Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal of a Wearable Personal Concierge System with Healthcare Using Speech Dialogue Technology

Our Speech dialogue system is wearable, so you can talk with computer graphics character animation Speech dialogue agent anytime and anywhere, providing daily information (desired information, etc.), daily health management and health advice, etc. This paper proposes the realization of a Speech dialogue system that resembles a personal concierge who is familiar to the user. In this system, we have also implemented a function that enables real-time detection of health deterioration caused by COVID-19 pneumonia and provides health management and advice to the user in real time by constantly acquiring real-time information on the user's pulse wave, SpO2 and body temperature.
Minimum-time trajectory planning for a 4-dof manipulator considering motion stability and obstacle avoidance

The trajectory planning with obstacle avoidance is a critical task for redundant manipulators in the complex operating environment. This paper proposes a time-minimum trajectory optimization technique to obtain a collision-free and jerk-continuous trajectory for a 4-dof redundant manipulator considering both kinematic and spatial constraints. In the optimization, the whole trajectory is divided into several sub-trajectories to ensure the reliability of the technique. The screw-based inverse kinematics (IK) model is used to obtain the angular displacements of joints corresponding to the end-effector final positions in each sub-trajectory. The spatial constraint is determined by assuming robotic links using cylinders with different bottom radius, and enveloping obstacles using spheres under different sizes. The quintic polynomial curve is utilized to interpolate in joint space to guarantee the continuous of the angular jerk of each joint. Finally, PSO algorithm is used to search for the optimal trajectory of the robot by taking the whole motion time as the optimization objective. Two cases are taken to validate the effectiveness of the proposed technique. The simulation results in MATLAB show that the robotic motion based on the optimal trajectory are safe, stable and efficient.
Compressing Neural Networks With Inter Prediction and Linear Transformation

Because of resource-constrained environments, network compression has become an essential part of deep neural networks research. In this paper, we found a mutual relationship between kernel weights termed as <italic>Inter-Layer Kernel Correlation</italic> (ILKC). The kernel weights between two different convolution layers share a substantial similarity in shapes and values. Based on this relationship, we propose a new compression method, <italic>Inter-Layer Kernel Prediction</italic> (ILKP), which represents convolutional kernels with fewer bits through similarity between kernel weights in convolutional neural networks. Furthermore, to effectively adapt the inter prediction scheme from video coding technology, we integrate a linear transformation into the prediction scheme, which significantly enhances compression efficiency. The proposed method achieved 93.77% top-1 accuracy with <inline-formula> <tex-math notation="LaTeX">$4.1\times $ </tex-math></inline-formula> compression ratio compared to the ResNet110 baseline model on CIFAR10. It means that 0.04% top-1 accuracy improvement was achieved by using less memory footprint. Moreover, incorporating quantization, the proposed method achieved a <inline-formula> <tex-math notation="LaTeX">$13\times $ </tex-math></inline-formula> compression ratio with little performance degradation compared to the ResNets baseline model trained on CIFAR10 and CIFAR100.
Building segmentation from satellite imagery using U-Net with ResNet encoder

As one of the most important types of artificial ground features, building images and outlines are widely used in map updating, GIS analysis, urban planning, and environmental modelling etc. Fast and accurate segmentation of buildings is one of the hot and difficult topics in remote sensing image processing research for many years. This paper tries to apply the U-Net deep neural network model with ResNet encoder to remote sensing image segmentation for building extraction. First, we obtain some remote sensing images through some open datasets, and then we outline the building images to get the building mask, then we divide the dataset into eight to two to form two datasets for training and validation, and then we use this dataset to train and validate the U-Net model. The results show that the model's MIoU (Mean Intersection over Union) reached 0.83, and the model achieved a good building segmentation effect. This model can be used for building segmentation with clear boundaries, however, there are also a few improvements to be resolved in further studies, more accurate results, more straight outlines, less misclassification, etc.
HOPE: Software Defect Prediction Model Construction Method via Homomorphic Encryption

Software defect prediction can predict the defective modules in the project in advance, which is helpful to optimize the allocation of test resources. Recently, privacy protection for datasets and models has gradually attracted the attention of researchers. In this study, we are the first to apply homomorphic encryption to software defect prediction model construction and propose a novel method HOPE. Specifically, we adopt an algorithm approximation strategy to approximate the sigmoid function and select the Paillier homomorphic encryption algorithm for Logistical regression. In our case study, we choose the MORPH dataset gathered from real-world open-source projects as our experimental subjects. Then we design three control groups to simulate three different scenarios based on whether the client sends the encrypted data to the server and whether the server uses the HOPE method. The final results show that if the server uses the original Logistic regression to construct the model on the encrypted data, the performance of the trained model is similar to random guess, which can guarantee the privacy protection of the data. Moreover, compared with the original Logistical regression method, the method HOPE only needs a small amount of computational cost, but there is no obvious performance decrease. We share our implementation scripts and datasets to encourage researchers to conduct more studies on this research direction.
Microprismatic Fresnel Lens for Formation of Uniform Light Circle

