Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative 3D interactions and their application on virtual, augmented and mixed reality interfaces

Spatial object manipulation is one of the most common tasks in 3D environments. It is the action of changing position, rotation and scale properties of virtual elements. The intangibility of the virtual requires the use of 3D user interfaces for robust interactions. Manipulations in 3D involve complex coordination of virtual transformations and demand a highly cognitive and physical effort. One alternative towards efficient 3D manipulations is to share the work to solve the task collaboratively. A collaborative environment provides more than one individual perspectives of the scene, which may also include shared and personal viewpoints. However, teamwork involves considerable negotiations and, as team members vary, team strategies and task-fulfillment processes also change. Modeling such human interactions raises novel concepts and interface design approaches when compared to those typically grounded in a single-user scenario. Our goal is to investigate collaboration in group work, identify the aspects of effective cooperative interactions and design 3D user interactions that enhance the team capability to solve complex manipulation tasks in virtual environments. We explore the use of handheld devices and virtual reality apparatus as input devices in our designs. Smartphones are ubiquitous in modern life which makes them useful for collaboration, and virtual reality headsets are becoming the standard for immersive virtual reality. First, we designed an interaction model to support simultaneous 3D manipulations in virtual and augmented reality scenarios and evaluated group performance and behavior in symmetric collaboration, where the team has the same interaction and visualization interface. After, we investigate asymmetric group interactions, integrating our symmetric collaborative solutions. This research contributes to the definition of a model for the design of collaborative 3D user interfaces flexible to work in multiple immersion scenarios. Furthermore, we present a series of studies that investigate the application of collaborative 3D interactions on Virtual, Augmented and Mixed Reality scenarios.
Intra-aortic Balloon Pump Therapy for Anterior ST-elevation Myocardial Infarction with Cardiogenic Shock: An Observational Cohort Study

Background As a mechanical circulatory assistance, intra-aortic balloon pump (IABP) has been widely used for cardiogenic shock (CS), although recent clinical trials questioned its impact on acute myocardial infarction patients, nothing is hitherto known on the contribution of IABP to CS patients after anterior wall infarction. The aim of this study was to investigate the efficacy and safety of IABP therapy in patients presenting with anterior ST-elevation myocardial infarction (STEMI) complicated by CS.Methods We conducted a retrospective study of 215 consecutive patients presenting with CS after STEMI in the anterior wall between January 2006 and August 2017, including 125 patients in the IABP group and 90 patients in the control group.Results At 30 days, 60 (48.0%) patients in the IABP group and 58 (64.4%) patients in the control group had died (P=0.017). The Kaplan-Meier survival curves showed the cumulative survival rate in the IABP group was consistently higher than control group (P=0.009 by Log-Rank test). Nevertheless, IABP increased the occurrence of thrombocytopenia (21.6% vs. 2.2%, P<0.001) and lower limb complications (20.0% vs. 2.2%, P<0.001) at the same time. Subgroup analyses by Cox regression showed a better trend of prognoses in patients aged less than 60 years old (HR=0.49, 95% CI=0.26-0.91, P=0.025), male (HR=0.53, 95% CI=0.34-0.83, P=0.005), no history of hypertension (HR=0.47, 95% CI=0.26-0.87, P=0.017) and systolic blood pressure less than 80 mm Hg (HR=0.40, 95% CI=0.22-0.73, P=0.009). At 12-month follow-up, all-cause mortality in the IABP group was obviously lower than the control group (52.5% vs. 74.1%, P=0.002), there were no significant differences in other adverse cardiovascular events (P=1.000). Conclusions The combination of IABP use is associated with reduced 30-day and 12-month mortality in patients with anterior STEMI complicated by CS, though thrombocytopenia and lower limb complications are frequently observed.
Development of Chitosan Films from Edible Crickets and Their Performance as a Bio-Based Food Packaging Material

Edible insects have gained attention due to their impressive nutritional composition, as well as their efficient use of natural resources. However, a research gap remains on the applications of insect chitosan, especially as it relates to their potential use as food packaging material. Chitosan from two reared cricket species (Acheta domesticus and Gryllodes sigillatus) was evaluated for use as food packaging material. Cricket chitosan films (CCF) were structurally similar to commercial shrimp chitosan films (SCF) at controlled glycerol levels, as seen by shared spectral peaks in FT-IR analyses. Mechanical properties of CCF showed they had equal or greater tensile strength when compared to commercial SCF, although flexibility was lower. Scanning electron microscopy showed increased roughness of microstructure, likely increasing the tortuosity. As a result, CCF had improved water vapor permeability compared to commercial SCF. Melanin complexes present in cricket chitin and chitosan increased hydrophobicity and decreased light transmittance. This study also revealed that intrinsic species differences, which occur during insect and crustacean exoskeleton development, could have effects on the functionality of chitosan packaging materials. Overall, CCF were found to be as effective as commercial SCF, while providing additional advantages. CCF derived from reared crickets have good mechanical and barrier properties, and improved water resistance and light barrier characteristics. Edible cricket chitosan has the potential to be used as bio-based packaging material for food and pharmaceutical applications.
Web-based Tree Editor for JastAdd Compilers

