Reviewerly/exHarmony huggingface dataset:
Perceptual Content-Aware Bitrate Adaptation for HTTP Streaming using Markov Decision Process

This paper presents a perceptual content-aware bitrate adaptation algorithm for the HTTP streaming services. Compared with the traditional throughput-based and buffer-based algorithms, the impact of visual perception on user's Quality of Experience (QoE) has also been considered. We model the content-aware bitrate adaptation problem into a Markov Decision Process (MDP) and develop a segmented value iteration method to solve this problem. We have integrated this adaptive algorithm into dash.js, on which we can compare our approach with the default throughput-based algorithm and well-known BOLA algorithm. The results have shown that our algorithm can not only reach the higher average quality on the premise of maintaining fluency but also enable the scenes with higher attention to obtain higher quality, thus ultimately improve QoE.
Bayesian Estimation of Single and Multiple Graphs

In this chapter, we discuss Bayesian approaches for the inference of both single and multiple networks. To begin, we provide an overview of Bayesian graphical modeling approaches to learn directed and undirected networks, from either Gaussian or non-Gaussian data. We then provide an in-depth description of Bayesian models for the joint estimation of multiple undirected networks, where either the inclusion of edges or the edge values themselves are related across settings. These approaches highlight some of the advantages of taking a Bayesian approach, as hierarchical priors enable sharing of information across groups, automatic learning of parameters, and posterior estimates of uncertainty regarding individual networks and their similarity. We include simulation studies comparing the performance of Bayesian and frequentist methods for the inference of multiple networks, as well as a case study on brain structural connectivity networks during the progression of Alzheimer's disease.
A ROBUST TECHNIQUE OF FAKE NEWS IDENTIFICATION USING ENSEMBLE FEATURE SELECTION

In the modern era, selecting the best feature from the high dimensional dataset with multiple variables has become a challenging task.It has become very prominent to train the model with relevant features eliminating the un-necessary feature.The traditional method feature selection methods are performing their excellence in the field of selecting the feature by creating a subset of feature from the dataset.Though it performs well, sometimes they are not helpful in learning the model by selecting single feature with single classifier.This overfits the model and leads to unnecessary confusion.Therefore, this work implements a Robust Framework of Ensemble feature selection Technique.The ensemble learning combines two or more outputs in which they may be the same type or different types, and they may or may not have been trained on the same training data.This study aims to extract the false news sub features by combining multiple subsets of features using ensemble technique.To get an efficient feature on the fake news opinion pool the Feature Score, Recursive Feature Selection and Elasticnet Feature selection has been used.Finally, feature importance has been created for each feature acts as an aggregator to select the final subset of features.The performance has been analysed with 5 classification algorithms of SVM RF, Logistic Regression, Gradient Boosting Classifier and Ridge classifier.The overall performance with accuracy has been evaluated with each classifier.The best classifier is determined by the highest accuracy rate.Our proposed implemented framework determines that Random Forest acquires a better performance in Accuracy.
A multi-purpose dataset of Devanagari script comprising of isolated numerals and vowels

This article presents handwritten isolated characters of the Devanagari script. Devanagari script contains ten numerals, 13 vowels, and 33 consonants. Devanagari Character dataset includes 23 different characters of numerals and vowels. 2400 handwritten samples are collected for each of the numerals and 1400 for each vowel. Collected samples are digitized and pre-processed. During pre-processing, images with noise are removed. In this context, a final dataset of 38,750 images were included, where 2,250 and 1,250 samples for each numeral and vowel, respectively. The data is available in images and comma-separated-values, along with attached labels. The dataset could be used for Optical Character Recognition research and deep learning. In India, the Devanagari script is the base script on which 120+ languages are evolved; hence this dataset serves as the base for Machine Learning research in these languages. The data set is publicly available at https://data.mendeley.com/datasets/pxrnvp4yy8/2.
Comparative analysis of AI-based models for short-term photovoltaic power forecasting in energy cooperatives

