Miracle-dz/UniFAR-FTUs huggingface dataset:
Prosody patterns of feedback expressions in Hungarian spontaneous speech

Speech communication incorporates non-verbal signals and semi-lexical vocal phenomena as well as words used as the listener’s responses to the speaker’s message. They are most common in conversation with various functions regardless of language. A specific subcategory is feedback expressions (FEs) that can be found in the listener’s production as well as in the current speaker’s speech production when reacting to the former speaker’s message. This paper reports on the temporal and intonational characteristics of four types of FEs identified in 20 interviews and conversations from the BEA Hungarian database. Altogether 262 occurrences were categorized into four discourse functions signaling ‘attention’, ‘comprehension’, ‘agreement’ and ‘other attitude’. Durations showed statistically significant differences across discourse functions. They were significantly longer in females than in males in all functions. The pitch range data revealed a statistically significant difference depending on discourse function and gender only in the case of the ‘attention’ function. The dominant frequency contour was a rise in the functions of ‘attention’ and ‘agreement’ (90%). The same contour was observed only in 75.5% of the ‘comprehension’ function. An integrated approach is proposed to analyze these phenomena in spontaneous speech.
Diffusion-weighted magnetic resonance imaging (DWI) is a technique based on diffusion of water molecules in tissues

Diffusion-weighted magnetic resonance imaging (DWI) is a technique based on diffusion of water molecules in tissues with clinical applications to a wide array of pathological conditions. In vitro measurements of diffusion coefficients for liquids based on their nuclear magnetic resonance signal using the pulse field gradient method were originally described in the 1960s [1] and improved in the early 1970s [2]. These were followed by in vivo measurements of molecular diffusion of water and applications to various pathologic states in human and animal studies, including cerebral ischemia and neoplasms in the mid-1980s and early 1990s [3–14]. However, because of significant technical requirements of DWI, there has only been widespread clinical application of this technique in the last decade. Currently, DWI is the most reliable method for detection of early and small ischemic infarcts in the brain. However, applications of DWI extend far beyond the work-up of acute ischemia. DWI is increasingly used for the evaluation of a large variety of neoplastic and nonneoplastic conditions affecting the brain, head, and neck. This chapter begins with a description of the basic mechanisms underlying DWI changes on magnetic resonance imaging (MRI) scans, focusing on the extensive work using experimental models of brain ischemia. This is followed by a discussion of currently established and experimental clinical applications of DWI, starting with the use of DWI for imaging acute ischemic stroke. The physics of DWI is reviewed in a separate chapter and is not discussed here. Basic Mechanisms Underlying Diffusion Changes in Pathologic States
The Past, Present and Future of Visual Journalism in Television News Reporting in Nigeria

Visual journalism encompasses the broadcast and print media, where sounds (words), images (pictures) are deployed to convey information in resolving visual noise. Visual noise is the physically seen or interpreted tangibles, meant to distort meaning in a visual message. However, the visual relevance of sounds and images in TV news report is a departure from those of the print media which are static and fixed. With technological advancement, information via these sounds and images in TV news reporting is conveyed with immediacy and accuracy. Findings show that in developed countries, raw visual journalism is being applied in TV news more than what obtains in Nigeria's news reporting. This is an aftermath of political, economic, technological and even cultural challenges. Based on the findings, it is recommended that more individuals should be encouraged to invest in the TV broadcast industry to create the needed healthy competition between government own-media outfits and that of private sector. The expectation is that this kind of competition will help break monopoly and brings about checks and balances.
Exploring the Eect of User Control in Recommender Systems on User Experience

Recommender systems are extremely popular in a range of application domains. ese systems aempt to recommend suitable items to users. e quality of these systems have been measured in experimental seings using metrics such as novelty, coverage and accuracy. ese metrics can be aected by the seing of several parameters in the recommender system. is work aempts to allow users control over some of these parameters; we explore in a small trial the eect of these changes on the user experience.
BANGKOK AS AN URBAN REGION OF HETEROGENEOUS LANDSCAPES