Focusing Fresnel lenses are used in many fields of applied optics. These devices are used in optical sensor systems for imaging and optoelectronic integration. The traditional Fresnel lens concentrates the light intensity on the center of the formed image. We present a microprismatic Fresnel lens that transforms a circular incident parallel light beam into a homogeneous light circle with the necessary diameter at a certain distance from the lens. These transforming Fresnel concentrators can be successfully used, for example, in monitoring devices to automatically adjust the output signal from four-quadrant photodetectors. Traditional focusing Fresnel structures are manufactured by photolithographic methods or adjustable direct laser recording with photoresists. These methods enable the formation of stepped optical structures, which have inherent surface defects, resulting in the formation of images that are not high in quality. The proposed specialized Fresnel concentrators can be easily fabricated via the diamond cutting method, which enables the manufacturing of flat working surfaces with exceedingly high optical quality. We also develop a method for simulating the Fresnel transforming lenses with flat conical working facets and calculate the geometric parameters of the circular concentrators. We then apply the simulation results to the diamond cutting method and fabricate the microprismatic light transforming lens samples. These samples are then investigated experimentally with a collimated laser beam. The obtained data agree with the theoretical predictions.
Can Web Search Queries Predict Prices Change on the Real Estate Market?

This study aims to explore whether the intensity of internet searches, according to the Google Trends search volume index (SVI), is a predictor of changes in real estate prices. The motivation of this study is the possibility to extend the understanding of the extra predictive power of Google search engine query volume of future housing price change (shift direction) by (i) the introduction of a research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying the multi-class HPI values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the SVI that relates to interests in both ‘real estate’ and ‘credit to buy real estate’; (iv) evaluation of the introduced approach in the context of the Polish real estate market. The main theoretical contribution of our work is a confirmation that the freely available information regarding Google user searches can provide an in-depth insight into enriching the generally accepted statistics on supply and demand in the real estate market. From the practical perspective, this research confirms that SVI can be associated as a sole determinant to anticipate the housing price change with time-lag sufficient for making decisions regarding the purchase (sale) of individual property or the real estate market control. Such findings can be also helpful for researchers who intend to use Google Trends data as an extra variable from demand side to improve the prediction accuracy if it is included in the model which is based on the existing housing prices determinants.
Polycarbonate Bladed Highway Wind Turbine: A Case Study

In the past decade, the energy demand has increased significantly in line with population rise. This increase is rapidly leading to depletion of conventional resources. Consequently, the leading countries are forced to combine bigger shares of renewable energy (RE) into their energy plans than the ones they already have. This scenario has enlightened some RE technologies such as; Solar Photovoltaic technology (PV), Concentrated Solar Power (CSP), Biomass, and Wind Energy. In the present work, one of the novel applications of wind energy is developed and studied experimentally. The main goal of this new application is to utilize the aerodynamic energy loss generated by moving vehicles; high-speed moving vehicles can produce strong wakes which contain considerable amount of localized wind energy. In the current work, airport road, Amman, Jordan was the selected site for wind energy assessment. A full site assessment including wind velocity, direction, and power content associated with wind speed from moving vehicles was conducted. The proposed system is not only capable of utilizing power from moving vehicles, but is also designed to have a relatively low cut-in wind speed to generated electricity from natural wind. The rotor of the experiment prototype was fully made of Polycarbonate sheets, owing to their light weight and high resilience against fatigue. The design of rotor is the Banki wind turbine, which includes multiple blades to harvest the maximum amount of kinetic energy. In results, the turbine featured a maximum power coefficient of 0.00924 and a cut in wind speed of 6.29 m/s. Furthermore, the turbine features a low starting torque and a rigid structure. These, among many other characteristics, indicated that the proposed system has a good performance in comparison with other configurations of vertical axis wind turbines.
Preparation of silver nanoparticles loaded graphene oxide nanosheets for antibacterial activity

