Abstract: This paper presents the experience in an eLearning training project that is being implemented for electrical planning engineers from the national Mexican utility Comision Federal de Electricidad (CFE) Distribution. This modality is implemented and will be used in the utility for training purposes to help personnel in their daily technical activities. One important advantage of this training project is that once it is implemented and applied, financial resources will be saved by CFE Distribution Company because online training will be used in all the country; the infrastructure for the eLearning training will be uploaded in computational servers installed in the National CFE Distribution Training Department, in Ciudad de Mexico, and can be used in workplaces of 16 Distribution Divisions and 150 Zones of CFE Distribution. In this way, workers will not need to travel to the National Training Department, saving enormous efforts, financial, and human resources.
Abstract: Anticholinergic medication has been associated with events such as falls, delirium, and cognitive impairment in older patients. To further assess this, anticholinergic burden scores have been developed to quantify risk. A risk model based on clustering was deployed in a healthcare management system to cluster patients into multiple risk groups according to anticholinergic burden scores of multiple medicines prescribed to patients to facilitate clinical decision-making. To do so, anticholinergic burden scores of drugs were extracted from the literature which categorizes the risk on a scale of 1 to 3. Given the patients’ prescription data on the healthcare database, a weighted anticholinergic risk score was derived per patient based on the prescription of multiple anticholinergic drugs. This study was conducted on 300,000 records of patients currently registered with a major regional UK-based healthcare provider. The weighted risk scores were used as inputs to an unsupervised learning algorithm (mean-shift clustering) that groups patients into clusters that represent different levels of anticholinergic risk. This work evaluates the association between the average risk score and measures of socioeconomic status (index of multiple deprivation) and health (index of health and disability). The clustering identifies a group of 15 patients at the highest risk from multiple anticholinergic medication. Our findings show that this group of patients is located within more deprived areas of London compared to the population of other risk groups. Furthermore, the prescription of anticholinergic medicines is more skewed to female than male patients, suggesting that females are more at risk from this kind of multiple medication. The risk may be monitored and controlled in a healthcare management system that is well-equipped with tools implementing appropriate techniques of artificial intelligence.
Abstract: Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.
Abstract: Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfil requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.
Abstract: In higher education, a great degree of importance is placed on the internationalisation of the student experience. This is seen as a valuable contributor to elements such as building confidence, broadening knowledge, creating networks, and connections and enhancing employability for current students who will become the next generation of managers in technology and business. The COVID-19 pandemic has affected all areas of people’s lives. The limitations of travel coupled with the fears and concerns generated by the health risks have dramatically reduced the opportunity for students to engage with this agenda. Institutions of higher education have been required to rethink fundamental aspects of their business model from recruitment and enrolment, through learning approaches, assessment methods and the pathway to employment. This paper presents a case study which focuses on student mobility and how the physical experience of being in another country either to study, to work, to volunteer or to gain cultural and social enhancement has of necessity been replaced by alternative approaches. It considers trans-national education as an alternative to physical study overseas, virtual mobility and internships as an alternative to international work experience and adopting collaborative on-line projects as an alternative to in-person encounters. The paper concludes that although these elements have been adopted to address the current situation, the lessons learnt and the feedback gained suggests that they have contributed successfully in new and sometimes unexpected ways, and that they will persist beyond the present to become part of the "new normal" for the future. That being the case, senior leaders of institutions of higher education will be required to revisit their international plans and to rewrite their international strategies to take account of and build upon these changes.
Abstract: The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.
Abstract: A common complaint from power system analysis students lies in the overly complex tools they need to learn and use just to simulate very basic systems or just to check the answers to power system calculations. The most basic power system studies are power-flow solutions and short-circuit calculations. This paper presents a simple tool with an intuitive interface to perform both these studies and assess its performance in comparison with existent commercial solutions. With this in mind, Elegant is a pure Python software tool for learning power system analysis developed for undergraduate and graduate students. It solves the power-flow problem by iterative numerical methods and calculates bolted short-circuit fault currents by modeling the network in the domain of symmetrical components. Elegant can be used with a user-friendly Graphical User Interface (GUI) and automatically generates human-readable reports of the simulation results. The tool is exemplified using a typical Brazilian regional system with 18 buses. This study performs a comparative experiment with 1 undergraduate and 4 graduate students who attempted the same problem using both Elegant and a commercial tool. It was found that Elegant significantly reduces the time and labor involved in basic power system simulations while still providing some insights into real power system designs.
