Miracle-dz/UniFAR-FTUs huggingface dataset:
An Open-Source Low-Cost Mobile Robot System With an RGB-D Camera and Efficient Real-Time Navigation Algorithm

Currently, mobile robots are developing rapidly and are finding numerous applications in the industry. However, several problems remain related to their practical use, such as the need for expensive hardware and high power consumption levels. In this study, we build a low-cost indoor mobile robot platform that does not include a LiDAR or a GPU. Then, we design an autonomous navigation architecture that guarantees real-time performance on our platform with an RGB-D camera and a low-end off-the-shelf single board computer. The overall system includes SLAM, global path planning, ground segmentation, and motion planning. The proposed ground segmentation approach extracts a traversability map from raw depth images for the safe driving of low-body mobile robots. We apply both rule-based and learning-based navigation policies using the traversability map. Running sensor data processing and other autonomous driving components simultaneously, our navigation policies perform rapidly at a refresh rate of 18 Hz for control command, whereas other systems have slower refresh rates. Our methods show better performances than current state-of-the-art navigation approaches within limited computation resources as shown in 3D simulation tests. In addition, we demonstrate the applicability of our mobile robot system through successful autonomous driving in an indoor environment.
Application of a multilayer perceptron neural network to phytoplankton concentration using marine reflectance measures

The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.
Robust-adaptive control of nonlinear singlevariable mechatronic systems and robots

The PhD thesis deals with the tracking control of nonlinear mechatronic systems with unknown parameters. In order to deal with possible modelling errors and external disturbances, the robustness of the control is increased by variable structure or supervisor control algorithms. To obtain the unknown parameters, parameter estimation algorithms are applied. The stability of the closed loop systems and the tracking performance are guaranteed using Lyapunov techniques. The applicability and performances of the developed control algorithms are demonstrated through simulations and experimental measurements. If in the adaptive control systems there are large modelling uncertainties, due to the imperfect estimation of the nonlinearities, high unimplementable control signal can appear (singularity problem), which can lead to poor control performances or even instability in the closed loop system. A modified adaptive control law is presented in which the singularity problem is avoided, but it modifies the control signal only if the adaptation works poor. The developed algorithm can be applied for the control of mechatronical systems with unknown parameters. For adaptive control systems in which the modelling error has a large influence on the quality of the control, a supervisor control law is elaborated to guarantee the stability of the adaptive closed loop system. One of the major source of uncertainty in mechatronic systems is represented by the friction phenomenon, because the friction force can be modelled with difficulty and the frictional parameters are slowly varying in time. A general friction model is proposed, which describes well the nonlinear and dynamic behavior of friction and at the same time can easily be introduced in adaptive control algorithms. A parameter identification method for the developed model is introduced. Based on the model, a friction compensation algorithm is developed, which guarantees high tracking accuracy in the presence of dominant frictional effects and can easily be implemented in simple industrial control architectures and embedded systems. An adaptive tracking control algorithm is proposed for robotic systems to solve simultaneously the friction compensation and payload estimation problem. The developed control law is based on the dynamic model of the robot and it can guarantee a prescribed tracking accuracy in the presence of unknown frictional parameters and payload mass.
Stochastic-constraints method in nonstationary hot-clutter cancellation. I. Fundamentals and supervised training applications

This paper considers the use of spatio-temporal adaptive array processing in over-the-horizon radar (OTHR) and airborne radar applications in order to remove nonstationary multipath interference, known as "hot clutter". Since the spatio-temporal properties of hot clutter cannot be assumed constant over the coherent processing interval (CPI), conventional adaptive techniques fail to provide effective hot-clutter mitigation without simultaneously degrading the properties of the backscattered radar signals, known as "cold clutter". The approach presented incorporates multiple "stochastic" (data-dependent) constraints to achieve effective hot-clutter suppression, while maintaining distortionless output cold-clutter post-processing stationarity.
CALIBRATION OF SAFETY FACTORS YM FOR COMPOSITE STEEL AND CONCRETE BEAMS IN BENDING.

