Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Leader-following Attitude Consensus of Rigid Body Systems over Switching Networks by a Fully Distributed Control Law

The leader-following attitude consensus problem of multiple rigid body systems over switching networks was solved by the distributed observer approach. The resulting control law requires that the control of every rigid body system know the dynamics of the exosystem that produces the desired angular velocity. In this paper, we further consider solving the same problem by the adaptive distributed observer approach. The latter approach is able to provide for each rigid body system the estimation of the dynamics of the exosystem, thus leading to a fully distributed control law for solving the leader-following attitude consensus problem of multiple rigid body systems.
MS-GAN: GAN-Based Semantic Segmentation of Multiple Sclerosis Lesions in Brain Magnetic Resonance Imaging

Automated segmentation of multiple sclerosis (MS) lesions in brain imaging is challenging due to the high variability in lesion characteristics. Based on the generative adversarial network (GAN), we propose a semantic segmentation framework MS-GAN to localize MS lesions in multimodal brain magnetic resonance imaging (MRI), which consists of one multimodal encoder-decoder generator G and multiple discriminators D corresponding to the multiple input modalities. For the design of the generator, we adopt an encoder-decoder deep learning architecture with bypass of spatial information from encoder to the corresponding decoder, which helps to reduce the network parameters while improving the localization performance. Our generator is also designed to integrate multimodal imaging data in end-to-end learning with multi-path encoding and cross-modality fusion. An additional classification-related constraint is proposed for the adversarial training process of the GAN model, with the aim of alleviating the hard-to-converge issue in classification-based image-to-image translation problems. For evaluation, we collected a database of 126 cases from patients with relapsing MS. We also experimented with other semantic segmentation models as well as patch-based deep learning methods for performance comparison. The results show that our method provides more accurate segmentation than the state-of-the-art techniques.
COST: A Cluster-Oriented Scheduling Technique for Heterogeneous Multi-cores

Development of efficient resource allocation strategies for real-time tasks on heterogeneous platforms has traditionally proved to be a challenging as well as a computationally expensive problem. However, strategies which can efficiently schedule real-time task sets on generic heterogeneous platforms having an arbitrary number of processor types, are rare. Most of the existing strategies are oriented towards systems with restricted number of processing core types. Hence, this paper proposes an effective low-overhead heuristic approach called COST, for scheduling a set of periodic tasks executing on a heterogeneous multi-core system. The proposed strategy works in three-phases namely, Core Clustering, Task Partitioning, and Task Scheduling. The Core Clustering step attempts to combine the available processing cores into a group of clusters. Each cluster consists of two cores and a disjoint subset of the given task set is assigned to it. The tasks assigned to a cluster are then allocated to the processing cores of the cluster and scheduled in a fair manner. Experimental studies show that our proposed scheme provides high resource utilisation with satisfactory acceptance ratios for a wide-range of task sets.
Observer Design for Nonlinear Systems with Implicit Output

This paper investigates the problem of observer design for nonlinear systems when only a transformed output is available for measurement. More precisely, we first assume that there exists an observer design that requires the output of the system. We then suppose that this disturbed output is only available under a transformation and we propose a new observer design. We establish small-gain type conditions ensuring the asymptotic convergence of the proposed observer and we provide an illustration example.
Rebalancing Frequency Considerations for Kelly-Optimal Stock Portfolios in a Control-Theoretic Framework

In this paper, motivated by the celebrated work of Kelly, we consider the problem of portfolio weight selection to maximize expected logarithmic growth of a trader's account. Going beyond existing literature, our focal point here is the rebalancing frequency which we include as an additional parameter in the maximization. The problem is first set up in a control-theoretic framework, and then, the main question we address is as follows: In the absence of transaction costs, does high-frequency trading always lead to the best performance? Related to this question is our prior work on Kelly betting which examines the impact of making a wager and letting it ride. Our prior results indicate that it is often the case that there are no performance benefits associated with high-frequency trading. In the present paper, we generalize the analysis from the single-asset case to a portfolio with multiple risky assets. We show that if there is an asset satisfying a certain dominance condition, then an optimal portfolio consists of this asset alone; i.e., if the trader puts “all eggs in one basket,” performance becomes a constant function of rebalancing frequency. Said another way, the problem of rebalancing is rendered moot. The paper also includes simulations which address practical considerations associated with real stock prices vis-a-vis the dominance condition.
ICT Competency, Network Interaction, Internet Self-Efficacy, and Mathematical Achievement: Direct and Mediating Effects

