on the history of science, which are part of the world’s cultural heritage.
Analyzing Natural Language Essay Generator Models Using Long Short-Term Memory Neural Networks

Essay generation falls under very rare and challenging cases of deep learning in which input data is way lower than the output data. It focuses on the generation of written texts in natural human language from some known semantic representation of topic information. Aim is to generate informative, diverse, and topic-consistent essays based on different topics. We implemented three different artificial intelligence models, i.e., topic average long short-term memory (TAV-LSTM), topic-attention LSTM (TAT-LSTM), multi-topic aware LSTM (MTA-LSTM) to find out the best suitable technology for the natural language generator. However, TAV and TAT LSTMs showed some valuable results, MTA-LSTM gave the most suitable outcomes. Experimental results verify that the MTA-LSTM model is able to generate topic-consistent text, diverse and essentially makes development as compared to strong baselines. After the implementation of the models, it was found that MTA-LSTM outperformed TAT-LSTM and TAV-LSTM in almost every metric. Overall MTA-LSTM outperformed TAT-LSTM and TAV-LSTM by 14.79 and 20.82% in human evaluation. Also, it performed better in BLEU score evaluation by 27.47% in TAV-LSTM and by 11.53% in TAT-LSTM.
Formation of professional competence of learners in teaching medicinal plants

&#x0D; Professional competence is a necessary component of a person's professionalism in any area. The aim of the research is the formation of the professional activity of a future competitive specialist in the educational process of universities for the system-structural organization of professional training of biology students. The analysis of a wide range of various works aimed at professional pedagogical training of future specialists leads to the conclusion that its structure, content and organization cannot withstand the current state of social development. Currently, the modernization of the target, content and technological structures of professional education is associated with a revision of its content, form and methods in accordance with the requirements of continuing education, the main goal of which is to ensure the full and complete development of the future specialist. In this article, the results of a pedagogical experiment were obtained, which revealed the effectiveness of a specially prepared educational and methodological manual for training medicinal plants in the training of biologists in universities for educational and training.&#x0D; &#x0D; Keywords: Competence, professional competence, medicinal plants, Elaeagnaceae;
A corpus-assisted discourse analysis of Taiwanese students’ sentiments toward Asianphobia on the news amid Covid-19

The anti-Chinese racist slurs and violence toward anyone who bears physical resemblance to a Chinese have escalated at the time the Coronavirus aka Covid-19 has globally spread its wrath. For Taiwanese university students who were also impacted by Covid-19 as their opportunity to travel and study abroad has been put to a halt, raising their awareness of the dangers of racism amid the pandemic has never been more important. This study draws on the students’ feedback on class activity that revolves around language and culture, conflict and resolution. The self-drawn corpus was therefore based on the collected students’ written responses. Critical discourse analysis based on corpus linguistics (Baker et al., 2008) using Voyant Tools (Sinclair &amp; Rockwell, 2016), a web-based text analysis tool, was conducted to investigate word patterns that rose from collocations and concordance data, and the correlational impact of these linguistic devices. These results were then interpreted using critical discourse analysis (CDA) theoretical framework of van Dijk (1984, 2015), Fairclough (1995, 2001) and Wodak (2001) to evaluate the students’ attitudes and social representations that they share. The findings suggest a high frequency of the lemma “Taiwanese” and the collocation “Taiwan can help” signifying a strong sense of pride as an overwhelming majority felt that Taiwan is successfully leading the fight against Covid-19, thus disassociating themselves from the “Chinese mainlanders.” Yet underneath this bold statement lies their deepest concern–a sense of fear for their own safety.
A Study of the Construction of Indicators for “IT-APP” Principals Technology Leadership for Industrial and Vocational High School in Taiwan

Objective of principals technology leadership contents, there are 32 topics covering 5 facet being prepared. Following the fuzzy Delphi method, 17 experts were invited to have Delphi rounds questionnaire. The lfacets and breakdowns comprise 26 topics covering 5 facets applied for the confirmation of Principals Technology Leadership indicators âIT-APPâ for Industrial and Vocational High School in Taiwan. Based on the determined indicator system, further with analytic hierarchy process (AHP), to determine the priority weigh for each indicator.
Robust approach for optimized path selection in Monarch Butterfly Optimization

Nature Inspired Computing or (NIC) strives to develop new computing technologies by observing how nature can inspired to solve complex problems under various environmental conditions. This has produced unconventional research in new fields such as neural networks, swarm intelligence, evolutionary computing, and artificial immune systems. NIC technology is used in almost every branch of physics, biology, engineering, economics and even management. In this paper, one of the nature-inspired approach namely Monarch Butterfly Optimization (MBO)is used for modifying the chromosome parameter in it. The new conditional path selection criteria are developed for the movement of individual subpopulation along with the amplitude parameter. Ackley function is implemented by using conditional path selection mathematical model and the effect of amplitude parameter with adjusting ratio has been identified. The results show better performance among the conditional path selection criteria in terms of route optimization selection.
Global Overview of Large-Scale Photovoltaic System and Its Electrical Energy Storage Implementation

