Reviewerly/exHarmony huggingface dataset:
Teachers' Digital Literacy as a Prerequisite for Technology-Enhanced Teaching

Teachers' digital literacy is an essential prerequisite for them to be able to use technology-enhanced teaching effectively in their pedagogical practice. In 2018, the National Institute of Certified Educational Measurements of the Slovak Republic launched the PIAAC – Programme for the International Assessment of Adult Competencies project, which includes research on key cognitive and non-cognitive competences, reading, mathematical and digital literacy of teachers working at the ISCED 2 level of education – PIAAC online. Teachers' digital literacy is defined as a set of problem-solving skills using information and communication technologies that teachers need for their full-fledged employment in modern society. The authors present in this article a preview of the main results of the PIAAC-online project focusing specifically on teachers' digital literacy. The presented findings of the project confirm a strong correlation between the teachers' level of ICT usage in their personal and professional life.
A Distributed Framework for Measuring Average Vehicle Speed Using Real-Time Traffic Camera Videos

Traffic accidents have dramatically grown in recent years as a result of an increase in the number of vehicles. The driver’s speed is one of the main contributors to these collisions since it is inappropriate for the traffic and road conditions. To stop driver infractions, numerous safety measures have been implemented. Today, radar equipment and camera speed restrictions are widely employed to prevent accidents brought on by speed-related infractions. The average speed of the vehicle is determined in this study utilizing the videos and pictures taken by the Electronic Detection System (EDS) installed at various points along the route of a vehicle. The vehicle is identified by its license plate number, and image processing determines its speed. The data provided by the cameras capturing the same vehicle are used to calculate the average speed. When a vehicle enters the field of vision of a camera, the camera records the vehicle’s time and speed. The distance between any two cameras is the final factor used to compute average speed. With the help of the Bing Maps API, the shortest path distance between any two cameras is determined.
Detecting Frauds in Financial Statements Using Deep-Forest

Detecting frauds in financial statements is an important task in maintaining a healthy financial environment. In recent years, researchers developed multiple different machine learning methods for the fraud detection problem. However, due to the difficulty of the issue, the performance of published methods is far from being comparable to the field standard. In this paper, we present a new model to detect fraud. Using the public dataset of listed companies in the United States, we show that our approach outperforms other models in the prediction outcome.
Didactic Tool for Intuitive Random Process Visualization

The use of educational tools is an essential part of achieving educational goals. All practicing educators have experienced the lack of appropriate illustrative tools. Their use helps students deepening existing or to achieve new knowledge, applicable to other learning situations. Our goal is the development and the presentation of a didactic visualization tool suitable in the intuitive illustration of random processes. This tool visualizes the binomial distribution. It can be used in three variants: a) based on hardware-generated true random numbers; b) based on software-generated pseudo-random numbers; c) also based on pseudo-random numbers, but the probability of the random process is determined by the tilt of the stand. With data from the measurement can be used for further analysis: e.g., using an external program the user can plot the histograms of frequencies, he can calculate the empirical mean and standard deviation of the distribution, he will be able to perform statistical tests etc. The scope of the demonstration is the intuitive visualization of some real-life random processes, when the outcome is influenced by a large number of elemental, practically unobservable random events.
Sentiment Enhanced Smart Movie Recommendation System

Sentiment Enhanced Movie Recommendation System is the new age movie recommender that considers the sentiments of the user on a higher level and recommends the perfect movie for every user. The movies being classified vaguely on the genres, do not always represent the right taste of the user. Therefore, the sentiment enhanced movie recommender considers all the important features in a user’s watch trend, makes a cumulative calculation as to which trends the user is following and recommend the best movie. It allows users to add movies to their watchlist which is also considered for recommendation. Thus, by considering the user’s interests at a deep level, more accuracy can be achieved in recommending the best movie to users and hence engage them for a longer time in the website. The recommendation is based on user-user collaborative, item-item collaborative, item-content and user-content, implemented using SVD, KNN and similarity measures like cosine-similarity.
Automated Detection of Malaria Parasite from Giemsa-Stained Thin Blood Smear Images

Malaria is a threat to the universe, especially in Asian and African countries. In malaria-affected nations, malaria is diagnosed through a visual examination of blood under a microscope. The reliability of the result depends upon the skills of the lab technician. Any Individual or community will be affected if they are misdiagnosed. To overcome this issue, an automated malaria diagnosis system using nearly 27,000 thin blood smear images has been proposed. It involves two steps: 1. Pre-processing the digitized blood smear images to remove the noise and segmenting them to extract the Region of Interest. 2. A lightweight customized deep learning model to classify digital smears either as infected or normal. We have also implemented an automated malaria detection system using various pre-trained models. The result revealed that our model performed with an accuracy of over 99% and a sensitivity of 100%.
Optimized Web Service Composition Using Hybrid Evolutionary Algorithms