Domain Specific Languages (DSL) are programming languages created with specific domains in mind. Programs in these domains may be more easily represented as graphs or other structures, rather than text. Structure editors do just that, represent programs as, for example, graphs (graphical editors) or trees (tree editors), by using the programs’ underlying structures, making structure editors useful tools for DSL programming. We will in this thesis present a generic web-based tree editor that works for creating programs in any language specified in the metacompilation system JastAdd. The tree editor performs semantic analysis and displays semantic errors directly in the web browser.
A study on shopping experiences of consumers in online shopping in Chennai city

Online shopping atmosphere is thus having an ever increasing responsibility in creating and maintaining relation between retailers and consumers and providing exceptional shopping experiences to consumers. The convenience, interactivity, excitement and offers are online shopping experiences of consumes. Significant difference exists between socio-economic profile of consumers and shopping experiences of consumers in online shopping. The convenience, offers, excitement and interactivity are positively and significantly impacting satisfaction of consumers towards online shopping. Thus, the online retailers should give adequate convenience to consumers and improve interactivity between web applications and consumers. They must provide more shopping excitement and realistic offers to consumers for their online shopping to enhance satisfaction of online shopping consumers.
Efficient Video-Based Vehicle Queue Length Estimation Using Computer Vision and Deep Learning for an Urban Traffic Scenario

In the traffic engineering realm, queue length estimation is considered one of the most critical challenges in the Intelligent Transportation System (ITS). Queue lengths are important for determining traffic capacity and quality, such that the risk for blockage in any traffic lane could be minimized. The Vision-based sensors show huge potentials compared to fixed or moving sensors as they offer flexibility for data acquisition due to large-scale deployment at a huge pace. Compared to others, these sensors offer low installation/maintenance costs and also help with other traffic surveillance related tasks. In this research, a CNN-based approach for estimation of vehicle queue length in an urban traffic scenario using low-resolution traffic videos is proposed. The system calculates queue length without the knowledge of any camera parameter or onsite calibration information. The estimation in terms of the number of cars is considered a priority as compared to queue length in the number of meters since the vehicular delay is the number of waiting cars times the wait time. Therefore, this research estimates queue length based on total vehicle count. However, length in meters is also provided by approximating average vehicle size as 5 m. The CNN-based approach helps with accurate tracking of vehicles’ positions and computing queue lengths without the need for installation of any roadside or in-vehicle sensors. Using a pre-trained 80-classes YOLOv4 model, an overall accuracy of 73% and 88% was achieved for vehicle-based and pixel-based queue length estimation. After further fine-tuning of model on the low-resolution traffic images and narrowing down the output classes to vehicle class only, an average accuracy of 83% and 93%, respectively, was achieved which shows the efficiency and robustness of the proposed approach.
Optimising the Energy Efficiency and Transient Response of Diesel Engines through an Electric Turbocharger

The electric turbocharger provides great potential for vehicle fuel efficiency improvement, exhaust emissions reduction and transient response acceleration. It makes the engine runs as a hybrid system so critical challenges are raised in energy management and control. This paper proposes a realtime energy management strategy for the electric turbocharger. A multi-variable explicit model predictive controller is designed to regulate the key variables in the engine air system, while the optimal setpoints of those variables are generated by a high level controller. The controllers work in a highly efficient way to achieve the optimal energy management. This strategy has been validated in simulations and experiments. Excellent tracking performance and high robustness demonstrate the effectiveness of the proposed method.
Application of fusion of two classifiers based on principal component analysis method and time series comparison to recognize maritime objects upon FLIR images

This paper presents a method of recognition of maritime objects based on FLIR (forward looking infra-red) sensor images using two methods: Principal Component Analysis (PCA) and Dynamic Time Warping (DTW). A combination of the Principal Component Analysis PCA with the eigenimages analysis method reduces the dimensionality of the recognition problem. DTW method finds the shortest distance between two time series allowing a transformation of time for both compared series. In the presented maritime objects FLIR images classifier the DTW method is used to compare the vertical brightness projection histograms of silhouettes for the recognized object and the object pattern. To determine the silhouette of a maritime object the Otsu thresholding algorithm is used. The paper describes the eigenimages method, the DTW method of comparing time series and the data fusion method combining conclusions both classifiers. In the final part of the paper are presented preliminary test results of the classification method for a set of maritime objects FLIR images registered in the Baltic Sea.
A Simple Application of FM0 Encoding in DSRC Applications

