Miracle-dz/UniFAR-FTUs huggingface dataset:
TO RANK AND CLASSIFY SEVERE WEATHER OUTBREAKS

In recent years, a project has been undertaken to investigate the degree to which synoptic-scale processes determine the occurrence or absence of tornado outbreaks (see Mercer et al. 2009; Shafer et al. 2009; Shafer et al. 2010a,b). These studies required a means of identifying prototypical tornado outbreaks for evaluation. Doswell et al. (2006 – hereafter D06) developed a technique to rank tornado outbreaks and primarily nontornadic outbreaks (separately) using characteristics of the severe reports from the 1970-2003 period. The highest-ranked cases of each type were considered to be prototypical outbreaks of each type, and agreed with subjective notions regarding the meteorological and societal significance of such events during this period. Mercer et al. (2009) and Shafer et al. (2009) found that tornado outbreaks and primarily nontornadic outbreaks could be discriminated reasonably well using synoptic-scale data as initial conditions in mesoscale model simulations up to three days in advance of the events. Shafer et al. (2010a) cautioned that outbreak discrimination was affected by the time of year in which the events occurred, but these studies nevertheless have shown that synoptic-scale processes play a role in the type of outbreak that occurs. Of course, most outbreaks of severe convection are not easily classified as major tornado or primarily nontornadic outbreaks. Therefore, we now must determine if synopticscale processes can be used to predict the relative severity of convective events. To conduct such studies, identification of prototypical major severe weather outbreaks (mostly but not only major tornado outbreaks) is necessary. This study seeks to rank severe weather outbreaks of any type based on their
Chapter 2 Right Chambers Quantification in Clinical Practice : Echocardiography Compared with Cardiac Magnetic Resonance Imaging

The right ventricle (RV) has its own particular morphology and functions, which are different when compared to the left ventricle (LV). In clinical practice, the right heart chambers are often overlooked, as most physicians tend to focus more on LV and left atrium (LA) morphology and functions. However, cardiac pathology is often associated with right chambers impair‐ ment, which can occur as a primary pathophysiological response to elevated pressure in the pulmonary arterial circulation associated with primary pulmonary artery hypertension [1-2], in pulmonary diseases associated with pulmonary venous or arterial hypertension [3-4], pulmonary embolism [5], but also in congenital heart disease [6]. Most often, RV dysfunction is triggered by left chamber impairment [7-9].
The Link Between Physical Experiment and Computer Model in a Science Classroom

The Bifocal Modeling Framework (BMF) is an inquiry-based approach for science learning that links students’ physical experimentation with their use of computer modeling. Our study is among the first implementations of a BMF approach in a science class. The study consisted of three conditions implemented with a total of 75 9th grade high-school students. The first and second conditions were assigned two different implementation modes of BMF: BMF-with-model-design and BMF-without-model-design. Both groups conducted a physical experiment, utilized a virtual-model, and compared the results between the two. However, only the BMF-with-design participated in the design-module, which consisted of developing an on-paper model of the scientific phenomenon under investigation. The third condition, employed as a control, received instruction in the school’s traditional approach. Our results indicate that students who participated in both implementations demonstrated better understanding of the content as well as improvement in their meta-modeling knowledge. However, only for the "BMF-with-design" group was the improvement statistically significant for both content and meta-modeling knowledge.
Fascicola 1-2 , 2002 Denoising Signals Corrupted by Speckle Noise

This work present a new method for the denoising of signals used in radar. The specific noise for such applications is the speckle noise. To remove this noise a method based on the Donoho’s de-noising method is used. A variant of the Donoho’s de-noising method is applied to the logarithm of the acquired signal. Then the logarithm is inverted obtaining the denoised signal. Some simulations of this new de-noising method are reported.
Parallel and Distributed Boolean Gröbner Bases Computation in SageMath

We introduce a parallel and distributed computation method of Boolean Gröbner bases, which are implemented in the computer algebra system SageMath using the PolyBoRi library. We present an easy way to parallelize Boolean Gröbner bases computation using a decorator for parallel computation supported in SageMath, we also present a way of distributed computation using a multiprocessing module in Python. Our software achieves satisfactory speed-up comparing to our sequential implementation in SageMath.
How the eye got its brain

Evolution provides an explanatory framework for biology. Our usual formula is to explain a trait by equating its present function with its adaptive reason for being, invoking natural selection as the causal force. But since there is no trait that we won’t try to explain by such necessarily post hoc scenarios of deterministic molding by selection, evolutionary explanations are often derided by critics as Just So stories. This is irritating, because we think we are going Kipling one better by saying How the Leopard Really Got His Spots (see Murray, 1997). But if we examine our stories more closely, it is difficult to know when we have the true ones right. The evolution of eyes illustrates this in many ways.
The effect of music Mnemonic and Flash Cards to increase fourth grade students’ english vocabulary acquisition