With the popularity of the Internet and information technology in education, a growing number of studies have been conducted on its influence on academic achievement, and further attention has been paid to the influence of gender factors. Through the structural equation model, this study used Internet self-efficacy as a mediator to compare and analyze the direct and indirect effects of the ICT competency and the network interaction on the mathematical achievement of the students of different gender. The result shows that the direct effects of male students' ICT competency and mathematical achievement are negative and significant, while female students are not. The direct effects of ICT competency and network interaction on mathematical achievement of male and female students were all negative. However, during the adjustment factor of Internet self-efficacy, the indirect effects were positive and significant. It could be seen the crucial role of Internet self-efficacy between technical use and mathematical achievement. Teachers should attach importance to the current situation of male students' Internet reverse use. At the same time, they should also pay attention to the cultivation of Internet self-efficacy of male and female students, so that technology can better help students improve their mathematical achievement.
On the Performance Analysis of APIs Recognizing Emotions from Video Images of Facial Expressions

This work is meant to provide insights towards state-of-the-art Application Programming Interfaces (APIs) commercially available today for recognizing emotions from video footprint of facial expressions. We analyze and compare performance of four such integrable commercial APIs using standard Extended Cohn-Kanade Dataset (CK+) containing over 10, 000 images with facial emotions and some randomly collected real life images. We also discuss issues to understand their limitations and adaptation as well as enhancement strategies. For performance analysis, we introduce the performance metric moving average to find the primary expression in the displayed emotion of a video sequence. Finally, using the popular valence-arousal dimensions, we show how to (lexicographically) order six basic emotions anger, happiness, sadness, surprise, fear and disgust. By providing neutral performance comparison, we hope to fill the existing notable gap between researchers, solution providers and application developers working on emotion based user modeling.
On-Chip Thermionic Electron Emitter Arrays Based on Horizontally Aligned Single-Walled Carbon Nanotubes

Thermionic electron emitter, a critical component in vacuum electronic devices, usually suffers from bulky size, difficulty to integrate, and slow temporal response. Here, to overcome these problems, we fabricate and test a new miniature electron emitter: on-chip thermionic electron emitter arrays based on horizontally aligned single-walled carbon nanotubes. The devices are fabricated and integrated on a Si chip with microfabrication technologies, providing compactness as well as fast temporal response. Emitter arrays with a packing density of up to $\sim \textsf {0.4}\times 10^{7}$ emitters/cm2 and an emission density of 1.73 mA/cm2 at a bias voltage of 2.64 V are realized. The turn-on/off response times of the devices are measured to be ~150/500 ns, which are 5 orders of magnitude faster than those of their bulky counterparts. The advantages of on-chip nature, high degree of integration, and fast temporal response make the thermionic emitter arrays promising in miniature vacuum electronic devices.
A Wide Impedance Bandwidth Printed Slot Antenna for 28, 38, and 60-GHz Applications

A printed slot antenna with 10-dB impedance bandwidth from 22.44 GHz to $> 67 \ \text{GHz}$ is presented. The proposed antenna is basically an elliptical slot antenna fed by a microstrip line on the backside of the substrate. To increase the bandwidth, particularly, a parasitic patch loaded by a cross-shaped slot was non-concentrically placed into the elliptical slot and an open-circuited stub was properly added to the feed line. Experimental results show that the fabricated antenna achieves a ratio bandwidth of $> 2.99;1$ for VSWR $\leq 2$ and at lower frequencies of the bandwidth it exhibits dipole-like radiation patterns with the peak gain of 8.4 dBi. The proposed antenna is suited to future 5G wireless communications at 28 GHz, 38 GHz, and 60 GHz.
Computation of Complete Lyapunov Functions for Three-Dimensional Systems

Complete Lyapunov functions are of much interest in control theory because of their capability to describe the long-time behaviour of nonlinear dynamical systems. The state-space of a system can be divided in two different regions determined by a complete Lyapunov function: the region of the gradient-like flow, where the Lyapunov function is strictly decreasing along solution trajectories, and the chain-recurrent set whose chain-transitive components are level sets of the Lyapunov function. There has been continuous effort to properly identify both regions and in this paper we discuss the extension of our methods to compute complete Lyapunov functions in the plane to the three-dimensional case, which is directly applicable to higher dimensions, too. When extending the methods to higher dimensions, the number of points for collocation and evaluation grows exponentially. To keep the number of evaluation points under control, we propose a new way to choose them, which does not depend on the dimension.
Fine-Grained Categorization by Deep Part-Collaboration Convolution Net