Abstract: Due to the COVID-19 pandemic, its impacts on education all over the world, and the problems arising from the use of traditional methods in education during the pandemic, it was necessary to apply alternative solutions to achieve educational goals. In this regard, electronic content production through screen recording became popular among many teachers. However, the production of screen-recorded videos requires special technical and pedagogical considerations. The purpose of this study was to extract and present the technical and pedagogical considerations for producing screen-recorded videos to provide a useful and comprehensive guideline for e-content producers. This study was applied research, the design was descriptive, and data collection has been done using qualitative method. In order to collect the data, 524 previously produced screen-recorded videos were evaluated by using an open-ended questionnaire. After collecting the data, they were categorized, and finally, 83 items as technical and pedagogical considerations in the form of 5 domains were determined. By applying such considerations, it is expected to decrease producing and editing time, increase the technical and pedagogical quality, and finally facilitate and enhance the processes of teaching and learning.
Abstract: This paper presents the Language Learning as a Means of Drive Engagement (LLMDE) theory, derived from a grounded theory analysis of interviews with Japanese university students. According to LLMDE theory, language learning can be understood as a means of engaging one or more of four self-fulfillment drives: the drive to expand one’s horizons (perspective drive); the drive to make a success of oneself (status drive); the drive to engage in interaction with others (communication drive); and the drive to obtain intellectual and affective stimulation (entertainment drive). While many theories of learner psychology focus on conscious agency, LLMDE theory addresses the role of the unconscious. In addition, supplementary thematic analysis of the data revealed the role of context in mediating drive engagement. Unexpected memorable events, for example, play a key role in instigating and, indirectly, in regulating learning, as do institutional and cultural contexts. Given the apparent importance of such factors beyond the immediate control of the learner, and given the pervasive role of habit and drives, it is argued that the concept of motivation merits theoretical reappraisal. Rather than an underlying force determining language learning success or failure, it can be understood to emerge sporadically in consciousness to promote behavioral change, or to protect habitual behavior from disruption.
Abstract: Sexuality remains a personal or a private matter of discussion in the Indian society and generally discussed among the same age group or gender. Complete absence of the sex education has caused serious implications for the students with disabilities in Indian society. There are widespread perceptions that student with disabilities are ‘asexual’, ‘unattractive’ and therefore cannot be considered sexually desirable. Such perceptions continue to reinforce the other perceptions that student with disabilities are somehow incapable of being in an intimate relationship in the life and therefore they do not need any learning related to the sex education. We need to understand that if a student has a disability, it does not mean that student have no emotional feelings, hormones and sexual desires like any other student without disability. Sexuality is an integral part of every human life and should not be seen as matter of shame and guilt. Unfortunately, the concept of the sex education is misunderstood in itself. Instead of realizing the crucial importance of sex education for the students with disabilities or non-disabilities, it is often considered mainly as an education about ‘how to have sexual intercourse’. One needs to understand that it is not just about sexual conduct but also about the gender and sexual identity, self-esteem, self protection and acceptance of self. This research paper examined issues and debates around the sex education, particularly in context of the students with disabilities in India and focuses on how students with disabilities themselves see the need of sex (health) education. To understand their perceptions, descriptive survey method was used. It was found that most of the students among respondent were comfortable and felt it as a strong need for such orientation during their schooling. The paper emphasizes that sex education is a need of the time and further a necessity. Hence it is important for our education system to implement it for the complete well being of the students with disabilities.
Abstract: Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.
Abstract: Music is ubiquitous in human lives. Ever since the foetus hears the sound inside the mother’s womb and later upon birth the baby experiences alluring sounds, the curiosity of learning emanates and evokes exploration. Music is an education than a mere entertainment. The intricate balance between music, education and entertainment has well been recognized by the scientific community and is being explored as a viable tool to understand and improve the human cognition. There are seven basic swaras (notes) Sa, Ri, Ga, Ma, Pa, Da and Ni in the Carnatic music system that are analogous to C, D, E, F, G, A and B of the western system. The Carnatic music builds on the conscious use of microtones, gamakams (oscillation) and rendering styles that evolved over centuries and established its stance. The complex but erudite raga system has been designed with elaborate experiments on srutis (musical sounds) and human perception abilities. In parallel, ‘rasa’- the emotions evoked by certain srutis and hence the ragas been solidified along with the power of language in combination with the musical sounds. The Carnatic music branches out as Kalpita sangeetam (pre-composed music) and Manodharma sangeetam (improvised music). This article explores the Manodharma sangeetam and its subdivisions such as raga alapana, swara kalpana, neraval and ragam-tanam-pallavi (RTP). The intrinsic mathematical strategies in its practice methods toward improvising the music have been discussed in detail with concert examples. The techniques on swara weaving for swara kalpana rendering and methods on the alapana development are also discussed at length with an emphasis on the impact on the human cognitive abilities. The articulation of the outlined conscious practice methods not only helps to leave a long-lasting melodic impression on the listeners but also onsets cognitive developments.
