Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Adaptive Tracking Controller Design for Stochastic Switched Pure-Feedback Nonlinear Systems With Input Saturation and Non-Lower Triangular Structure

This paper concentrates on the design of intelligent adaptive tracking controller for stochastic switched nonlinear pure-feedback systems with input saturation and non-lower triangular structure. It needs to be emphasized that both the issues of pure-feedback structure and non-differential saturation nonlinearity are involved in the studied system. With the help of the mean-value theorem, a novel intelligent adaptive tracking controller is developed in this work to overcome the difficulty resulted from pure-feedback structure, and the inherent property of Gaussian functions is utilized to handle functions that are unknown and include all state variables. Moreover, through the universal intelligent approximation technology, a novel control strategy is constructed under the framework of backstepping, which guarantees that the tracking error can converge to a small neighborhood near the origin in the sense of mean quartic value and all signals of the nonlinear closed-loop system can be bounded in probability. Eventually, the effectiveness of the presented scheme is further illustrated by the simulation of two practical examples.
A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples. This poses new threats to the security-critical applications in cybersecurity. Currently, there is still a short of study on adversarial examples in the domain of cybersecurity. In this paper, we propose a new method known as the brute-force attack method to better evaluate the robustness of the machine learning classifiers in cybersecurity against adversarial examples. The proposed method, which works in a black-box way and covers some shortages of the existing adversarial attack methods based on generative adversarial networks, is simple to implement and only needs the output of the target classifiers to generate adversarial examples. To have a comprehensive evaluation of the attack performance of the proposed method, we use our method to generate adversarial examples against the common machine learning based security systems in cybersecurity including host intrusion detection systems, Android malware detection systems, and network intrusion detection systems. We compare the attack performance of the proposed method against these security systems with that of state-of-the-art adversarial attack methods based on generative adversarial networks. The preliminary experimental results show that the proposed method, which is more efficient in computation and outperforms the state-of-the-art attack methods based on generative adversarial networks, can be used to evaluate the robustness of various machine learning based systems in cybersecurity against adversarial examples.
Light Field Image Quality Assessment via the Light Field Coherence

In this paper, a novel full-reference image quality assessment (IQA) method for evaluating the quality of the distorted light field (LF) image against its reference LF image is proposed, called the log-Gabor feature-based light field coherence (LGF-LFC). Based on the fact that to compare two LF images, it essentially boils down to measure how coherent of these two LF images, we attempt to measure the degree of their LF coherence (LFC). To pursue this goal, the salient features from the reference and distorted LF images under comparison need to be extracted. By considering that the Gabor feature has the ability to well characterize the human visual system (HVS) perception, and the special characteristics of the LF images, the multi-scale and single-scale Gabor feature extraction schemes are developed to extract the multi-scale log-Gabor features from the sub-aperture images (SAIs) and the single-scale log-Gabor feature from the epi-polar images (EPIs), respectively. Note that the former can reflect the image details (via the SAIs), while the latter indicates the viewing consistency (via the EPI’s depth information). The similarity measurements are subsequently conducted on the comparison of their SAIs and that of their EPIs separately, followed by combining them together for arriving at the final score. Extensive simulation results have clearly demonstrated that the proposed LGF-LFC is more consistent with the perception of the HVS on the quality evaluation of the LF images than multiple classical and state-of-the-art IQA methods.
Learning-Supported Approximated Optimal Control for Autonomous Vehicles in the Presence of State Dependent Uncertainties

The control and operation of autonomous systems often involve different decision layers. The higher control levels are responsible for the planning and perception and operate on a slow time scale. To achieve good performance and disturbance rejection, often additional lower level controllers are used, which act on a fast time scale. Designing lower level controllers for autonomous systems is often challenged by the available, typically limited computational power. Furthermore, for safety and reliability reasons the low level controllers need to be verified, need to guarantee constraint satisfaction and correct operation under all circumstances. We consider the problem of low level continuous time controller design for autonomous systems subject to state dependent uncertainties. The main idea is to derive offline an approximated explicit solution of an optimal feedback law based on a series expansion via Al’brekht‘s Method. To decrease the impact of state dependent uncertainties, the controller is parametrized in terms of disturbance parameters, which are adapted - learned online. As shown, the proposed strategy can be implemented in realtime even on computationally limited embedded platforms. We outline local stability results of the explicit solution for polynomial systems. The effectiveness and robustness of the proposed strategy to learn and mitigate the effect of the external disturbances are validated via simulation results for a quadcopter with different uncertainty scenarios.
Economic Nonlinear Model Predictive Control of a U-loop Bioreactor