: The dedicated short-range communication (DSRC) system is the evolving technique used in the field of intelligent transport system (ITS) and Electronic Toll Collection (ETC). The DSRC standard employs FM0 encoding techniques to obtain dc-balance and enhances signal reliability. The codeword structure of FM0, thus limiting the hardware potential of existing DSRC systems. Dedicated short-range communications are one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards. The performance of this paper is evaluated on the simulation Using Xilinx. The maximum operation frequency is 900 MHz for FM0 encodings. The power consumption is 1.14 mW at 900 MHz for FM0 encoding. The encoding capability of this paper can fully support the DSRC standards of America, Europe, and Japan.
Augmented Linear Quadratic Tracker for Enhanced Output-Voltage Control of DC-DC Buck Converter

This paper presents a methodic approach to synthesize a robust affine state-feedback controller to enhance the output-voltage tracking control performance of a DC-DC buck converter circuit. The proposed control scheme primarily utilizes a conventional linear-quadratic-tracker (LQT) that renders optimal control decisions based on the state-feedback of output-voltage and inductor-current. Additionally, it employs a feed-forward control term to track the time-varying reference voltage trajectories. Despite its optimality, the LQT lacks robustness in eliminating the steady-state errors and compensating the effects of bounded exogenous disturbances that are caused by high-frequency noises, load-step transients, and modeling errors. In this research, the conventional LQT is equipped with auxiliary tools to dynamically compensate the aforementioned parametric uncertainties. The existing state-space model of the system is augmented with an additional integral-of-error state-variable to eliminate the steady-state fluctuations in output-voltage response. The controller is also retrofitted with a self-tuning capacitor-current control term in order to emulate and deliver the derivative control effort. It rejects the disturbances, compensates the hysteresis effect rendered by the parasitic impedances, and improves the error convergence-rate of the response. The proposed augmented tracking controller is rigorously analyzed via experimental tests to validate its effectiveness.
Sunshine-Based Trajectory Simplification

Sunshine-based trajectory analysis plays a significant impact on comprehensive scenarios, including driving safety and routing planning for mobile solar collection. To the best of our knowledge, however, a little work analyzes the impact of sunshine on mobile objects on different trajectories. This paper investigates the relationship between sunshine and the trajectories of mobile objects. We introduce a sunshine orientation model to integrate sunshine information with trajectories. A sunshine-based trajectory simplification model is proposed to remove redundant points in trajectories, which carry a little sunshine-related information. The sunshine-based trajectory simplification model is indeed versatile. We apply this model to help users efficiently query the sunshine information of their trips. Our design is evaluated with extensive simulations based on three-month general transit feed specification data from August to October in 2017 in five USA cities, including Washington, DC, USA, and Minneapolis. Furthermore, we develop a sunshine query system for mobile scenarios by embedding our sunshine-based trajectory simplification model and conduct a field study to show the validity and efficiency of this model. The evaluation results show that our design reduces 87.4% position points in trajectories while only sacrificing 9.6% of sunshine information in the field tests.
Unsupervised and semi-supervised learning for efficient opacity annotation of diffuse lung diseases

Research on computer-aided diagnosis (CAD) for medical images using machine learning has been actively conducted. However, machine learning, especially deep learning, requires a large number of training data with annotations. Deep learning often requires thousands of training data, but it is tough work for radiologists to give normal and abnormal labels to many images. In this research, aiming the efficient opacity annotation of diffuse lung diseases, unsupervised and semi-supervised opacity annotation algorithms are introduced. Unsupervised learning makes clusters of opacities based on the features of the images without using any opacity information, and semi-supervised learning efficiently uses the small number of training data with annotation for training classifiers. The performance evaluation is carried out by the classification of six kinds of opacities of diffuse lung diseases: consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal, and the effectiveness of the methods is clarified.
Do Tax Credits Stimulate R&D Spending Bi-Ming

