Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing Children’s Physical Literacy: How well prepared are prospective teachers?

While the known health impacts of sedentary lifestyles have focused attention on children’s outdoor activity, the development of their physical literacy – the physiological, social, cultural, cognitive, expressive, and psychological dimensions of their physicality – is much less in focus. Developing children’s physical literacy is embedded in the Early Years Learning Framework and Primary curriculum: Health and Physical Education, and the performing arts subjects within The Arts. This study asks “How well prepared are pre-service teachers to implement a program that contributes to developing children’s physical literacy? ” This mixed methods study includes an environmental scan of BEd courses at 12 Australian universities; and at one university, a content analysis of units, a review of 227 students’ practicum records, and an online survey of 57 pre-service teachers. The study finds that pre-service teachers are generally well-informed, committed and confident but have limited diagnostic and practical pedagogical skills to effectively develop children’s physical literacy.
Preparation of aqueous extract of guava leaves :

The aim of the present study is to investigate the ameliorative effect of aqueous guava leaf extract (AGLE) with different doses (200, 350, 500 & 650 mg/kg b.wt.) taken p.o in rats fed high fat high cholesterol (HFC) diet for 8 weeks. The four doses of AGLE cause a significant decrease in food intake, final body weight, gain in body weight, serum TAG, VLDL-C, risk ratio as well as liver TAG and malondialdehyde level. Also doses of AGLE (350,500 and 650 mg/kg b.wt.) cause a significant decrease in relative liver weight, serum AST and LDL-C but a significant increase in HDL-C and leptin hormone. While doses (500 & 650 mg/kg b.wt.) cause a significant decrease in serum TC. The highest dose causes a significant decrease in serum ALT and adiponectin hormone. Total liver cholesterol was decreased significantly in groups given (200 & 350 mg/kg b.wt.) but no significant decrease in doses (500 & 650 mg/kg b.wt.). As well as ghrelin hormone does not show significant decrease in the four treated groups fed HFC diet and given AGLE compared to rats fed HFC diet only.
Interactions of Nanoparticles and Biosystems: Microenvironment of Nanoparticles and Biomolecules in Nanomedicine

Nanotechnology is a multidisciplinary science covering matters involving the nanoscale level that is being developed for a great variety of applications. Nanomedicine is one of these attractive and challenging uses focused on the employment of nanomaterials in medical applications such as drug delivery. However, handling these nanometric systems require defining specific parameters to establish the possible advantages and disadvantages in specific applications. This review presents the fundamental factors of nanoparticles and its microenvironment that must be considered to make an appropriate design for medical applications, mainly: (i) Interactions between nanoparticles and their biological environment, (ii) the interaction mechanisms, (iii) and the physicochemical properties of nanoparticles. On the other hand, the repercussions of the control, alter and modify these parameters in the biomedical applications. Additionally, we briefly report the implications of nanoparticles in nanomedicine and precision medicine, and provide perspectives in immunotherapy, which is opening novel applications as immune-oncology.
A HOLISTIC GLOTTAL PHASE-RELATED FEATURE

This paper addresses a phase-related feature that is time-shift invariant, and that expresses the relative phases of all harmonics with respect to that of the fundamental frequency. We identify the feature as Normalized Relative Delay (NRD) and we show that it is particularly useful to describe the holistic phase properties of voiced sounds produced by a human speaker, notably vowel sounds. We illustrate the NRD feature with real data that is obtained from five sustained vowels uttered by 20 female speakers and 17 male speakers. It is shown that not only NRD coefficients carry idiosyncratic information, but also their estimation is quite stable and robust for all harmonics encompassing, for most vowels, at least the first four formant frequencies. The average NRD model that is estimated using data pertaining to all speakers in our database is compared to that of the idealized Liljencrants-Fant (LF) and Rosenberg glottal models. We also present results on the phase effects of linear-phase FIR and IIR vocal tract filter models when a plausible source excitation is used that corresponds to the derivative of the L-F glottal flow model. These results suggest that the shape of NRD feature vectors is mainly determined by the glottal pulse and only marginally affected by either the group delay of the vocal tract filter model, or by the acoustic coupling between glottis and vocal tract structures.
CliffGuard : Towards Workload-Resilient Database Designs