In the limit state design of composite beams, distinct partial safety factors are required for structural steel, concrete, reinforcement, and shear connection. A new method for the statistical determination of these factors is developed from the existing procedure for calibrating for steel components, applicable to any structural element composed of more than one material. It is applied to test data on 121 beams with full or partial shear connection. The values given in the ENV version of Eurocode 4: Part 1.1 are found to provide the intended margins of safety, and to have appropriate ratios, one to another. Any proposals to use a lower value should be supported by evidence (or linked with procedures to ensure) that the relevant coefficient of variation in practice is correspondingly lower than the value assumed here. These values are based mainly on calibration work for Netherlands building codes, and should be checked against EN specifications, when available. (A)
Exploring protocols for multidisciplinary disaster response using adaptive workflow simulation

The unique and dynamic changing nature in which a disaster unfolds forces emergency personnel involved with the mitigation process to be greatly flexible in their implementation of protocols. In past disasters the incapability of the disaster organization to swiftly adjust the workflow to the changing circumstances, has resulted in unnecessary delays and errors in mitigation. Addressing this issue, we propose and demonstrate a method for simulating disasters for work and protocol optimization in disasters response (TAID), based on the BRAHMS multi-agent modeling and simulation language. Our hypothesis is that this low fidelity simulation environment can effectively simulate work practice in dynamic environments to rearrange workflow and protocols. The results from an initial test simulation of the Hercules disaster at Eindhoven airport in the Netherlands look promising for future and broader application of our disaster simulation method.
Conducting Multimodal Rhetorical Analysis of TV Ads with Atlas.ti 7

Abstract This paper lays out the steps involved in the process of analyzing the rhetorical structure of TV ads with the employment of the content analytic software Atlas.ti 7. By drawing on a corpus of 87 TV commercials from brands that make up the world’s most valuable brands according to WPP’s BrandZ 2012 report and a resulting pool of 561 ad filmic segments, it will be shown (i) how TV ads may be segmented, (ii) how individual segments may be coded with one or more verbo-visual rhetorical figures in the light of three distinctive levels of analysis (on an intra-segment level, between two succeeding segments, against the background of an ad film’s “global semantic structure”) and (iii) how statistical output pertaining to the rhetorical structure of TV ads may be produced. This technically oriented analysis, which is part of ongoing research in the field of multimodal advertising rhetoric (cf. Rossolatos 2013a, 2013b, 2013c), is intended to contribute to scholarly research about the benefits that stem from combining an interpretive approach to the analysis of ads’ rhetorical configuration that is edified on film semiotics, with a quantitative approach, enabled by the employment of the content analytic software Atlas.ti 7.
Opening Up but Staying Local: Insights from Partnership Formations between Established and Startup Firms

In this paper, we develop and test a theoretical framework that considers how established firms forming partnerships with startups may be subject to spatial and temporal myopia and how these tendencies are moderated by the established firms’ histories of experiencing essential failures and successes in solving R&D problems. We argue that prior failures and successes differ in the extent to which they challenge established firm’s conventional ways of solving problems and in the extent to which they allow firms to balance long term and short term organizational demands, so that prior failures and successes very differently shape myopic behavior in partnership formations. The study argues for the value of integrating perspectives on myopia and local search with studies examining events of failures and successes to organizational decisions in technological search.
Continual Auxiliary Task Learning

Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather useful data for those off-policy predictions. In this work, we investigate a reinforcement learning system designed to learn a collection of auxiliary tasks, with a behavior policy learning to take actions to improve those auxiliary predictions. We highlight the inherent non-stationarity in this continual auxiliary task learning problem, for both prediction learners and the behavior learner. We develop an algorithm based on successor features that facilitates tracking under non-stationary rewards, and prove the separation into learning successor features and rewards provides convergence rate improvements. We conduct an in-depth study into the resulting multi-prediction learning system.
Acceptance of Smartphones by Users in BiH Through Extended Technology Acceptance Model

The main objective of this research paper is to measure acceptance of Smartphones and empirically identify factors which are crucial influencers for Smartphone acceptance. Technology Acceptance Model (TAM) is extended by adding two additional variables, ―Perceived enjoyment‖ (PE) and ―Security and privacy‖ (SP). Survey has been prepared based on variables and measuring items retrieved from literature review, and one hundred and forty nine Smartphone users were surveyed. According to results, users of Bosnia and Herzegovina are slightly agreeable when it comes to all variables except security and privacy. This study is important insight not only for companies operating in market of Bosnia and Herzegovina when it comes to factors that influence people to use Smartphones, but also for mobile oriented companies outside of country with potential to enter Bosnian market. Limitations of this work are too general sample with no specific target group of people, and small sample size. Therefore, this work could serve as stimuli for new researchers to do similar research but with improved sample aspects.
The Application of Dental Complexity Metrics on Extant Saurians

