Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Autonomous Sensor Network for Multilevel Damage Identification

If there is anything industrial application can learn from nature, then it is flexibility and adaptiveness. Humanity has invented, designed and implemented a increasingly complex set of technological solutions to achieve more, make life more easy and reach further than our natural capacity allows us. However, only recently developments started to hand over the control to systems. While not disregarding the potentially negative sides of this, the concept of autonomous operation systems does exhibit a significant positive contribution to for example monitoring systems. Control of these systems includes the decision on what to monitor when and how and ultimately which action is to be taken based on the outcome of the monitoring. Normally, this is a task assigned to humans, which are generally considered as trustworthy and sufficiently flexible to recognize out of the ordinary responses. The objectiveness of the human judgment is however a weak point and the inability to recognize new, yet unknown outliers in a signal is increasingly pushed back by new technologies to resolve or circumvent this issue. Bringing this line of thought to the application of an actual application of a damage identification system for a safety critical composite structure, results in the concept of a smart autonomous sensor network using piezo-electric sensors. The use of piezo-electric sensors is crucial, due to their chameleon characteristic: they can be used as sensor, actuator and harvester. Focusing on the first two functionalities, a given set of piezo-electric transducers (PZTs) is embedded in a composite skin stiffener structure, which is excited by a shaker mimicking an operational, vibrating condition. In normal mode, the transducers measure the dynamic response of the system. Depending on several possible triggers, such as time, an impact event or anomaly identification based on the dynamic response, the mode of operation switches to a specific form of active, for example, Vibro-Acoustic Modulation (VAM) or acousto-ultrasonic (AU) measurement at a specific location in the network. All without human interference: all decisions on what to do when and how are taken by the control unit of the network. Ultimately, the outcome of the analysis of the system includes an operational control action, such as stopping the system (shut down) or limiting the maximum power. A leaner version simply issues a warning to the user that a specific component needs attention.
Interfaces Cerveau-Machine

R. Flamary 7 janvier 2015 1 / 53 Sommaire Introduction Cadre général Définitions Paradigmes et Applications Potentiels Évoqués Imagerie motrice Autres paradigmes Acquisitions ICM Mesures invasives Mesures non-invasives Traitement du signal, extraction de caractéristique Filtrage Caractéristiques Classification des signaux Méthodes de classification Validation et mesures de performances Bibliographie
Mining High Utility Itemsets using Up-Tree Algorithm

: Mining high utility itemset from XML database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing large number of candidate itemset for high utility itemsets. Such a large number of candidate itemset degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains large number of long transactions or long high utility itemsets (HUIs). The algorithm used here is UP-Growth (Utility Pattern Growth) for mining high utility itemsets with a set of techniques for pruning candidate itemsets. The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans of the database. UP-Growth not only reduces the number of candidates effectively but also out performs other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions. We can efficiently store and retrieve the data’s in and from the XML databases than relational database.
Spam in User Generated Content Platforms: Developing the HaBuT Instrument to Measure User Experience

We are in an era of user-generated content (UGC) but our understanding of the impact of UGC spam on user experience is limited. Most prior instruments to measure user experience were developed in the context of traditional spam types like web-spam or email-spam. In this paper, we develop the 15 item HaBuT scale, consisting of three sub-scales: Happiness, Burden and Trust that measures user experience with respect to UGC spam. The items in the instrument are analyzed using confirmatory factor analysis with a sample of 700 responses from internet users. This process resulted in an instrument of high reliability and validity. The instrument is a valuable tool for researchers and practitioners interested in designing, implementing, and managing systems that rely on user-generated content and to those studying the impact of UGC spam on user experience. We demonstrate a real-world application of the HaBuT scale by applying it to investigate the impact of review spam on mobile apps users. We present the study results of online experiments with 3300 participants across US, India and South Korea.
Machine Translation -- Why couldn't we do it? Why are we starting to be able to now?