A fundamental problem in database systems is choosing the best physical design, i.e., a small set of auxiliary structures that enable the fastest execution of future queries. Almost all commercial databases come with designer tools that create a number of indices or materialized views (together comprising the physical design) that they exploit during query processing. Existing designers are what we call nominal; that is, they assume that their input parameters are precisely known and equal to some nominal values. For instance, since future workload is often not known a priori, it is common for these tools to optimize for past workloads in hopes that future queries and data will be similar. In practice, however, these parameters are often noisy or missing. Since nominal designers do not take the influence of such uncertainties into account, they find designs that are sub-optimal and remarkably brittle. Often, as soon as the future workload deviates from the past, their overall performance falls off a cliff, leading to customer discontent and expensive re-designs. Thus, we propose a new type of database designer that is robust against parameter uncertainties, so that overall performance degrades more gracefully when future workloads deviate from the past. Users express their risk tolerance by deciding on how much nominal optimality they are willing to trade for attaining their desired level of robustness against uncertain situations. To the best of our knowledge, this project is the first to adopt the recent breakthroughs in the theory of robust optimization to build a practical framework for solving some of the most fundamental problems in databases, replacing today’s brittle designs with a principled world of robust designs that can guarantee predictable and consistent performance.
Continuous-actionplanning for discounted infinite-horizon nonlinear optimal controlwithLipschitz values ⋆

We consider discrete-time, infinite-horizon optimal control problems with discounted rewards. The value function must be Lipschitz continuous over action (input) sequences, the actions are in a scalar interval, while the dynamics and rewards can be nonlinear/nonquadratic. Exploiting ideas from artificial intelligence, we propose two optimistic planning methods that perform an adaptive-horizon search over the infinite-dimensional space of action sequences. The first method optimistically refines regions with the largest upper bound on the optimal value, using the Lipschitz constant to find the bounds. The second method simultaneously refines all potentially optimistic regions, without explicitly using the bounds. Our analysis proves convergence rates to the global infinite-horizon optimum for both algorithms, as a function of computation invested and of a measure of problem complexity. It turns out that the second, simultaneous algorithm works nearly as well as the first, despite not needing to know the (usually difficult to find) Lipschitz constant. We provide simulations showing the algorithms are useful in practice, compare them with value iteration and model predictive control, and give a real-time example.
Optical Music Recognition by Long Short-Term Memory Recurrent Neural Networks

Optical Music Recognition is the task of transcribing a music score into a machine readable format. Music scores today are often in a sheet format and society can not enjoy it. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Thus, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. Moreover, Bidirectional LSTM has been tested in order to improve the recognition task insomuch as the two direction reduces the ambiguity in some predictions. For training, a synthetic dataset of more than 40K images of incipts from RISM database has been used to validate the proposed approaches. It is labeled at primitive level. Finally, this knowledge acquired by the BLSTM has been transferred to a handwritten dataset that has been created from music scores belonging CVC-MUSCIMA database to validate the proposed approach in a real scenario.
Explorer Learning Factored Markov Decision Processes with Unawareness

Methods for learning and planning in sequential decision problems often assume the learner is fully aware of all possible states and actions in advance. This assumption is sometimes untenable: evidence gathered via domain exploration or external advice may reveal not just information about which of the currently known states are probable, but that entirely new states or actions are possible. This paper provides a model-based method for learning factored markov decision problems from both domain exploration and contextually relevant expert corrections in a way which guarantees convergence to near-optimal behaviour, even when the agent is initially unaware of actions or belief variables that are critical to achieving success. Our experiments show that our agent converges quickly on the optimal policy for both large and small decision problems. We also explore how an expert’s tolerance towards the agent’s mistakes affects the agent’s ability to achieve optimal behaviour.1
Crowdjump: Investigating a Player-Driven Platform Game