A simple, facile method to fabricate successfully silver nanoparticle (AgNPs) decorated on graphene oxide (GO) layers via grafted thiol groups. Samples were prepared with different concentrations of AgNO3. Resulting AgNPs were quasi-spherical in shape and attached on the layers of GO. Physical properties were confirmed by X-ray diffraction (XRD), zeta potential, dynamic light scattering (DLS), Fourier transform infrared (FTIR) spectra, thermogravimetric analyzer (TGA), transmission electron microscopy (TEM) and field emission scanning electron microscopy (FE-SEM). Antimicrobial test was effectively showed using MRSA (Staphylococcus areus). The GO-Ag NPs with appropriate Ag NPs content of 0.2 M AgNO3 exhibited the strongest antibacterial activity at 48.77% inhibition after 4 hours incubation.
OTT_A_248237 5017..5032

Andrea Forschner 1 Stephanie Weißgraeber Dirk Hadaschik Martin Schulze Maria Kopp Sabine Kelkenberg 2 Tobias Sinnberg 1 Claus Garbe Saskia Biskup Florian Battke 2 1Center for Dermatooncology, Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany; 2Center for Genomics and Transcriptomics (CeGaT) GmbH, Tuebingen, Germany; 3Practice for Human Genetics, Tuebingen, Germany Purpose: BRAF and MEK inhibitors significantly improved the prognosis of metastatic melanoma. Nevertheless, initial treatment response may be only temporary. Liquid biopsies (LB) offer a possibility to monitor patients by measuring circulating tumor DNA (ctDNA). We sought to find out whether ctDNA can be used to reliably determine progressive disease under targeted therapy. In addition, we wanted to check whether ctDNA may represent a possible prognostic marker for survival. Patients and Methods: We included 19 melanoma patients with BRAF and MEK inhibitor therapy. For each patient, a 710 gene panel was analyzed on the latest available tumor tissue before the start of therapy. Repetitive LB were collected in which BRAF V600E/K mutations were monitored using digital droplet PCR (ddPCR). We correlated radiological staging results and overall survival with ctDNA results. Results: In 13 patients, ctDNAwas detectable when starting targeted therapy, whereas in six patients, ddPCR was always negative, which we confirmed with ultra-deep sequencing. All patients with initially detectable ctDNA had ctDNA values declining to zero during followup, increasing again at the time of extracerebral progression or even slightly before detection by imaging. Survival was significantly worse for patients with elevated LDH (p=0.034) or detectable ctDNA (p=0.008) at the start of targeted therapy. Conclusion: Therapy monitoring by ctDNA seems to be a reliable method for detecting extracranial progression, even more sensitive and specific than LDH or S100B. However, due to the small number of cases in our study, further studies are necessary.
Effects of surface and top wind shear on the spatial organization of marine Stratocumulus-topped boundary layers

15 The convective nature of Stratocumulus topped boundary layers (STBL) involves the 16 motion of updrafts and downdrafts, driven by surface fluxes and radiative cooling, re17 spectively. The balance between shear and buoyant forcings at the surface can determine 18 the organization of updrafts between cellular and roll structures. We investigate the ef19 fect of varying shear at the surface and top of the STBL using Large Eddy Simulations, 20 taking DYCOMS II RF01 as a base case. We focus on spatial identification of the fol21 lowing features: coherent updrafts and downdrafts, and observe how they are affected 22 by varying shear. Stronger surface shear organizes the updrafts in rolls, causes less well23 mixed thermodynamic profiles, and decreases cloud fraction and liquid water path (LWP). 24 Stronger top shear also decreases cloud fraction and LWP more than surface shear, by 25 thinning the cloud from the top. Features with stronger top than surface shear are as26 sociated with a net downward momentum transport and show early signs of decoupling. 27 Classifying updrafts and downdrafts based on their vertical span and horizontal size con28 firms the dominance of tall objects spanning the whole STBL. Tall objects occupy 30% 29 of the volume in the STBL while short ones occupy less than 1%. For updraft and down30 draft fluxes these tall objects explain 65% of the vertical velocity variance and 83% of 31 the buoyancy flux, on average. Stronger top shear also weakens the contribution of down32 drafts to the turbulent fluxes and tilts the otherwise vertical development of updrafts. 33 Plain Language Summary 34 Stratocumulus clouds form in the atmospheric boundary layer, close to the Earths 35 surface. Turbulence in this boundary layer causes large circulation composed of strong 36 motions going up and down called updrafts and downdrafts. At the surface and top of 37 the boundary layer, wind speed can change abruptly, generating wind shear. We inves38 tigate the effect of changes in wind shear on the organization of the clouds, updrafts, and 39 downdrafts. By simulating atmospheric flow, we observe that stronger surface wind re40 duces the amount of clouds, strong top shear modifies the shape of cloud tops, and only 41 when shear is strong both at the surface and top, clouds are elongated in the wind di42 rection. Of all the updrafts and downdrafts found, the tallest ones dominate in volume 43 occupied and in the transport of momentum, heat, and moisture in the boundary layer. 44
Towards Uncertainty Quantification of LES and URANS for the Buoyancy-Driven Mixing Process between Two Miscible Fluids—Differentially Heated Cavity of Aspect Ratio 4

