Reviewerly/exHarmony huggingface dataset:
Prosodic Analysis and Enhancement of Dysarthric Speech

Dysarthria is a disorder that is caused in the nervous system. It is caused by damage in some parts of the brain such as cerebellum. Because of the damage in brain it causes weakness in muscles used for speech therefore it happens as in mumbled, slurred or slow speech that human and the machine find it difficult to understand such slurred speech. The Automatic Speech Recognizers which were designed for speech intelligibility perform poorly on dysarthric speech recognition. This paper focuses on the transformation of voice for dysarthria to enhance its intelligibility Formant tracking, pitch and energy estimation with durational cues from dysarthric speech facilitate the modification of these trajectories to more closely approximate the desired intelligible target speech. The transformation of the speech is done by using formant re-synthesis, pitch change and duration morphing. The results of such transformation results indicate that the transformation of the pitch and duration step enhances the intelligibility of dysarthric speech and make it easy to understand for humans and machines.
Seminar Nasional Fisika (SNF) 2018

At the beginning of 2019, we would like to present an international proceeding that contains the selected papers from the National Physics Seminar or Seminar Nasional Fisika (SNF 2018), entitled "Earthing Physics and Learning Physics in Building Global Wisdom" that was organized by the Physics Department of FMIPA Unesa - Surabaya on August 11, 2018.
Optimization of Feature Loss for Image Enhancement

Image-transformation problem is a problem in which an input image is transformed to an output image. In most of the recent methods, a feed-forward neural network is defined which utilizes per-pixel loss between the output image and the ground-truth image. In this paper we have showcased that high-quality images can be generated by defining a feature-loss function which is based on high-level perceptual features extracted from pre-trained convolutional networks. We have combined both the approaches that have been formerly mentioned and have proposed a feature-loss function for training a feed-forward neural network capable of image transformation tasks. We have compared out method with that of an optimization based approach, similar to the one utilized in Generative Adversarial Networks (GANs) and our method produced visually appealing results whilst fully capturing the intricate details of the object in the image.
Dark Web

Internet is already recognised and being used extensively during this period .We explore information, watch videos and movies, interact through social media such as Facebook, Kik and Twitter and do almost everything on the internet .Yet, maximum number of people don't know that the regular use of Web is just about 27% of the entire World Wide Web.The greater part of World Wide Web usage is on the deep web which includes the dark web.What is confidential in the deep web?The deep web contains countless unlawful activities and unlawful materials even criminal involvement.It has become the breeding ground for the cyber offenders where by it's a place where cyber offenders get together and collect to spread the darkness.Drug dealer, Hackers, virus creators, terrorist, sex addict, stalkers, paedophiles, bullies and all inconceivable people are there.Phishing, identity theft, virus and malware is there all the time.A small fault can cause indispensable consequences, you can be tracked, exposed, kidnapped or even assassinate if you are not cautious.The deep web typically has 3 configurations, search engine, browser and website.It is alike to the surface web and using altered tools and configurations.The issues while in the deep web is how the work happens there, maximum of it is by the use of crypto currency as its leading transaction.Deep web is the number one habitation for the hacker, they are travelling around and do activities to analyse, training and test their capabilities.Therefore, it is suitable that deep web is the shell for them.Don't complicate between Dark Web and Deep Web, the dark web is the part of the deep web and the deep web has a greater explanation from dark web.To get in the dark web you need to use a distinct browser named TOR Browser.it is not to be confused with something that you do not apprehend, you essentially learn to shelter when you access dark web.Officials must gain a thoughtful of the Dark Web in command to include rationally in the discussion and pass actual dark web strategy.
Factors Influencing Learners' Motivation in Mobile-Assisted Language Learning: A Case Study on Four Chinese EFL Learners

As mobile-assisted language learning (MALL) has become increasingly popular among learners of English, their motivation in using mobile technologies to learn the language has attracted the attention of researchers.This study investigates possible factors that may influence learners' motivation in MALL in natural learning environments.A case study was conducted involving four Chinese adult EFL learners who used a mobile application named MintReading for eight weeks.Weekly learning journals and semi-structured interviews were conducted to collect in-depth data from each participant.The results indicated that the following affected learners' motivation as they were learning: (1) interesting and useful reading content; (2) language difficulties; (3) affordance of mobile technologies; (4) motivational design of the application.On the other hand, their interaction with an online community, that of the social media app WeChat, failed to contribute to their learning motivation.Discussions of the research findings are based on self-determination theory, and implications and limitations of this study are also addressed.
Research on Salient Object Detection using Deep Learning and Segmentation Methods

