Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Reference Tracking Control Solutions for a Visual Servo System Based on a Virtual State from Unknown Dynamics

This paper focuses on validating a model-free Value Iteration Reinforcement Learning (MFVI-RL) control solution on a visual servo tracking system in a comprehensive manner starting from theoretical convergence analysis to detailed hardware and software implementation. Learning is based on a virtual state representation reconstructed from input-output (I/O) system samples under nonlinear observability and unknown dynamics assumptions, while the goal is to ensure linear output reference model (ORM) tracking. Secondary, a competitive model-free Virtual State-Feedback Reference Tuning (VSFRT) is learned from the same I/O data using the same virtual state representation, demonstrating the framework’s learning capability. A model-based two degrees-of-freedom (2DOF) output feedback controller serving as a comparisons baseline is designed and tuned using an identified system model. With similar complexity and linear controller structure, MFVI-RL is shown to be superior, confirming that the model-based design issue of poor identified system model and control performance degradation can be solved in a direct data-driven style. Apart from establishing a formal connection between output feedback control, state feedback control and also between classical control and artificial intelligence methods, the results also point out several practical trade-offs, such as I/O data exploration quality and control performance leverage with data volume, control goal and controller complexity.
The super-browser: a new paradigm for web applications

—The modern web browser performs a multitude of tasks, which were never contemplated in its original design. This work investigates the roles and responsibilities of the various components that comprise the web browser, and a traditional web application. We review the web browser’s role in the greater architecture and propose that a ”Super Browser” concept need not mean greater responsibilities for the web browser application. Instead, the solution described in this work introduces a distributed approach that is capable of executing applications composed of distinct components. The paper presents an implementation of this concept, and a comparison of this approach with a traditional web application framework.
Mechanically pattern reconfigurable antenna using metasurface

A radiation pattern reconfigurable antenna designed using metasurface (MS) is proposed in this study. The pattern reconfigurable metasurfaced (PaRMS) antenna is composed of a planar semi-circular MS placed directly atop of a planar circular patch antenna with a diameter of 70 mm (1.2 λ 0 ), making it compact and low profile. The main-beam direction of the antenna has an angle of 32° from the boresight direction. By rotating the MS around the centre of the patch antenna, the antenna beam can be steered continuously. The PaRMS antenna is studied and designed to operate at around 5.5 GHz. To verify the results of simulation, the PaRMS antenna is fabricated and measured. Results show that the antenna has an operating bandwidth from 5.4 to 5.6 GHz and measured peak realised gain of 7.2 dBi.
Financing Efficiency Evaluation and Influencing Factors of Hydrogen Energy Listed Enterprises in China

Existing studies of financing efficiency concentrate on capital structure and a single external environment or internal management characteristic. Few of the studies include the internal and external financing environments at the same time for hydrogen energy industry financing efficiency. This paper used the data envelopment analysis (DEA) model and the Malmquist index to measure the financing efficiency of 70 hydrogen energy listed enterprises in China from 2014 to 2020 from both static and dynamic perspectives. Then, a tobit model was constructed to explore the influence of external environment and internal factors on the financing efficiency. The contributions of this paper are studying the internal and external financing environments, and integrating financing cost efficiency and capital allocation efficiency into the financing efficiency of hydrogen energy enterprises. The results show that, firstly, the financing efficiency of China’s hydrogen energy listed enterprises showed an upward trend during the years 2014–2020. Secondly, China’s hydrogen energy enterprises mainly gather in the eastern coastal areas, and their financing efficiency is more than that in western areas. Thirdly, the regional economic development level, enterprise scale, financing structure, capital utilization efficiency and profitability have significant effects on the financing efficiency. These results can promote the achievement of “carbon neutrality” in China.
Ameliorated End-to-End Deep Learning System for Self Learning Cars

The introduction of self-driving automobiles in today's society necessitates the development of high-quality algorithms to guide them. Convolutional neural networks are extensively utilised because of their ability to classify images based on observed attributes. The proposed solution entails steering a car autonomously using just the windshield view as input. This is accomplished by using Convolutional neural networks in an end-to-end deep learning strategy, as proposed by NVIDIA for self-driving automobiles. To improve the accuracy of existing models, the neural network will be supported with inputs from image processing modules that identify lane markers and cars.Reduced costs, increased safety, and increased mobility are all advantages of providing such an algorithm for retrofitting existing cars.
GOOD AND BAD SOCIOLOGY: DOES TOPIC MODELLING MAKE A DIFFERENCE?

