Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Investigation and Controller Development for Variable Speed Wind-Driven DFIG

This paper investigates the performance of wind-driven Doubly fed Induction Generator (DFIG) under sub-synchronous and super-synchronous speed. A simple controller for DFIG has been proposed using low cost 555 timer ICs, flip-flops and an IGBT inverter. The controller can be adjusts the frequency of inverter pulse to obtain a desired frequency despite the variations in the rotor speed of the DFIG. A prototype of the system has been built by fabricating the controller and a three-phase IGBT bridge inverter. The controller has been tested on a 4-pole, three-phase, 400V, 5kW, delta connected prototype machine driven by a DC motor under wide speed operation. The simplified steady state analysis of the system is presented. The results of the both calculated and experimental are furnished in the paper.
Kernel and Moment Based Prediction and Planning : Applications to Robotics and Natural Language Processing

This thesis focuses on moment and kernel-based methods for applications in Robotics and Natural Language Processing. Kernel and moment-based learning leverage information about correlated data that allow the design of compact representations and efficient learning algorithms. We explore kernel algorithms for planning by leveraging inherently continuous properties of reproducing kernel Hilbert spaces. We introduce a kernel based robot motion planner based on gradient optimization, in a space of smooth trajectories, a reproducing kernel Hilbert space. We further study a kernel-based approach in the context of prediction, for learning a generative model, and in the context of planning for learning to interact with a controlled process. Our work on moment-based learning can be decomposed into two main branches: spectral techniques and anchor-based methods. Spectral learning describes a more expressive model, which implicitly uses hidden state variables. We use it as a means to obtain a more expressive predictive model that we can use to learn to control an interactive agent, in the context of reinforcement learning. We propose a combination of predictive representations with deep reinforcement learning to produce a recurrent network that is able to learn continuous policies under partial observability. We introduce an efficient end-to-end learning algorithm that is able to maximize cumulative reward while minimizing prediction error. We apply this approach to several continuous observation and action environments. Anchor learning, on the other hand, provides an explicit form of representing state variables, by relating states to unambiguous observations. We rely on anchor-based techniques to provide a form of explicitly recovering the model parameters, in particular when states have a discrete representation such as in many Natural Language Processing tasks. This family of methods provides an easier form of integrating supervised information during the learning process. We apply anchor-based algorithms on word labelling tasks in Natural Language Processing, namely semi-supervised part-of-speech tagging where annotations are learned from a large amount of raw text and a small annotated corpus.
ENGLISH AS A LINGUA FRANCA IN EUROPE: CHALLENGES FOR APPLIED LINGUISTICS

The omnipresence of English in Europe has led to numerous discussions about its widespread functions and special status compared to all other European languages. Yet, many of these discussions conceive of Europe as a group of nation states where English is either a first or a foreign language. This chapter seeks to question this well-established distinction by investigating what is in fact the most common use of English in Europe, namely English as a lingua franca (ELF). The chapter suggests a different way of conceptualizing the language in European contexts and provides an updated overview of empirical research into its lingua franca use. Examples of a particular approach to ELF research are provided in the form of two case studies focussing on different aspects of ELF interactions. These studies demonstrate how users of ELF exploit the possibilities intrinsic in the language to achieve their own communicative purposes. Finally, the chapter highlights some future directions for linguistic research and addresses the challenges that the emergence of ELF poses for various areas of applied linguistics.
Enhanced Confidentiality Multi-Keyword Top-k Identical Search Over Encrypted Data

: Cloud computing provides people and enterprises huge computing power and climbable storage capacities to support a spread of massive information applications in domains like health care and research, thus a lot of and a lot of information homeowners area unit concerned to source their information on cloud servers for nice convenience in information management and mining. However, information sets like health records in electronic documents sometimes contain sensitive info, that brings concerning privacy considerations if the documents area unit discharged or shared to part untrusted third-parties in cloud. A sensible and wide used technique for information privacy preservation is to cipher information before outsourcing to the cloud servers, that but reduces {the information|the info|the information} utility and makes several ancient data analytic operators like keyword-based top-k document retrieval obsolete. during this paper, we tend to investigate the multi-keyword top-k search downside for large encoding against privacy breaches, ANd commit to establish an economical and secure answer to the current downside. Specifically, for the privacy concern of question information, we tend to construct a special tree-based index structure and style a random traversal rule, that makes even a similar question to provide completely different visiting methods on the index, and might conjointly maintain the accuracy of queries unchanged underneath stronger privacy.
Breast Cancer Classification and Biomarker Discovery from Microarray Data Using Silhouette Statistics and Genetic Algorithms