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection has attracted a lot of interest in computer vision and recently various heuristic computational models have been designed. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. The aim of this review work is to study about the details of methods in salient object detection. It not only focuses on the methods to detect saliency objects, but also reviews the works related to spatio temporal video attention detection technique in video sequences. It also discusses the open issues in terms of evaluation metrics and dataset bias in model performance and suggests future research directions. The evaluation metrics are classified into mean absolute error (MAE), Accuracy and Run-Time complexity
An Extended Laplacian Score Algorithm for Unsupervised Feature Selection

Experts from various sectors, utilize data mining techniques to discover most useful information from the huge amount of data, to improve their quality of outcomes. The Presence of irrelevant and redundant features affects the accuracy of mining result. Before applying any mining technique, the data need to be preprocessed. Feature selection, a preprocessing step in data mining provides better mining performance. In this paper, we propose a new two step algorithm for unsupervised feature selection. In the first step Laplacian Score is used to select the important features. And in the second step, Symmetric Uncertainty is used to remove redundant features. The experimental results show that the proposed algorithm outperforms the Laplacian Score algorithm.
Study Of Digital Image Correlation-A Computer Vision Technique for Stress Analysis: Its Accuracy and Reliability

The computer vision methods for stress analysis were emerged in early 1980s. Among them the method of digital speckle/image correlation (DSC) was first proposed in the year of 1983 (by Sutton et al). The latest technology advance has made possible to build affordable systems to handle applications that conventional techniques are unable to deal with. Researchers worldwide have been studying to improve the accuracy and reliability of the DSC in order to extend its applications to emerging areas of science and engineering. The study of DSC at Ryerson was initiated in early 1990s and the existing system at Ryerson, named “AutoStrain,” was made available early in 2000. The recent applications have been concentrated in areas where stress analysis in the microscopic scale is needed. The applicability of the method has been studied and verified by the predecessors in many ways, mostly via comparison with the results obtained from analytical solutions, numerical modelling and experimental measurement using proven techniques. Yet new applications usually demand revelation of stress/strain for problems with no prior knowledge and under unusually subtle conditions (e.g., new material/structure of micro or sub-micro scale of geometry, multi-material interface, ultra-hassle environment and etc.). For an application facing such challenges, the accuracy and reliability of the measurements are constantly a concern. The goals of the current project are as follows: to review the recent research on the sources resulting in errors from both fundamental and technical fronts; to give a close analysis of the key factors that cause errors in the existing system; and to finally propose measures to cope with the factors and to lead to improvements. The study has covered these aspects o f the method including the image formation and the effect of light sources, the speckle patterns, the algorithms of the search schemes used and the proper interface among these schemes, the convergence criterion and the adaptive method to determine/adjust the values of the criterion to fit specific applications, etc. The project has reached the goals by having made substantial improvements as follows. First, the importance has been clarified of consistency among the different coordinate systems defined in the different parts of the software and improved the interface among these parts. Second, an adaptive method aiming at improving the convergence criterion for Newton-Raphson iterative algorithm has been proposed and implemented. The test results have demonstrated the effectiveness of these improvements.
AI and Individualized Education in Phys Ed and Sport

The development of AI technologies in recent years has opened the doors to a wide array of application domains. One field of study in particular has great potential to use this knowledge: physical education and sport. New devices and software have made their way into the field of physical activity, motivating the investigation of AI-driven technologies as a tool in developing a new type of educational system. The general scope of this chapter is to analyze the use of AI individualized education in one major field of study: physical education and sport. The use of AI technologies in training future teachers is also discussed and analyzed. The implementation of AI software in sport is brought to attention, along with relevant case studies. Limitations and hurdles in constructing intelligent support systems for training were identified. Accessibility of an AI-driven educational system was detailed, pointing out barriers with regard to culture, social environment, and age differences.
Large Scale Digital Book Initiatives: Checking in with Google Books, HathiTrust, Project Gutenberg and More

Large Scale Digital Book Initiatives: Checking in with Google Books, HathiTrust, Project Gutenberg and More
Estimation of Monthly Average Daily Diffuse Solar Radiation using Empirical Models For Kathmandu Nepal