Energy communities can support the energy transition, by engaging citizens through collective energy actions and generate positive economic, social and environmental outcomes. Renewable Energy Sources (RES) are gaining increasing share in the electricity mix as the economy decarbonises, with Photovoltaic (PV) plants to becoming more efficient and affordable. By incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed to provide added value to energy communities. In this context, the scope of this paper is to compare Machine Learning (ML) and Deep Learning (DL) algorithms for the prediction of short-term production in a solar plant under an energy cooperative operation. Three different cases are considered, based on the data used as inputs for forecasting purposes. Lagged inputs are used to assess the historical data needed, and the algorithms’ accuracy is tested for the next hour’s PV production forecast. The comparative analysis between the proposed algorithms demonstrates the most accurate algorithm in each case, depending on the available data. For the highest performing algorithm, its performance accuracy in further forecasting horizons (3 hours, 6 hours and 24 hours) is also tested.
DEVELOPING DIGITAL - BASED COMIC MEDIA IN TEACHING MATHEMATICS FOR ELEMENTARY SCHOOL STUDENTS

This research aims to develop digital-based comic media on addition and subtraction materials of mathematics for elementary school students at grade II. This research was motivated by a lack of knowledge of students on addition and subtraction materials. The novelty of this media is in the form of files without using sheets of paper. This study used ADDIE model (Anlysis, Design, Develop, Implement, and Evaluate). Based on the five stages of development of comic media resulted from the feasibility of 90% while the practicality of teachers obtained a percentage of 90% and practicality of students obtained a percentage of 93%. The use of products gets a percentage of 86% with the category "excellent". This study has an implication that that the use of learning media makes students more actively involved in the learning process especially those in elementary schools. Therefore, teachers are supposed to maintain their creativity in teaching.
Real-Time Image Detection of Traffic Light in Complex Intersection Traffic Environment

In order to improve traffic safety at the road intersections when vehicles passing by, a traffic light detection technology based on the traditional image processing method for complex intersection traffic environments is proposed. Firstly, a dataset of traffic images was collected, and distribution laws of the HSV pixel values were statistically analyzed to produce a set of segmentation thresholds under some specified probabilities. Secondly, distribution characteristics of the geometric shape parameters for traffic lights in real drive situations were resolved so as to made their numerical intervals certain. Then, with the help of a defined 9-dimensional pixel density feature, a support vector machine was trained to judge whether the image was a traffic light or not and recognize its type, by which way a large number of interference images were filtered out. Finally, another support vector machine trained by a defined 1764-dimensional feature of the extended image regions was used to verify the survived images to reduce the wrong detection rate. Experiment results showed that the detection algorithm worked with a 98.41% accuracy rate and 1.08% missed detection rate, meanwhile showed a strong adaptability and good real-time performance.
COACHING AS A FORM OF ACTIVE LEARNING IN FOREIGN LANGUAGE CLASSES

The authors analyse the interpretations of the concept of coaching in scientific thought in general, and as a new direction in the educational environment in particular. It has been found out that special-pedagogical coaching originated in the USA as «Content-Focused Coaching» and was used in teaching staff training. Theoretical and methodological aspects of pedagogical coaching, which contributes to the growth of motivation of higher education students, both in personal and communicative development and helps to realize their potentialities, have been investigated. The key aspects of pedagogical coaching such as: implementation of activities on the principles of partnership, joint preparation for the class and implementation of its stages, and reflection have been highlighted. It has been found out that «linguistic coaching» is widely used in foreign language teaching, which is aimed at creating optimal conditions for acquisition, and effective and quick transmission of language knowledge from a teacher (coach) to a student, providing, first of all, the student’s awareness of learning objectives and resources, increasing the variety of learning ways, raising the responsibility of a higher education student for the course and the results of learning. The importance of the coach’s role has been emphasized, the principles and algorithms of his/her actions in organizing and conducting a foreign language class using linguistic coaching have been determined. It has been stated that the coaching technologies under analyzing are realized by means of dialogic communication and cover 4 stages: motivation, planning, realization, reflexion. The article substantiates the main stages of a session with the use of coaching forms of training, reveals the principles and methods of work. The coaching technique is gaining popularity and is more and more often used in the educational process of higher educational institutions. Key words: coaching, foreign language, foreign language teaching, linguistic coaching, active learning methods.
Improving productivity through corporate hackathons: A multiple case study of two large-scale agile organizations