angkok and fi ve provinces in the vicinity, called Bangkok Metropolitan Region, BMR, occupies 7,650 Square Kilometers of the most signifi cant delta area and productive agricultural lands of Thailand. According to 2012 database of the Land Development Bureau, Thailand, the BMR has more than 140 types of land-use classifi cations related to various agricultural usage; paddy fi elds, crop fi elds, orchards, perennial plots, horticulture, farming facilities and aquaculture lands. It could be said that BMR’s surrounding landscapes, the richness of patches and the diversity of ecology are defi ned by complex patterns of mixed land-uses. The goal of this study is to understand the overview of BMR’s ecological landscape and its changes. By studying landscape ecology, by focusing on agricultural land-use change, and by using the computer software analysis “Fragstats”, the changes of landscape metrics reveals that BMR’s ecological landscape patterns have previously been more complex. Each selected BMR landscape has changed its pattern and has unique spatial characteristics. It also appears that the loss of landscape diversity is possibly related to the increased dissimilarity of landscape composition. The Ecological landscape metrics were used as research parameters to reveal spatial characteristics of the complexity of ecological landscapes in the Extended Metropolitan Region.
Business Intelligence And E-banking: A Study Of Bi Importance In Banking Sector

Business intelligence (BI) is used to analyzing business data. The main idea in BI is to support flow of business information around and within the organization by identifying and processing the information into useful managerial knowledge and intelligence. BI can be used by banks for analyzing performance budgeting, historical trends, performance analytics, employee performance measurement etc. In this paper authors have tried to emphasize on the importance of BI in E-banking arena and also tries to find out problems that banks have encountered while implementing BI in their banking systems.
An Introduction to the BDgraph for Bayesian Graphical Models

This introduction to the R package BDgraph is a (slightly) modiﬁed version of Mohammadi and Wit (2019a), published in the Journal of Statistical Software . Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce an R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decomposable) with continuous, discrete, and mixed variables. The package eﬃciently implements recent improvements in the Bayesian literature, including that of Mohammadi and Wit (2015) and Dobra and Mohammadi (2018). To speed up computations, the computationally intensive tasks have been implemented in C++ and interfaced with R , and the package has parallel computing capabilities. In addition, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature and are used to describe the package capabilities. The paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package.
MuRET as a software for the transcription of historical archives

The transcription process from historical handwritten music manuscripts to a structured digital encoding has been traditionally performed following a fully manual workflow. At most, it has received some technological support in particular stages, like optical music recognition (OMR) of the source images, or transcription to modern notation with music edition applications. Currently, there is no mature and stable enough solution for the OMR problem, and the most used music editors do not support early notations, such as the mensural notation. A new tool called MUsic Recognition, Encoding, and Transcription (MuRET) has been developed, which covers all transcription phases, from the manuscript image to the encoded digital content. MuRET is designed as a machine-learning based research tool, allowing different processing approaches to be used, and producing both the expected transcribed contents in standard encodings and data for the study of the transcription process. Keywords-Optical music recognition, Early music notation, Handwritten manuscripts, Software tools.
Air Pollution Control Policies in China

Published online : 22 May, 2019 Abstract:In developing countries, feeble environmental regulatory institutions regularly undermine ordinary summon and-control policies. Accordingly, these nations are progressively trying different things with elective methodologies that intend to produce new gauges for the health harm of air pollution over a 13-year plan and age, from 2017 to 2030. The Chinese government defines required objectives of reduction of emission and decrease of energy in its thirteenth Five-Year Plan period, green belt activities and 2030 sustainable development goals. This investigation utilizes China to demonstrate the health impacts of air pollution and health advantages coming about because of different air pollution control situations in China, outline how policies and measures have been actualized by and by in the region as a reaction to the National Environmental Five Year Plan issued by the central government and show how the different reactions have added to meeting targets.
Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears

Malaria, caused by Plasmodium parasites, continues to be a major burden on global health. Plasmodium falciparum (P. falciparum) and Plasmodium vivax (P. vivax) pose the greatest health threat among the five malaria species. Microscopy examination is considered as the gold standard for malaria diagnosis, but it requires a significant amount of time and expertise. In particular, the automated and accurate detection of P. vivax is difficult due to the low parasitemia levels as compared to P. falciparum. In this work, we develop a rapid and robust diagnosis system for the automated detection of P. vivax parasites using a cascaded YOLO model. This system consists of a YOLOv2 model and a classifier for hardnegative mining. Results from 2567 thin blood smear images of 171 patients show the cascaded YOLO model improves the mean average precision about 8% compared to the conventional YOLOv2 model.
Journal of Double Star Observations V5, No4

