Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-RBF Networks Revisited: Robust Classification with Rejection

One of the main drawbacks of deep neural networks, like many other classifiers, is their vulnerability to adversarial attacks. An important reason for their vulnerability is assigning high confidence to regions with few or even no feature points. By feature points, we mean a nonlinear transformation of the input space extracting a meaningful representation of the input data. On the other hand, deep-RBF networks assign high confidence only to the regions containing enough feature points, but they have been discounted due to the widely-held belief that they have the vanishing gradient problem. In this paper, we revisit the deep-RBF networks by first giving a general formulation for them, and then proposing a family of cost functions thereof inspired by metric learning. In the proposed deep-RBF learning algorithm, the vanishing gradient problem does not occur. We make these networks robust to adversarial attack by adding the reject option to their output layer. Through several experiments on the MNIST dataset, we demonstrate that our proposed method not only achieves significant classification accuracy but is also very resistant to various adversarial attacks.
An Overview on Principles for Energy Efficient Robot Locomotion

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied.
Speech Denoising by Adaptive Filter LMS in the EMD Framework

This work introduces a new speech de noising method, which combines adaptive Least Mean Squares (LMS) filter with Empirical Mode Decomposition (EMD). The basic idea of this method is the signal reconstruction with IMFs previously filtered, using the adaptive (LMS) filter. The LMS algorithm has been extensively used in many applications as a consequence of its simplicity and robustness. Thanks to the data driven decomposition of the EMD, the application of the adaptive filter on the IMFs gives better results than use alone LMS filtering of the noisy signal. The proposed EMD-LMS denoising method is applied to noisy speech signal with different noise levels and the results are compared with those of the LMS filter. The obtained results show that the proposed denoising schemes perform better than the LMS filter. The study is limited to signals corrupted by additive white Gaussian noise.
Construction of the Driving Cycle of Vehicles Queuing at Toll Station

Through analysis on the data concerning the driving cycle of vehicles queuing at the expressway toll station, in combination with the proposed clustering analysis and Markov process, a space-based driving cycle model is built in relation to vehicles queuing. Through comparison with the actual driving cycle in speed-acceleration joint distribution, the model is proved to be capable of well-fitting the driving cycle of vehicles queuing. This model can provide a good analysis basis for the emission and energy consumption of vehicles queued in front of the toll station.
Roadgraph Generation and Free-Space Estimation in Unknown Structured Environments for Autonomous Vehicle Motion Planning

Automotive manufacturers and customers wish to have fully automated driving functionality available in a huge set of locations, scenarios, and markets. This raises the need for universally applicable scene understanding and motion planning algorithms that do not rely on highly accurate maps or excessive infrastructure communication. In this paper we introduce two novel approaches for extracting a topological roadgraph with possible intersection options from sensor data along with a geometric representation of the available maneuvering space. Also, a search and optimization-based path planning method for guiding the vehicle along a selected track in the roadgraph and within the free-space is presented. We compare the methods presented in simulation and show results of a test drive with a research vehicle. Our evaluations show the applicability in low speed maneuvering scenarios and the stability of the algorithms even for low quality input data.
Utilizing Spare Inverter Capacity for Distribution Grid Voltage Support: An Adaptive Control Scheme

Grid-tie inverters for connecting renewables to the grid are generally rated based on the peak power rating of the sources feeding them but, owing to the inherent variability and uncontrollability of most renewable energy sources, much of the inverter capacity is not used the majority of the time. This results in significant spare converter capacity that may be used for other purposes. This paper proposes an adaptive, plug-and-play means of utilizing this unused capacity for controlling reactive power, with the aim of providing grid voltage support on distribution networks, where reactive power control has traditionally not been economical. The method described uses an active impedance estimation algorithm to determine the gains required for reactive power controllers without requiring manual tuning of large numbers of converters. Both simulated and experimental results validating the techniques described are provided.
Posterior Cramér-Rao Lower Bound for Angle-Only Filtering in 3D

In our previous work, we compared the performance of a number of nonlinear filters for the angle-only filtering (AOF) problem in 3D using bearing and elevation measurements from a single maneuvering sensor. These filters used Cartesian coordinates and modified spherical coordinates for the state vector and were based on discrete-time dynamic and measurement models. The target followed a nearly constant velocity motion. In this paper, we compute the posterior Cramer-Rae lower bound (PCRLB) for the problem so that the performance of the nonlinear filters can be judged relative to the best possible performance. Results from Monte Carlo simulations show that as the measurement accuracy decreases, the difference between root mean square position and velocity errors and corresponding PCRLBs increases.
Impacts of supercapacitors on battery lifetime in hybrid energy storage system in building integrated photovoltaic DC micro-grid