Softwaredevelopment companies organize hackathons to encourage innovation.Despite many benefits of hackathons, in large-scale agile organizations where many teams work together, stopping the ongoing work results in a significant decrease in the immediate output.Motivated by the need to understand whether and how to run hackathons, we investigated how the practice affects productivity on the individual and organizational levels.By mapping the benefits and challenges to an established productivity framework, we found that hackathons improve developers' satisfaction and wellbeing, strengthen the company culture, improve performance (as many ideas are tested), increase activity (as the ideas are developed quickly), and improve communication and collaboration (because the social network is strengthened).Addressing managerial concerns, we found that hackathons also increase efficiency and flow because people learn to complete work and make progress quickly, and they build new competence.Finally, with respect to virtual hackathons we found that developers work more in isolation because tasks are split between team members resulting in less collaboration.This means that some important, expected hackathon values in virtual contexts require extra effort and cannot be taken for granted.
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topics from Scientific Papers

A common writing style for statistical results are the recommendations of the American Psychology Association (APA). In practice, writing styles vary as reports are not 100% following APA-style or parameters are not reported despite being mandatory. In addition, the statistics are not reported in isolation but in context of experiment conditions investigated and the general experiment topic. We address these challenges by proposing a flexible pipeline STEREO based on wrapper induction and unsupervised aspect detection to extract experiment statistics, conditions, and topics. Thus, in contrast to existing rule-based tools like statcheck with a pre-defined set of rules, we learn rules via induction. It required only 0.25% of the CORD-19 corpus (about 500 documents) to learn statistics extraction rules that cover 95% of the sentences in CORD-19. The statistic extraction has 100% precision on APA-conform statistics, which is identical with statcheck. In addition, STEREO can extract non-APA writing styles with precision, which statcheck does not support. Extracting non-APA conform statistics is important as they make more than 99% of all 113k extracted statistics. We could extract in 46% the correct conditions from APA-conform reports (30% for non-APA). The best model for topic extraction achieves a precision of 75% on statistics reported in APA style (73% for non-APA conform).
Collaborative Multi-Resolution MSER and Faster RCNN (MRMSER-FRCNN) Model for Improved Object Retrieval of Poor Resolution Images

Object detection and retrieval is an active area of research.This paper proposes a collaborative approach that is based on multi-resolution maximally stable extreme regions (MRMSER) and faster region-based convolutional neural network (FRCNN) suitable for efficient object detection and retrieval of poor resolution images.The proposed method focuses on improving the retrieval accuracy of object detection and retrieval.The proposed collaborative model overcomes the problems in a faster RCNN model by making use of multiresolution MSER.Two different datasets were used on the proposed system.A vehicle dataset contains three classes of vehicles and the Oxford building dataset with 11 different landmarks.The proposed MRMSER-FRCNN method gives a retrieval accuracy 84.48% on Oxford 5k building dataset and 92.66% on vehicle dataset.Experimental results show that the proposed collaborative approach outperform the faster RCNN model for poor-resolution conditioned query images.
Active Learning for Argument Strength Estimation

High-quality arguments are an essential part of decision-making.Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining.However, the annotation effort for this task is exceptionally high.Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled.Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.
SSE: A Smart Framework for Live Video Streaming based Alerting System

Automated surveillance is always been a matter of curiosity due to its applications and the freedom through implicit monitoring. A smart implicit monitoring needs to be smarter with un-intervening inference-based classification, decision, and alerting processes. In this same sequence, detection and classification of unusual activities is the utmost curiosity among researchers. The entrance of Artificial Intelligence and the various computing ways (like Machine Learning and Deep Learning methods) of achieving it has been proven the most influential and promising computing revolution in the last decade. AI&ML-based object detection, segmentation, and identification has proven its vulnerability towards the achievement of these such goals and making computer vision smarter than ever before. In this paper, we are proposing a framework for an intelligent surveillance system based on AI&ML for video-based live surveillance. The proposed framework will provide a pathway to the intelligent system design for automated monitoring and alerting for unusual events based on detected objects. Basically, it would be a live streaming-based altering system.
Democratizing University Seat Allocation using Blockchain

Online seat allocation processes such as Joint Seat Allocation Authority in India have streamlined the university seat allocation process and reduced the risk of seats being vacant. Similar centralized online counseling processes are used for many universities in different countries. In-spite of being a collaborative process involving different stakeholders, such systems are centralized having their inherent limitations including lack of transparency, risk of censorship, manipulation, and single point of failure. In this demonstration, we showcase a decentralized ledger technology based system and application for democratizing the university seat allocation process. We demonstrate that the user experience of the proposed system is almost identical to the traditional centralized one, in spite of having the additional benefits of transparency, auditability, and non-repudiability of the decentralized architecture.
The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