The purpose of this quasi-experimental study was to examine the effect of music mnemonic and flash cards in enhancing grade 4 students’ English vocabulary acquisition. The participants were 30 students, aged 9-11 years old, who had low capability in second language (English) vocabulary acquisition. The result of the study showed that music mnemonic and flash cards could increase students’ vocabulary acquisition, p = 0.001; p < 0.01. Music mnemonic group has the highest score due to the multimodality of the various methods of memorization that impact to the chunking effect on working memory, so that the new information can be more easily to transfer to the long-term memory. Teachers and school should combine several methods in providing appropriate learning based on the students’ cognitive development stage.
Survey : Machine Translation for Indian Language

This paper presents Literature survey on Machine Translation and disuses the important tools and various approaches used for translating from one language into another language. There are various Machine Translations system has been developed for Indian languages such as Google Translator, BabelFish Translator etc. but they are failing to provide a good quality of translation. There are various challenges faced in Machine Translation such as Parts of speech tagging, morphological analyser, parsing, Word sense disambiguation and Translation. These are important tool of Natural Language processing.
Internet Adoption and Task-based Comparative Advantage between OECD Countries

I use sector-level bilateral trade data between 21 high-income OECD countries in 2006, to show that internet adoption affects the composition of trade between developed countries. Specifically, I find that OECD countries with higher broadband rankings have comparative advantage in less routine sectors. The broadband ranking index for each country is from ITIF, which examines three aspects of the internet: number of users per 100 households, average speed, and price of the fastest technology. The routine task intensity evaluates the importance of analytical or interactive tasks in each sector, and it is obtained by aggregating different occupations’ routine index with the weight as occupations’ corresponding employment shares in each sector of US. The underline mechanism of the finding is that information and communication technology (ICT) complements workers in performing non-routine tasks. It has been firstly highlighted by Autor, Levy and Murnane (2003) but never applied in studying countries’ comparative advantage. As the network effect of the bilateral internet adoption seems missing, the pattern suggests that the internet facilitate trade within developed countries via a channel other than simply reducing the bilateral transaction costs. The pattern survives various robustness checks such as adding all sorts of controls, changing the sample year(s) to 2007 or 2002-2006, and using alternative measures of routine task intensity. By using alternative internet measures and switching to ICT development index or computer density, I find that broadband is more important for workers to perform the interactive tasks than the analytical tasks. I also use PPML (Poisson Pseudo Maximum Likelihood) and IV (Instrument Variable) approach to tackle problems brought by zero-trade flows and possible endogeneity, and the baseline pattern sustains.
Prosody , Tone , and Intonation

Prosody refers to all suprasegmental aspects of speech, including pitch, duration, amplitude and voice quality that are used to make lexical and post-lexical contrasts, and to convey paralinguistic meanings. Tone refers to pitch patterns that make lexical, grammatical or morphological contrasts in many languages. Intonation refers to the melodic facet of prosody, although the two terms are sometimes interchangeable. Though often treated as separate research subjects, these three areas are heavily overlapped with each other not only in terms of the phenomena they cover, but also with regard of the underlying mechanisms they share. A common perspective that can link them all is the articulatory-functional principle, which views speech as a system for transmitting communicative meanings with the human articulators (Xu, 2005). From this perspective, lexical, post-lexical, affective and social meanings are all conveyed in parallel, each encoded by its own unique imprints on the properties of various underlying articulatory targets. The articulation process turns these function-loaded targets into the acoustic patterns ultimately perceived by listeners, which consist of not only all the functional contributions, but also all the traces and artefacts of the articulation process itself.
Hardware Implementation and Applications of Deep Belief Networks

Deep learning is a subset of machine learning that contributes widely to the contemporary success of artificial intelligence. The essential idea of deep learning is to process complex data by abstracting hierarchical features via deep neural network structure. As one type of deep learning technique, deep belief network (DBN) has been widely used in various application fields. This thesis proposes an approximation based hardware realization of DBNs that requires low hardware complexity. This thesis also explores a set of novel applications of the DBN-based classifier that will benefit from a fast implementation of DBN. In my work, I have explored the application of DBN in the fields of automatic modulation classification method for cognitive radio, Doppler radar sensor for detection and classification of micro unmanned aerial systems, cyber security applications to detect false data injection (FDI) attacks and localize flooding attacks, and applications in social networking for prediction of link properties. The work in this thesis paves the way for further investigation and realization of deep learning techniques to address critical issues in various novel application fields.
Improving efficiency of first-arrival traveltime tomography by stochastic optimization