In this paper, we present an algorithm for economic nonlinear model predictive control (NMPC) of single-cell protein production in a U-loop bioreactor. The model of the U-loop bioreactor consists of both stochastic ordinary and partial differential equations. Using a typical finite-volume discretization, the model contains 87 state variables. The NMPC algorithm is based on the continuous-discrete extended Kalman filter and a simultaneous collocation method. We present a closed-loop simulation which demonstrates the computational feasibility of real-time implementation of the NMPC algorithm for startup and steady state operation of the U-loop reactor.
Energy and Spectral Efficiency Tradeoff in NOMA: Multi-Objective Evolutionary Approaches

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.
Information-Criterion-Based Agent Selection for Cooperative Localization in Static Networks

In this paper we propose a Bayesian agent network planning algorithm for information-criterion-based measurement selection for cooperative localization in static networks with anchors. This allows to increase the accuracy of the agent positioning while keeping the number of measurements between agents to a minimum. The proposed algorithm is based on minimizing the conditional differential entropy (CDE) of all agent states to determine the optimal set of measurements between agents. Such combinatorial optimization problems have factorial runtime and quickly become infeasible, even for a rather small number of agents. Therefore, we propose a Bayesian agent network planning algorithm that performs a local optimization for each state. Experimental results demonstrate a performance improvement compared to a random measurement selection strategy, significantly reducing the position RMSE at a smaller number of measurements between agents.
Sulfated Polysaccharides in Red and Brown Algae

IN RED ALGAE 42 Agars 42 Carrageenans . 42 Biological Techniques in Polysaccharide Chemistry . 43 Sulfated Heteroglycans 43 IN BROWN ALGAE 44 BIOLOGICAL AND ENVIRONMENTAL FACTORS AFFECTING SULFATED POLYSACCHARIDES 44 BIOSYNTHESIS 45
Crowdsourcing-Based Learning Data Collection for Real-Time Sensor Error Correction in Indoor Environments

Sufficient training data and high positioning accuracy are crucial components of indoor positioning. However, the collection of learning data consumes much time and manual effort, inhibiting the global spread of indoor positioning technology. The use of crowdsourcing-based data collection that does not require user intervention can reduce deployment effort, but results in loss of positioning accuracy. Pedestrian dead reckoning partly resolves the problem by using a variety of sensors to provide a relatively accurate position; however, the accumulation of errors is yet to be successfully addressed. In this study, we introduce a highly accurate positioning method that implements error correction based on a crowdsourced database. The proposed method constructs learning data without manual effort and the need of reference points in the target area, and improves positioning accuracy by continuously learning and correcting the error distribution of the inertial sensor. The obtained positioning accuracy was approximately 3.38 m for roughly 83% of the collected fingerprints. Furthermore, the error correction algorithm improved the moving distance and direction accuracy by up to 2.86% and 25.7%, respectively. The proposed method was verified through experiments in an office building where it successfully constructed a learning dataset and reflected a dynamic environment by deriving accurate tracking results.
GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis

Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-norm, which can be highly influenced by outlier data. In recent years, there has been growing interest in the development of more robust PCA methods. Recent works explore alternative norms, such as the <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula>-norm or the more general <inline-formula><tex-math notation="LaTeX">$L_p$</tex-math></inline-formula>-norms, which significantly improve robustness over the <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-norm. In this work, we present the Grassmann Iterative P-norm PCA (GrIP-PCA) method, which uses an iterative Grassmann manifold optimization approach to find the solution to the highly non-convex <inline-formula><tex-math notation="LaTeX">$L_p$</tex-math></inline-formula>-norm PCA problem. The increased flexibility of this iterative optimization approach allows for the first ever direct comparison between the projection maximization and reprojection minimization objective functions for general <inline-formula><tex-math notation="LaTeX">$L_p$</tex-math></inline-formula>-PCA. Our results demonstrate that the underutilized reprojection formulation leads to improved robustness of PCA in multiple experiments.
Hybrid Capsule Network Architecture Estimation for Melanoma Detection