The changing social reality, which is increasingly digitally networked, requires new research methods capable of analysing large bodies of data (including textual data). This development poses a challenge for sociology, whose ambition is primarily to describe and explain social reality. As traditional sociological research methods focus on analysing relatively small data, the existential challenge of today involves the need to embrace new methods and techniques, which enable valuable insights into big volumes of data at speed. One such emerging area of investigation involves the application of Natural Language Processing and Machine-Learning to text mining, which allows for swift analyses of vast bodies of textual content. The paper’s main aim is to probe whether such a novel approach, namely, topic modelling based on Latent Dirichlet Allocation (LDA) algorithm, can find meaningful applications within sociology and whether its adaptation makes sociology perform its tasks better. In order to outline the context of the applicability of LDA in the social sciences and humanities, an analysis of abstracts of articles published in journals indexed in Elsevier’s Scopus database on topic modelling was conducted. This study, based on 1,149 abstracts, showed not only the diversity of topics undertaken by researchers but helped to answer the question of whether sociology using topic modelling is “good” sociology in the sense that it provides opportunities for exploration of topic areas and data that would not otherwise be undertaken.
Performance Analysis Of Ga And Pso Over Economic Load Dispatch Problem

Economic load dispatch problem is one of the most popular concerns in power system engineering. Many method have been proposed in past to solve this. Genetic algorithm and particle swarm optimization are the most popular algorithms in term of optimization. This paper is implementation of GA and PSO over the Economic Load Dispatch problem. Comparison of both algorithms is shown here with a standard example when considering Loss and no Loss conditions. Economic Load Dispatch (ELD) is one of an important optimization tasks which provides an economic condition for a power systems. In this paper, Particle Swarm Optimization (PSO) as an effective and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. In this paper, a piecewise quadratic function is used to show the fuel cost equation of each generation units, and the B-coefficient matrix is used to represent transmission losses. The feasibility of the proposed method to show the performance of this method to solve and manage a constraint problem is demonstrated in 4 power system test cases, consisting 3, 6 generation units with neglected losses in two of the last cases.
Rural Patients’ Perceptions of Their Potentially Preventable Hospitalisation: A Qualitative Study

Potentially preventable hospitalisations (PPHs) occur when patients receive hospital care for a condition that could have been more appropriately managed in the primary healthcare setting. It is anticipated that the causes of PPHs in rural populations may differ from those in urban populations; however, this is understudied. Semi-structured interviews with 10 rural Australian patients enabled them to describe their recent PPH experience. Reflexive thematic analysis was used to identify the common factors that may have led to their PPH. The analysis revealed that most participants had challenges associated with their health and its optimal self-management. Self-referral to hospital with the belief that this was the only treatment option available was also common. Most participants had limited social networks to call on in times of need or ill health. Finally, difficulty in accessing primary healthcare, especially urgently or after-hours, was described as a frequent cause of PPH. These qualitative accounts revealed that patients describe nonclinical risk factors as contributing to their recent PPH and reinforces that the views of patients should be included when designing interventions to reduce PPHs.
Probabilistic risk assessment of infrastructure networks subjected to hurricanes

An initial investigation towards a probabilistic risk assessment framework for infrastructure systems subjected to hurricanes is presented. An idealized probabilistic spatial-temporal hurricane model is proposed for the purpose of investigating infrastructure performance and redundancy. The investigated infrastructure is an electrical network, corresponding to a synthetic IEEE benchmark system, which is modeled by a physical power flow model. The response of the infrastructure network to hurricane events is evaluated in terms of the overall power loss, considering the power flow characteristics of the network and potential cascading failures. It is found that while the network component failure probabilities are strongly dependent on the hazard characteristics, this does not hold for the overall network damage after simulation of the cascading failures. The computed average power loss level tends to be rather insensitive to the number and combination of initially triggered network components.
Mid-term evaluation report of Vong Tay Lon Program

Vong Tay Lon is a participatory development communication (PDC) learning and networking program in Vietnamese language. It is jointly implemented by five Vietnamese universities, namely, 1) Hue University of Agriculture and Forestry (HUAF), 2) Hanoi Agricultural University (HAU), 3) Thai Nguyen University of Agriculture and Forestry (TUAF), 4) Tay Nguyen University; and 5) Nong Lam University of Agriculture and Forestry. Two other participating institutions are NETNAM Corporation and Information Centre for Agriculture and Rural Development (ICARD).
Comparison of alternative ways of deriving panel data from the annual demographic files of the current population survey.