Discriminating heterogeneous cancers by microarrays is a topic of much interest in bioinformatics. A number of methods have been proposed and successfully applied to this problem. In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as discriminant function to classify breast cancers for biomarker discovery. Distance metrics and classification rules based on silhouette statistics have also been discussed to improve our algorithms for high classification accuracy. Finally, the proposed method is compared to previously published methods. Many experimental results show that our method is effective to discriminate breast cancer subtypes and find many potential biomarkers to help cancer diagnosis.
Change management for very large software systems

The INFUSE change-management facility's methodology is briefly explained and its use of a hierarchy of experimental databases for controlling and coordinating changes is described. The authors then present the algorithm which INFUSE uses to automatically build and maintain this hierarchy. Some results of the application of INFUSE are given; one presented hierarchy is similar to one independently identified by a human expert. <<ETX>>
Neural networks applied to the classification of spectral features for automatic modulation recognition

The use of back-error propagation neural networks for the automatic modulation recognition (AMR) of an intercepted signal is demonstrated. In all, ten modulation types are considered and a variety of spectral preprocessors are investigated for feature extraction. For the given training and test sets, the Welch periodogram is found to give the best results. For classification, experimental results show that neural networks match and even outdo the performance of the conventional k-nearest neighbor (k-NN) classifier for this preprocessor. Moreover, optimization of selected neural networks is demonstrated using the optimal brain damage (OBD) pruning technique. <<ETX>>
Improve Security of Cloud Storage by Using Third Parity Authentication, One Time Password and Modified AES Encryption Algorithm

Cloud computing is a new term to provide application and hardware as service over the internet. Demand for cloud has increased dramatically in recent years. However, a major drawback for cloud adoption is lack of security so that we will try to solve some security issues related to cloud storage by design and implement a secure system to store privet data in cloud storage. This secure system provide secure login to cloud by using third parity authentication (smart phone) and one time password depend on chaotic system to prevent unauthorized people from get access to cloud and modified AES algorithms to encrypt the data in the cloud storage.
Burdett-Mortensen Model of On-the-Job Search with Two Sectors

The focus of this paper is on the steady state of a two-sector economy with undirected search where employed and unemployed workers can search for jobs, both within a sector and between the sectors. As in the one-sector model, on-the-job search generates wage dispersion among homogeneous workers. The analysis of the two-sector model uncovers a property called constant tension that is responsible for analytical tractability. We characterize the steady state in all cases with constant tension. When time discounting vanishes, constant tension yields the endogenous separation rate in each sector as a linear function of the present value for a worker. The one-sector economy automatically satisfies constant tension, in which case the linear separation rate implies that equilibrium offers of the worker value are uniformly distributed. Constant tension also has strong predictions for worker transitions and value/wage dispersion, both within a sector and between the two sectors. When constant tension does not hold, we compute the steady state numerically and illustrate its properties.
Modeling Under MATLAB by ANFIS of Three-Phase Tetrahedral Transformer Using in Microwave Generator for Three Magnetrons Per Phase

This work deals with the modeling of a new three-phase tetrahedral transformer of HV power supply, which feeds three magnetrons per phase. The design of this new power supply is composed of three single-phase with magnetic shunt transformers coupling in star; each one is size to feed voltage-doubling cells, thereby feeds a magnetron. In order to validate the functionality of this power supply, we simulate it under Matlab-Simulink environment. Thus, we modeled nonlinear inductance using a new approach of neuro-fuzzy (ANFIS); this method based on the interpolation of the curve B(H) of ferromagnetic material, the results obtained gives forms of both voltages and currents, which shows that they are in accordance with those of experimental tests, respecting the conditions recommended by the magnetron manufacturer
DPCM with a recurrent neural network predictor for image compression

A new predictor for DPCM based on a recurrent neural network is proposed. The proposed predictor which uses the bilinear recurrent neural network (BLRNN), has shown good performance for time-series prediction problems, and it is applied to DPCM for image compression with predictive coding. The performance of DPCM with BLRNN predictor is compared with the conventional DPCM with different predictors, such as the linear predictor and median predictor. The results show that the proposed method gives improved results over the conventional DPCM with linear predictor or median predictor in terms of PSNR or reconstructed images.
Distribution error in quantitative digital image analysis systems and its correction algorithm