As uncommon as its incidence is, melanoma is considered to be one of the most aggressive and deadly skin cancers today. Risk factors for the development of the disease are related to exposure to ultraviolet rays and family history. Currently, the diagnosis is made through dermatoscopy, a noninvasive technique that aims to observe the structures of the skin. The diagnosis of melanoma is linked to the professional's experience in identifying the disease. With the advancement of computing, solutions based on deep learning were suggested to help diagnose the disease. This work has as main objective the adaptation of a capsule neural network for the task of detecting melanoma in medical images. The proposed architecture consists of the VGG16 neural network combined with a capsule neural network. The results obtained by the proposed model fed with the ISIC Archive database proved to be promising when compared to the literature, obtaining an accuracy of 92.6, sensitivity of 90.9 and specificity of 92.8.
Video Popularity Prediction: An Autoencoder Approach With Clustering

Autoencoders implemented by artificial neural networks (ANNs) are utilized to learn the latent space representation of data in an unsupervised manner, and they have been widely used in recommender systems. For instance, several collaborative denoising autoencoder (CDAE) models have shown that their performance gains outperform that of the collaborative filtering based (CF-based) models. In this work, a near-optimal Top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> forecasting solution is proposed for our advanced autoencoder recommender systems. We propose a method which utilizes CDAE model in predicting the Top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> popular videos in an upcoming time period. In order to improve the prediction accuracy, we also propose an autoencoder based recommendation algorithm with the help of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means clustering that upgrades the performance of the original autoencoder model. The experimental results show that our method increases significantly the Average Precision (AP) and Recall values by nearly 30%. We then further utilize our proposed autoencoder model with clustering in predicting Top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> popular videos. The applications of predicting Top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> popular videos can be used in the video delivery for the Mobile Edge Computing (MEC) environment to avoid bottleneck in the constricted capacity of backhaul link. Namely, the performance gain will be upgraded if our proposed method precisely predicts and caches the Top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> popular videos in advance with the help of a better forecasting model.
Question Answering using Automatically Generated Semantic Networks – the case of Swahili Questions

Question Answering systems are important in natural language processing applications such as web search and information extraction. Various gold standard curated datasets exist for QA mostly in English. QAs are solved using syntactic, semantic or even learning systems. Low resource languages, such as Swahili, have not been easy to process due to few resources for training, testing and learning. By recognizing that Swahili generally follows subject-verb-object format, which is also the general structure of semantic networks, we use this fact to create a semantic network. Scarcity of public domain corpus has meant that we use Swahili frequently asked questions from public websites, and school texts for Swahili language. We create a semantic network and subject it to QA tasks. The results show good accuracy of about 80% for any typical set of one fact comprehension questions. Challenges such as scarcity of corpora and lack of gold standard test sets remain.
Stage Cost Formulations of Online Rainflow-counting for Model Predictive Control of Fatigue

The material fatigue cost formulation of Direct Online Rainflow-counting is reformulated to an Economic Stage Cost and a Tracking Stage Cost. This enables implementation in the majority of standard MPC frameworks, and even more importantly, this will enable further theoretical analysis. Key enablers for this reformulation are decoupling of fatigue cost in time, pre-simulation of the system-dynamics before the MPC, and periodic update of time-varying cost-parameters. Simulation-based tests of the cost calculation are shown which provide further insight and verify the correspondence to a classical moving-window a posteriori fatigue analysis. A brief theoretical discussion on the influence of presented approximations is provided. Finally, dynamic closed-loop simulations are presented which show the effectiveness of the formulations in attenuating oscillations of mechanical stress for a wind turbine model.
Cloud Services for Culture Aware Conversation: Socially Assistive Robots and Virtual Assistants