Abstract: Quantifying morphology has become increasingly popular with the advent of widely available high-powered computing. Orientation patch count rotated (OPCR) is an important measurement of dental surface complexity and has been shown to relate to diet, with carnivores possessing simple teeth and herbivores exhibiting complex dentitions. This method, however, has primarily been applied to mammals with comparatively little research focusing on dentigerous reptiles. Three methods are currently used to measure dental complexity (Surfer Manipulator, molaR, and MorphoTester), and the comparability of these methods remains untested in saurian datasets. Here, we test for differences among the three dental complexity methods using a dataset comprising lepidosaur and crocodylian teeth. Additionally, we seek to test the hypothesis that phenotypic dental complexity is related to dietary ecology using the molaR and MorphoTester OPCR methods. We find that Surfer Manipulator and 3D-OPCR methods (molaR and MorphoTester) generate different complexities, with molaR and MorphoTester consistently producing higher complexity values. The relationship between diet and dental complexity was recovered using molaR and MorphoTester, with carnivores possessing simple teeth and herbivores exhibiting significantly more-complex teeth, but this trend was not as distinct as when using Surfer Manipulator. Dental complexities were highly dependent on model preparation, in particular the triangle count of each 3D model, with surfaces that possess a greater number of faces exhibiting higher complexities. We present a series of recommendations that will help guide future studies of nonmammalian dentitions. Ultimately, 3D-OPCR methods are a fruitful area of research and represent a relatively untapped avenue of quantitative herpetology.
Predicting urban flooding susceptibility of public transit systems using machine learning approaches: a case study of the largest city in Canada

Urban floods often cause the functional disruption of public transit systems, thereby impeding people's mobility and resulting in adverse socio-economic consequences. Climate change, rapid urbanization, and unplanned disaster management further increase trends of urban floods with higher frequency and intensity. This study employs data-driven machine learning (ML) models for predicting the flooding susceptibility of public transit systems in Toronto, ON, Canada. Four ML approaches are employed to evaluate the future risks of public transit systems being inundated by flooding events: 1) Random Forest (RF); 2) eXtreme Gradient Boosting (XGBoost); 3) K Nearest Neighbor (KNN); and 4) Naïve Bayes (NB). We estimate flooding probability based on the relationship between flood inundation events and their contributing factors. Flood-plain maps by Toronto and Region Conservation Authority (TRCA) are used to generate flood and non-flood locations as a basis for training (70% of samples) and validating (30% of samples) ML models. We use the Area-Under Receiver Operating Characteristic (AUROC) curve to evaluate the prediction results of ML models. All four models show a high level of accuracy higher than 95%. Our results demonstrate public transit systems near major river channels are highly susceptible to floods which corroborated with historical flooding incidents in 2013 and 2018. The outcome of the study can be helpful to enhance the resilience of public transit systems in the city of Toronto and can facilitate evidence-based planning and policy to make cities more sustainable, livable, and resilient against flood hazards.
Hydrologic and Economic Uncertainties and Flood-Risk Project Design

Hydrologic, hydraulic, and economic uncertainties impact the economic attractiveness of flood-risk management projects. In particular, uncertainty in the calculation of expected annual damages introduces uncertainty into economic evaluations. This study develops a hypothetical basin to explore the performance of designs identified by different economic-risk decision models. A new decision model includes an explicit quantification of uncertainty in the marginal net benefits and hence in the reliability of net benefits generated by the last increment of a project. Monte Carlo analyses show that the uncertainty in total project benefits may not reveal large uncertainties associated with the last increments of a project. In terms of economic performance of the different descriptions of hydrologic risk, expected probability and traditional maximum likelihood flood-risk estimators are roughly equivalent. The new decision model identified less costly projects that achieved nearly the same average net benefits and had a greater probability of recovering the last dollar spent.
Reinforcement learning for autonomous vehicles