One of the major sources of the coefficient of variation in quantitative digital image analysis systems is the distribution error. It is caused by uneven optical density distribution within the measuring area. In digital image analysis systems, the measuring area is an individual pixel. The distribution error would be eliminated if we could measure the average optical density within the measuring area. However, most cameras used with light microscopes measure the average transmittance of a pixel. The image analysis system then converts the measured transmittance into an optical density. If the optical density distribution within a pixel is not uniform, the average optical density converted from the measured transmittance is different from the true optical density of the pixel. The authors call this conversional distribution error (CDE). They have analyzed and characterized this error, and developed an algorithm to minimize CDE by estimating the optical density distribution within a pixel. <<ETX>>
Monte Carlo simulations of surface clutter in GPR scenarios

The clutter caused by scattering from a rough air-ground interface is analyzed numerically. The simulations have been done using Monte Carlo approach. Statistical properties of the scattered field have been analyzed. It has been found that the phase of the reflected electromagnetic field nearby a rough air-ground interface follows the profile of the interface and this phase modulation of the reflected field is responsible for the forming of the clutter. Furthermore it is demonstrated that the correlation function of the phase of the reflected field coincides with the correlation function of the rough surface, while for the correlation of the reflected field it is valid only if the magnitude of the surface clutter is considerably less than the mean value of the ground reflection.
Quality-time-complexity universal intelligence measurement

Purpose With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method. Design/methodology/approach This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents. Findings By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment. Originality/value This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.
Virtual Machine Migration through an Intelligent Mobile Agents System for a Cloud Grid

During the last years, Virtual Machine technologies have become more and more widespread. Possible Virtual Machine integration is on the Cluster Grid Computing environment. So, the implemented idea described on this paper is based on Virtual Machine computing service. For these reasons, the main purpose of this research is to validate integration functionalities and performances of a Load Balancing System into a Cloud Grid with Virtual Compute Servers. Tests have focused on the restart of a batch job after a hardware crash and on the stopping of a job or its transfer to another host for hardware maintenance. A new algorithm has been created for automatic migration of a job, from a low performance node to a higher performance node.
Propose retina identification system based on the combination of SURF detector and BRISK descriptor

In this paper the design of hybrid retina matching algorithm that is used in identification systems is considered. Retina based recognition is apparent as the most secure method for identification of an identity utilized to differentiate persons. The characteristics of Speeded up Robust Feature (SURF) and Binary Robust Invariant Scalable Key-Points (BRISK) algorithm have been used in order to produce a fast matching algorithm than the classical ones, those characteristics are important for real-time applications which usually need quick processing of a growing quantity of data. The algorithm is divided into three stages: retinal image processing and segmentation, extracting the local features from the retinal images using SURF and BRISK descriptors and finally matching those features using KNN matcher. The algorithm was trained with structured analysis of retina (STARE) database giving accuracy that reach 100%.
Evaluating Password Behavior at a Small University

No matter how sophisticated an organization’s security system is, it remains vulnerable due to the human factor. In this study, we surveyed and analyzed the patterns practiced by users when generating passwords at a small-sized university. We found that users are not as aware of security requirements and practices as they think. Moreover, the vast majority of users’ passwords are breakable within days or shorter. Interestingly, we found that the use of numbers and uppercase letters is prevalent among users. However, numbers are mostly used at the end of the passwords and uppercase letters are mostly used at the beginning of passwords. The existence of such trends makes it easier for attackers to generate more effective dictionaries. Based on the analysis in this study, we make recommendations to the IT department to improve the password policy. Additionally, we provide recommendations to the faculty, staff, and students on how to strengthen their passwords.
Using virtual data for training deep model for hand gesture recognition

Deep learning has shown real promise for the classification efficiency for hand gesture recognition problems. In this paper, the authors present experimental results for a deeply-trained model for hand gesture recognition through the use of hand images. The authors have trained two deep convolutional neural networks. The first architecture produces the hand position as a 2D-vector by input hand image. The second one predicts the hand gesture class for the input image. The first proposed architecture produces state of the art results with an accuracy rate of 89% and the second architecture with split input produces accuracy rate of 85.2%. In this paper, the authors also propose using virtual data for training a supervised deep model. Such technique is aimed to avoid using original labelled images in the training process. The interest of this method in data preparation is motivated by the need to overcome one of the main challenges of deep supervised learning: using a copious amount of labelled data during training.
Ultra Low Power Circuits for Internet of Things and Deep Learning Accelerator Design with In-Memory Computing