We study the accuracy of various photometry packages and AstroImageJ for measuring the separation and position angle of close double stars using a traditional imaging method. In particular, we focus on twelve systems with separations at and above the stars’ full width at half maximum (FWHM) separations ranging from two to seven arcseconds. Gaia DR2 serves as our reference to judge accuracy. Our images have a FWHM of 2.0", and AstroImageJ’s residuals were roughly -0.1" down to just over twice the FWHM. The photometric algorithms presented mixed results. DOPPhot had the lowest residuals of the photometries, yet still presented a comparatively large random error of ±0.2". However, it outperformed AstroImageJ at closer separations, maintaining this ±0.2" error down to 1.5x the FWHM, 3.0". These results are surprisingly accurate given the modest imaging equipment used a 100mm telescope operating at 714mm focal length. Figure 1. Plot of STF619’s historical data, with the measurements of AstroImageJ, DAOPhot, and Gaia Data Release 2 overlaid. The historical measurements are plotted chronologically, with the earliest measurements lightest in color. Received May 13, 2020. Vol. 16 No. 4 September 1, 2020 Page 373 Journal of Double Star Observations A Comparison of Photometric Techniques for Astrometry of Close Double Stars were developed by Bertin and Arnouts (Bertin and Arnouts, 2018). To assess their accuracy, we compared our measurements to the values reported in Gaia Data Release 2 (DR2) because it is the most accurate and recent astrometric data available (Lindegren, et al. 2018; Marakov, 2007). The AIJ software is popular for double star data reduction because it has features designed for double star astrometry, as shown in Figure 2. In AIJ, a weighted average aperture photometry algorithm is used to calculate star centroids (Howell, 2006).
Survey on Various Attacks and Message Authentication Schemes in WSN

Wireless sensor networks (WSN) are gaining more importance in area of research and development Because of its wide range of applicationsand usage of less battery power. Despite making such sensor networks possible, the very wireless nature of the sensors presents a number of security threats when deployed for certain applications like military,surveillances etc. The problem of security is due to the wirelessnature of the sensor networks and constrained nature of resources on the wireless sensor nodes.Hence the traditional security architecture used for wireless networks are not always valid for WSN.Since the nodes are always placed in a dangerous environment, the nodes are physically not protected.This is an added vulnerability to WSN. Number of schemes have been developed to prevent unauthorized access of messages. One of such scheme is Message authentication which is the one of the most efficient way to prevent unauthorized and corrupted messages from being forwarded in wireless sensor networks (WSNs). There are various methods for authentication such as Message Authentication Code, Signedecryption, and Key Aggregate System . This paper is about investigating various methods for authentication.Other goals are to give an introduction to general security in wireless sensor networks. Each message authentication methods have their own problems such as threshold overhead and key management and computation overhead and scalability. To solve such problem we developed a new authentication scheme which uses the elliptic curve cryptography, in which any node can transmit n number of message without threshold problem. This paper is to do survey before actually implementing it. Keyword: Authentication, elliptic curve cryptography, symmetric key, public key.
Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee

“Bots” playing games is not a new concept, likely going back to the first video games. However, there has been a new wave recently using machine learning to learn to play games at a near optimal level essentially using neural networks to “solve” games. Depending on the game, this can be relatively straight forward using supervised learning. However, this requires having data for optimal play, which is often not possible due to the sheer complexity of many games. For example, solitaire Yahtzee has this data available, but two player Yahtzee does not due to the massive state space. A recent trend in response to this started with Google Deep Mind in 2013, who used Deep Reinforcement Learning to play various Atari games [4]. This project will apply Deep Reinforcement Learning (specifically Deep Q-Learning) and measure how an agent learns to play Yahtzee in the form of a strategy ladder. A strategy ladder is a way of looking at how the performance of an AI varies with the computational resources it uses. Different sets of rules changes how the the AI learns which varies the strategy ladder itself. This project will vary the upper bonus threshold and then attempt to measure how “good” the various strategy ladders are in essence attempting to find the set of rules which creates the “best” version of Yahtzee. We assume/expect that there is some correlation between strategy ladders for AI and strategy ladders for human, meaning that a game with a “good” strategy ladder for an AI indicates that game is interesting and challenging for humans.
Role of External Shocks in Small Open Economy: a DSGE Approach with Collateral Constraints and Housing Market