Abstract While machine translation has been a long-held dream dating back to the appearance of the first computers, for many years it had not reached a level that was able to stand up to real use. However, over the past ten or so years, there have been huge advances in the technology, and machine translation is finally seeing large-scale use in real situations. This paper describes 5 major elements that have contributed to these enormous steps forward in machine translation systems.
Uso de técnicas de machine learning para realizar mapping en robótica móvil

espanolEl trabajo presentado consiste en un estudio de diversos clasificadores basados en machine learning como herramienta para llevar a cabo la tarea de mapeo y localizacion en robotica movil. En concreto, estos clasificadores son utilizados para solventar la tarea de localizacion "gruesa", la cual forma parte de los procesos a realizar para resolver la localizacion jerarquica. El proceso de localizacion llevado a cabo por el robot consistira en (1) capturar una imagen desde una posicion desconocida, (2) calcular su correspondiente descriptor de apariencia global, (3) introducir dicha informacion al clasificador y obtener la estancia en la cual se encuentra el robot en ese instante. Tras esto, (5) el robot realizara el problema de image retrieval con toda la informacion visual de entrenamiento contenida en la estancia seleccionada (localizacion - na). Este trabajo evalua el uso de tres clasificadores (Na ve Bayes, SVM y clasificador basado en red neuronal) los cuales se entrenan con tres posibles descriptores de apariencia global (HOG, gist y un descriptor obtenido a partir de una CNN). Los experimentos se llevan a cabo mediante el uso de un dataset que contiene imagenes omnidireccionales capturadas en entornos de interior y que presenta cambios dinamicos (personas andando, cambios de mobiliario, etc.). Los resultados obtenidos demuestran que el metodo propuesto es una alternativa eficiente para realizar la tarea de localizacion jerarquica en cuanto error de localizacion y tiempo de computo. EnglishThis work introduces a study regarding the use of several classi ers based on machine learning tools to carry out the mapping and localization task in mobile robotics. These classi ers are used to solve the rough localization, which is part of the hierachical localization process. Therefore, the localization tackled by the robot consists in (1) obtaining an image from an unknown position, (2) calculating its related global appearance descriptor, (3) puting this information into the classifer to estimate the current room. Afterwards, (5) the robot carries out the image retrieval problem with all the visual information provided by the training dataset contained in the selected room ( ne localization step). This work evaluates the use of three types of classi ers (Na ve Bayes, SVM and a classi er based on neural networks) which are trained with three possible global appearance descriptors (HOG, gist and a descriptor calculated from a CNN). The experiments are carried out through the use of a dataset which contains omnidirectional images captured indoor under dynamic changes (people walking, furniture changes, etc.). The results obtained show that this method proposed is an e cient alternative to tackle the hierarchical localization regarding the localization error and the computing time.
Resource Management and Scheduling in WSNs Powered by Ambient Energy Harvesting

The collaborative nature of industrial wireless sensor networks (WSNs) brings several advantages over traditional wired systems, including selforganization, rapid deployment, flexibility, and intelligent-processing capability [9]. Recently, WSNs have found their way into a wide variety of applications and systems with varying requirements and characteristics [1], [14]: ocean water monitoring and bathymetry, avalanche rescue, object tracking, remote monitoring of oil and gas reservoirs, and, preventive and predictive maintenance (PdM)1, which is considered to be an important and relevant example of the class of industrial sensor networking applications that provide measurable value in real deployments. Resource management is just as critical in the industrial environment as in other deployment scenarios. This may seem counterintuitive since most Predictive maintenance is a general term applied to a family of technologies used to monitor and assess the health status of a piece of equipment (e.g., a motor, chiller, or cooler) that are in service [14].
Bio‐Multifunctional Smart Wearable Sensors for Medical Devices

Advances in digital health care have driven innovations in high‐performance wearable and smart sensors. One requirement in this field is establishing healthy, secure, and reliable medical devices for precisely monitoring vital signs of the human body or the surrounding environment through flexible sensors with not only high‐sensing performance but also excellent biofunctionality. Smart wearable sensors with excellent biofunctionality furnish medical devices with various smart functions such as biocompatibility, biodegradability, and self‐healing, which have attracted widespread interest from device engineers and materials scientists. Herein, a comprehensive review of the latest progress concerning these smart wearable sensors is presented with a focus on bio‐multifunctional (biocompatible, biodegradable, and self‐healing) device designs. The medical applications of bio‐multifunctional smart wearable sensors are also briefly covered, and to conclude, a discussion of the challenges, opportunities, and future perspectives is provided.
Face Recognition Based on Joint Sparse Representation of Multiple Features for Public Safety