We present Crowdjump, a player-driven online platform game: players could post ideas on how to improve the game, the platform, or any related aspect. Continuously, all players could then choose ideas which were implemented and released on a daily basis. We conducted an exploratory study with 25 players over the course of 23 days and aimed at releasing two features per day. We analyzed idea types and could show that players did not change an idea voting scheme based on up- and down-votes and were more focused on changing the game than any other component. In contrast, features that would improve the community feeling or would help to improve ideas of others were not often suggested or selected. Nonetheless, the experience was rated as quite enjoyable by players, showing the appeal of such a player-driven game design approach and the relevancy for further research in this context.
ENVIRONMENTAL IMPACTS OF COAL SUBSIDIES IN TURKEY : A REGIONAL GENERAL EQUILIBRIUM ANALYSIS

In this study we aim at providing an analytical quest for Turkey to study the true social costs of the existing coal subsidization scheme and identifying viable alternative policy instruments and economic measures to complement its e¤orts of further greening and de-carbonizing its economy. To this end we develop an applied general equilibrium model and utilize both macro and micro level data to assess the impact of the current arsenal of energy policy instruments (in particular coal subsidies) and public policy intervention mechanisms to accelerate technology adoption and achieve higher employment, energy security, and sustainable growth patterns. Spanning over 2015-2030, our analytical apparatus focuses exclusively on considering the scal implications as well as the environmental repercussions of the CO2 and other gaseous emissions and the relevant market instruments of abatement. With the aid of a set of alternative policy scenarios against a business as usual path, we study the aggregate and sectorial performances of growth, employment, investment and capital accumulation, consumption/welfare and trade balance. JEL: C68, O44, Q56, Q58
Tightly-Coupled Aided Inertial Navigation with Point and Plane Features

This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.
STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction

As many location-based applications provide services for users based on traffic conditions, an accurate traffic prediction model is very significant, particularly for long-term traffic predictions (e.g., one week in advance). As far, long-term traffic predictions are still very challenging due to the dynamic nature of traffic. In this paper, we propose a model, called Spatio-Temporal Convolutional Neural Network (STCNN) based on convolutional long short-term memory units to address this challenge. STCNN aims to learn the spatio-temporal correlations from historical traffic data for long-term traffic predictions. Specifically, STCNN captures the general spatio-temporal traffic dependencies and the periodic traffic pattern. Further, STCNN integrates both traffic dependencies and traffic patterns to predict the long-term traffic. Finally, we conduct extensive experiments to evaluate STCNN on two real-world traffic datasets. Experimental results show that STCNN is significantly better than other state-of-the-art models.
Chapter 7 Machine Learning Algorithm for Wireless Indoor Localization

Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.
Inverse Optimal Control : What do we optimize ?

How do we design the behavior of an autonomous system? Optimal control says, give us a cost function and we’ll give you an optimal policy. Can’t get any better than that, right? So we go to work designing a cost function. But for all our efforts to stay principled, we’ve all been there, in the lab, late at night, tuning parameters, weighing cost components, designing cost features, and tweaking, tweaking, tweaking to get the darned thing to do what we want. Optimal control methods are optimizers, but in reality, we don’t really know what we should be optimizing. This document addresses the question of how we can, instead, leverage our strong intuition of how we’d like the robot to behave to learn a cost function that embodies demonstrations of that behavior. So defines Inverse Optimal Control (IOC). Rather than computing an optimal policy given a cost function, IOC uncovers the cost function that best explains demonstrated optimal behavior. This problem is strongly related to a broad class of very large scale multiclass classification problems (Structured Prediction) in machine learning, and we start by deriving and discussing that connection in detail. We then derive a simple gradient-based method for learning that that solves IOC. The algorithm consists of an outer loop optimization that repeatedly solves the optimal control problem under successively improving hypotheses and subsequently corrects errors the perceived behavior. The algorithm effectively automates the manual guess and check process that plagues the design of optimal controllers. 1 Cost functions are arbitrary Optimal control is built on an abstraction of the form min u1:T ,s1:T+1 T ∑ t=1 ct(st,ut) + ψ(sT+1) (1) s.t. st+1 = f(st,ut). But what does this actually mean in practice? Defining optimal control in terms of cost functions ct posits that we know explicitly how to rank states and actions relative to one another. But how bad is it really if a robot gets close to a wall and applies a particular torque to one of its joints? And more importantly, is
Segmentation-based Method for Text-Dependent Speaker Recognition in Embedded Applications

