A Comparison of Algorithms for Text Classification of Albanian News Articles

Text classification is an essential work in text mining and information retrieval. There are a lot of algorithms developed aiming to classify computational data and most of them are extended to classify textual data. We have used some of these algorithms to train the classifiers with part of our crawled Albanian news articles and classify the other part with the already learned classifiers. The used categories are: latest news, economy, sport, showbiz, technology, culture, and world. First, we remove all stop words from the gained articles and the output of this step is a separate text file for each category. All these files are then split in sentences, and for each sentence the appropriate category is assigned. All these sentences are then projected to a single list of tuples sentence/category. This list is used to train (80% of the overall number) and to test (the remained 20%) different classifiers. This list is at the end shuffled aiming to randomize the sequence of different categories. We have trained and then test our articles measuring the accuracy for each classifier separately. We have also analysed the training and testing time. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

A Business Perspective on Internet of Things

Sensors are rapidly being integrated everywhere, in every aspect of our lives, ranging from home automation to wearable sensors for pervasive healthcare management. We hear ubiquitously for ubiquitous devices. In recent years, increasingly we are listening and talking about phrases like “Internet of Things”, “M2M”, “Context awareness”, “Web of Things”, which announce the new era to come, where we will be surrounded with smart devices, which communicate with each other to easer and beautify our lives. This article reports on our findings about the business perspective on internet of things. It involves literature review on the internet of things world and its impact in the business domain. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

The Who in VR/AR for Education: A Scoping Review from IEEE Publications

HEIs have continuously been the vanguard of novel technologies, pushing progress and establishing the afterward generation of scientists, businesspersons, and engineers. Virtual and augmented reality technologies are on the borderline of enlargement nowadays. To better understand who are essential actors in this new research area, we have extracted metadata of all published papers by IEEE, dating from 1990 to 2021, using keywords VR, AR, and Education. For each publication, we have extracted the list of all the authors, creating a co-authorship dataset. This dataset is then further processed using social network analysis metrics. The results are discussed to understand research publications\u27 main actors driving VR/AR/Education trends

Albanian Text Classification: Bag of Words Model and Word Analogies

Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%

A Google Classroom-Based Learning Management System: Empirical Evidence from SEEU

The use of e-learning in a higher education institution is identified by the implementation of Learning Management Systems (LMS). South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC). However, despite adoption of these systems, there are considerable challenges facing the usage of the systems. Hence, a tool has been developed to track the activity of the teachers in the system and to analyze the factors that maximize its usage. Moreover, a module for course and users’ management was also implemented. The purpose of this paper is to introduce a new approach of investigating the usage of GC, i.e. identifying the determinants of undertaking GC activities, by conducting empirical analysis for the case of SEEU. Using SEEU Usage Google Classroom Report &amp; Analysis Data for 2016–2017 (SUGCR dataset 2017), we argue that (i) GC activities are affected by demographic characteristics and (ii) level, number of courses, and department affect the usage of GC. We apply appropriate estimation technique such as mlogit methodology. Identifying factors which encourage GC activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering GC. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

LMS Solution: Evidence of Google Classroom Usage in Higher Education

Background: Learning Management Systems (LMS) represent one of the main technology to support learning in HE institutions. However, every educational institution differs in its experience with the usage of these systems. South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC) as an LMS solution. Objectives: Identifying factors which encourage LMS activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering LMS. Methods/Approach: This paper introduces new approach of investigating the usage of LMS, i.e. identifying the determinants of increasing usage of LMS activities, by conducting empirical analysis for the case of SEEU. We apply appropriate estimation technique such as OLS methodology. Results: Using SEEU Usage Google Classroom Report & Analysis Data for spring semester (2016–2017) and winter semester (2017–2018) - SUGCR dataset 2017, we argue that (i) LMS activities are affected by demographic characteristics and (ii) the students’ LMS usage is affected by level and resources of instructors’ LMS usage. Conclusions: The empirical results show positive relationship between student and instructors’ LMS usage