This paper reports on a study designed to increase understanding of the reliability of alternative ways of deriving panel data from the Annual Demographic Files (ADF). The ADF is a public use collection of machine-readable completed March questionnaires compiled from the Census Current Population Surveys (CPS) since 1967. To exploit tha panel features of the ADF it is necessary to link the records of persons interviewed in the 2 successive Marches. Because identity codes for persons within households are incomplete and there are genuine changes from year to year the matching of identical records pairs is necessarily imperfect. An optimal linkage rule specifies a set of comparatively stable individual charactertistics or match keys and dictates the circumstances under which consistencies or inconsistencies in the keys are to be interpreted as denoting a matching of records for the identical person or a mismatch subject to a predetermined rick of misclassification. This work is based on the related rules of Du Bois and Fellefi-Sunter who suggest preinspecting subsamples of matched record pairs which are reasonably likely either to be true linkages or true nonlinkages. From these conditional likelihoods of configuration match keys are derived from which it is possible to deduce the error characteristics of competing linkage rules. Consistency on age appears to be the best single indicator of joint consistencies in the remaining keys. Comparison of the linkage rules followed by the Bureau of Labor Statistics (BLS) with competing approaches indicate the BLS methods are overly conservative. The results show the desirability of more flexible and user-oriented approaches to the derivation of panel data from the ADF.
Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume

This paper describes an ensemble segmentation trained by the AdaBoost algorithm, which finds a sequence of weak hypotheses, each of which is appropriate for the distribution on training example, and combines the weak hypotheses by a weighted majority vote. In our study, a weak hypothesis corresponds to a weak segmentation process. This paper shows a procedure for generating an ensemble segmentation algorithm using AdaBoost, and applies it to a liver lesion extraction problem from a contrast enhanced abdominal CT volume. A leave-one-patient-out validation test using 16 CT volumes demonstrated the effectiveness of the generated ensemble segmentation algorithm. In addition, we evaluated the performance by applying the algorithm to unknown test data provided by the �3D Liver Tumor Segmentation Challenge 2008�.
Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.
HER-PDQN: A Reinforcement Learning Approach for UAV Navigation with Hybrid Action Spaces and Sparse Rewards

Reinforcement learning (RL) equipped with neural networks has recently led to a wide range of successes in learning policies for unmanned aerial vehicle (UAV) navigation and control problems. The success of RL relies on two human-designed heuristics: appropriate action space deﬁnition and reward function engineering. The commonly used fully continuous or fully discrete action spaces in optimal control and decision making problems may lack control authority and remove the inherent problem structure, which can negatively aﬀect learning performance. Besides, reward engineering requires a lot of human eﬀort and may lead to unwanted behavior. In this paper, we address these challenges by proposing a new oﬀ-policy RL algorithm called HER-PDQN which incorporates Hindsight Experience Replay (HER) with Parameterized Deep Q-Networks (P-DQN). In simulation experiments, HER-PDQN is used to train an agent to fulﬁll a UAV navigation task in a 2-dimensional environment. The results indicate the eﬀectiveness of P-DQN algorithm in dealing both with the hybrid action space and sparse rewards. This paper can be considered as the ﬁrst attempt at applying RL in sparse reward setting for UAV navigation with hybrid action spaces.
Maximal orders containing a given sub-order for local fields of even characteristic

Abstract We extend, to the case of local fields of even characteristic, our previous computations for the set of maximal orders containing given quaternions as concrete sub-graphs of the Bruhat–Tits tree. Computing the relative position of such sub-graphs, the branches, is useful, for instance, to explicitly compute relative spinor images, thus solving the selectivity problem. They also play a role in the description of quotient graphs, which are useful to study matrix groups of arithmetical importance. As in earlier work, we concentrate on orders generated by two quaternions, but extending our method to larger generating sets is quite straightforward. In our previous work, the results were given mainly in terms of the quadratic defect. In the present context, we introduce and characterize an analogous concept for Artin–Scheier extensions, to take care of quaternions generating Galois field extensions. The latter extensions have no non-trivial elements of null trace. For this reason, we state our results in terms of a wider family of algebra presentations, in contrast to the aforementioned work, where we chose a pair of pure quaternions as generators of the order.
Resilient adaptive event-triggered dissipative control for networked control systems with DoS attacks