Numerical simulations are subject to uncertainties due to the imprecise knowledge of physical properties, model parameters, as well as initial and boundary conditions. The assessment of these uncertainties is required for some applications. In the field of Computational Fluid Dynamics (CFD), the reliable prediction of hydrogen distribution and pressure build-up in nuclear reactor containment after a severe reactor accident is a representative application where the assessment of these uncertainties is of essential importance. The inital and boundary conditions that significantly influence the present buoyancy-driven flow are subject to uncertainties. Therefore, the aim is to investigate the propagation of uncertainties in input parameters to the results variables. As a basis for the examination of a representative reactor test containment, the investigations are initially carried out using the Differentially Heated Cavity (DHC) of aspect ratio 4 with Ra=2e9 as a test case from the literature. This allows for gradual method development for guidelines to quantify the uncertainty of natural convection flows in large-scale industrial applications. A dual approach is applied, in which Large Eddy Simulation (LES) is used as reference for the Unsteady Reynolds-Averaged Navier–Stokes (URANS) computations. A methodology for the uncertainty quantification in engineering applications with a preceding mesh convergence study and sensitivity analysis is presented. By taking the LES as a reference, the results indicate that URANS is able to predict the underlying mixing process at Ra=2e9 and the variability of the result variables due to parameter uncertainties.
Information Systems Values: A Study of the Intranet in Three French Higher Education Institutions

Evaluation of performance of the Information System (IS) function has long been a concern of senior management in organisations. The IS function can impact competitive advantage which is why executives regularly look for evidence of returns on IT investments. The paper reports on a study which investigated how patrimonial value (inherent or actual quality of hardware, software and know‑how) and use value (contribution to the business) influence the success and organisational impact of an intranet. This paper proposes a set of principles for the conduct and implementation of analysis of these two intangible values of IS function. Research was conducted in three field studies of business and engineering schools in France. The data collected in questionnaires were analysed using the PLS structural equation method. This research provides theoretical contributions with a model giving a deeper comprehension of patrimonial value and use value of information systems, as well as explaining the relationships between these two values, success and organisational impact.
Stock return determinants in Indonesia

The purpose of this study is to provide empirical evidence regarding the effect of capital structure, company, size, earnings quality on stock returns with stock liquidity as an intervening variable. The method used in this study is descriptive and verification methods. This study uses 17 listed firms in Indonesia Stock Exchange as the sample specifically for the textile and garment industry over the period of 2014 to 2018 and analyzed by path analysis. The results show that capital structure, firm size, and earnings quality have significant and positive effects directly on stock returns and indirectly through stock liquidity. These findings imply that capital structure, firm size, earnings quality, and stock liquidity shall form positive information to investors under condition high trust of investors as the impact of decreasing asymmetric information. Consistent with signaling theory, this study proves that positive information on investors will be formed if there is an increase in investor confidence as a result of reduced information asymmetry.
Mineralization, Alteration Assemblages and Stable Isotopes of the Intermediate-Sulfidation Epithermal Strauss Deposit, Drake Goldfield, North-Eastern NSW, Australia

: The intermediate-sulfidation epithermal Strauss deposit is part of the Drake Goldfield of north-eastern NSW, Australia. The mineralization is gold dominant with minor silver and significant zinc, copper and lead. Strauss has a combined total resource of 2.66 Mt @ 1.5 g/T Au and 1.9 g/T Ag. Petrographic analysis has shown the deposit to be hosted by varying layers of dacitic ash flow tuffs and a cross-cutting lamprophyre dyke though previous authors classified some of the tuffs as intrusive andesites. Mineralization mainly occurs in the form of vein stockworks of three main orientations. Structural analysis indicates that these resulted from two fault system events and one conjugate fault system. Petrographic and SEM analysis shows that there are two different mineralization styles, a pyrite dominant and chalcopyrite dominant assemblage, with three main mineralization events. Correlation analysis for the assay data indicates that Au has a strong relationship with Ag and a moderate correlation with Pb. XRD analysis indicates widespread argillic-phyllic and propylitic alteration. Silver tellurides have been found as late-stage grains associated with the chalcopyrite mineralization assemblage. Sr shows a significant depletion due to the intense and pervasive alteration at Strauss. The carbon and oxygen isotopes of the vein carbonates suggests that the carbon from the late-stage carbonates was derived from a magmatic source with a significant input of low temperature meteoric water, while the sulfur isotopes indicate a magmatic sulfur source.