Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Gestures for Virtual Object Rotation

A Comparison of Gestures for Virtual Object Rotation Brandon Michael Garner School of Technology, BYU Master of Science The fields of virtual reality and gesture-based input devices are growing and becoming more popular. In order for the two technologies to be implemented together, an understanding of gestures in relation to virtual objects and users’ expectations of those gestures needs to be understood. Specifically, this thesis focuses on arm gestures for the rotation of virtual objects. Participants in the study were first asked to freely perform an arm gesture they felt should execute a task. Next, participants were asked to perform specific rotation tasks with preconfigured arm gestures on four objects. There were two types of objects: those that could only be rotated on one axis and those that could be rotated on two axes. Each object type was represented by a familiar small and large object: combination lock, water wheel, baseball and beach ball. Data on how quickly they could complete the rotation tasks was collected. After performing the tasks on each of the four objects, participants were asked to rate the intuitiveness of each gesture as well as their preferred gesture for the specific task. The captured data showed that when users were presented with virtual representations of familiar physical objects, most of them expected to rotate the objects with the same gestures they would use on the actual physical objects. Considering 1-axis objects, an arm-based twist gesture outperformed other arm-based gestures in both intuitiveness and efficiency. Also with 2-axis objects, an arm-based horizontal/vertical gesture outperformed others in both intuitiveness and efficiency. Interestingly, those gestures were most efficient for each object type regardless of the size of the object being rotated. This would indicate that users are able to mentally separate the physical and virtual experiences. Larger objects require different rotation gestures than smaller objects in the physical world, but that requirement is non-existent in a virtual world. However, while the mind can separate between the physical and virtual worlds, there is still an expected connection. This is based on the fact that the gestures most preferred for the rotation tasks are the same gestures used for similar physical tasks.
To design flat scattering parameters at 434 MHz frequency region by using AWRP

In this study, an Ultra High Frequency (UHF) flat gain power amplifier which has 434 MHz centre frequency and 12 MHz bandwidth frequency is designed. The main purpose of the work is to design and obtain flat scattering parameters and the other figure of merit at these frequency values. Approximately 4 MHz flatness from 432 MHz to 436 MHz at gain characteristics has been obtained by using Microwave Office AWRP (Applied Wave Research Program) supported by National Instruments. Limitation borders have been put to the graph and other optimization techniques are used on the program so that the gain graph becomes flat along 4 MHz at the peak side. Efficiency and linearity are also examined but the flatness is the main object for this project.
A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor

Pedestrian detection is a fundamental problem in various computer vision applications and is addressed by complex solutions involving complex feature extractor and classification techniques. In this paper, feature selector is used along with Histogram of Significant Gradients (HSG) descriptor and linear SVM classifier to enhance the detection accuracy and reduce the processing time. A feature selector organizes the extracted features in decreasing order of their significance. A comparative study has been done employing different classifiers and feature selectors and a system is proposed with better detection capability. The proposed scheme is found to have a 50 % reduction in feature size, 67 % reduction in processing time and 3.93 % improvement in accuracy.
Effect of Different UML Diagrams to Evaluate the Size Metric for Different Software Projects

In Software Engineering, an important activity is measuring of the Software in different ways. For Measuring the Software, appropriate metrics are needed. Using Software metrics we are able to attain the various qualitative and quantitative aspects of Software. To measure the Software in terms of quality, size, efforts, efficiency, and reliability, performance etc. we have different metrics available in Software Engineering and it has been an area of interest for the various researchers. Measures of specific attributes of the process, project and product are used to compute Software metrics. This work proposes a similar approach of measuring software using various UML diagrams and applied Software size metric to evaluate the size of the Software. This paper discusses the proposed approach using two different case studies and their source codes. This paper discusses the different results obtained using different perspectives of the Software size metric measurements and maintainability of the Software.
A formal framework for linguistic tree query