Role of External Shocks in Small Open Economy: DSGE approach with collateral constraints and housing market Hyejin Park Department of Economics The Graduate School Seoul National University This paper extends the DSGE model of Iacoviello (2005) with housing market and collateral constraints to small-open economy version to see how financial friction affects transmission mechanism of external shocks on small open economy. It constructs a structural quasi-closed economy model which stands for the external world in order to identify foreign structural shocks that affect small open economy. Then it incorporates the model with small open economy version whose measure is negligible compared to the large one. Using data of South Korea and the U.S. after 2000, this paper estimates model parameters with Metropolis-Hastings random walk sampling and compare the cross-correlations and forecasting error variance shares calculated based on the model to the empirical ones and results from Bayesian VAR with lagged block-recursive restriction estimated by Gibbs algorithm by Zha (1999). It turns out that the model is able to replicate forecasting error variance shares that structural shocks from the U.S. have on the cyclical fluctuations of output and interest rates in South Korea. However, the model underestimates the cross-correlation between Korean variables and lagged U.S. ones and highly underestimates the forecasting error share of U.S. shocks in inflation and housing price dynamics. In particular, the model does not reproduce highly positive and persistent co-movement of housing prices across countries and highly positive correlation between U.S. housing price and Korean output, which suggests modelling interdependent capital and housing market across countries is essential for further research. keywords : small open economy, DSGE, transmission mechanism, financial friction, collateral constraints, Bayesian estimation Student Number : 2013-22853
Quo Vadis, Math Information Retrieval

With the exponential growth of information in the digital form, information retrieval and querying digital libraries is of paramount importance, and mathematical and technical STEM documents are not an exception. The key for precise searching is the adequate and unambiguous representation of documents, paragraphs, sentences and words, which we are going to evaluate. We are presenting a roadmap to tackle the problem of searching and question answering in the digital mathematical libraries, and discuss the pros and cons of promising approaches primarily for the key part, namely the document representation: several types of embeddings, topic mixtures and LSTM. The listed representation learning options will be evaluated at the next ARQMath evaluation lab of CLEF 2020 conference.
HyperPatches-a pipeline for reconstructing 3D plant models with registered hyperspectral data

Hyperspectral imaging has become accessible and affordable in recent years; this has increased research potential in precision horticulture for the early detection of stress and disease within crops. Plants are being imaged at close proximity, such that features on individual leaves can be resolved and used in stress detection. Ultimately such analysis will have to happen in a high throughput manner. For this to happen crops need to be imaged in their normal growth state. In the past, effective hyperspectral measurement required the removal of leaves or fruit, ideally pinning down and imaging flat. This removes artefacts caused by lighting and orientation; however it takes time, will cause an unwanted stress response, and requires manual labour. To improve the process, plants should be imaged in situ; however the leaves are now at different inclinations and distances to the camera. Imaging such 3D objects with a hyperspectral camera can affect the reflectance profile (spectral signature) because the incline and angle of the object increases or decreases the light reflected [4]. A second influence over the profile can happen with shadows over the object [6]. Existing work points to using 3D information (such as point clouds) to assist hyperspectral data analysis [1]. One such approach utilises multiple hyperspectral images, but this requires a large amount of data and processing time [3]. Here, instead we propose to combine several side view RGB images of the plant with a single top-down view of the plant from a hyperspectral camera, producing a 3D model with the hyperspectral information mapped onto it.
Abiotic differences and anthropogenic interference affect foothill yellow-legged frog breeding

As human presence rapidly introduces novel environmental pressures to native ecosystems, many species experience population declines. Sensitive taxa such as amphibians may be particularly vulnerable to such environmental disturbances. Rana boylii, the foothill yellow-legged frog and a California State Species of Special Concern, serves as a model for understanding the effect of anthropogenic interference, such as changing climate and disturbed habitats, on reproductive success. In this study, we measured egg mass abundance, abiotic characteristics of oviposition microhabitats, and developmental conditions of egg masses at two locations with different levels of human interference along the South Fork Eel River in the pacific northwest of California. We found that abiotic conditions affected egg mass proximity to nearest neighbor, hatching, and predation, whereas the level of human interference affected egg mass size and desiccation. These findings further confirm the need to identify and protect R. boylii oviposition habitats and be cautious about drastically modifying the climate to which they are adapted.
Stress-testing technics for commodities futures portfolios, using extreme-value theory and copulas

Recent crashes in the markets have demonstrated the limitations of traditional riskmanagement tools, such as VaR, and backward looking simulations based on the Normal distribution or purely historical data. The purpose of this paper is firstly to explore the potential improvements extreme value distributions offer compared to traditional t and Normal distributions in modelling asset returns of commodities futures. The second part will show how t-distributed copulas outperform Gaussian copulas for stress-tests of a portfolio composed of ten different commodities futures, and focus on the economic analysis of the simulations’ results. Our study will span several time periods, as structural breaks on commodities prices are an important feature.