In this paper, a fast algorithm for text-dependent speaker recognition is proposed for real-time applications on embedded platforms. Considering the similarity and effectiveness, we introduce the Non-Linear Partition (NLP) method used for isolated word speech recognition to textdependent speaker recognition, where both training and verification can be performed in terms of segments instead of frames and which can save the execution time compared with dynamic programming based methods. However, due to the segmentation rule of distance accumulation, NLP is not robust enough, often resulting in an unreasonable segmentation result in case of noise. Accordingly, the simple rigid application of NLP will cause an equal error rate (EER) increase by 23.6%. To address this problem, an improved NLP algorithm is proposed, in which an utterance is segmented according to the Mahalanobis distance measure of between-frame changes. Experimental results show that the proposed algorithm can significantly improve the text-dependent speaker recognition in both recognition performance and computational efficiency.
Entity Matching with Quality and Error Guarantees

Given two sets of entities X and Y , entity matching aims to decide whether x and y represent the same entity for each pair (x, y) ∈ X × Y . In many scenarios, the only way to ensure perfect accuracy is to launch a costly inspection procedure on every (x, y), whereas performing the procedure |X| · |Y | times is prohibitively expensive. It is, therefore, important to design an algorithm that carries out the procedure on only some pairs, and renders the verdicts on the other pairs automatically with as few mistakes as possible. This article describes an algorithm that achieves the purpose using the methodology of active monotone classification. The algorithm ensures an asymptotically optimal tradeoff between the number of pairs inspected and the number of mistakes made.
Modular Platooning and Formation Control

Moving vehicles in formation, or platooning, can dramatically increase road capacity. While traditional control methods can manage fleets of vehicles, they do not address the issues of dynamic road conditions and scalability (i.e., sophisticated control law redesign and physics modeling). We propose a modular framework that averts daunting retrains of an image-to-action neural network, provides flexibility in transferring to different robots/cars, while also being more transparent than previous approaches. First, a convolutional neural network was trained to localize in an indoor setting with dynamic foreground/background. Then, we design a new deep reinforcement learning algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. MPG is sucessfully applied to the platooning problem with obstacle avoidance and intra-group collision avoidance.
Super-Resolution on Degraded Low-Resolution Images Using Convolutional Neural Networks

Single Image Super-Resolution (SISR) has witnessed a dramatic improvement in recent years through the use of deep learning and, in particular, convolutional neural networks (CNN). In this work we address reconstruction from low-resolution images and consider as well degrading factors in images such as blurring. To address this challenging problem, we propose a new architecture to tackle blur with the down-sampling of images by extending the DBSRCNN architecture [1]. We validate our new architecture (DBSR) experimentally against several state of the art super-resolution techniques.
A ug 2 01 7 Machine Learning Inspired Energy-Efficient Hybrid Precoding for MmWave Massive MIMO Systems

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.
Electromagnetic Wave Attenuation and Phase Rotation by Charged Dust Particles

Electromagnetic wave propagating in dust storms is susceptible to attenuation and phase rotation by suspended charged particles. Considerable attention has therefore been paid to the development in the recent past by researchers. This paper presents a theoretical approach for predicting the attenuation and the phase rotation of an electromagnetic wave propagation in dust storms environment with charged particles. Mathematical model based on forward scattering amplitude Rayleigh theory is given in terms of monodisperse medium (equal sized distribution) and polydisperse medium (exponential size distribution). The attenuation and the phase rotation are calculated, and the results are analysed. The effect caused by the charged particles is notably significant. It is found that the attenuation and the phase rotation with charged dust particles are greater than where there is no charge. The coefficients of attenuation and phase rotation increase with the magnitude of charges carried by the dust particles depicted by the ratio of charge-to-mass. The results also show the variation of both attenuation and phase rotation with the charge distribution angles under different charge-to-mass ratios.