The analysis of human communication, in all its forms, increasingly depends on large collections of texts and transcribed recordings. These collections, or corpora, are often richly annotated with structural information. These data sets are extremely large so manual analysis is only successful up to a point. As such, significant effort has recently been invested in automatic techniques for extracting and analyzing these massive data sets. However, further progress on analytical tools is confronted by three major challenges. First, we need the right data model. Second, we need to understand the theoretical foundations of query languages on that data model. Finally, we need to know the expressive requirements for general purpose query language with respect to linguistics. This thesis has addressed all three of these issues. Specifically, this thesis studies formalisms used by linguists and database theorists to describe tree structured data. Specifically, Propositional dynamic logic and monadic second-order logic. These formalisms have been used to reason about a number of tree querying languages and their applicability to the linguistic tree query problem. We identify a comprehensive set of linguistic tree query requirements and the level of expressiveness needed to implement them. The main result of this study is that the
Case‐only trees and random forests for exploring genotype‐specific treatment effects in randomized clinical trials with dichotomous end points

Discovering gene–treatment interactions in clinical trials is of rising interest in the era of precision medicine. Non‐parametric statistical learning methods such as trees and random forests are useful tools for building prediction rules. We introduce trees and random forests to the recently proposed case‐only approach for discovering gene–treatment interactions and estimating marker‐specific treatment effects for a dichotomous trial's end points. The motivational example is a case–control genetic association study in the prostate cancer prevention trial, which tested the hypothesis whether finasteride can prevent prostate cancer. We compare this novel approach with the interaction tree method previously proposed. Because of the modelling simplicity—directly targeting at interaction—and the statistical efficiency of the case‐only approach, case‐only trees and random forests yield more accurate prediction of heterogeneous treatment effects and a better measure of variable importance, relative to the interaction tree method which uses data from both cases and controls. Application of the proposed case‐only trees and random forests to the prostate cancer prevention trial study yielded a discovery of genotypes that may influence the prevention effect of finasteride.
An improved retinal blood vessel segmentation for diabetic retinopathy detection

Abstract Diabetic Retinopathy (DR) is an eye disorder that has progressively grows towards people who suffered from diabetes. The complications in diabetes cause the damage of blood vessel at the back of the retina. In extreme cases, DR may lead to vision loss or blindness. However, this serious effect was able to be in control through timely treatment and early detection. Recently, this issue is spreading rapidly especially in working area which eventually forced the demand of diagnosis of this illness from the earliest stage. Hence, the detection of retinal blood vessel plays significant role in controlling the progressions of this illness. The advance stages of DR such as neovascularisation which leads to the growth of abnormal vessel can be controlled by extraction of retinal vessel. Therefore, the aim of this research is to develop an approach for blood vessel detection. The proposed method comprises several techniques namely contrast enhancement, background exclusion, filtration, h-maxima transform, multilevel thresholding and morphological operation. The performance of the algorithm was evaluated based on True Positive Fraction, False Positive Fraction and accuracy which achieved the result of 0.9779, 0.0408 and 0.9686, respectively.
Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification

Prime objective of this letter is to select the most relevant features from the original set and perform two steps of feature extraction operations on those selected set, for the classification of hyperspectral remote sensing (HSRS) images. Neighborhood rough sets (NRSs)-based method is used for feature selection because of its excellent neighboring information capturing ability. On these selected features, two steps of extraction operations are performed using the stationary wavelet transform (WT) and stacked deep autoencoder (SDAE). Stationary WT extracts the features by exploiting the spectral–spatial information and stacked DAE extracts through representative learning of input information. The wavelet features and the original input spectral features are cascaded to feed as input to the stacks DAE for feature extraction and classification tasks. The proposed classification model with these operational steps possesses the ability to capture more informative features with improved spectral–spatial information that are highly beneficial for the classification of complex data sets, like HSRS images. Simulation results with two HSRS images justified the efficacy of the proposed model compared to other similar methods in terms of different performance measurement indexes.
Stabilized finite elements for time‐harmonic waves in incompressible and nearly incompressible elastic solids

The propagation of waves in elastic solids at or near the incompressible limit is of interest in many current and emerging applications. Standard low‐order Galerkin finite element discretization struggles with both incompressibility and wave dispersion. Galerkin least squares stabilization is known to improve computational performance of each of these ingredients separately. A novel approach of combined pressure‐curl stabilization is presented, facilitating the use of continuous, equal‐order interpolation of displacements and pressure. The pressure stabilization parameter is determined by stability considerations, while the curl stabilization parameter is determined by dispersion considerations. The proposed pressure‐curl–stabilized scheme provides stable and accurate results on a variety of numerical tests for incompressible and nearly incompressible elastic waves computed with linear elements.
An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects.
Previsão de Convulsões com o Uso de Redes Complexas