As an important policy instrument to effectively intervene and stimulate the R&D investment of enterprises, tax incentives have been used by more and more countries and have strong evidence of theory and practice. The study found that the preferential tax policies of the state and the R&D investment of small and high-tech enterprises did not achieve the expected incentive effect, which is consistent with the conclusions derived from the mathematical model. Finally, a brief summary and relevant policy Suggestions are presented. Introduction In 2008, in order to encourage enterprises to carry out research and development activities, increase levels of innovation, and to standardize enterprise research and development expenses of pre-tax deduction and preferential tax policies of execution, the Chinese government introduced measures for the management of enterprise research and development expense deductions (proposed). In 2013 and 2015, these methods were revised and improved respectively: the pre-tax deduction standard for research and development expenses were further improved. Therefore, from the policy content and purpose, the intention of stimulating enterprise R&D investment is very obvious. Thus, is it true that the policy implementation process has achieved its intended goals? This is subject to further evaluation and analysis. Empirical Model Construction and Analysis The purpose of this article is to analyze the implementation on January 1, 2013 of the state administration of taxation on research and development of the notice of claim for additional deduction fees and tax policies (hereinafter referred to as the "notice") to the enterprise of R&D investment. Relative to the 2008 implementation of the measures for the management of the enterprise research and development expense deductions (try out)", the notice’s biggest characteristic is to expand the scope of enterprise R&D expenses deducted, to ensure the enterprise can be in the costs associated with R&D deductions, so as to reduce the tax burden of enterprises, and to stimulate enterprise R&D investment to a certain extent. High-tech enterprises and small and micro businesses that enjoy low tax rates (less than 25 per cent of the income tax) may have more preferential access. Data Source and Processing The data of the empirical analysis in this part are all from the CSMAR database. Since this article aims to analyze the impact of policy changes on R&D investment in enterprise R&D, all selected companies should have access to the data on R&D investment. Based on this, the criteria for selecting sample data are: (1) China's Shanghai and Shenzhen a-share listed companies are non-ST. (2) The R&D expenditure for 2010-2015 was positive. According to the above criteria, there were 2,069 sample companies selected.
Joint Power Allocation for the Multi-User NOMA-Downlink in a Power-Line-Fed VLC Network

Visible light communications (VLC) constitute as a promising downlink technique of supporting the ever-increasing tele-traffic. Since the ubiquitous mains power line constitutes a natural backbone feed for VLC, a combined power line and VLC network is conceived, where the source data are fed by power line communications (PLC) into the VLC network. We refer to it as a PLC-VLC network, where the power line connects the light emitting diodes without requiring a duplicated filter-backbone. Then, the information is forwarded to multiple users via visible light. Furthermore, to increase the downlink bit rate, non-orthogonal multiple access (NOMA) is invoked in VLC. For the sake of maximizing the sum-throughput, we conceive a joint PLC-VLC power allocation (JPA) strategy, where the power allocated both to the PLC and to the VLC links is jointly determined upon analyzing the power relationship between them. Our simulations demonstrate that NOMA significantly outperforms orthogonal multiple access (OMA) with the aid of the proposed JPA.
A multi-resolution approach for spectral graph matching

This project presents an innovative way of solving the inexact graph matching problem of weighted graphs. The goal is to find the correspondence between the vertices of two similar graphs. This problem is solved through a multi-resolution approach in the spectral domain. Spectral graph matching provides a framework that allows to represent both graphs in a different dimensional space where the problem is more tractable. Furthermore, the multi-resolution approach improves the performance of the graph matching algorithm, since lower resolutions reveal new structural patterns. To obtain lower resolutions of both graphs, the proposed graph downsampling methods must make sure that the vertices selected for the lower resolutions should be the ones that are more likely to be correctly matched. If this property is not accomplished, the matching in this lower resolution will not provide useful information for the final matching. A novel solution to common challenges in graph matching, i.e, sign ambiguity of the eigenvectors, are provided in order to improve the correspondence between the vertices of both graphs. Different graph matching applications are presented to illustrate the effectiveness of the proposed technique. A comparison with other spectral graph matching algorithms demonstrates the benefits of the proposed approach.
Genetic Polymorphism Analysis of Mitochondrial DNA from Chinese Guangdong Liannan Yao ethnic group

Background Genetic polymorphism and haplotype distribution characteristics analysis of mitochondrial DNA in Chinese Guangdong Liannan Yao group was conducted in this study, to provide genetic basis for tracing the origin and historical migration of Liannan Yao people.Results 46 mutation sites were found, and among which single nucleotide transition was the most commonly observed variant (86.17%). Multiple (sub)haplogroups were detected in Liannan Yao ethnic group, among which haplogroup D was the most common haplogroup (29.80%), and the least were C and Y(0.48% respectively). Conclusions The Liannan Yao population had the commonalities of the ethnic groups in southern China, but it was significantly different from other Chinese ethnic populations. The present results revealed that Liannan Yao ethnic group was genetically closer related to Fujian She ethnic population, Yunnan Yao population, and Hunnan Miao population. The data enriched the Chinese mtDNA database and provided a reference for forensic identification and screening for potential pathogenic mutations.