The purpose is to respond to the call of the government on the artificial intelligence (AI) policy of music online education. First, wireless network technology and AI technology are described in detail; the application principle of AI technology in traditional music teaching mode and online teaching mode is analyzed; then, the combination model of AI technology and music online education system in 5th-generation (5G) wireless network environment is proposed. These models include music online composition teaching model, music online teaching scoring model, and music online lyric writing teaching model. Finally, the models are verified in the application of AI technology. The results show that AI technology has good application performance in the models above. Moreover, the direction of music online teaching reform and innovation along the combination of 5G network environment and AI is correct under the background of the new era. This exploration expands the application scenario of AI technology and provides ideas for the reform and innovation of music online education in the context of the new era.
Cloud Architecture Based Learning Kit Platform for Education and Research – A Survey and Implementation

In today’s scenario technology has become an important part of life. Technology plays a very vast and significant role in the tremendously increasing education system. Having technical education has become mandatory in current time for education and research. Every day new technologies and researches are coming up to improve the teaching methodologies providing a refined and easier way of learning and practical approaches. Cloud computing is a rapidly emerging technology for education 4.0 in recent years. Cloud computing is all about enabling resources to users according to their need over the internet. Cloud computing, mainly provides the services like servers, databases, networking, storage etc. This paper presents implementation of a learning kit consisting of cloud integration with computing platform i.e. raspberry pi; multiple sensors have been connected to it. The main aim of integration of cloud computing with raspberry pi is to collect and monitor the real time data generated via cloud. This paper describes the implementation, challenges and other aspects of cloud based learning kit in education and research area and how to use these types of kits for learner, educator and researcher.
Can AI be music to our ears? [Artificial intelligence - music]

From &apos;deepfakes" of hits by Elvis and Nirvana, to real-time soundtracks for streamers, to otherworldly sounds generated by cutting-edge instruments, AIis transforming the way music is created and experienced. AI music systems now exploit artificial neural networks, modelled on the human brain, which adapt and learn from past patterns. When a large quantity of audio files or musical scores are fed in, the systems analyse the data, identify patterns such as melodies, drum breaks or chord progressions and produce new material in a similar style. musicians and producers work with the output to edit and enhance it using conventional techniques to arrive at a final composition. Musicians working with AI often see it as a tool to enhance creation and a democratising force, but others worry the technology will become more dominant and ultimately displace musicians from their jobs.
Critical Factors to Approach the Emergency Online Teaching Due to the COVID-19

This study analyzed the critical factors to approach the emergency online teaching due to the COVID-19 pandemic in a business school in Latin America. The data were collected through a semi-structured online questionnaire administered to 94 full-time and part-time faculty members. The study identified 10 critical factors for the successful implementation of emergency online teaching. The originality of the study lies in analyzing the experience in migrating to online teaching in a Hispanic higher education academic institution.
Deep Learning-based Image Super-resolution Algorithms - A Survey

Image super-resolution (SR) is a technique of enhancing image by increasing spatial resolution of the image.By performing image SR, the pixel intensity in an image is increased.Based number of input images, the SR technique is categorized into two types as single-image SR (SISR) and multi-image SR (MISR).This work analyses different characteristics of SISR on different image datasets like medical and real-world images by using different quality factors such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and perceptual index (PI).Also, it examines the complexity of SISR schemes based on their computation time.Based on the detailed study it is identified that the deep-leaning-based SR using component learning is a best method in terms of quantitative, qualitative and computation time of generating SR image.Further, it is suggested that to improve the quality a greater number of convolutional network layers can be used in SR algorithms.
Software Defect Fault Intelligent Location and Identification Method Based on Data Mining

Abstract With the advancement of the times, computer technology is also constantly improving, and people’s requirements for software functions are also constantly improving, and as software functions become more and more complex, developers are technically limited and teamwork is not tacitly coordinated. And so on, so in the software development process, some errors and problems will inevitably lead to software defects. The purpose of this paper is to study the intelligent location and identification methods of software defects based on data mining. This article first studies the domestic and foreign software defect fault intelligent location technology, analyzes the shortcomings of traditional software defect detection and fault detection, then introduces data mining technology in detail, and finally conducts in-depth research on software defect prediction technology. Through in-depth research on several technologies, it reduces the accidents of software equipment and delays its service life. According to the experiments in this article, the software defect location proposed in this article uses two methods to compare. The first error set is used as a unit to measure the subsequent error set software error location cost. The first error set 1F contains 19 A manually injected error program, and the average positioning cost obtained is 3.75%.