In part-based categorization context, the ability to learn representative feature from quantitative tiny object parts is of similar importance as to exactly localize the parts. We propose a new deep net structure for fine-grained categorization that follows the taxonomy workflow, which makes it interpretable and understandable for humans. By training customized sub-nets on each manually annotated parts, we increased the state-of-the-art part-based classification accuracy for general fine-grained CUB-200-2011 dataset by 2.1%. Our study shows the proposed method can produce more activation to discriminate detail part difference while maintaining high computing performance by applying a set of strategies to optimize the deep net structure.
Energy-aware networked control systems under temporal logic specifications

In recent years, event and self-triggered control have been proposed as energy-aware control strategies to expand the life-time of battery powered devices in Networked Control Systems (NCSs). In contrast to the previous works in which their control objective is to achieve stability, this paper presents a novel energy-aware control scheme for achieving high level specifications, or more specifically, temporal logic specifications. Inspired by the standard hierarchical strategy that has been proposed in the field of formal control synthesis paradigm, we propose a new abstraction procedure for jointly synthesizing control and communication strategies, such that the communication reduction in NCSs and the satisfaction of the temporal logic specifications are guaranteed. The benefits of the proposal are illustrated through a numerical example.
Binary Signaling under Subjective Priors and Costs as a Game

Many decentralized and networked control problems involve decision makers which have either misaligned criteria or subjective priors. In the context of such a setup, in this paper we consider binary signaling problems in which the decision makers (the transmitter and the receiver) have subjective priors and/or misaligned objective functions. Depending on the commitment nature of the transmitter to his policies, we formulate the binary signaling problem as a Bayesian game under either Nash or Stackelberg equilibrium concepts and establish equilibrium solutions and their properties. In addition, the effects of subjective priors and costs on Nash and Stackelberg equilibria are analyzed. It is shown that there can be informative or non-informative equilibria in the binary signaling game under the Stackelberg assumption, but there always exists an equilibrium. However, apart from the informative and non-informative equilibria cases, under certain conditions, there does not exist a Nash equilibrium when the receiver is restricted to use deterministic policies. For the corresponding team setup, however, an equilibrium typically always exists and is always informative. Furthermore, we investigate the effects of small perturbations in priors and costs on equilibrium values around the team setup (with identical costs and priors), and show that the Stackelberg equilibrium behavior is not robust to small perturbations whereas the Nash equilibrium is.
Bias Evaluation of Professors' Reviews

Typically, the overall performance of a university professor is assessed based on three criteria: teaching, research and service. The teaching component is based on students' ratings and comments. However, comments and ratings are highly subjective, thus making the assessment difficult, especially given conscious and unconscious bias. RateMyProfessor.com is a popular publicly accessible website where students rate professors. We use this large corpus to evaluate bias and answer the following questions: 1) what words do students use to describe their professors? 2) is there any correlation between the words used and the rating given? and, 3) are there significant differences across gender and discipline? To answer these questions, we model the reviews using Latent Dirichlet Allocation (LDA) to automatically identify topics and we run several regression models to analyze if there are statistically significant factors that influence the overall rating.
Neural Machine Translation Advised by Statistical Machine Translation: The Case of Farsi-Spanish Bilingually Low-Resource Scenario

In this paper, we propose a sequence-to-sequence NMT model on Farsi-Spanish bilingually low-resource language pair. We apply effective preprocessing steps specific for Farsi language and optimize the model for both translation and transliteration. We also propose a loss function that enhances the word alignment and consequently improves translation quality.
Strided U-Net Model: Retinal Vessels Segmentation using Dice Loss

Accurate segmentation of vessels is an arduous task in the analysis of medical images, particularly the extraction of vessels from colored retinal fundus images. Many image processing tactics have been implemented for accurate detection of vessels, but many vessels have been dropped. In this paper, we propose a deep learning method based on the convolutional neural network (CNN) with dice loss function for retinal vessel segmentation. To our knowledge, we are the first to form the CNN on the basis of the dice loss function for the extraction of vessels from a colored retinal image. The pre-processing steps are used to eliminate uneven illumination to make the training process more efficient. We implement the CNN model based on a variational auto-encoder (VAE), which is a modified version of U-Net. Our main contribution to the implementation of CNN is to replace all pooling layers with progressive convolution and deeper layers. It takes the retinal image as input and generates the image of segmented output vessels with the same resolution as the input image. The proposed segmentation method showed better performance than the existing methods on the most used databases, namely: DRIVE and STARE. In addition, it gives a sensitivity of 0.739 on the DRIVE database with an accuracy of 0.948 and a sensitivity of 0.748 on the STARE database with an accuracy of 0.947.
CNN-Based Detection and Classification of Grasps Relevant for Worker Support Scenarios Using sEMG Signals of Forearm Muscles