A face recognition method based on joint sparse representation of multiple features is proposed in this paper. First, principle component analysis (PCA), kernel PCA (KPCA), and non-negative matrix factorization (NMF) are used to extract feature vectors of face images. The three features could provide complementary descriptions for face images. Then, in the classification stage, joint sparse representation is employed to classify the three features thus considering their correlations. Finally, the total reconstruction errors of the three features on different kinds of training classes are calculated to determine the label of test sample. Experiments are conducted on AR and Yale-B databases to validate the effectiveness of the proposed method.
Cloud Computing Resource Load Forecasting based on Bat Algorithm Optimized SVM

For the problem of resource load forecasting in cloud computing, the optimized bat algorithm is combined with SVM for forecasting. Firstly, the bat algorithm adopts the reverse learning strategy for population initialization, and secondly, the weighting factor in the particle swarm optimization is used for individual optimization. Finally, the individual is selected using the Gaussian mutation method. Two important parameters in the SVM are optimized using the improved algorithm. In the simulation experiment, the SVM is optimized by the particle swarm optimization and compared with the genetic algorithm optimizing SVM, and a better forecasting effect is obtained.
Solving Future Different-Layer Nonlinear and Linear Equation System Using New Eight-Node DZNN Model

In this article, a future different-layer nonlinear and linear equation system (DLNLES) is investigated. First, based on a zeroing neural network (ZNN) method, a zeroing equivalency theorem is proposed. Then, a continuous ZNN (CZNN) model is developed for continuous DLNLES solving. Next, a new eight-node Zhang et al. discretization formula is proposed to discretize the CZNN model, and thus, an eight-node discrete ZNN (DZNN) model is proposed for the future DLNLES solving. Five-node and four-node DZNN models are also developed for the same problem solving. Besides, numerical experiments are executed to substantiate the validity and superiority of the proposed eight-node DZNN model. Finally, the path-tracking control problem of a four-link redundant robot arm is formulated as a specific future DLNLES problem and can, thus, be solved by the three DZNN models. Comparative numerical results further indicate that the proposed eight-node DZNN model is much superior to the other two DZNN models.
Segmentation of multispectral satellite images based on seeded region growing and instance-based learning

In order to reach the balance between the fulfillment of human needs and the protection of the environment, it is necessary to have detailed and accurate information about natural resources. Such information can be obtained through thematic maps, a product of remote sensing. In remote sensing, the generation of accurate thematic maps presents many research challenges, being one of them, image segmentation. In this thesis, a novel segmentation algorithm based on seeded region growing and instance based learning is proposed. The algorithm includes a novel automatic seed generation approach that uses a histograms analysis, a new weighted instance-based learning algorithm (WIBK) which obtains one or more weights per feature per class, a novel region growing algorithm (SRG-WIBK) that uses WIBK as decision criteria, and a novel region-merging scheme based on ownership tables which allows to merge regions according to user needs. The WIBK algorithm was experimentally evaluated on several databases from the UCI repository, and compared against instance-based and non instance-based learning algorithms showing a very competitive performance. The SRG-WIBK algorithm was tested on multispectral synthetic images and compared against the algorithms implemented in the ERDAS software showing very even results.
Energy Efficient Virtual Machine Placement in Data Center

The rising trend of e-commerce and cloud computing are just two reasons for the growing demand for Data Centers (DCs). DCs are among the largest global energy consumers in relation to the total global energy consumption. The rising numbers of DCs are an increasingly negative impact on the environment. This may be caused by the DCs themselves as well as by the power generation that is needed by the DCs. Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem to achieve multiple goals. To overcome those problems we design the allocator in order to accept as many VM requests as possible, taking into account the power consumption of the network devices. It can be covered through various approaches such as allocator policy (Best Fit/Worst Fit), allocation strategy (Single/Multi-objective optimization), and network resources. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion.
Network on Chip Low-power Mapping Method based on Tabu Search Genetic Algorithm

In order to effectively map more and more complex application tasks to the network on chip processing unit to complete the related tasks with less energy consumption, a new low power mapping method with a combination of genetic algorithm and tabu search algorithm is proposed. The method makes full use of the powerful global search ability of the genetic algorithm, and combines the local search ability of the tabu search and the prominent mountain turning features to compensate for the weak local search ability and premature defects of the genetic algorithm, so as to achieve better low power consumption effect on NoC. The experimental results show that under the same experimental platform and power consumption model, the new algorithm has a great energy efficiency improvement compared with the early genetic algorithm, and has energy efficiency advantages compared with the later improved MGA and AGA algorithms.
Trading Memory versus Workload Overhead in Graph Pattern Matching on Multiprocessor Systems