Near-surface imaging plays a significant role in producing high quality data from land or shallow marine environments. First-arrival traveltime tomography is a common approach for solving the nearsurface imaging problem from which results are needed to calculate robust and accurate static solutions. However, traveltime tomography involves inverting massive first-arrival picks. We believe that there is redundant information in traveltime picks and we propose to explore randomization methods to decrease the number of first-arrival picks that are used to compute near surface models. The latter leads to an improvement in terms of the efficiency of traveltime tomography leading to faster turnaround times. An efficient inversion also permits to explore solutions in terms of the trade-off parameters that control the smoothness of tomographic inversion. We adopt a Stochastic Approximation (SA) strategy to use only a small part of the data to retrieve high quality near surface models. A synthetic and a real data example are used to test the validity of the method. We also use these tests to understand the amount of data reduction that the method can tolerate before degrading the solution. For this purpose, a detailed statistical analysis was carried out. Relatively large deviations from the inverted data using all the data and the data that uses only a small percentage of picks occurs in areas of poor ray coverage. However, this effect is negligible in terms of the statics solutions that one can obtain by the tomographic inversion. For the real data example, the long-wavelength statics calculated from the velocity model derived by the new method is identical to model obtained by classical non-linear optimization using all the picks. However, a saving of 96% in terms of computational cost was achieved using the SA strategy.
SELECTED PROBLEMS OF EMISSION INVENTORIES – GEOSTATISTICAL PERSPECTIVE

This paper presents approach that develops currently applied methodology for obtaining spatial surrogates for spatial disaggregation of air emission inventories. Considered sector was small residential combustion in the Katowice Urban Zone, situated in the southern part of Poland, due to its importance in emission budget, also potential health threatening of the ‘low emission’ sector.Results presented in this paper use geostatistical techniques: ordinary and indicator kriging to show dependence between spatial distribution of the urban density development (described by the built areas) and distribution of the district heating infrastructure. Obtained results should be useful for emission inventory compilers, for air quality modelers as well as policymakers.
CHARACTERIZATION OF ZINC OXIDE ( ZnO ) THIN FILM COATED BY THERMAL EVAPORATION TECHNIQUE

Thin Films of zinc oxide have been deposited by thermal evaporation of zinc metal. The annealing temperature varied from 350°C to 450°C for fixed one hour annealing time. XRD results show the presence of (100), (101) and (002) diffraction peaks for all the films of ZnO. The intensity of the diffraction peaks increase with increase in annealing temperature. The FWHM values of the peaks decrease with increase in annealing temperature showing better crystallinity of the thin films. The SEM study shows the needle like morphology of the thin films. The films are found optically transparent for visible region of the electromagnetic spectrum from the VIS-UV data. RBS study showsthe thickness of films 180nm.
User Influence on Twitter Hashtags Evolution: A Case Study from Career Opportunities Groups

Hashtags on Twitter are used to make groups, initiate conversational subjects, share life events and post personal experiences. Due to the enormous growth of social media networks, researchers conducted many studies on the evolution of groups on such networks to predict how they retain their members, attract new ones and grow over time. In this study, we focus on the factors causing people to participate in Twitter hashtags, which in turn affects the evolution of these hashtags. In order to study this evolution, a dataset of some Twitter hashtags related to career opportunities was collected for a period of two months between February and March 2016. The collected dataset allowed us to study the reciprocal effect of the users’ topological features and their activity levels. In this paper, three new measures are introduced (two influence measures and one topological measure). Those measures, in addition to the measures available in the literature, are used to spot the measures that can be used for influencing a user to attract other users to a certain hashtag.
Design of an Affordable Socially Assistive Robot for Remote Health and Function Monitoring and Prognostication

To address shortages in rehabilitation clinicians and provide for the growing numbers of elder and disabled patients needing rehabilitation, we have been working towards developing an affordable socially assistive robot for remote therapy and health monitoring. Our system is being designed to initially work via remote control, while addressing some of the challenges of traditional telepresence. To understand how to design a system to meet the needs of elders, we created a mobile therapy robot prototype from two commercial robots and demonstrated this system to clinicians in two types of rehabilitation care settings, a daycare setting and a inpatient rehabilitation setting. We propose to introduce the prototype as a social and therapy agent into clinician-patient interactions with the aim of improving the quality of information transfer between the clinician and the patient. This paper describes an investigative effort to understand how clinicians who work with elders accept this prototype. Clinicians from each setting differed in their needs for the robot. Those in daycare settings preferred a more social robot to encourage and motivate elders to exercise as well as monitor their health. Clinicians in the inpatient rehabilitation setting desired a robot with more therapeutic and treatment capabilities. Both groups wanted a robot with some autonomy that was portable, maintainable, affordable, and durable. We discuss these results in detail along with the ethical implications of increasing the robots autonomy and suggest additional requirements for achieving a smarter robot that can meet the clinicians social, health monitoring and prognostication desires.
Offer Categorization for Price Comparison Websites: Word Embedding Approaches