Abstract: Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.
Abstract: The use of brain stem auditory evoked potential (BAEP) is a common way to study the hearing function of people, a way to learn the functionality of a part of the brain neuronal groups that intervene in the learning process by studying the behaviour of wave VI. The latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of innocuous, low-cost and easy-access techniques such as, among others, the BAEP that can help us to detect early possible neurodevelopmental difficulties for their subsequent assessment and cure. To date and the authors best knowledge, only the latency data obtained, observing the first to V waves and mainly in the left ear, were taken into account. This work shows that it is essential to consider both ears; with these latest data, it has been possible to diagnose more precisely some cases than with the previous data had been diagnosed as “normal”despite showing signs of some alteration that motivated the new consultation to the specialist.
Abstract: Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of acquiring new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used in this work is to analyze the dynamics of different brain areas during a cognitive activity to find the relationships between the other areas analyzed to understand the functioning of neural networks better. Also, the latest advances in neuroscience have revealed the exis-tence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neurodevelopmental difficulties for their subsequent assessment and therapy. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process, specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho-pedagogical plans that allow obtaining an optimal integral development of the affected people.
Abstract: Software engineering curriculums generally consist of industry-based practices such as project-based learning (PBL) which mainly focuses on efficient and innovative product development. These approaches can be tailored and used in project-based modules in software engineering curriculums. However, there are very limited attempts in the area especially related to Sri Lankan context. This paper describes a tailored pedagogical approach and its results of using design sprint which can be used for project-based modules in software engineering (SE) curriculums. A controlled group of second year software engineering students was selected for the study. The study results indicate that all of the students agreed that the design sprint approach is effective in group-based projects and 83% of students stated that it minimized the re-work compared to traditional project approaches. The tailored process was effective, easy to implement and produced desired results at the end of the session while providing students an enjoyable experience.
Abstract: Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.
Abstract: Entrepreneurship is an essential source of economic growth and a prominent factor influencing socio-economic development. Entrepreneurship education educates and enhances entrepreneurial activity. This study aims to understand current trends in entrepreneurship education and evaluate the effectiveness of diverse entrepreneurship education programs. An increasing number of universities offer entrepreneurship education courses to create and successfully continue entrepreneurial ventures. Despite the prevalence of entrepreneurship education, research studies lack inconsistency about the effectiveness of entrepreneurship education to promote and develop entrepreneurship. Strategies to develop entrepreneurial attitudes and intentions among individuals are hindered by a lack of understanding of entrepreneurs' educational purposes, components, methodology, and resources required. Lack of adequate entrepreneurship education has been linked with low self-efficacy and lack of entrepreneurial intent. Moreover, in the age of digitisation and during the COVID-19 pandemic, digital learning platforms (e.g. online entrepreneurship education courses and programs) and other digital tools (e.g. digital game-based entrepreneurship education) have become more relevant to entrepreneurship education. This paper contributes to the continuation of academic literature in entrepreneurship education by evaluating and assessing current trends in entrepreneurship education programs, leading to better understanding to reduce gaps between entrepreneurial development requirements and higher education institutions.
Abstract: Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially the fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma-chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.
Abstract: The COVID-19 pandemic has affected the way Higher Education Institutions (HEIs) have given their courses. From emergency remote where all students and faculty were immediately confined to home teaching and learning, the continuing evolving sanitary situation obliged HEIs to adopt other methods of teaching and learning from blended courses that included both synchronous and asynchronous courses and activities to HyFlex models where some students were on campus while others followed the course simultaneously online. Each semester brought new challenges for HEIs and, subsequently, additional emotional reactions. This paper investigates the affective side of teaching and learning in various online modalities and its toll on students and faculty members over the past three semesters. The findings confirm that students and faculty who have more self-efficacy, flexibility, and resilience reported positive emotions and embraced the opportunities that these past semesters have offered. While HEIs have begun a new semester in an attempt to return to ‘normal’ face-to-face courses, this paper posits that there are lessons to be learned from these past three semesters. The opportunities that arose from the challenge of the pandemic should be considered when moving forward by focusing on a greater emphasis on the affective aspect of teaching and learning in HEIs worldwide.