Reviewerly/exHarmony huggingface dataset:
A Topic Coverage Approach to Evaluation of Topic Models

Topic models are widely used unsupervised models capable of learning topics - weighted lists of words and documents - from large collections of text documents. When topic models are used for discovery of topics in text collections, a question that arises naturally is how well the model-induced topics correspond to topics of interest to the analyst. In this paper we revisit and extend a so far neglected approach to topic model evaluation based on measuring topic coverage - computationally matching model topics with a set of reference topics that models are expected to uncover. The approach is well suited for analyzing models' performance in topic discovery and for large-scale analysis of both topic models and measures of model quality. We propose new measures of coverage and evaluate, in a series of experiments, different types of topic models on two distinct text domains for which interest for topic discovery exists. The experiments include evaluation of model quality, analysis of coverage of distinct topic categories, and the analysis of the relationship between coverage and other methods of topic model evaluation. The paper contributes a new supervised measure of coverage, and the first unsupervised measure of coverage. The supervised measure achieves topic matching accuracy close to human agreement. The unsupervised measure correlates highly with the supervised one (Spearman's $\rho \geq 0.95$). Other contributions include insights into both topic models and different methods of model evaluation, and the datasets and code for facilitating future research on topic coverage.
The research of personal and professional development features of college graduates in the university: content and methods

The features of the personal and professional development of the future specialists are studied in the aspect of their specialization. These are revealed through the use of projective research methods - the essays "My professional plans" and "My usual day in … years", incomplete sentence method, and a questionnaire to assess the level of socio-psychological adaptation. To study the college graduates’ adaptation peculiarities to educational process in the university, the questionnaire was used, designed to determine the level of educational and professional adaptation in psychophysiological, professional and socio-psychological aspects. The article describes the use of the "Incomplete sentences" method, its modification depending on the indicators of the respondents group. The results of testing students are presented in detail. Other research methods are also described.
Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study

A model developed at the University of Tomsk, Russia, for high latitudes (over 55° N) is proposed and applied to the analysis and observation of the solar resource in the state of Sonora in the northwest of Mexico. This model utilizes satellite data and geographical coordinates as inputs. The objective of this research work is to provide a low-cost and reliable alternative to field meteorological stations and also to obtain a wide illustration of the distribution of solar power in the state to visualize opportunities for sustainable energy production and reduce its carbon footprint. The model is compared against real-time data from meteorological stations and satellite data, using statistical methods to scrutinize its accuracy at local latitudes (26–32° N), where a satisfactory performance was observed. An annual geographical view of available solar radiation against maximum and minimum temperatures for all the state municipalities is provided to identify the photovoltaic electricity generation potential. The outcomes are proof that the model is economically viable and could be employed by local governments to plan solar harvesting strategies. The results are generated from an open source model that allows calculating the available solar radiation over specific land areas, and the application potential for future planning of solar energy projects is evident.
Efficient and Secure Strategy for Energy Systems of Interconnected Farmers′ Associations to Meet Variable Energy Demand

Since ancient times, agriculture has been one of the most important resources of national development. At a national level, clean energy is a strategic objective of Romania, in accordance with the EC directive 2016/30.11.2016 (“Clean Energy for All”). At a European level, the European Commission published in January 2019 the “Towards a Sustainable Europe by 2030” strategy, highlighting the strategic importance of the Internet of Things (IoT) and blockchain technologies. In this context, the synergy between the energy management of a hybrid energy system and blockchain technology, applied to farmers’ associations, represents a priority research direction in the field of information and communication technology, blockchain, and security. This paper presents the integration of the management of the energy produced by photovoltaic panels owned by farmers’ association, to support the variable energy demand (necessary for water pumps, charging stations of the electric agricultural machines, the animal farms, and the auxiliary equipment) based on the IoT, DLT, blockchain technologies and smart contracts applied to farmers associations registered as users of the SmartFarm platform.
Breast cancer mitotic cell detection using cascade convolutional neural network with U-Net