Internet Web services are used to build electronic business applications so they can be interconnected and provide flexibility. When a user needs more than one service at a time, a composition of the available services is carried out in order to fulfil the user’s service request. When a wide variety of internet services are available, we demand a proper procedure of composing the services based on the aspects affecting service quality. Now a days for a single web service, there are multiple services with same functionalities are available. We made an effort to put together the web services in our recommended work with the highest overall QoS values based on the requests made by the user’s. We used particle swarm optimization (PSO) and ant colony optimization (ACO) techniques to address the service composition problem using quality of service (QoS) parameters.
A Comparative Analysis of Single Image Dehazing Techniques for Real Scene

The visibility of an image is highly limited by bad weather conditions and environmental pollution. Loss of color, contrast and original brightness of the scene results in a hazy image. The goal of a single image dehazing technique is to recover the clear output by using single hazy input. Recently, different techniques are used on synthetic scene, where the haze environment in an image is created by haze generator. Sometimes, these techniques will not generate a similar result for a real and synthetic scene. In this paper different single image dehazing techniques have been implemented on real scene captured by a camera especially in a haze-prone environment. The outputs obtained from different techniques are compared by using the Ciede2000 parameter for both low and dense haze.
Meta Embeddings for LinCE Dataset

Language Identification in code-mixed social media text contest aimed at Multilingual Meta Embeddings (MME), a productive method to learn multilingual representations for Language Identification. Language mixing occurs at a sentence boundary, within a sentence, or a word in code-mixing. This paper proffers an MME-driven language identification mechanism for code-mixed text. This study zeroed in on the comparison of different classifiers on Hindi-English code-mixed text data obtained from LinCE Benchmark corpus. LinCE is a centralized benchmark for linguistic code-switching evaluation that integrates ten corpora from four different code-switched language pairings with four tasks. Each instance in the dataset was a code-mixed sentence, and each token in the sentence was associated with a language label. Then we experimented with using different classifiers such as convolutional neural network, Gated Recurrent Unit, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit and we observed BiLstm outperformed well. A multilingual meta embedding technique was empirically evaluated for language identification.
Modes of Interaction with the Digital Environment in Music Composition; Lateral Thinking

Now-a-days musicians including composers are tempted to encompass in their strategy of composing not only the solid features implied by the traditional emergent formulas (of writing gestural sequences on sheets of paper) showing the need of recurring the patriarchal patterns. They aim to stop the imitative, sedimented expression (depicting a canonical crescendo), being prepared to meet ideas from outside the ‘western ear’. With an agnostic attitude most of composers who live in an authentical present are insisting to start thinking on the “zero degree” occurence, that factor of atypical, ex-musical (non-fundamental) processes from whose specific vacuum arises an immersive media, the display of new art-work possibilities. Considering important the mission of interacting with art (let it be musical, visual etc.) by means of intellectual channels capable to communicate technological innovations, or a (super)praxis with computer as the main ambassador within the globalized culture, the composers call dominantly for a interruption of the rustic paradigm of ‚canonical crescendo(s)’ bringing closer the dialog with the digital world. Our paper respondes to questions with regard to the need of translating − just this complex part − into performative composing strategies. Then emphasizes the function of media heterogenity absorbtion which integrates a cross-modal perception as generative setting for digital communication environment.
The RRRS Methodology Using Self-Regulated Strategies with ICT to Homogenize the English Language

The Research, Revision, Review, and Self-assessment (RRRS) methodology is used in establishing regulated self-learning among the students of the National and Foreign Languages Career. This research work aims to analyze the application of this methodology by using the resources based on Information and Communication Technologies (ICT); the research comes under an analytical experimental type by utilizing a quantitative information analysis approach, and several strategies were applied by using ICT resources such as videos, use of mobile devices, web 2.0 tools; the technique used here was the application of experiments by using digital and technological resources. This study was applied to twenty-nine students in a sixteen-week period during the intervention process with two hours daily; the methodology applied for the development of technological resources was ADDIE, the same one that focuses on the pedagogical development mediated by technology in students. As a result, it was possible to demonstrate the improvement that exists while applying ICT-based strategies and self-regulated learning, and finally, it is concluded that the use of technological resources improves the learning of English language and the skills that are evaluated in this language.
Image Binarization with Hybrid Adaptive Thresholds