Intuitive man-machine interaction is one of the main challenges towards a wider use of exoskeletons as assistive systems to reduce fatigue and alleviate the risk of injuries of workers during manual handling or overhead work. In this context reliable human grasp detection and classification is a basic but key component to estimate current state and context. While hand gesture recognition using surface electromyography (sEMG) has been addressed in a number of studies, there has been only little research regarding the detection and classification using sEMG of grasps relevant for worker support scenarios, which is the scope of the present study. Specifically, we investigated detection and classification of one-handed power grasps (relevant for tool manipulation) and two-handed grasps (typical while handling crates and boxes) using the instantaneous sEMG vector of the forearm muscles activity acquired using a consumer-grade wearable sensor bracelet. Detection and classification was performed by convolutional neural networks (CNN) trained using datasets including multiple grasps, positions, sessions and users. The accuracy of grasp detection and classification was evaluated in different scenarios with increasing level of signal variability, including sample cross-validation, inter-session scenario and inter-person scenario. We found that high accuracy of grasp detection can be achieved, even in the inter-person scenario. For one-handed grasps, reasonable accuracy of classification of the object manipulated by the user was possible in the inter-person scenario. For two-handed grasps, reasonable accuracy of grasp type classification was possible for some of the grasps even in the inter-person case. These results, achieved with a simple, low-profile acquisition device, show the potential of proposed approach in the context of worker support scenarios.
A Hybrid Lower Bound for Parameter Estimation of Signals With Multiple Change-Points

Change-point estimation has received much attention in the literature as it plays a significant role in several signal processing applications. However, the study of the optimal estimation performance in such context is a difficult task since the unknown parameter vector of interest may contain both continuous and discrete parameters, namely the parameters associated with the noise distribution and the change-point locations. In this paper, we handle this by deriving a lower bound on the mean square error of these continuous and discrete parameters. Specifically, we propose a hybrid Cramér–Rao–Weiss-Weinstein bound and derive its associated closed-form expressions. Numerical simulations assess the tightness of the proposed bound in the case of Gaussian and Poisson observations.
Object-Based Temporal Segment Relational Network for Activity Recognition

Video understanding is the next frontier of computer vision, in which activity recognition plays a major role. Despite the recent improvements in holistic activity recognition, further researching part-based models such as context may allow us to better understand what is important for activities and thus improve our current activity recognition models. This work tackles contextual cues obtained from object detections, in which we posit that objects relevant to an action are related to its spatial arrangement regarding an agent. Based on that, we propose Egocentric Pyramid to encode such spatial relationships. We further extend it by proposing a data-centric approach named Temporal Segment Relational Network (TSRN). Our experiments give support to the hypothesis that object spatiality provides an important clue to activity recognition. In addition, our data-centric approach shows that besides such spatial features, there may be other important information that further enhances the object-based activity recognition, such as co-occurrence, relative size, and temporal information.
Experimental and numerical modeling of corona discharge generated in an electrostatic precipitator

The present article is devoted to the numerical and experimental study of positive and negative corona discharge in air at atmospheric pressure in a multi-wire-to-planes geometry, which is widely exploited in the design of the electrostatic precipitator (ESP). A detailed experimental procedure was carried out on a scaled-down laboratory prototype developed for the purpose to determine the corona discharge characteristics. Different electrode layouts have been tested to improve the performance of the precipitator. In addition, the corona governing equations are successfully implemented and solved using Comsol Multiphysics. It has been found that the level and polarity of the applied voltage as well as the geometrical parameters of the studied configuration have a very significant influence on the intensity of the corona discharge current.
Low-RCS Reflectarray With Phase Controllable Absorptive Frequency-Selective Reflector

This paper presents a methodology to control the reflection phase response from a 3-D bandstop structure backed with an absorbing material. An absorptive frequency-selective reflection structure using the cascaded arc-shaped strip resonators and absorbers is utilized to obtain an absorption–reflection–absorption response. The reflection phase response of the structure within the reflection band is then flexibly controlled by top-loaded metallic patches with variable size. An application of this methodology is demonstrated by designing a high-gain and low-radar cross section (RCS) reflectarray. The in-band phase profile of the reflecting surface is constructively designed to collimate the beam in the far field. Meanwhile, the out-of-band scattering from the planar reflector is significantly reduced. Compared with a conventional reflectarray, the radiation performance of the proposed antenna is maintained with an aperture efficiency of 50.1% and directivity of 23.5 dBi. Significant RCS reduction has been achieved with a fractional bandwidth of 76.9% and 17.1% for the lower and upper bands with reduction levels of 10 and 8 dB, respectively.