The number of mitotic tumor cells detected in each slide is one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counts are still a difficult problem for pathologists and related experts. Traditional methods use manual design algorithms to extract features of mitotic cells, and most methods rely on sliding windows to achieve pixel-level classification through deep learning. However, the complex background and high resolution of pathological images make the above methods time-consuming and ineffective. In order to solve the above problems, we propose a new cascaded convolutional neural network UBCNN (cascaded CNN based on UNet), which consists of three parts: semantic segmentation and classification to detect mitosis. First, we use an improved UNet ++ segmentation network to locate the candidate set of mitotic targets. Secondly, an adequately labeled cell nucleus data set is sent to an improved two-dimensional VNet network, and the cell nucleus is located by means of semantic segmentation to obtain accurate image blocks of mitotic and non-mitotic cells. Finally, the obtained cell image block is used to train a convolutional neural network to achieve binary classification, and the candidate set area is screened to retain the final result of mitosis cells. This paper verifies the detection effect of the above-mentioned cascade detection algorithm on the ICPR 2012 and 2014 mitosis automatic detection competition data sets. The evaluation indicators include accuracy, recall and F-score. Our cascade detection algorithm based on segmentation and classification reached 0.831 on the ICPR 2012 data set and 0.576 on the ICPR 2014 data set. Compared with other existing algorithms, the detection effect was improved, which was very competitive.
Redox element record shows that environmental perturbations associated with the T-OAE were of longer duration than the carbon isotope record suggests – the Aubach section, SW Germany

The Early Jurassic Toarcian Oceanic Anoxic Event (T-OAE) with its associated carbon-isotope excursion (CIE) was possibly one of the most pronounced periods of widespread oxygen deficiency in the Mesozoic ocean.The event has been extensively studied in order to understand the processes triggering the environmental perturbations and the extreme oxygen depletion in many marine basins.However, comparatively little focus has been placed on the end of the positive CIE and the stratigraphic coherent end of anoxiceuxinic conditions.In the present study, we constrain the stratigraphic extent of anoxic-euxinic conditions and define the termination of the positive CIE in the Swabo-Franconian Basin covering the Lower Toarcian strata using carbon-isotope ratios, organic matter pyrolysis and redox-sensitive element concentrations of outcrop samples from the Aubach section.Bulk organic carbon-isotope values, corrected for changes in type of organic matter using the Hydrogen Index (HI), suggest that the amplitude of the negative CIE in organic matter is as little as 3.3-3.5 ‰, in contrast to 4.5 ‰ change in δ 13 C carb in the same section.Enrichment in redox-sensitive proxies (V/Al and DOP-T) and %TOC suggest that environmental perturbations associated with the T-OAE continued until the upper falciferum Zone in the Aubach section.This indicates that anoxic-euxinic conditions terminated in the same stratigraphic interval in which δ 13 C values return to steady, light values at ~-28 ‰ (termination of positive CIE).This synchronism in the return to normal marine conditions is also observed in the southern Paris Basin, but not in the Cleveland Basin.
A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.
The Effect Of Online Learning Service Quality On Student Satisfaction During COVID19 Pandemic In 2020

The coronavirus disease in 2019 (COVID 19) becomes the focus of all the countries in the world to finish. This virus is so dangerous that all efforts get done to stop it. Unfortunately, it is impossible to do it, but what can do is by the prevention of its spread. To do it, moreover, the government of Indonesia advises all Indonesians to work from home in all sectors, including education. As one of the responsible higher institutions for educating the students, Universitas Kristen Krida Wacana obeys this suggestion by the work from home for the lecturers to carry on online learning for the students. This study intends to investigate and analyze the online learning quality on the satisfaction of the management students in the faculty of economics and business, taking the subject of human resource management and organizational behavior in 2020. Furthermore, they become the population of this study. By the stratified random sampling method from the community consisting of 76 students and a variance-based structural equation model (SEM), this research grabs 64 students as the sample. It analyzes the collected data associated with latent variables. After testing and analyzing the data, this study infers that the better quality of service online learning, the more satisfied the students. In other words, the implementation of this online learning is successful. Likewise, some practical and theoretical recommendations based on this evidence are available.&#x0D; &#x0D; Keywords – lecturer, online learning service quality; the spread of COVID 19; student satisfaction; work from home
SU-NLP at SemEval-2020 Task 12: Offensive Language IdentifiCation in Turkish Tweets