Carbon-emission reduction is one of the great challenges emitted by fossil fuels during power generation. This can be mitigated at a large scale by using solar photovoltaic technology, which is one of the unmatched electrical energy generation techniques with the help of solar energy. Energy crises are increasing day by day as the demand for electrical energy is increasing, and hence to meet the global demand, we need to expand the capacity of PV generation at a large scale. The increased capacity of the solar photovoltaic system may vary from few megawatts (MWs) to few kilowatts (KWs) depending upon the types of level of generation. A traditional generating plant emits carbon and to eliminate this carbon emission, solar PV penetration in the power system can be done at a large scale. Since solar energy is a daily phenomenon and due to this uncertainty in PV power generation, electrical energy storage (EES) systems need to be installed to enhance system capacity and performance. Due to advanced technological development in the electrical storage system, such as Li-ion batteries, lead acid, and nickel–cadmium batteries, has given increasing energy sector participants by providing a cost-effective approach, quality improvement, and energy management. This paper focuses on the PV system penetration at a large scale to the existing grid system and a complete and comprehensive overview of the electrical storage system. Forecasting of new technologies and recent development in EES has been discussed. The current scenario and basic building block of the PV solar system have also been focused. Different gaps in recent work have been identified and indicated about the future development of EES for large-scale PV systems.
Evaluación Remota Emergente En Tiempos De La Pandemia De Covid-19: Análisis De Experiencia En Educación Superior

At the Escuela Superior Politécnica del Litoral (ESPOL), a classroom course in Geographic Information Systems (GIS) takes place in a laboratory equipped with high-end computers and free of distractions. These conditions place the student in a comfortable environment to exploit their learning potential adequately. On the other hand, the results of this course in virtual modality are subject to the possession and quality of the resources of each teacher and student such as: internet connection, computer performance, and family environment. As a consequence, new ways for the evaluation of the GIS virtual course are proposed to offer (as much as possible) equity among all students. A geo-referenced survey of 40 students of the GIS course was conducted to learn about the possession and quality of technological resources, and ongoing training activities were carried out during the course. In addition, the performance of the students' computers was measured using the Novabench software. Finally, the results show no relationship between final grades and computer performance. It is possible that this is the result of the implementation of multiple evaluations, feedback, and self-regulation instruments by the teacher.
Weakly Supervised Sketch Based Person Search

Person search often requires a query photo of the target person. However, in many practical scenarios, there is no guarantee that such a photo is always available. In this paper, we define the problem of sketch based person search, which uses a sketch instead of a photo as the probe for retrieving. We tackle this problem in a weak supervision setting and propose a clustering and feature attention based weakly supervised learning framework, which contains two stages of pedestrian detection and sketch based person re-identification. Specially, we introduce multiple detectors, followed by fuzzy c-means clustering to achieve weakly supervised pedestrian detection. Moreover, we design an attention module to learn discriminative features in subsequent re-identification network. Extensive experiments show the superiority of our method.
An Integrated Framework for Two-Pass Personalized Voice Trigger

In this paper, we present the XMUSPEECH system for Task 1 of 2020 Personalized Voice Trigger Challenge (PVTC2020). Task 1 is a joint wake-up word detection with speaker verification on close talking data. The whole system consists of a keyword spotting (KWS) sub-system and a speaker verification (SV) sub-system. For the KWS system, we applied a Temporal Depthwise Separable Convolution Residual Network (TDSC-ResNet) to improve the system's performance. For the SV system, we proposed a multi-task learning network, where phonetic branch is trained with the character label of the utterance, and speaker branch is trained with the label of the speaker. Phonetic branch is optimized with connectionist temporal classification (CTC) loss, which is treated as an auxiliary module for speaker branch. Experiments show that our system gets significant improvements compared with baseline system.
Novel Approach of Data Transformation for Privacy Assurance using Optimization of Genetic Algorithm

Data mining techniques were used to extract important information from the data. There is an inherent risk to the data's privacy when taking data mining. Privacy preservation in data mining process employs approaches that operate on sensitive data that is hidden even from the algorithm's operator. The majority of privacy-preserving approaches rely on reducing the granularity of data representation. This results in data loss, but it improves protection. As a result, there is a trade-off in PPDM between information loss and privacy. Effective techniques that do not jeopardize security mechanisms are needed. Randomization approach, k-anonymity model, l-diversity, and distributed privacy preservation are some of the strategies suggested for privacy preservation. The k-anonymity model is based on a quasi-identifier, which is a set of database attributes that serves as the data's identifier. All of the data in the database is supposed to be organized into a series of tables, with each tuple containing information about one of the tables. K-anonymity strategies depend on the use of pseudo-identifiers to reduce granularity in data representation.. The actual value of the attribute is entirely eliminated using the suppression process. However, these two approaches result in a loss of some information, which can compromise accuracy. In this paper, we proposed a optimization of genetic algorithm for privacy preservation in data mining process.
Text-Aware Recommendation Model Based on Multi-attention Neural Networks

With the rapid development of information technology, Internet of Things and other technologies, various applications in the Internet space are emerging in an endless stream, triggering an explosive increase in data scale. Intelligent recommendation systems can efficiently filter out massive data sentiment analysis is one of the key technologies of Natural Language Processing (NLP) and intelligent recommendation systems. Finding out the reasons behind certain emotional expressions in texts through information technology has received widespread attention. Based on this, this paper proposes a text-aware recommendation model based on multi-attention neural network model to solve this problem. First, we use a modified LDA and paragraph vector learning framework to obtain the text vector representation, then capture the context information of the text through the Bi-LSTM layer, and finally input the representation into the CNN layer to classify and predict emotional factors. Experimental results show that our proposed method is significantly better than the most advanced baseline method.
Adaptive Sparse Representation of Continuous Input for Tsetlin Machines Based on Stochastic Searching on the Line

This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially hundreds of them). We evaluate the performance of the new scheme empirically using five datasets, along with a study of interpretability. On average, TMs with SSL feature representation use 4.3 times fewer literals than the TM with static threshold-based features. Furthermore, in terms of average memory usage and F1-Score, our approach outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees (XGBoost), and Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs. Our approach further outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in terms of the Area Under Curve while performing competitively on COMPAS.