This paper focuses on a study of an hybrid energy storage system constituted by lead acid batteries and supercapacitors and designed to work in a low voltage DC microgrid supplying by photovoltaic sources and dedicated to building application. We proposed a simulation methodology in order to evaluate supercapacitors performances and behaviors for long term analysis. We quantity their impacts on battery lifetime and on the levelized cost of electricity of our storage system. This analysis has been validated on the micro-grid developed in LAASCNRS in ADREAM building integrated photovoltaic. The power profiles used for our work correspond to 1 years production and consumption data from 100m2 floor of the building. Results of comparative studies with different load profiles, SC costs and methods to estimated battery lifetime are presented and discussed.
EveryLite: A Lightweight Scripting Language for Micro Tasks in IoT Systems

Processing the computational tasks on the devices at the edge can significantly reduce computing load, network transmission load, and response latency. However, programming on these devices is difficult due to the resource-constrained and diversity features. This paper presents a lightweight scripting language, called EveryLite, to address this issue. EveryLite features a new @-expression to access the resources on connected devices via the REST Web interfaces and focuses on the micro tasks with limited complexity in Internet of Things (IoT) systems. We design an elastic runtime environment with a core of 37 KB and some extending modules to address the IoT devices' diversity problem. Experimental results show that the applications developed by EveryLite can be run on heterogeneous devices without modification and consume less memory than those developed by other scripting languages such as Lua and Python.
In-Situ Resource Provisioning with Adaptive Scale-out for Regional IoT Services

In an era where billions of IoT devices have been deployed, edge/fog computing paradigms are attracting attention for their ability to reduce processing delays and communication overhead. In order to improve Quality of Experience (QoE) of regional IoT services that create timely geo-spatial information in response to users' queries, it is important to efficiently allocate sufficient resources based on the computational demand of each service. However since edge/fog devices are assumed to be heterogeneous (in terms of their computational power, network performance to other devices, deployment density, etc.), provisioning computational resources according to computational demand becomes a challenging constrained optimization problem. In this paper, we formulate a delay constrained regional IoT service provisioning (dcRISP) problem. dcRISP assigns computational resources of devices based on the demand of the regional IoT services in order to maximize users' QoE. We also present dcRISP+, an extension of dcRISP, that enables resource selection to extend beyond the initial area in order to satisfy increasing computational demands. We propose a provisioning algorithm, in-situ resource area selection with adaptive scale out and in-situ task scheduling based on a tabu search, to solve the dcRISP+ problem. We conducted a simulation study of a tourist area in Kyoto where 4,000 IoT devices and 3 types of IoT services were deployed. Results show that our proposed algorithms can obtain higher user QoE compared to conventional resource provisioning algorithms.
Particle Swarm Optimization Based Maximum Power Point Tracking Algorithm for Photovoltaic Energy Conversion System

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) method is one of the most popular and widely used and it has always been applied in photovoltaic energy conversion system. However, this method exhibits fluctuations among the maximum power point (MPP) due to the nature of unpredicted and changes of the environmental parameters. Therefore, it is significant to include an intelligent controller that can track the maximum peak regardless of parameters variations such as: irradiation and temperature. This paper describes the design and development of particle swarm optimization (PSO) based maximum power point tracking (MPPT) algorithm for photovoltaic energy conversion system. The proposed MPPT is simple, flexible, accurate and efficient in maximum photovoltaic power tracking. In this work, MATLAB/Simulink simulation package is used to simulate the performance of the proposed MPPT algorithm. The performance of the proposed PSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy. The simulation results demonstrate that the tracking capability of the PSO algorithm is more efficient, comparing to the traditional one, particularly under parameters variation conditions.
A Novel Architecture for Automatic Document Classification for Effective Security in Edge Computing Environments

Existing automatic document classification architectures are underpinned by the transfer of raw document data streams to the cloud computing environment for processing and analysis. This operation is expensive, has limitation to meet the real-time processing needs, and exposes potential security and privacy risks at the data transfer process. In this paper, we present a novel architecture for document classification with enhanced security that incorporates mechanism to facilitate the processing and analysis of raw data streams near the source of the data.
Centralized Multiple-View Information Fusion for Multi-Object Tracking Using Labeled Multi-Bernoulli Filters