Autonomous vehicle control presents a significant challenge for artificial intelligence and control theory. The act of driving is best modeled as a series of sequential decisions made with occasional feedback from the environment. Reinforcement learning is one method whereby the agent successively improves control policies through experience and feedback from the system. Reinforcement learning techniques have shown some promise in solving complex control problems. However, these methods sometimes fall short in environments requiring continual operation and with continuous state and action spaces, such as driving. This dissertation argues that reinforcement learning utilizing stored instances of past observations as value estimates is an effective and practical means of controlling dynamical systems such as autonomous vehicles. I present the results of the learning algorithm evaluated on canonical control domains as well as automobile control tasks.
The Influence of a Walking Technique on Presence in Virtual Reality 1

This paper presents an interactive technique for moving through an immersive virtual environment (or "virtual reality"). The technique is suitable for applications where locomotion is restricted to ground level. The technique is derived from the idea that presence in virtual environments may be enhanced the stronger the match between proprioceptive information from human body movements, and sensory feedback from the computer generated displays. The technique is an attempt to simulate body movements associated with walking. The participant "walks in place" to move through the virtual environment across distances greater than the physical limitations imposed by the electro-magnetic tracking devices. A neural network is used to analyse the stream of coordinates from the head-mounted display, to determine whether or not the participant is walking on the spot. Whenever it determines the walking behaviour, the participant is moved through virtual space in the direction of gaze. We discuss two experimental studies to assess the impact on presence of this method in comparison to the usual hand pointing method of navigation in virtual reality. The studies suggest that subjective rating of presence is enhanced by the walking method provided that participants subjectively associate with the virtual body provided in the environment. An application of the technique to climbing steps and ladders is also presented.
Patient and public involvement in a UK National Institute for Health Research Programme Grant for Applied Research: experiences from the Chronic Headache Education and Self-management Study (CHESS)

Abstract Background: Patient and public involvement (PPI) plays a crucial role in ensuring research is carried out in conjunction with the people that it will impact upon. In this article, we present our experiences and reflections from working collaboratively with patients and public through the lifetime of an National Institute for Health Research (NIHR) programme grant; the Chronic Headache Education and Self-management Study (CHESS) which took place between 2015 and 2020. PPI over the course of CHESS: We worked closely with three leading UK migraine charities and a lay advisory group throughout the programme. We followed NIHR standards and used the Guidance for Reporting Involvement of Patients and the Public checklist. We consulted our PPI contacts using a variety of methods depending on the phase of the study and the nature of the request. This included emails, discussions, and face-to-face contact. PPI members contributed throughout the study in the programme development, in the grant application, ethics documentation, and trial oversight. During the feasibility study; in supporting the development of a classification interview for chronic headache by participating in a headache classification conference, assessing the relevance, and acceptability of patient-reported outcome measures by helping to analyse cognitive interview data, and testing the smartphone application making suggestions on how best to present the summary of data collected for participants. Due to PPI contribution, the content and duration of the study intervention were adapted and a Delphi study with consensus meeting developed a core outcome set for migraine studies. Conclusions: The involvement of the public and patients in CHESS has allowed us to shape its overall design, intervention development, and establish a core outcome set for future migraine studies. We have reflected on many learning points for the future application of PPI.
Load-bearing Capacity Of H-shaped Steel Columns Under Local Buckling At Elevated Temperature

When a compartment fire occurs in a building of steel structure, large bending deformations are produced at the ends of columns and beams, inducing local buckling at these points. In this study, the load-bearing capacity of H-shaped steel columns under local buckling is clarified from pure compression tests and bending compression tests at constant temperatures ranging from room temperature to 600°C. On the basis of the tests, we propose, in relation to the fire resistance of H-shape steel stub columns, a relation between safety usable steel temperature and constant axial load ratio.
Life estimation of DC insulation systems in the presence of voltage-polarity inversions

A phenomenological model to estimate life of insulation systems subjected to DC stress with or without polarity inversions is presented. The model is obtained applying Miner's law to life deriving from the decomposition of the supply stress in a DC voltage plus impulse voltages. The latter accounts for accelerated aging due to inversion. The model is validated resorting to life tests with and without inversions, carried out on polyethylene materials for cable application.