One key task for price comparison websites is to categorize offers collected from online stores. Classification accuracy impact on searching, recommendation, and website reputation. There are few studies on this topic in the literature, and none of them apply the successful technique of word embedding nor perform a detailed analysis of features. In this work, we compare two different word embedding approaches with the traditional bag-of-words approach, for the task of offer categorization. Firstly, we employ an unsupervised approach in which the embedding is learned from millions of offers using the well known word2vec tool. Secondly, we develop a supervised approach in which the embedding and the offer classifier comprise a Convolutional Neural Network (CNN) and both are jointly learned. Additionally, we perform a detailed analysis of several features regarding their relevance to offer categorization. We assess our models on a dataset comprising more than 11 million offers collected and manually annotated by the most popular Latin American price comparison website. In our experiments, the CNN model substantially outperforms the other models. We present detailed experimental results that highlight the contribution of different parts of the CNN model. Regarding feature engineering, we notice that all evaluated offer attributes contribute to enhance the classifier performance.
SKETCHING CONCATENATIVE SYNTHESIS: SEARCHING FOR AUDIOVISUAL ISOMORPHISMS IN REDUCED MODES

This paper presents a prototype allowing the control of a concatenative synthesis algorithm using a 2D sketching interface. The design of the system is underpinned by a preliminary discussion in which isomorphisms between auditory and visual phenomena are identified. We support that certain qualities of sound and graphics are inherently cross-modal. Following this reasoning, a mapping strategy between low-level auditory and visual features was developed. The mapping enables the selection of audio units based on five feature data streams that derive from the statistical analysis of the sketch
An Objective Evaluation of Pathological Voice Based on Complexity Method

In order to evaluate the Pathological Voice’s signal by parameters objectively, in this paper we adopt HNR, LempelZvi Complexity (LZC) and C0 complexity to analyze Pathological Voice’s signal . Pathological voice and normal voice that classify as 4 degrees by the GRBAS system Total hoarseness degree (G) . The result shows that, the existing acoustic parameter HNR is directly proportional to subjective hearing characteristics of Total hoarseness degree, HNR is more effective to distinguish the pathological voice from normal voice; but in the same time, not only the C0 is on good terms with subjective point of view, but also the C0 complexity shows the voice’s hoarse degree more effectively, better able to evaluate Pathological Voice objectively than HNR. It belongs to the voice painless and contactless identification method, which will use as the effect of voice medical process objective evaluation method of sound quality.
L OCUS : An environment description language for J ASON

JASON is an AGENTSPEAK interpreter for multi-agent system development, in which agents are described in the AGENTSPEAK language. Therefore, we only have to describe the agent behavior, but the environment does not follow this style, it requires a Java description of how the actions and perceptions operate. This choice of implementation guarantees that even complex environments can be created for JASON, but it requires knowledge about both Java and JASON’s Application Programming Interface (API). In this paper we aim to fill the gap between the languages with an AGENTSPEAK-like description of the environment. To overcome this gap we propose LOCUS, a source-to-source compiler which generates a Java description of the environment for JASON, providing the user with an easier starting point to create complex environments with a consistent description for both the agents and the environment. The output of LOCUS can be further modified if required, not limiting the user to the features already provided by LOCUS.
Quality versus quantity in two-sided markets competition : Evidence from crowdfunding websites

In this paper, we study how mechanisms like minimum quality standards shape competition in two-sided markets in terms of quantity and quality of members. We investigate the reward-based crowdfunding industry, a growing and (yet) weakly regulated model, where entrepreneurs pitch to receive financial support from investors and receive pledges in exchange for special prizes. In our setting, two platforms compete head to head, and one of them softens its minimum quality standards. By potentially opening up its system to lower quality entrepreneurs, the platform sharply increases entry in comparison to its rival while the relative average quality decreases. Our results highlight the complex competitive dynamics in two-sided markets, as changes on the one side also impact the other. In particular, we feature the challenging task of balancing quantity and quality in platform competition as well as the potential usefulness of the findings to platform operators set their strategies.