In many applications, the states of an unknown number of objects need to be estimated using measurements that are acquired from multiple sensors with different fields of view. When object labels are part of their states, the problem is called the multi-sensor multi-object tracking problem. This paper presents a new solution for statistical fusion of multisensor information in such problems where the sensors form a centralized network. Assuming that a labeled multi-Bernoulli (LMB) filter is running at each sensor node, we suggest a new approach to fuse the multiple LMB posteriors in a centralized manner. The fused posterior is designed to incorporate all the information provided by multiple sensor nodes for each object label. Numerical experiments involving challenging multi-sensor multi-object tracking scenarios show that the proposed method outperforms the state of the art.
Toward Initiative Decision-Making for Distributed Human-Robot Teams

The brief history of human-robot teams can be traced through the changing perspective of a robot's role within the team, which has evolved from being treated as a tool to a recent shift toward the desire to have the robot act as an equal partner. While researchers have made tremendous strides in recent years, "making robots into team players" [1] that can work with humans as peers still presents a multitude of challenges. One key characteristic of a synergistic team is the ability to intervene or backup each other as necessary (e.g., when the other is underperforming). Hence, in this article, we formulate distributed human-robot teamwork in the framework of mixed-initiative interaction, which is an interaction strategy that lets the best-suited member of the team to perform the work by allowing team members to interleave their contributions to the overall team performance through opportunistic seizure and relinquishment of task initiatives. Specifically, this paper aims to address the issue of initiative decision-making - that is, when should a robot take over (relinquish) control from (to) a human teammate.
Multiclass SVM for Affect Recognition with Hardware Implementation

Emotion recognition is an interested field in the affective computing domain thanks to its important applications in Human Machine Interaction. Recent investigated studies proved the effectiveness and the relevance of physiological response as a modality to detect the human emotional states. This paper deals with an improved emotion recognition system that aims to classify the affective states into three defined areas in Arousal-Valence Space. Basing on previous works, we explored only two physiological signals, namely, electrocardiogram and respiration amplitude which were collected in the publicly available database MAHNOB-HCI. After preprocessing the signals, we extracted and normalized emotionally relevant features from these two signals. Prior to the classification stage using multiclass support vector machine as a classifier, we applied a level feature fusion. Furthermore, we implemented the classification stage on the Raspberry Pi III Model B using Python platform. In this work, we achieved 60.41 % for Arousal and 59.57% for Valence. The obtained classification rates are promising compared to recent related works.
Geo-location Based Augmented Reality Application For Cultural Heritage Using Drones

Augmented Reality (AR) already is very interesting topic for cultural heritage since it can be very easy to reconstruct the ancient building on the site without any physical intervention. With the AR technology, 3D models of ancient buildings can be overimposed to remains of the site. In this way historical sites become more interesting and appealing places. In this study, we developed a geo-location based AR application for Roman Baths of Ankara which is placed in Turkey. Novel part of this study is using drones for video source which model of the bath will be overlaid to it.
A Model-Based Approach to Simulate Rain Effects on Automotive Surround Sensor Data

Intelligent vehicles use surround sensors to perceive their local environment. However, already small errors in sensor data measurement and interpretation can lead to severe accidents, thus, testing the reliability of sensors and safety systems before market introduction is of high importance. The downside of real-world testing is that the increasing complexity of safety systems results in increased test effort and, as a consequence, longer development time. To counteract, simulation have to play a central role in future development and test of automated driving functions. It is common knowledge that adverse weather conditions such as rain degrade a sensor's performance due to absorption and scattering by water particles in the air. As a result, the effects of rain on sensor raw data can lead to incorrect environmental perception. This behavior is highly random but still needs to be replicated in the simulation environment to enable virtual tests with a high degree of realism. This work gives an overview on the related work, rain physics, and basic sensor theory. Further, we present a novel approach for simulating the effects of rain on data from camera, Iidar, and radar sensors.
Fuzzy Droop Control for Voltage Source Inverter Operating in Standalone and Grid Connected Modes

