Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Background Modeling and Subtraction using Modified Space and time Local Binary Pattern

: Conventional background modeling and subtraction techniques have a hypothesis that the views are not of dynamic edifices with restricted disorder. These procedures will perform inadequately in dynamic scenes. In this paper, we present a resolution to this difficulty. We first extend the local binary patterns from the space domain to the spatiotemporal domain and give a distinct dynamic texture extraction operator, named modified spatiotemporal local binary patterns (Modified Space and Time Local Binary Pattern). We present an innovative and practical method for dynamic background modeling and subtraction using Modified Space and Time Local Binary Pattern. In this proposed method, each pixel is represented as a group of Space and Time Local Binary Pattern to dynamic texture histograms which connect spatial texture and temporal motion knowledge together. The histograms collected are processed to get normalized histogram which is the inadequacy of existing Space and time local binary pattern method. Compared with traditional methods, empirical results show that this method accommodates swiftly to the changes in the dynamic background. It gets correct detection of moving objects and succeeds most of the false detections for dynamic changes of natural scenes.
BER Analysis of NOMA-Enabled Visible Light Communication Systems With Different Modulations

Visible light communication (VLC) and non-orthogonal multiple access (NOMA) are deemed two promising technologies in the next generation wireless communication systems in achieving high capacity and massive connectivity. In this paper we study the performance of a NOMA-enabled VLC system using different modulation schemes. In particular system level bit error rate (BER) is derived for different modulation schemes. Conventional methods used for analyzing the BER under orthogonal multiple access cannot be directly applied to NOMA. In order to obtain the closed-form BER expressions for the NOMA-enabled VLC systems, an analytical framework based on bitwise-decision axis and signal space is proposed.Moreover, the analysis method can be extended to the any wireless communication networks with NOMA. Simulation results demonstrate the accuracy of the theoretical analysis. The study shows that the BER gap among users decreases with the increase of the modulation order but at the cost of a higher power consumption in order to achieve a better signal to noise ratio. It is observed that 8-PSK modulation in NOMA-enabled VLC systems strikes a good tradeoff between the power cost and the achievable BER.
Motion robust imaging photoplethysmography in defocus blurring

Non-contact, imaging photoplethysmography (IPPG) uses video sequence to measure variations in light absorption, caused by blood volume pulsations, to extract cardiopulmonary parameters including heart rate (HR), pulse rate variability, and respiration rate. Previous researches most focused on extraction of these vital signs base on the focus video, which require a static and focusing environment. However, little has been reported about the influence of defocus blur on IPPG signal’s extraction. In this research, we established an IPPG optical model in defocusing motion conditions. It was found that the IPPG signal is not sensitive to defocus blur by analysis the light intensity distribution in the defocus images. In this paper, a real-time measurement of heart rate in defocus and motion conditions based on IPPG was proposed. Automatically select and track the region of interest (ROI) by constructing facial coordinates through facial key points detection, obtained the IPPG signal. The signal is de-noised to obtain the spectrum by the wavelet filtering, color-distortion filter (CDF) and fast Fourier transform (FFT). The peak of the spectrum is corresponded to heartbeats. Experimental results on a data set of 30 subjects show that the physiological parameters include heart rate and pulse wave, derived from the defocus images captured by the IPPG system, exhibit characteristics comparable to conventional the blood volume pulse (BVP) sensor. Contrast experiment show that the difference between the results measured by both methods is within 3 beat per minute (BPM). This technology has significant potential for advancing personal health care and telemedicine in motion situation.
On the use of Low-Cost IMUs for GNSS Spoofing Detection in Vehicular Applications

This paper examines the use of low cost IMUs as a means of GNSS spooﬁng detection. The self-GNSS spooﬁng scenario is considered, where the holder of a GNSS receiver may shield the antenna from the genuine signals and introduce counterfeit ones to lead the receiver to compute an incorrect PVT. A method of us-ing uncalibrated, low cost inertial measurement units is proposed, which can detect spooﬁng based on a co-herency test between the GNSS and inertial measurements. Results of live testing under vehicular dynamics suggest that reasonable spooﬁng detection accuracy can be achieved with a time-to-alarm of three minutes
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure.
OCD: Online Crowdsourced Delivery for On-Demand Food