A epilepsia e definida como uma desordem cerebral caracterizada por uma pre-disposicao a geracao de convulsoes. Tais convulsoes sao fracamente controladas em mais de 30% dos pacientes epilepticos, sendo necessarias intervencoes medicas. Desta forma, existe a necessidade de tratamentos inovadores para o tratamento desta desordem. As atividades neuronais extra e intracraniana de individuos sadios e doentes podem ser obtidas por meio da tecnica de EletroEncefaloGrafia (EEG). Essa tecnica consiste no registro grafico das correntes eletricas desenvolvidas no encefalo, obtido atraves de eletrodos aplicados no couro cabeludo, na superficie encefalica, ou ate mesmo dentro da substancia encefalica [6]. [. ]
Selection of methods to analyse body weight and feed intake data used as inputs for nutritional models and precision feeding in pigs

The progress of technologies (sensors, automates) in precision livestock farming enables the development of innovative feeding techniques such as precision feeding of individual animals. In addition to the design of adapted feeders, precision feeding requires decision-support tools to manage data and apply nutritional models that calculate the optimal feed composition and allowance. These calculations require to forecast body weight (BW) and feed intake (FI) of individual pigs according to past performance. To select the most accurate forecasting method, three statistical methods were tested on a dataset of measurements of BW and FI for 117 pigs: the double exponential smoothing (DES) method, multivariate adaptive regression splines (MARS), and the knearest neighbours (kNN) method. These methods were tested in relation to data sampling frequency (i.e., daily or weekly measurements) and data availability. The capacity to forecast BW or FI was evaluated through the mean error of prediction. The kNN method appeared suitable if few historical data are available as it requires not more than 3 historical data. The MARS method was better than the DES method to forecast daily BW, but the DES method was better in forecasting the daily cumulated FI. The DES method also seemed more appropriate for weekly BW data, requiring only 3 historical data to make a forecasting. These methods can be used for performance forecasting in a decision-support tool for precision feeding. This study was performed in the Feed-a-Gene Project funded by the European Union’s H2020 Programme (grant agreement no 633531).
Strict dissipativity analysis for classes of optimal control problems involving probability density functions

Motivated by the stability and performance analysis of model predictive control schemes, we investigate strict dissipativity for a class of optimal control problems involving probability density functions. The dynamics are governed by a Fokker-Planck partial differential equation. However, for the particular classes under investigation involving linear dynamics, linear feedback laws, and Gaussian probability density functions, we are able to significantly simplify these dynamics. This enables us to perform an in-depth analysis of strict dissipativity for different cost functions.
Biometriese egtheidsbevestiging tydens intydse bankdienste : referaatopsomming

A system for biometric authentication in online banking Online banking is under attack by cyber criminals using methods such as phishing, spoofing, and man-in-the-middle attacks. The system proposes using a biometrically secured USB stick containing the client's fingerprint, a unique key S, and a limited web browser and other information, which is provided to the client by the banking institution. To log on, the client allows his or her fingerprint to be read by the USB stick, and access is granted if the print matches the one previously stored on the USB stick. The browser on the USB stick is then activated, taking the client to the bank's real online banking web page. Every transaction is then secured by means of randomly generated session keys using the shared key S, the transaction information and other information. This system prevents many of the currently known malicious attacks.
$W$ -Band High-Gain Substrate Integrated Cavity Antenna Array on LTCC

A <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> high-gain substrate integrated cavity (SIC) antenna array using multilayered low-temperature cofired ceramic (LTCC) technology is proposed and designed for <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band applications. The TM<sub>211</sub> mode of the SIC is utilized. Two parasitic patches are introduced on the top surface of the SIC to adjust the aperture electromagnetic field distribution of the TM<sub>211</sub> mode in the cavity for obtaining a high-gain broadside radiation pattern. A bow tie shape is adopted for the parasitic patches to improve the matching characteristic of the SIC. The SIC is modified to a stepped flared shape to improve the gain characteristic of the antenna element. Subsequently, a two-layer low-loss substrate integrated waveguide (SIW) feeding network is designed for the <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> antenna array on the basis of the proposed high-gain SIC radiating element. The proposed antenna array shows a wide measured bandwidth of 17.48% from 81.45 to 97.05 GHz at <inline-formula> <tex-math notation="LaTeX">$\vert \textrm {S}_{11} \vert < -10$ </tex-math></inline-formula> dB and a measured gain of up to 20.3 dBi.
TEACHING SPEAKING THROUGH INFORMATION TRANSFER TASK AT SECOND GRADE STUDENTS OF SMP