An accurate knowledge and data of solar radiation and its component are indispensable for the utilization of solar energy. However radiation data are often inaccessible. In this regard, the empirical models are reliable tools. This paper aims to develop and evaluate three simple empirical models (linear, quadratic, and cubic) for estimation of monthly average daily diffuse solar radiation of Kathmandu. Developed Models correlates diffuse fraction with clearness index are based on the satellite data from the NASA Langley Research Center. The performance of models is found to be statistically significant which has been analyzed in terms of statistical indicators like RMSE, MBE and R2. Among the three models, the cubic model is best fit on the basis of statistical parameters. So that cubic model is recommended for the estimation of monthly average daily diffuse radiation at Kathmandu and similar climatic sites of Nepal.
Orders from key target markets provide a platform for future growth for De.mem

Australian–Singaporean water and wastewater treatment company De.mem Ltd has received two purchase orders, worth A$350 000, from customers in new, important target markets and geographies – providing a platform for future growth.
Integration of one-dimensional functions

This textbook is a comprehensive introduction to the key disciplines of mathematics - linear algebra, calculus, and geometry - needed in the undergraduate physics curriculum. Its leitmotiv is that success in learning these subjects depends on a good balance between theory and practice. Reflecting this belief, mathematical foundations are explained in pedagogical depth, and computational methods are introduced from a physicist's perspective and in a timely manner. This original approach presents concepts and methods as inseparable entities, facilitating in-depth understanding and making even advanced mathematics tangible. The book guides the reader from high-school level to advanced subjects such as tensor algebra, complex functions, and differential geometry. It contains numerous worked examples, info sections providing context, biographical boxes, several detailed case studies, over 300 problems, and fully worked solutions for all odd-numbered problems. An online solutions manual for all even-numbered problems will be made available to instructors.
A hybrid Grey Wolf optimizer with multi-population differential evolution for global optimization problems

The optimization field is the process of solving an optimization problem using an optimization algorithm.Therefore, studying this research field requires to study both of optimization problems and algorithms.In this paper, a hybrid optimization algorithm based on differential evolution (DE) and grey wolf optimizer (GWO) is proposed.The proposed algorithm which is called "MDE-GWONM" is better than the original versions in terms of the balancing between exploration and exploitation.The results of implementing MDE-GWONM over nine benchmark test functions showed the performance is superior as compared to other stat of arts optimization algorithms
Speech Recognition System for Isolated Tamil Words using Random Forest Algorithm

ASR is the use of system software and hardware based techniques to identify and process human voice. In this research, Tamil words are analyzed, segmented as syllables, followed by feature extraction and recognition. Syllables are segmented using short term energy and segmentation is done in order to minimize the corpus size. The algorithm for syllable segmentation works by performing the STE function of the continuous speech signal. The proposed approach for speech recognition uses the combination of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC). MFCC features are used to extract a feature vector containing all information about the linguistic message. The LPC affords a robust, dependable and correct technique for estimating the parameters that signify the vocal tract system.LPC features can reduce the bit rate of speech (i.e reducing the measurement of transmitting signal).The combined feature extraction technique will minimize the size of transmitting signal. Then the proposed FE algorithm is evaluated on the speech corpus using the Random forest approach. Random forest is an effective algorithm which can build a reliable training model as its training time is less because the classifier works on the subset of features alone.
A Reverie of Gardens

A quatrain from Ralph Waldo Emerson introduces this chapter and the idea of the "garden sentiment," referred to here as "garden-desire," is further explored. Garden-desire is not based on materialism, since it is cheaper to buy plants than to grow them. Instead, it is based on the simple idea of self-expression through the growing of plants, which breaks us out of "the long estrangement of our artificial lives." Bailey also warmly describes the process of a germinating plant, likening it to the germinating garden sentiment. Lastly, Bailey introduces an elsewhere-repeated trope of describing plants as "old friends," a companionship "of things that are real and clean." The chapter concludes with a reference to Emerson's fellow Transcendentalist, Henry David Thoreau.
9. Passives of ditransitives: the gradual rise of a Recipient passive in Italian

This chapter investigates the properties and the diachronic development of a poorly described Italian construction, vedersi ‘to see oneself’ followed by the infinitive, with the aim of defending its Recipient passive interpretation. According to the recent typological literature (Malchukov, Haspelmath, and Comrie 2010), Italian, as a language that follows an indirective coding pattern, should not allow the passivization of the Recipient argument in ditransitives. The case under scrutiny represents a special strategy whose motivations are to be searched for, on the one hand, in the tendency for Recipients to take the subject role in passivization (Haspelmath 2015), and on the other at the pragmatic level in a response to the need for expressing the (mostly negative) subject affectedness. The diachronic analysis illustrates the grammaticalization path of vedersi + infinitive leading from the reflexive construction, in which vedersi has a perceptive meaning, to a Recipient passive construction through texts from Old to Modern Italian. The semantic expansion of the Recipient construction beginning in the late 19th century, through its ability to attract a variety of ditransitive predicates, is illustrated with corpus-based examples. The earlier but parallel development of a Theme passive in the construction vedersi + past participle (Giacalone Ramat 2017) confirms the main lines of change and suggests a new assessment of passivization patterns in Modern Italian.
“INTRODUCTORY PART” OF KOREAN LITERATURE RELATED COURSES AS A MOTIVATIONAL TOOL