Online-to-offline (O2O) commerce connecting service providers and individuals to address daily human needs is quickly expanding. In particular, on-demand food, whereby food orders are placed online by customers and delivered by couriers, is becoming popular. This novel urban food application requires highly efficient and scalable real-time delivery services. However, it is difficult to recruit enough couriers and route them to facilitate such food ordering systems. This paper presents an online crowdsourced delivery (OCD) approach for on-demand food. Facilitated by Internet-of-Things and 3G/4G/5G technologies, public riders can be attracted to act as crowdsourced workers delivering food by means of shared bicycles or electric motorbikes. An online dynamic optimization framework comprising order collection, solution generation, and sequential delivery processes is presented. A hybrid metaheuristic solution process integrating the adaptive large neighborhood search and tabu search approaches is developed to assign food delivery tasks and generate high-quality delivery routes in a real-time manner. The crowdsourced riders are dynamically shared among different food providers. Simulated small-scale and real-world large-scale on-demand food delivery instances are used to evaluate the performance of the proposed approach. The results indicate that the presented crowdsourced food delivery approach outperforms traditional urban logistics. The developed hybrid optimization mechanism is able to produce high-quality crowdsourced delivery routes in less than 120 s. The results demonstrate that the presented OCD approach can facilitate city-scale on-demand food delivery.
Cross-lingual intent classification in a low resource industrial setting

This paper explores different approaches to multilingual intent classification in a low resource setting. Recent advances in multilingual text representations promise cross-lingual transfer for classifiers. We investigate the potential for this transfer in an applied industrial setting and compare to multilingual classification using machine translated text. Our results show that while the recently developed methods show promise, practical application calls for a combination of techniques for useful results.
The Effects of Career Broadening on Leadership Development

Abstract : The goal of this research was to improve the Air Force's knowledge of the effects of career-broadening jobs on the leadership development of its officer corps. Specifically, the study sought to find significant relationships between incidents of career broadening in the officers' background and their odds of being selected for promotion and in-residence professional military education (PME). Selection under these two areas is considered recognition of an officer's ability to handle more responsibility and greater leadership challenges. Therefore, they are logical assessments of an officer's leadership development. Duty histories of officers who met the Major, Lieutenant Colonel, and Colonel promotions boards in 2004 and 2005, as well as those officers who competed for selection to in-residence PME programs at the intermediate and senior development levels in 2003 and 2004, were analyzed to determine the impact on the odds of selection provided by career-broadening experiences. Results indicate that the Air Force needs to communicate the value of career broadening more effectively to its officers. Additionally, the developmental aspects of career-broadening jobs should be explored in the future.
Binocular Vision Pose Estimation Based on PSOPF

In order to realize robot autonomous localization and navigation, binocular vision localization based on particle swarm optimized particle filter (PSOPF) is put forward according to the characteristics of nonlinear and non-Gaussian distribution complex system. The localization of 6-DOF robot only depends on binocular vision in the method. At first, road signs are obtained by SIFT feature matching points of binocular vision. The second, initial pose estimation is obtained by four elements. Finally, robot pose is estimated accurately by PSOPF, and the algorithm overco mes the shortcoming of particle filter (PF) and imp rove estimation accuracy. Experiment results show that this algorithm has high computing accuracy and robustness.
ABC-NN: Approximate Bayesian Computation with Neural Networks to learn likelihood functions for efficient parameter estimation

In cognitive neuroscience, computational modeling offers a principled interpretation of the functional demands of cognitive systems. Bayesian parameter estimation provides information about the full posterior distribution over likely parameters. Importantly, the set of models with known likelihoods is dramatically smaller than the set of plausible generative models. Approximate Bayesian Computation (ABC) methods facilitate sampling from posterior parameters for models specified only up to a data generating process, overcoming this limitation to afford bayesian estimation of complex stochastic models (Wood, 2010; Beaumont, 2010; Akeret, Refregier, Amara, Seehars, & Hasner, 2015; Turner & P., 2014). Relying on model simulations to generate synthetic likelihoods, these methods however come with substantial computational cost at inference where simulations are typically conducted at each step in a MCMC algorithm. We propose a method that learns an approximate likelihood over the parameter space of interest, using multilayered perceptrons (MLPs). This incurs a single upfront cost, but the resulting network comprises a usable likelihood function that can be freely used in standard inference algorithms. We test this approach in the context of drift diffusion models, a class of cognitive process models commonly used in the cognitive sciences to jointly account for choice and reaction time data in a variety of experimental settings (Ratcliff, Smith, Brown, & McKoon, 2016).
Investigations of a possible link between age-related macular degeneration and atherosclerosis.