Penelitian ini bertujuan untuk mengetahui apakah information transfer task dapat meningkatkan kemampuan berbicara bahasa Inggris siswa dan untuk mengetahui persepsi siswa tentang belajar berbicara menggunakan teknik information transfer task . Ada dua instrumen yang digunakan dalam penelitian ini, yang pertama tes berbicara dan yang kedua angket persepsi siswa. Penelitian tindakan kelas ini dilaksanakan dalam dua siklus, hasil siklus pertama 17 siswa (65.38%) mencapai nilai ≥ 70, akan tetapi hasil siklus pertama belum memenuhi indikator keberhasilan penelitian ini, maka dari itu, siklus kedua pun dilaksanakan. Hasil siklus kedua menunjukkan ada 22 siswa (84.61%) mencapai nilai ≥ 70. Lalu, hasil angket persepsi siswa menunjukkan 20 siswa (76.92%) menyukai pembelajaran menggunakan information transfer task . Jadi, dapat disimpulkan bahwa dari hasil penelitian tindakan kelas ini ditemukan bahwa information transfer task dapat meningkatkan kemampuan berbicara siswa dan membuat siswa puas dengan pembelajaran. This research was conducted to find out whether information transfer task could improve students’ speaking ability and to analyze students’ perception about the implementation of information transfer task. There were two instruments in this research i.e. speaking test and questionnaire. This classroom action research was conducted in two cycles and in the cycle one there were 17 students (65.38%) who reached score ≥ 70, because the indicator had not been fulfilled in the cycle one, the researcher did cycle two. In cycle two there were 22 students (84.61%) who got ≥ 70. Then, the result for students’ perception questionnaire showed that 20 students (76.92%) stated that they were active and satisfied with teaching learning process through information transfer task. So, it could be concluded that from the result of this classroom action research it was found that information transfer task could improve students speaking ability and make the students satisfied with the teaching learning process. Keywords : car, information transfer, speaking ability, teaching speaking
Black Intragroup Empathic Responding to Police Interracial Violence: Effects of Victim Stereotypicality and Blacks’ Racial Identification

Despite the public outrage in response to police violence against unarmed Black men, work on the psychological dynamics of reactions to these incidents is relatively rare. The present research examined whether empathy for a Black male victim of White police interracial violence would vary as a function of victim stereotypicality (stereotypic/counterstereotypic) and Black participant racial identity. In Study 1, 140 Black participants were recruited from Amazon Mechanical Turk (MTurk). As hypothesized, Black participants low in racial identification reported less empathy for the stereotypical relative to the counterstereotypical victim. Those high in racial identification showed relatively high levels of empathy regardless of the characteristics of the Black victim. Study 2 replicated these effects with 263 Black MTurk participants. This research highlights the value of considering individual differences in the Black observers (racial identification) and the characteristics of Black victims to better understand the psychological processes involved in intragroup responses to police violence.
Variable-scale maps in real-time generalisation using a quadtree data structure and space deforming algorithms

ABSTRACT Variable-scale maps have been advocated by several authors in the context of mobile cartography. In the literature on real-time map generalisation, however, corresponding methods that resolve cartographic conflicts by deformation of the underlying map space together with the map foreground, are underrepresented. This paper demonstrates how the concept of a malleable space can be applied as a part of the generalisation process and incorporated into the overall methodology of point generalisation. Two different algorithms are used, a density-equalising cartogram algorithm and Laplacian smoothing. Both methods work in real-time and are datadriven. In addition, they allow for a parameterisation in combi-nation with a quadtree data structure, as well as a combination with 'classic' generalisation operators (e.g. selection, aggregation, displacement) based on the quadtree. The quadtree serves both as a spatial index for fast retrieval and search of points, and as a density estimator to inform generalisation operators. The use of the quadtree as a common spatial index provides a tool to combine variable-scale maps with classic generalisation. A combination of the two allows, at small map scales, the maintenance of detail in dense areas and data reduction in sparse areas. Additionally, it facilitates building a modular workflow for real-time map generalisation.