The issue of teaching Korean literature and motivating students has been important due to the recent changes in Korean studies students’ expectations (interest to Korean popular culture and preference to practically oriented subjects). Literature gradually becomes a discipline demanding new educational approaches and additional efforts as students tend to doubt its relation to the competences necessary for their professional success. The paper presents one of the methods elaborated by the author to make students more motivated and involved in learning Korean literature, i. e., an “introductory course” opening the main course and gives its outline. It aims at giving a clear understanding of what the students will achieve, familiarizing the students with basic specific features of Korean literature relevant for the most of the material to be studied (time saving factor). The introduction provides the “road map” of the course, makes the bridges between literature and the targeted professional level (a motivational tool). This method can be applied to other literatures as well.
Threefold similarity analysis: a case study on crowdsourcing feeds

Crowdsourcing is a valuable social sensing for the smarter city. We present a framework of crowdsourcing feeds similarity analysis from a threefold point of view, namely image, text, and geography, which is based on similarity analysis, founded on a sequence that goes from coarse to thinner similarity filters. The main idea is to extract feeds within a specific geographic range, and then to analyse similarity of image colour and text in clustered feed sets. The framework enables to identify feeds that report the same issue, and hence to filter redundant information. Based on proved methods and algorithms, such framework has been implemented in a software application, called CITY FEED, which is used by the Municipality of Pavia.
The Effect of the Smart Navigation System Based on Augmented Reality

Currently, virtual technology is applied to everyday life. AR (Augmented Reality) has widely become a challenger technology that brings virtual 3D images into the real world through a camera. Augmented Reality is a technology that brings virtual images that is a 3D model simulated into the real world through cameras and processing that brings objects to overlap into one image and AR can help people to understand the content easily. Hence, in increasing the efficiency of services and publicizing various information, this paper presents the smart navigation system using augmented reality based on a smartphone in the case study of Benjakitti Park, Thailand. The application can navigate users to the POI destination and the system based on mobile devices is composed of two parts: the navigation application and the bone collector game. This project produced user satisfaction at a good level and the proposed application was able to support the significant information for navigation in aspects of performance, usability, and effectiveness.
Sexual Offences in the Cyber World: Emerging Technological Challenges

The aim of the study is to analyze the effectiveness of the protection provided for privacy and the related interests of women in cyberspace. This research provides a review and analysis of state changes, guidelines, etc. regulatory tools to protect women's privacy and related interests in cyberspace. These instruments constitute an area of law and politics that has reached considerable maturity, diffusion and normative importance over the decades. Awareness of the law and policies in this area is the main objective and will reflect the state of India. All about regulation is a large body of academic commentary that analyzes privacy issues in cyberspace from different angles. This research will present a predictive analysis of sexual and cybercrime crimes against women in India and the laws that prevent cyber victimization in general and women in particular
Pengaruh Faktor Fisiologis, Lingkungan Sekolah, dan Motivasi Belajar Terhadap Hasil Belajar Ekonomi Di SMAN 2 Painan

This study aims to: 1) To see the effect of physiological factors on students 'economic learning motivation, 2) To see the influence of the school environment on students' economic learning motivation, 3) To see the effect of physiological factors on student economic learning outcomes, 4) To see the effect school environment on student economic outcomes, 5) To see the effect of student learning motivation on student economic learning outcomes. This type of research is associative descriptive research. The sample used as many as 90 people using Nonprobability Sampling. Data collection techniques used were observation and questionnaire / questionnaire. The collected data were analysis with the help of SPSS 21.0 program. This research uses path analysis. The results of this study indicate that 1) physiological factors significantly influence students' economic learning motivation, 2) School environment has a significant effect on students' economic learning motivation, 3) Physiological factors significantly influence students' economic learning outcomes, 4) School environment has a significant effect on student economic learning outcomes, 5) learning motivation has a significant effect on economic learning outcomes of students of class XI IPS SMAN 2 PainanKeyword: physiological, school environment, learning motivation, learning outcomes.