This paper focuses the design of resilient control for networked control systems with an adaptive event-triggered mechanism in the presence of denial-of-service attacks, actuator saturation and actuator faults. The model for denial-of-service attacks (DoS-As) in networked control systems, which mainly destroy the control channel and occur aperiodically with an unknown attack strategy, is constructed. An adaptive event-triggered mechanism (AETM) is introduced to reduce unimportant communication transmissions in a network provided adaptive varying threshold while maintaining satisfactory system performance. By using the Lyapunov function approach, linear matrix inequality (LMI)-based sufficient conditions are obtained to ensure the exponential stability and satisfying the prescribed performance index of the closed-loop systems under DoS-As. Moreover, the feasibility of the jointly developed schemes for the control gain and event-triggered parameters is calculated using the presented LMIs. The potentiality of the proposed scheme is validated using a numerical example.
Multi Atlas Segmentation with Active Shape Model Refinement for Multi-Organ Segmentation in Head and Neck Cancer Radiotherapy Planning

We describe a segmentation method that was used in the Head and Neck Auto Segmentation Challenge held at the MICCAI 2015 conference. The algorithm consists of two building blocks. First, we employ a multi-atlas segmentation to obtain an initial segmentation for the considered organs at risk. Secondly, we use an Active Shape Model (ASM) segmentation to refine the initial segmentation of some of the organs. Leave-one-out experiments with the training data were used to determine suitable parameters for the individual steps of the segmentation. The ASM refinement resulted in improved segmentation for the optic nerves and submandibular glands, while for the brain stem, parotid glands, chiasm, and mandibular bone, the multi-atlas segmentation was preferable. Our submission achieved the second rank in the challenge.
The value of small-scale student projects in undergraduate research training

This article reports on the undertaking of small-scale projects for final-year Occupational Therapy (OT) students training at the University of the Free State. An Action Learning Action Research (ALAR) approach was used to investigate the impact of such projects in a clinical setting. A tree analogy is used to describe the student projects, providing the body of the investigation as a whole, and sharing the experience gained in determining the constituents of the "fertile soil" and conditions for "optimum growth". The impact of ALAR on practical experience is explained as a first step towards establishing a research culture among undergraduate students in OT as well as in similar training contexts. Die waarde van kleinskaalse studenteprojekte in voorgraadse navorsingsopleiding Hierdie artikel lewer verslag oor studenteprojekte van beperkte omvang wat in die finale jaar van Arbeidsterapie-opleiding aan die Universiteit van die Vrystaat onderneem word. 'n Aksieleer en -navorsingsbenadering is gevolg om die impak van hierdie projekte in 'n kliniese omgewing te ondersoek. 'n Boom-analogie van die volledige projek word gebruik om die studenteprojekte te beskryf. Hierdie beeld orden die ondervindings wat tot die samestelling van "vrugbare grond" en die toestande vir "optimale groei" bygedra het. Die impak van aksienavorsing op praktiese ondervinding word beskou as 'n eerste stap in die daarstelling van 'n navorsingskultuur onder voorgraadse studente in Arbeidsterapie en soortgelyke opleidingsomgewings.
Visual-Guided Solutions in Automated Zebrafish Larva Heart Micro-Injection

Micro-injection is one of the most efficient approaches to deliver foreign materials into zebrafish larva heart. Well-trained micro-injection technicians are required for such operation, and still the efficiency remains low and the work is laborious. As a result, automated micro-injection that is able to improve the efficiency and injection consistency is in great demand. Barriers like auto-focusing after changing of microscope magnification, accurate tracking of injection pipette, and zebrafish larva heart detection in three dimensions make the automation challenging. The proposed coarse-fine searching strategy and the depth of field optimized step lengths result in fast and accurate auto-focusing on the zebrafish and injection pipette. The injection pipette is tracked in real-time thanks to the proposed dynamical region of interest. Besides, the combination of y-Sobel filter and alpha-beta filter improves the noise resistance during pipette tracking, which helps to reduce the tracking error. With the proposed in-plane and out-of-plane heart detection algorithms, the zebrafish heart is detected in three-dimensions efficiently and the accuracy is promising. The success rate for automated zebrafish larva heart micro-injection reached 90% with the proposed visual algorithms.