Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining

AbstractOne of the greatest challenges in speech technology is estimating the speaker's emotion. Most of the existing approaches concentrate either on audio or text features. In this work, we propose a novel approach for emotion classification of audio conversation based on both speech and text. The novelty in this approach is in the choice of features and the generation of a single feature vector for classification. Our main intention is to increase the accuracy of emotion classification of speech by considering both audio and text features. In this work we use standard methods such as Natural Language Processing, Support Vector Machines, WordNet Affect and SentiWordNet. The dataset for this work have been taken from Semval -2007 and eNTERFACE’05 EMOTION Database

Livelihood security policy can support ecosystem restoration

Earth faces an unprecedented ecological crisis: the destruction of its ecosystems. Despite increasing interest in restoration, including through the UN Decade on Ecosystem Restoration (Decade), lack of financing and resources mean efforts to reverse degradation have advanced slowly. Restoration efforts require new approaches to ensure the needs of different stakeholders are met. However, analyses of policies and opportunities that help to finance restoration while improving socioecological outcomes, are lacking. This paper analyzes livelihood security funding and opportunities for ecosystem restoration, drawing on India's Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), the world's largest livelihood security program. The paper analyzes MGNREGA's performance between financial years 2013–2021, focusing on the financing of ecosystem restoration-related works, community mobilization and policy implementation in the early part of the COVID-19 pandemic. Then, the paper reflects on the benefits and shortcomings of MGNREGA and considers wider lessons for the Decade. MGNREGA generated significant funding flows and numbers of projects nationally, which can contribute to ecosystem restoration. Policy design enabled the continuation and increase of works even during the first year of the COVID-19 pandemic. Our findings demonstrate the potential of linking ecosystem restoration with development policies to unlock funds, on a national scale. To maximize contributions to ecosystem restoration nevertheless requires capacity building, inclusion of environmental indicators and integration of best ecosystem restoration practices

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Adoption of Web-Enabled Student Evaluation of Teaching (WESET)

The “student voice” movement, which advocates for the critical importance of seeking and applying student input into educational decisions such as curriculum development and teaching methods, has been gaining momentum. We examine “student voice” through the vehicle of “Student Evaluation of Teaching (SET)” in the context of higher education. We treat Web-Enabled Student Evaluation of Teaching (WESET) in higher educational institutions as an innovation and apply Diffusion of Innovation theory to study its adoption. We study WESET rates of adoption by analyzing data from 45,934 anonymous student feedbacks of 427 teachers by 1102 students over a period of five years covering both undergraduate and graduate programs at an Indian university. Data from 589 courses in three distinct academic disciplines were collected and analyzed. The adoption rate of the students is primarily attributed to three factors: (a) the guarantee that the system will maintain anonymity, (b) expectation that student feedback will result in positive changes, and (c) ease of use as WESET was integrated into an existing system already used by students. Student evaluations for the same courses significantly improved over each subsequent semester, suggesting that faculty had incorporate student feedback into their curriculum and teaching methods

Exploiting label dependency and feature similarity for multi-label classification

Exploiting label dependency and feature similarity for multi-label classificatio

Early Research Trends on ChatGPT: Insights from Altmetrics and Science Mapping Analysis

In the four months following its launch in December 2022, ChatGPT, the LLM bot employing deep learning algorithms to generate human-like responses, has been the subject of numerous research articles. Identifying early attention to this research is highly intriguing. As citations for these publications may take time to accumulate, our study focused on examining the early attention of ChatGPT research using the Altmetric Attention Score (AAS), a composite attention score developed by Digital Science. Our findings from the total set of publications and the top publications according to the highest AAS scores reveal the following trends: (i) The United States, Japan, and the United Kingdom are the top countries that published most of the top research articles related to ChatGPT. (ii) The most frequently mentioned source titles include journals like Nature, Science and preprint sources like medRxiv and arXiv. (iii) Among the fields of research (FoR) to which ChatGPT publications align, ‘information and computing sciences’ and ‘biomedical and clinical sciences’ received the highest mentions. (iv) Five major clusters were identified in the network formed by the interlinkage of FoRs, and the most prominent themes discussed in top articles within these five clusters include ChatGPT usage in medical writing and determining ChatGPT’s role in scientific publishing. (v) Scientists are found to be the major user category demonstrating the highest level of interest in ChatGPT research. By capturing these early trends in ChatGPT research and the early attention to this research, our work offers valuable insights for ChatGPT enthusiasts, researchers, and policymakers in fields such as education, information sciences, biomedical sciences, scientific publishing, and many others

Evaluating Holistic Education and Digital Learning model for advancing SDG4: a longitudinal mixed-effects modeling approach

AbstractThis longitudinal study evaluates the Holistic Education and Digital Learning (HEDL) model within rural Indian contexts, contributing to United Nations Sustainable Development Goal 4 (SDG4). The holistic education includes activities such as yoga, environmental activities, cultural programs, cleanliness drives and substance abuse ambassador programs while the digital learning encompasses applications for language learning, numeracy, touch writing and vocabulary enhancement. The dataset comprises 8869 students from 78 HEDL centers across 21 Indian states, monitored over 5 years through standardized assessments, attendance metrics and digital teacher supervision. Employing mixed-effects models with nested random effects for centers and students, the findings indicate that the HEDL model significantly elevates literacy and language skills in these settings. The digital learning component alone contributes to a 0.5% average weekly literacy gain. Furthermore, the holistic educational components demonstrate a statistically significant correlation with improved literacy outcomes: a 25% increased likelihood of achieving grade-level reading and a 63% increased likelihood of attaining grade-level writing. The results are found to be reliable and robust across time and a large number of locations across India. The results contribute to understanding the dual role of blended learning and holistic education in rural education and underscore the potential of such pedagogical models