Graph pattern matching (GPM) is a core primitive in graph analysis with many applications. Efficient processing of GPM on modern NUMA systems poses several challenges, such as an intelligent storage of the graph itself or keeping track of vertex locality information. During query processing, intermediate results need to be communicated, but target partitions are not always directly identifiable, which requires all workers to scan for requested vertices. To optimize this performance bottleneck, we introduce a Bloom filter based workload reduction approach and discuss the benefits and drawbacks of different implementations. Furthermore, we show the trade-offs between invested memory and performance gain, compared to fully redundant storage.
An improved solution for deformation simulation of nonorthotropic geometric models

Physically based deformation simulation has been studied for many years in computer graphics. In order to simulate more complex geometric models and better meet the designer's requirements, many anisotropic approaches have been proposed in recent years. However, most of the approaches focus on simulating orthotropic models. In comparison with orthotropic models, nonorthotropic ones allow the objects to have anisotropic behaviors along nonorthogonal directions. In this paper, we introduce an improved approach to simulate nonorthotropic geometric models under large deformation. The improvements are mainly twofold. First, a frame field is specified on a given undeformed object, that is, each point of the object is equipped with a frame. In each local frame, we construct three independent vectors and form a nonorthogonal coordinate. Second, we design the deformation properties along each axis in the local nonorthogonal coordinate to get a local constitutive model. The final nonorthotropic model is generated by transforming the designed model from local nonorthogonal coordinates to the global standard Cartesian coordinate. To improve the stability, we introduce a time‐varying method to simultaneously track the local coordinates reorientation by pushing forward the original frame field to the deformed frame field. Experiments show that the deformation simulation using the designed nonorthotropic models exhibits anisotropic behaviors along different directions and are more stable than previous methods.
IMPLEMENTATION OF DUAL-CORE MULTITHREADED PROCESSOR ON XILINX SPARTAN-III FPGA

Technology is emerging at a very rapid way and the need for smaller and faster systems has been increasing today at an exponential rate along with time. This need for design and implementation of new systems at a low time to market has given rise to the concept of the digital system. The enhancement to the speed of operation of a system majorly depends on the speed of operation of the core-processing unit. The enhancement to the speed of operation of a processor comes to a bottleneck for designer while realizing multitasking of operation sequentially. The major difficulty arises with the synchronization of data operation. This paper work aims towards the realization of a fast processing 5-stage dual core pipelined microprocessor for the enhancement of operational efficiency of a system. The processor is implemented with 5-stage fine parallelism controlled using the multithreading concept. The proposed task is implemented using VHDL language and simulated using Modelsim tool for functional verification. The synthesis, route and placement and Floor planning of the implemented design are carried out on XILINX Spartan-III FPGA.
Internal Model Control of PMSM Position Servo System: Theory and Experimental Results

Much recently, an internal model approach from output regulation theory has been adopted to solve the speed tracking control problem of the permanent magnet synchronous motor (PMSM) system on simulation level. A striking advantage of this approach is that it can achieve multiple goals including trajectory tracking, disturbance rejection, and robustness simultaneously. This article further studies a position tracking control problem of the PMSM system under nonlinear load torque disturbance. A nonlinear internal model based output feedback controller is proposed, which can achieve exact position tracking and allow certain parameter uncertainties. Besides simulation results, a real-time experimental setup is built and experimental results are provided to illustrate the effectiveness of the proposed controller. It is worth mentioning that the proposed controller can lead to a high precision position tracking performance under nonlinear load torque disturbance.
Visualization of a two-dimensional tree modeling using fractal based on L-system

The purpose of this paper is to present a visualization of two-dimensional trees using fractals and L-Systems. Grammar in L-System consisting of letters, axioms, and production rules can be used to construct trees in various forms according to the production rules used. With the formation of grammar from the tree pattern in a fractional manner, the grammar can be reused to construct other tree patterns at a higher level. The iterations made on certain production rules visually affect the tree model. Experiments were carried out on the L-System generator to see the results of tree visualization by testing various grammars. Testing was done by trial and error in production rules to get a realistic visualization of a two-dimensional tree model. From the experimental results produced a variety of grammar, where modifications to the production rules produced different tree models. Visually, from the iteration results, the higher the iteration value, the two-dimensional tree was not formed properly. When the iteration value or n > 5 the two-dimensional tree model results from an iteration it lost detail from the branch.