This paper summarizes our group's efforts in the offensive language identification shared task, which is organized as part of the International Workshop on Semantic Evaluation (Sem-Eval2020).Our final submission system is an ensemble of three different models, (1) CNN-LSTM, (2) BiLSTM-Attention and (3) BERT.Word embeddings, which were pre-trained on tweets, are used while training the first two models.BERTurk, which is the first BERT model for Turkish, is also explored.Our final submitted approach ranked as the second best model in the Turkish sub-task.
Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Abstract Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naïve Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.
Self-Adaptive Image Encryption Algorithm Based on Quantum Logistic Map

This study proposes a self-adaptive image encryption algorithm based on the quantum logistic map. First, the initial values are substituted into the quantum logistic map based on an iteration process to generate three random sequences. After preprocessing, three new random sequences with better randomicity were obtained from the generated random sequences. In particular, the correlation coefficients for random sequences before and after preprocessing are compared to determine the best model to make the correlation coefficients closer to zero. Second, one random sequence in the scrambling stage is selected with respect to the plain image. The selected random sequence is then used to perform row-column perturbation on the plain image. Finally, the remaining two random sequences are used to perform forward and reverse diffusion to obtain the final cipher image. Because of the dependence on the plain image, the algorithm can frustrate the chosen-plaintext and known-plaintext attacks. Experimental results show that the proposed encryption algorithm can achieve secure communications.
Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline. ELM is a single-hidden layer feedforward neural network with the hidden layer parameters randomly generated in advance and the output layer parameters calculated optimally using the least-square algorithm directly using the training samples. ELM is used as the heartbeat classification algorithm due to its high accuracy training accuracy, fast training speed, and good generalization ability. Experimental testing is carried out using the public MIT-BIH arrhythmia dataset to perform a 16-class classification. Experimental results show that the proposed approach achieves a superior classification accuracy with fast training and recognition speeds, compared with existing classification algorithms.
Pre-service Teachers Perspectives of Google Expedition

This article explores pre-service teachers' personal experiences as they implemented immersive virtual reality using Google Expedition during a summer STEM camp for primary students. The research is situated around semi-structured interviews conducted to determine how pre-service teachers view their experiences using Google Expedition in primary classrooms, and the pedagogical challenges they encountered during this process. The semi-structured interviews provided insight into the planning, the pedagogical applications, and the obstacles or challenges they encountered during the planning stage and execution of their lesson using a highly interactive, three-dimensional tool such as Google Expedition. The data indicated that pre-service teachers had confidence in implementing virtual reality lessons in the class and considered it a new horizon for teaching. Yet, it also identified pedagogical challenges associated with implementing virtual reality in a primary classroom.
Sentiment Analysis in Turkish Based on Weighted Word Embeddings

In the era of big data, natural language processing becomes an important research discipline, owing to the immense quantity of text documents and the progresses in machine learning. Natural language processing has been succesfully employed in many different areas, including machine translation, search engines, virtual assistants, spam filtering, question answering and sentiment analysis. Recent studies in the field of natural language processing indicate that word embedding based representation, in which words have been represented in dense spaces through fixed length vectors, can yield promising results. In this study, we evaluate the predictive performance of 36 word embedding based representation obtained by three word embedding methods (i.e., word2vec, fastText and DOC2vec), two basic weighting functions (i.e., inverse document frequency and smooth inverse document frequency) and three vector pooling schemes (namely, weighted sum, center based approach and delta rule). Experimental analysis indicates that word2vec based representation in conjunction with inverse document frequency based weighting and center based pooling, yields promising results for sentiment analysis in Turkish.
Use of Blockchain Technology to Curb Novel Coronavirus Disease (COVID-19) Transmission