Collecting data from environment and converting gathered data into information is the key idea of Internet of Things (IoT). Miniaturized sensing devices enable the idea for many applications including health monitoring, industrial sensing, and so on. Sensing devices typically have small form factor and thus, low battery capacity, but at the same time, require long life time for continuous monitoring and least frequent battery replacement. This thesis introduces three analog circuit design techniques featuring ultra-low power consumption for such requirements: (1) An ultra-low power resistor-less current reference circuit, (2) A 110nW resistive frequency locked on-chip oscillator as a timing reference, (3) A resonant current-mode wireless power receiver and battery charger for implantable systems. Raw data can be efficiently transformed into useful information using deep learning. However deep learning requires tremendous amount of computation by its nature, and thus, an energy efficient deep learning hardware is highly demanded to fully utilize this algorithm in various applications. This thesis also presents a pulse-width based computation concept which utilizes in-memory computing of SRAM.
Exploiting Structure for Scalable and Robust Deep Learning

Deep learning has seen great success training deep neural networks for complex prediction problems, such as large-scale image recognition, short-term time-series forecasting, and learning behavioral models for games with simple dynamics. However, neural networks have a number of weaknesses: 1) they are not sample-efficient and 2) they are often not robust against (adversarial) input perturbations. Hence, it is challenging to train neural networks for problems with exponential complexity, such as multi-agent games, complex long-term spatiotemporal dynamics, or noisy high-resolution image data. This thesis contributes methods to improve the sample efficiency, expressive power, and robustness of neural networks, by exploiting various forms of low-dimensional structure, such as spatiotemporal hierarchy and multi-agent coordination. We show the effectiveness of this approach in multiple learning paradigms: in both the supervised learning (e.g., imitation learning) and reinforcement learning settings. First, we introduce hierarchical neural networks that model both short-term actions and long-term goals from data, and can learn human-level behavioral models for spatiotemporal multi-agent games, such as basketball, using imitation learning. Second, in reinforcement learning, we show that behavioral policies with a hierarchical latent structure can efficiently learn forms of multi-agent coordination, which enables a form of structured exploration for faster learning. Third, we showcase tensor-train recurrent neural networks that can model high-order mutliplicative structure in dynamical systems (e.g., Lorenz dynamics). We show that this model class gives state-of-the-art long-term forecasting performance with very long time horizons for both simulation and real-world traffic and climate data. Finally, we demonstrate two methods for neural network robustness: 1) stability training, a form of stochastic data augmentation to make neural networks more robust, and 2) neural fingerprinting, a method that detects adversarial examples by validating the network’s behavior in the neighborhood of any given input. In sum, this thesis takes a step to enable machine learning for the next scale of problem complexity, such as rich spatiotemporal multi-agent games and large-scale robust predictions.
Real-time cloud sensing for efficiency improvement of optical high-resolution satellite remote sensing

Cloud sensing provides the ability to point the optical axis of the satellite remote sensing instrument to cloud-free areas on the basis of previously acquired cloud cover information. In this way the effective imaging capacity of the mission can be improved. The cloud cover information may be acquired in real-time for optimal performance. This paper explores a number of real-time cloud sensing concepts. The performance of the different options is assessed by analysis and simulation. It is concluded that the imaging capacity of a high-resolution optical remote sensing satellite can be improved by up to 100%.
Placement of a Team of Surveillance Vehicles Subject to Navigation Failures

We consider the problem of performing surveillance of a region with vehicles subject to failures that prevent them from navigating through the environment. This work is based on a Voronoi coverage control problem formulation. In this formulation, failed vehicles can cause other vehicles to be trapped in a con guration that provides poor coverage of the region. To avoid this, each vehicle performs self-monitoring to detect failures. Upon detection of a failure, a negotiation mechanism is triggered and vehicles compare variations of a performance measure to decide whether when it is bene cial to help another vehicle. The proposed approach is evaluated experimentally using a eet of three wheeled mobile robots.