The process of image binarization divides pixel values into two groups, with the back-and-forth represented by white and black, respectively. A novel image binarization approach is presented in this research, “Image Binarization with Hybrid Adaptive Thresholds (IBHAT)” with two adaptive thresholds being proposed. It associates two threshold values which are based on global and local mean for each pixel that is to be transformed to either in background or foreground. One of the threshold values has to be selected for the concerned pixel. Hence the term hybrid adaptive is used. The local contrast of pixels inside a block of 3 $$\times $$ 3 is used to make the decision. This technique can switch to three different modes such as local, global, or hybrid adaptive thresholding technique simply by using a specific control parameter value. Global adaptation technique gives a result similar to global techniques, local technique gives a result similar to other local techniques, while the hybrid technique gives a result which is not similar to the previous two but is a very effective one. So it is convenient to apply in degraded document image binarization. This technique is compared with other global as well as local techniques at different conditions of the controlling factor based on the types of the image applied. Since it uses only 3 $$\times $$ 3 block size, its computational time is similar to the global one. In comparison to previous relevant strategies, the suggested methodology takes less time to compute and produces effective results, according to the results of the experiment.
BEN-CNN-BiLSTM: A Model of Consequential Document Set Identification of Bengali Text

Document set identification assigns a text to its predefined text set. Therefore, the objective of any classification work is to create a model that can categorise various texts and objects into distinct classes. In this paper, three consequential deep learning based models, viz. BiLSTM, BiLSTM with attention layer, and CNN-BiLSTM have been used, which have the auto-learning capability in Bengali corpora. The CNN-BiLSTM model has been designated the classification model for its best performance in categorising Bengali text documents. This consequential model, named BEN-CNN-BiLSTM, was learned with Bengali text documents to determine the category of an unknown Bengali document. At first, more than four lac news articles from renowned Bengali newspapers are processed. After that, the training data is processed and entered into the proposed model. Finally, the model performance is assessed using the test dataset to calculate recall, precision, F-score, and accuracy. Compared to other standard classification algorithms in Bengali text classification, our proposed BEN-CNN-BiLSTM model achieved 93.94% accuracy. Thus, it can be said that the proposed BEN-CNN-BiLSTM model can be a new document set identification technique for Bengali datasets.
Classification of Kathakali Asamyuktha Hasta Mudras Using Naive Bayes Classifier and Convolutional Neural Networks

Indian’s most famous classical dance named as Kathakali consists of tough body movements, elegant hand gestures, background music and facial expressions. Due to the challenges involved in kathakali hasta mudras, it is very tough to recognize the expression of kathakali hand gestures. We have used a dataset of kathakali hasta mudras and dug into different methods to understand kathakali hasta mudras which are used to devolve the narration of a story by various artists by using machine learning (ML) and deep learning (DL) approaches in our work. A total 24 varieties of hasta mudras exist in kathakali which are used to express a story by the artist. We introduced a naive Bayes classification technique and CNN model which are used to classify the hasta mudra images among the 24 different varieties. We compared and differentiated the efficiency of ML techniques and DL techniques. Our outcomes reveal that DL techniques gave up to 79%accuracy. Various preprocessing methods have been used for data preprocessing and finally a deep learning approach is applied for Kathakali hand mudras.
BLRS: An Automated Land Records Management System Using Blockchain Technology

Land registry and management system is a crucial component of every government system since they preserve records of land ownership in the country. Traditional land registration and transfer is a complex and arduous process that requires numerous intermediaries and is prone to fraud and forgery. One solution to this problem is to digitize land records, but even if the records are stored in a database, they can be altered due to its centralized nature and lack of adequate security. Blockchain technology has the potential to close all of these loopholes by increasing trust, security, transparency, and traceability of data shared across a network through the use of cryptographic primitives. This paper proposes a novel blockchain-based land records management system which, unlike existing approaches, considers several crucial factors as follows: (1) involvement of all existing stakeholders in the system, (2) land fragmentation and merging during transfer, (3) classification of lands like industrial, farming, barren, (4) marking lands that are under some court dispute, (5) overall Government monitoring, (6) automated pricing of lands with land search feature, and (7) support of proper payment channels. As a proof of concept, we provide a prototype implementation of the system written in solidity on the Ethereum platform with a user interface using ReactJS. We conduct an experimental evaluation to establish the feasibility and effectiveness of our proposed system using Hyperledger Caliper which demonstrates the system performance in terms of execution gas costs, CPU utilization, average latency, and transaction throughput.
Unmanned aerial systems, geology’s newest aerial technology