6 ACKNOWLEDGEMENT 8 LIST OF FIGURES 9 LIST OF TABLES 10 ABBREVIATIONS AND SYMBOLS 11 CHAPTER 1: INTRODUCTION 16 1.1 Age-related macular degeneration 16 1.1.1 Definition of age-related macular degeneration 16 1.1.2 Diagnosis of age-related macular degeneration 17 1.1.3 The patho-physiology of AMD 20 1.2 Atherosclerosis 21 1.2.1 Definition 21 1.2.2 Pathogenesis 21 1.3 The link between atherosclerosis and AMD 22 1.3. 1 Hypotheses 22 1.3. 2 Epidemiological evidence of co-morbidity of Atherosclerosis and AMD 23 1.4 Aims and Objectives of this project 32 CHAPTER 2: EXTRACELLULAR MATRIX 35 2.1 Bruch’s Membrane and the Vascular Intima: Is There a Common Basis for Age-related Changes and Disease? 35 2.1.1 Structural Comparison of the Bruch’s Membrane and the Vascular Wall 35 2.1.2 Macroscopic Changes of the BM and the Vascular Wall 36 2.1.3 Cell biology of the BM and the vascular wall 37 2.1.4 Lipid accumulation 39 2.1. 5 Matrix dysregulation 40 2.1. 6 Matrix metalloproteinases 43 2.1. 7 Conclusion from Literature Review 44 2.2 Serum Elastin Derived Peptides in Age-related Macular Degeneration 45 2.2.
Familial Aggregation of Physical Activity Levels in the Strong Heart Family Study

Physical inactivity is a major risk factor for complex metabolic diseases such as obesity, diabetes, and hypertension. Traditionally, these diseases and conditions have been the specific burden of adulthood, but they are now being diagnosed more frequently in younger populations. There has been some suggestion that, as is true in adults, children are becoming less physically active and that this decline in physical activity may help to explain the sudden increase in the incidence of metabolic diseases among children. Understanding the factors that are impacting on decreased physical activity levels in children will provide clues on how to approach this problem.Similar to findings in obesity, where obese parents tend to have overweight and obese children, physical activity appears to be related between parent and offspring. Although the literature regarding familial aggregation of physical activity levels is limited, it does allude to an association of physical activity levels among parents and children to varying degrees. Using several different approaches, we examined the familial resemblance of physical activity levels, determined by pedometry, in 96 extended Native American families from three geographic locations (Arizona, North/South Dakota, Oklahoma) in the Strong Heart Family Study. Based upon correlational analyses, physical activity levels were significantly and positively related among parent-offspring pairs. More specifically, correlations between father-daughter pairs (<,18 years of age) and father-son pairs (>,18-30 years of age) were rho = 0.30 and 0.26, respectively (p = 0.01). No significant associations were noted between physical activity levels of mother-offspring pairs. For our main investigation, we examined the familial effects, of physical activity using variance components analyses. Despite the fact that this study was conducted in three separate geographic locations, had limited household data, was not designed specifically to examine aggregation of physical activity levels, and was not limited to 2-parent families, when modeled as a heritable effect, physical activity achieved statistical significance (p = 0.007) explaining approximately 9% of the trait variance. Since physical activity can provide health benefits in youth, and since many young people are not meeting established guidelines, improving physical activity levels of youth is a key public health challenge. However, in order to develop effective physical activity interventions for youth, factors that impact on a child's physical activity levels need to be better understood. The findings of the current study indicate a significant, albeit weak, degree of aggregation of physical activity levels among parent-offspring pairs and among families. This supports the need to establish family based lifestyle interventions studies where both parent and child may benefit.
A Learning-Based Prediction Model for Baby Accidents

According to the statistics in the United Kingdom, more than two million babies and toddlers experienced accidents every year. Despite the places where accidents happened, most of the accidents could’ve been predicted and prevented. In order to avoid causing injuries by accident, a temporal-pyramid long short-term memory (TP-LSTM) network along with the temporal attention mechanism is proposed to predict whether an accident will happen in the future or not. The proposed network is capable of capturing important information of the video at different temporal resolution and selecting crucial frames that contribute to the accident most. Moreover, the proposed early exponential loss (EEL) function is incorporated to achieve better prediction. The baby video dataset (BVD) containing 670 videos is collected from several video-sharing websites. 320 of which are with accidents and the others are without accidents. The experimental results show that the proposed network attains average precision of 61.13% and the accidents are foreseen 4.196 seconds before the occurrence with 80% recall.
Comparison between Modified MPC and DTC Control Method for Permanent Magnet Synchronous Motor