This paper introduces a new Cloud platform providing services for culturally competent interaction, that has been developed to expand the capabilities of Socially Assistive Robots and virtual assistants interacting with older persons. The rationale behind the proposed architecture is discussed, by outlining key principles as well as the functionalities provided, with a specific focus on verbal interaction. Three case studies, the humanoid robot Pepper, a robotic medicine dispenser Pillo, and a custom-built Android-based virtual assistant, are analyzed in detail, by showing how robots and other assistants may easily access culturally competent Cloud services to expand their interaction capabilities. Transcripts of conversations are reported and commented, in order to outline both the positive features and the limitations of the system.
Robust Secure Resource Allocation for Downlink Two-User MISO Rate-Splitting Systems

In this paper, we consider max-min fairness for a downlink two-user multi-input single-output (MISO) system with imperfect channel state information available at transmitter (CSIT) taking into account the total power constraint and the physical layer security. To handle the worst-case channel uncertainty of a potential eavesdropper (PE), we study the robust secure resource allocation algorithm which maximizes the minimum achieved worst-case secrecy rate among single-antenna legitimate users. In contrast to existing schemes adopted in the literature, we propose a rate-splitting (RS)-based secure transmission scheme, which advocates the dual use of a common message serving both as a desired message and artificial noise (AN) for legitimate users and the PE, respectively. Considering the non-convex resource allocation algorithm design problem, we apply the majorization maximization (MM) method to obtain a suboptimal iterative algorithm. In each iteration, a rank-constrained semi-definite program (SDP) is solved optimally by SDP relaxation (SDR). Simulation results demonstrate that our proposed robust secure beamforming scheme in the MISO-RS secure transmission system outperforms that of several baselines.
Non‐parametric multivariate analyses of changes in community structure

Abstract In the early 1980s, a strategy for graphical representation of multivariate (multi-species) abundance data was introduced into marine ecology by, among others, Field, et al. (1982). A decade on, it is instructive to: (i) identify which elements of this often-quoted strategy have proved most useful in practical assessment of community change resulting from pollution impact; and (ii) ask to what extent evolution of techniques in the intervening years has added self-consistency and comprehensiveness to the approach. The pivotal concept has proved to be that of a biologically-relevant definition of similarity of two samples, and its utilization mainly in simple rank form, for example ‘sample A is more similar to sample B than it is to sample C’. Statistical assumptions about the data are thus minimized and the resulting non-parametric techniques will be of very general applicability. From such a starting point, a unified framework needs to encompass: (i) the display of community patterns through clustering and ordination of samples; (ii) identification of species principally responsible for determining sample groupings; (iii) statistical tests for differences in space and time (multivariate analogues of analysis of variance, based on rank similarities); and (iv) the linking of community differences to patterns in the physical and chemical environment (the latter also dictated by rank similarities between samples). Techniques are described that bring such a framework into place, and areas in which problems remain are identified. Accumulated practical experience with these methods is discussed, in particular applications to marine benthos, and it is concluded that they have much to offer practitioners of environmental impact studies on communities.
Implementation of an IoT based Pet Care System

As pet ownership is soaring each year, the demands for a higher quality of pet care products are increasing as well. This has driven the development of the Internet of Things (IoT) technology in this field. Using the technology of IoT, pet owners can remotely track their pet's activity and location, monitor their pet's health condition or even interact with their pets. All these smart pet care products are playing an indispensable role in the pet owner's daily life. In the present project, we apply the IoT technology to implement an integrated system including pet food feeder, water dispenser, and litter box, which are the three most fundamental elements that pet owners will be concerned about when they are busy or away from their pets. The three subsystems are connected to the local network with Arduino Uno boards and Wi-Fi modules. Furthermore, the data collected from each sensor are processed and displayed on a smartphone application. Thus, pet owners through only one single interface, they can obtain all the information regarding pet's food consumption, water consumption, as well as defecation timing, duration, and frequency. Additionally, a controlling function is also enabled in the application for the pet owners to dispense food anytime and anywhere. An overall statistical chart with the mentioned values is presented in the application, updating from time to time. With this pet care system in a smartphone application, we provide pet owners an efficient, convenient and low-cost tool for pet care.