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Beacons and Blockchains in the Mobile Gaming Ecosystem: A Feasibility Analysis

We explore the adoption of the Internet of Things (IoT) and Distributed Ledger Technologies (DLTs), such as blockchains, in mobile gaming, focusing on ecosystem expansion and diversification, customer attraction and retention, exploitation of context sensitive and personalized advertisements, and improved monetization of in-game assets. We evaluate the cost and transaction delay of DLTs in a location-based mobile game ecosystem using two types of blockchains (permissioned and permissionless or public), based on defined Key Performance Indicators (KPIs). Our evaluation shows the advantages of using both types of blockchains as well as interledger technologies that combine them. Permissioned blockchains enable high performance, e.g., in terms of throughput and delay, and low cost, while permissionless (public) blockchains, through their transparency, immutability, and openness, support trust and facilitate interactions among unrelated parties. Finally, we show that the combination of IoT devices and DLTs in mobile gaming offers new business opportunities and enables innovative business models for both traditional mobile gaming companies and other participants in the ecosystem, e.g., game players, cafes, malls, and similar establishments, advertising companies, and independent programmers.
AUGMENTED REALITY TECHNOLOGIES IN EDUCATION - A LITERATURE REVIEW

"Digital technologies advancements have reshaped the framework of the traditional classroom and taken educational curriculum to a different new level. More than ever, the education systems all over the world, strongly affected by the shock of the COVID-19 crisis, need to rely on innovation and digital resources. Augmented Reality (AR) technology can enable educators and students to access specialised materials beyond time and space. This paper presents a systematic review of literature on AR in education in the last 3 years. In total 30 research papers were analysed in this review, by using the content analysis method. Further research topics concerning the affordances of AR applications in education have been identified. "
Remote Controlling Distributed Parallel Computing System over the Cloud (RCDPCSC)

Parallel Distributed Processing approaches are relatively new. A distributed cloud is the application of cloud computing technologies to interconnect data and applications served from multiple geographic locations. A distributed information technology (IT) means that something is shared among multiple systems that may also be in the same or different locations. The amount of data and exhausted time to process them and regulate the expected results efficiently and in as lowest as possible time has been expanded enormous. Therefore, it is essential to prepare approaches to enhance parallel processing techniques via cloud computing.
Multilevel Fault-Tolerance Aware Scheduling Technique in Cloud Environment

In cloud computing, the resources are delivered to the users on demand at a considerable cost. Due to low maintenance and high scalability services, enterprises wish to deploy their newly developed application towards the computing environment. For large scale applications, fault tolerance is an essential task that guarantees the reliability and availability of computing services. In this paper, a multi-level fault tolerance scheduling mechanism is proposed that overcomes the real-time failure in the system. In the first phase, non-functional testing and decision making algorithm is used to find the reliability of virtual machines. Here, the reliability criterion is achieved by Reliable Decision K-Nearest Neighbor (RDK-NN) algorithm that considers only the best reliable virtual machine. In the second phase, high availability is achieved using a scheduling algorithm. For this purpose, a Teaching-Learning Based Optimization (TLBO) scheduling is proposed that provides a better-scheduled set of tasks for the corresponding users. The evaluation of the proposed approach is carried out under Cloudsim platform. The performance is determined in terms of makespan time, failure ratio, performance improvement rate, response time and rejection ratio to estimate the scheduling task. The result shows that the system achieves high reliability and availability of data with a multi-level format in the cloud environment.