Direct torque control (DTC) and model predictive control (MPC) are widely used in the control of permanent magnet synchronous motor (PMSM). However, DTC for PMSM can cause large torque ripples and flux ripples, high harmonic distortion of the stator current, and high acoustic noises. Compared to DTC, MPC considers all possible switching states which can reduce the ripples of torque and flux. MPC with one-step delay compensation for PMSM and DTC with one-step delay compensation and two hysteresis comparators for PMSM have been proposed to solve some drawbacks of these two controllers. This paper makes a detailed comparison between these two improved control methods through Simulink and hardware experiments results to analyse the four indicators-torque ripple, flux ripple, transient time and THD of inverter current.
End-to-End Deep Image Matting Platform

Image matting is a basic feature in most of image quality improvement applications and is considered as a fundamental problem in the computer vision field. In this paper, an Endto-End Image Matting Platform is proposed to segment the foreground and background by creating an alpha matte image. My end-to-end image matting algorithm is a learning based network which contains two stages. The first stage is to create a tri-map image from an RGB image using segmentation neural network where tri-map images are used to locate the expected foreground objects with rough outlines. The second stage is an image matting neural network, and it takes the outputs from the first stage as prior knowledge to predict precise alpha matte images. With the help of image matting formula and the outputs from the second stage, contents in an RGB image can be easily split into foreground and background. I applied both training and evaluating on Adobe matting benchmark and Car Media 2.0’s car oriented image matting dataset, and the outcomes demonstrated the convenience and superior performance of our algorithm compared to existing state of the art methods. This paper put forward a web platform structure to integrate deep learning algorithms. I applied multiple strategies to enhance the performance of the platform. By using this platform, multiple users can work with different deep learning applications at the same time which dramatically increases the efficiency of the server usage. iv
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Much of model-based reinforcement learning involves learning a model of an agent's world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware---e.g., a brain---arose as the byproduct of competing evolutionary pressures for survival, not minimization of a supervised forward-predictive loss via gradient descent. That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances. Crucially, this optimization process need not explicitly be a forward-predictive loss. In this work, we introduce a modification to traditional reinforcement learning which we call observational dropout, whereby we limit the agents ability to observe the real environment at each timestep. In doing so, we can coerce an agent into learning a world model to fill in the observation gaps during reinforcement learning. We show that the emerged world model, while not explicitly trained to predict the future, can help the agent learn key skills required to perform well in its environment. Videos of our results available at https://learningtopredict.github. io/
An Overview of the Development of Antenna-in-Package Technology for Highly Integrated Wireless Devices

Antenna-in-package (AiP) technology, in which there is an antenna (or antennas) with a transceiver die (or dies) in a standard surface-mounted device, represents an important antenna and packaging technology achievement in recent years. AiP technology has been widely adopted by chipmakers for 60-GHz radios and gesture radars. It has also found applications in 77-GHz automotive radars, 94-GHz phased arrays, 122-GHz imaging sensors, and 300-GHz wireless links. It is believed that AiP technology will also provide elegant antenna and packaging solutions to the fifth generation and beyond operating in the lower millimeter-wave (mmWave) bands. Thus, one can conclude that AiP technology has emerged as the mainstream antenna and packaging technology for various mmWave applications. This article will provide an overview of the development of AiP technology. It will consider antennas, packages, and interconnects for AiP technology. It will show that the antenna choice is usually based on those popular antennas that can be easily designed for the application, that the package choice is governed for automatic assembly, and that the materials and processes choices involve tradeoffs among constraints, such as electrical performance, thermal–mechanical reliability, compactness, manufacturability, and cost. This article also shows a probe-based setup to measure mmWave AiP impedance and radiation characteristics. It goes on to give AiP examples implemented, respectively, in a low-temperature co-fired ceramic, an embedded wafer level ball grid array process, and a high-density interconnect processes. Finally, this article will summarize and present some recommendations on research topics to further the state of the art of AiP technology.
A Proposal of Control Method Considering the Path Switching Time of SDN and Its Evaluation

A R T I C L E I N F O A B S T R A C T Article history: Received: 17 June, 2019 Accepted: 31 July, 2019 Online: 16 August, 2019 Recently, communication demands often change because of the various network services in companies and individuals. Software Defined Networking (SDN) has emerged as a viable control paradigm that allows flexible communication, using OpenFlow as its default standard and enabler. However, when changes happen frequently in SDN networks due to unforeseen reasons -such as a network failure or topology changesit takes a long time to perform all the operations. For instance, to change a routing path, first new paths must be calculated, then the controller must transmit the commands to the network elements, which has to process those commands. This process can cause a delay, or even disruption, in the communication service. Therefore, this paper proposes a network control method to reduce the time to change a path using OpenFlow.