Understanding the role of emotions, usability components and design features in HCI

This paper has discussed the observations of various emotions which are invoked in the human computer interface and how these emotions affect the decisions of the users online. This paper has also discussed usability test methods in the human computer interface design and its relation with the usability of the interfaces. Also some principles are put forward which should be implemented for the development of better interfaces

Review and Analysis on Solar Energy Forecasting Using Soft Computing and Machine Learning Methodologies

Traditional power producing methods can't keep pace with India's growing need for electricity. New Delhi to Kolkata were all without power as of July 30, 2012, due to the world's largest blackout. In the next five years, India's power generation capacity will expand by 44 percent. Demand for power develops as India's population and economy expand. To reduce power outages and satisfy future energy needs, what needs to be changed? India has made the decision to move away from fossil fuels in favor of renewable energy sources, both for economic and environmental reasons. There has been an increase in the use of solar PV panels as a sustainable energy source in recent years. With improved access to data and computing power, machine-learning algorithms can now make better predictions. Machine learning and time series models can assist many stakeholders in the energy industry make accurate projections of solar PV energy output. In this study, various machine learning algorithms and time series models are evaluated to find which is most effective. While much research has already gone into wind energy forecasting, solar energy forecasting is only now beginning to see an uptick in interest. A detailed review and analysis model is presented in this study. Power system operational planning has become a major issue in today's world. In order for the power system to function properly, a range of factors must be anticipated with the utmost accuracy over various forecasting horizons. It is important to note, however, that scholars have devised a variety of methods for forecasting distinct factors. Exogenous variables play an important role in the implementation and analysis of new forecasting models that have recently been published in the literature. In order to predict renewable energy resources, an intelligent approach is needed. Achieving the best accurate forecasts for these variables while minimizing computing effort is a work in progress because of the rising complexity of the power system. Solar power forecasting as well as wind power forecasting will be the focus of this research in light of these concerns. Comparing these models' outcomes to the results of previous models will also be done

SPEECH RECOGNITION SYSTEMS – A COMPREHENSIVE STUDY OF CONCEPTS AND MECHANISM

Speech Recognition Systems now-a-days use many interdisciplinary technologies ranging from Pattern Recognition, Signal Processing, Natural Language Processing implementing to unified statistical framework. Such systems find a wide area of applications in areas like signal processing problems and many more. The objective of this paper is to present the concepts about Speech Recognition Systems starting from the evolution to the advancements that have now been adapted to the Speech Recognition Systems to make them more robust and accurate. This paper has the detailed study of the mechanism, the challenges and the tools to overcome those challenges with a concluding note that would ensure that with the advancements of the technologies, this world is surely going to experience revolutionary changes in the near future

An Effective Precision Enhancement Approach to Estimate Software Development Cost: Nature Inspired Way

In recent years, many researchers and practitioners have explored the possibility of estimating effort and cost using nature inspired algorithms. The purpose of this paper is to investigate the relevance of bacterial foraging optimization algorithm (BFOA) for optimizing the COCOMO model coefficients to estimate the software development time. The goal of this research is to minimize the fitness function value which is the measure of the deflection of estimated time from the real time taken in the software development. Results of the experimental study conducted shows that the proposed approach produces promising results in comparison to COCOMO model and other existing approaches listed in literature. Results show that COCOMO model and other existing approaches are less accurate in comparison to BFOA with MMRE as 0.16 and PRED(25) as 0.9. Thus BFOA can help software industry in predicting accurate and reliable values for planning and maintenance of software project

Patrilocality and Child Sex Ratios in India

In multi-level and multi-layered foundations of gendered approaches for understanding the kinship system, son preferences, and male-skewed child sex ratios in India; patriarchy, and patrilineality have received greater attention than patrilocality. To fill this gap, in this study, we construct a measure of patrilocality and examine its association with skewed child sex ratios. We hypothesize that households practice sex selection and daughter discrimination because of patrilocal norms that dictate the later life co-residence between parents and sons. Our findings reveal that the child sex ratio, the sex ratio at birth, and the sex ratio at last birth are positively correlated with the patrilocality rates across states and districts of India. The relationship holds across the multiple robustness checks. Findings, although not surprising, emerge from the robust empirical analyses at a time when child sex ratios continue to worsen in India, notwithstanding the country’s socio-economic progress. We conclude that in the absence of strong social security measures and lack of preference for old-age homes amidst the accepted practice of patrilocality coupled with increasing lower fertility norms, the dependency on sons will continue and further lead to the continuation of sex selection in India

SeamlessM4T-Massively Multilingual & Multimodal Machine Translation

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More specifically, conventional speech-to-speech translation systems rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages. To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual system capable of translating from and into English for both speech and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Finally, all contributions in this work are open-sourced and accessible at https://github.com/facebookresearch/seamless_communicatio

Spectrum of choices to restore the smile of a child: An update on current pediatric anterior crowns

Esthetic treatment of severely damaged primary teeth is one of the greatest challenges to pediatric dentists. A wide variety of full coverage esthetic crowns for primary teeth are commercially available in the market. A practitioner should choose convenient, durable, and reliable solution which is fulfilled with complete knowledge of different crown forms. This article on esthetic crowns for children gives an outline of various anterior crowns in a tabular form, reviews their composition, advantages, disadvantages, and selection criteria

A Comparison of Changes in the Mean Arterial Blood Pressure and Mean Uterine Artery Pulsatility Index from 11–14 to 19–24 + 6 Gestation Weeks in Low-Risk and High-Risk Asian Indian Pregnant Women

Aim The aim of this study was to determine the changes in the mean arterial blood pressure (MAP) and mean uterine artery (UtA) pulsatility index (PI) from 11-14 to 19-24 + 6 gestation weeks in Asian Indian pregnant women. Methods Clinical and demographic details, MAP, and mean UtA PI measures were ascertained for pregnant women at 11 to 14 gestation weeks and 19–24 + 6 gestation weeks. Women were categorized as a high-or-low risk for preterm preeclampsia using the Fetal Medicine Foundation algorithm and 1 in 150 cutoff. High-risk pregnant women were recommended low-dose aspirin 150 mg daily at bedtime. Changes in MAP and mean UtA PI were compared for gestational age intervals and high-and-low risk women using nonparametric tests. Results The study analyzed the results of 1,163 pregnant women. Both MAP (mean difference: 5.14, p < 0.001) and mean UtA PI (mean difference: 0.14, p < 0.001) remained significantly higher at the second-trimester assessment in high-risk pregnant women compared to low-risk pregnant women. Seventy-seven (35.16%) of the 219 pregnant women with abnormal mean UtA PI in the first trimester had an abnormal mean UtA PI in the second-trimester assessment. One hundred (10.59%) of the 944 pregnant women with normal mean UtA PI in the first trimester had an abnormal mean UtA PI in the 19-24 + 6 weeks assessment. Seventy-seven pregnant women (6.62% of 1,163 women, 95% confidence interval: 5.33, 8.20) had an abnormal mean UtA PI at both gestation age intervals. High-risk pregnant women taking low-dose aspirin daily showed a larger reduction in mean UtA PI compared to high-risk pregnant women that did not report the use of low-dose aspirin (0.89 vs. 0.62, p <0.001) Conclusion MAP and mean UtA PI decreased significantly from the first to the second trimester of pregnancy. Sequential assessment of the MAP and mean UtA PI in the first and second trimesters of pregnancy will be useful for fetal radiologists in India to identify a subgroup of women with abnormal mean UtA PI at both trimesters that may need more intense surveillance and follow-up till childbirth