According to the electrical network stability, this study was interested in Micro-Grid (MG) which can be operate in grid-connected mode and standalone mode. The MG is composed by wind generator and battery/Supercapacitor hybrid storage system and is related with RL load. The purpose of this work is to investigate a decentralized control based on Fuzzy Droop Control (FDC) method. The objective of this control is to improve simultaneous the grid frequency and voltage and make them in optimum margins by export on import active and reactive powers exchanged with grid assuring its stability. The robustness of the proposed FDC is validated by its effect on the grid frequency and voltage variations after regulation compared with the performance of conventional droop control. According to the simulation results, it is remarkable that the levels of the voltage and frequency of the grid are well minimized and become in optimum zones.
The Analysis Between Traditional Convolution Neural Network and CapsuleNet

Convolution neural networks(CNNs) have made a series of breakthroughs in the fields of autonomous driving, robotics, medical treatment and so on. The powerful feature learning and classification abilities of CNNs have attracted wide attention of scholars. With the improvement of the networks structure and the increasing of the networks depth, the performance of tasks such as image classification, detection, recognition, and segmentation has been improved greatly. Recently, CapsuleNet, which is different from CNNs, has been proposed. In CapsuleNet, vectors replace traditional scalar points to better characterize the relationships between feature information and acquire more attributes of things. In this paper, the basic structure and algorithm principle of classical CNN models were described firstly, and then the basic neural network, network structure, parameter update and distribution process for the CapsuleNet model were analyzed and summarized. Finally, several group of training experiments were conducted to compare and analyze the image classification and feature expression canabilities of CansuleNet and CNN models.
[Regular Paper] Texture Biomarkers of Alzheimer's Disease and Disease Progression in the Mouse Retina

In this paper, we imaged the retina of wild-type and the triple-transgenic mouse model of Alzheimer’s disease (3xTg- AD) with optical coherence tomography to assess changes in the retinal tissue associated with the Alzheimer’s disease. Texture analysis allowed to identify differences between groups at the age of four months, and to find biomarkers of disease progression. Furthermore, our findings suggest that specific layers of the retina may play a fundamental role in the assessment of early changes associated with the Alzheimer’s disease.
Mutual Information-based Feature Selection Approach to Reduce High Dimension of Big Data

As increasing the massive amount of data demands effective and efficient mining strategies, practitioners and researchers are trying to develop scalable mining algorithms, machine learning algorithms and strategies to be successful data mining in turning mountains of data into nuggets. Data of high dimension significantly increases the memory storage requirements and computational costs for data analytics. Therefore, reducing dimension can mainly improve three data mining performance: speed of learning, predictive accuracy and simplicity and comprehensibility of mined result. Feature selection, data preprocessing technique, is effective and efficient in data mining, data analytics and machine learning problems particularly in high dimension reduction. Most feature selection algorithms can eliminate only irrelevant features but redundant features. Not only irrelevant features but also redundant features can degrade learning performance. Mutual information measured feature selection is proposed in this work to remove both irrelevant and redundant features.
Comparison of Machine Learning Methods for Code Smell Detection Using Reduced Features

We examine a machine learning approach for detecting common Class and Method level code smells (Data Class and God Class, Feature Envy and Long Method). The focus of the work is selection of reduced set of features that will achieve high classification accuracy. The proposed features may be used by the developers to develop better quality software since the selected features focus on the most critical parts of the code that is responsible for creation of common code smells. We obtained a high accuracy results for all four code smells using the selected features: 98.57% for Data Class, 97.86% for God Class, 99.67% for Feature Envy, and 99.76% for Long Method.
Immunoexpression of N-cadherin, Twist and Vimentin in Bladder Urothelial Carcinomas

Purpose: Epithelial mesenchymal transition consists in the acquisition of neoplastic epithelial cells of a mesenchymal phenotype the process being involved in cancers invasion and metastasis. In this study were analyzed the expression of N-cadherin, Twist and Vimentin in bladder urothelial carcinomas according to the main prognostic parameters. Material/Methods: The study included 20 bladder urothelial carcinomas which were analyzed histopathological, immunohistochemical and statistical. Results: N-cadherin was identified in 45% of cases, which belonged to high-grade carcinomas with deep invasion and lymph node metastases. Twist immunoreaction was identified in all cases and was significantly increased in advanced stages carcinomas. Vimentin was present only in the advancing edge in 25% of cases, which belonged to highly invasive carcinomas. Urothelial carcinoma metastases were N-cadherin and Twist and Vimentin negative. We found a linear positive distribution of N-cadherin and Twist values. Conclusion: the used markers are useful for identifying aggressive urothelial carcinomas in the context of reciprocal stimulation mechanisms inside of urothelial epithelial-mesenchymal transition process.