The original geologist toolkit includes: a hand lens, compass, rock hammer, writing instrument and a field notebook, paired with a love for the outdoors and the desire to understand the earth and the processes that shape it. Today, the latter part remains true, but the toolkit has innumerous variations that can provide multitudes of new information, by enhancing accuracy, altering perspectives, and offering completely new capabilities. It has been said that in the last 100 years humanity has entered a ‘technological renaissance.’ This rapid period of innovation and exploration includes a significant amount of observation equipment like digital cameras, global positioning systems (GPS), satellites, drones, and even smartphones. Widespread, accessible surveillance devices certainly pose a serious threat to privacy, but the ‘eye(s) in the sky’ can be used for good as well. The foundation of science is solid observation and geologists have many things to observe. Geologists work tirelessly to evaluate and mitigate geologic hazards such as floods, mass movements, earthquakes, and volcanic activity. Unmanned aerial systems (UASs) also known as drones, can carry observatory equipment into the most dangerous or inaccessible places during and after these catastrophic events without compromising safety to gather data.
A YOLOv5-based Target Detection Algorithm Design

At the beginning of 2020 Gengzi, a new coronavirus pneumonia (COVID - 19) that swept the world from the sky ravaged the land of God. In order to effectively organize the massive spread of the epidemic, this paper proposes a system that combines YOLOv5 to provide detection of faces wearing masks. The system is in a situation where one or more persons wearing masks in different scenarios can be detected. The design first uses a collection of mask face data under a variety of different wearing conditions and obtains a trained detection model using the above method to achieve the detection of whether a face is wearing a mask. The detection system can effectively detect the face mask wearing situation detected in the local picture elements, local video elements and the camera real- time shooting screen. The recognition effect of the system is verified to be 0.945, which is a significant improvement compared with other algorithms.
Geolocational Soundwalk as ecological choreography: Walking and listening towards ecological awareness

This article, which is based on a thesis written for an MA in Contemporary Theatre, Dance and Dramaturgy at Utrecht University, considers the ecological potential of performing arts through a lens of a newly proposed concept of ecological choreography with reference to a stimulation of ecological experience of interconnectedness and an attention on the format of a geolocational soundwalk (through a study case of Sandbox). A geolocational soundwalk is an example of ecological choreography due to its characteristics of practical mechanics and the use of sound. Choreography, here defined as an organisation of things in space and time, refers to performance’s way of producing such ecological experiences. Relying on a combination of geographical location and sound excerpts instigated by these coordinates, a geolocational soundwalk’s audience experiences interconnectedness immediately when encountering the medium itself. The interdependence of its elements is ingrained into the artwork’s structure and any missteps from the audience which disturb the interconnectedness are visible, revealing the web of complex connections. In combination with presenting topics such as degradation of public space and the return of natural sounds during the pandemic, Sandbox produces an experience of interconnectedness for the attendee. When listening to sound excerpts and stories about the physical place they are walking, the web of connections between the location, the mechanics of the soundwalk as well as ecological topics is revealed. The embodied experience, which is the ecological contribution of performance, is what makes the soundwalk an example of the concept of ecological choreography.
Alter Ego reveals the future metaverse: Reality is fiction

In fall, Americans were introduced to the first network game show employing virtual reality, aptly titled Alter Ego . Its premise, spinning off several prior reality shows, gave music performers opportunities to compete nationally, but this time contestants assumed the persona of an Alter Ego .
Target Tracking Based on Spatiotemporal Saliency and Multiscale Appearance Cue Fusion

In this paper, an unsupervised approach for tracking moving targets in videos is proposed. The proposed approach combines multi-scale appearance cues and spatiotemporal saliency. Firstly, the gradient distribution of the optical flow field at lower spatiotemporal scales is analyzed to get a global saliency map, estimating the initial range of the moving target. Then, the structured forest algorithm is used to extract more refined edge information. Appearance clues including HOG, color histogram, color contrast and spatial color variance are fused to describe local spatial information. The spatial saliency map is calculated by the geodesic distance-based method according to the spatial feature difference of superpixels. The temporal saliency map is generated based on the position and color deviation of the superpixels from two adjacent frames. Finally, the global saliency map, the spatial map and the temporal saliency map are fused to obtain the spatiotemporal saliency map, indicating the region of moving targets. Experimental results on two benchmark video datasets show that our approach does not need prior information and can achieve superior experimental results compared with four previous classical algorithms.