Miracle-dz/UniFAR-FTUs huggingface dataset:
Second Law Analysis of a Reactive MHD Couple Stress Fluid Through Porous Medium

In this work, effect of magnetic field on the entropy generation rate of a reactive couple stress fluid through porous medium is investigated. The equations governing the fluid flow are formulated, nondimensionalised and solved using the rapidly convergent semi-analytical Adomian decomposition method (ADM). The obtained velocity and temperature profiles are utilised to compute the entropy generation rate, irreversibility ratio and Bejan number. The effects of pertinent flow parameters on velocity, temperature, entropy generation rate and Bejan number are analyzed graphically��
DATA ARRANGEMENT AND GENERATION USING DEEP BELIEF NETWORK

According to research printed on eMarketer, about 70 five percent inside the content printed by Facebook users contains photos. The very best data from various modalities will likely have semantic correlations. Most of the existing works make use of a bag-of-words to model textual information. Because we advise having a Fisher kernel framework to represent the textual information, we put it on aggregate the SIFT descriptors of images. We advise to incorporate continuous word representations to deal with semantic textual similarities and adopted for mix-media retrieval. The dwelling block inside the network located in the job could be the Gaussian restricted Boltzmann machine. However, Fisher vectors are frequently high dimensional and dense. It limits the usages of FVs for giant-scale applications, where computational requirement should be studied. Finally, hamming distance allows you to determine the similarities concerning the hash codes inside the converted FV as well as other hash codes of images. We look at the recommended method SCMH on three generally used data sets. SCMH achieves better results than condition-of-the-art methods with a few other the lengths of hash codes. A Skip-gram model was placed on produce these 300-dimensional vectors for a lot of million keywords. For generating Fisher vectors, we utilize the implementation of INRIA. In this work, we compare the key factor entire recommended approach as well as other hashing learning methods. Although the offline stage inside the recommended framework requires massive computation cost, the computational complexity of internet stage is small or much like other hashing methods.
Interpreter and Applied Development Environment for Learning Concepts of Object Oriented Programming

The programing languages are classified by its paradigms, some paradigms are easier to understand than others, or at least we have that impression. But when we learn our first language, what paradigm is the best? Many people could say that structured is better, because is a recipe to follow, or maybe an object oriented, because it-s a natural definition, others could say that functional is better. Actually the debate of which should be the first language to learn is growing, and it will continue growing. Nowadays we decide which language to learn as a result of a necessity, more than analyse which one is easier to understand or which should be the correct learning process. This paper will present a tool in Spanish that could be used as an instrument to understand these concepts of object oriented and its management before starting the programming.
Advanced Control Design of an Autonomous Line Painting Robot

Painting still plays a fundamental role in communication nowadays. For example, the paint on the road, called road surface marking, guides the traffic in order and maintains the high efficiency of the entire modern traffic system. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, the mechanical and electronic design of a Painting Robot was discussed. The overall design was to fulfill the requirements of the line painting. Computer vision techniques were applied to this thesis since the camera was selected as the major sensor of the robot. Advanced control theory was introduced to this thesis as well. Three different controllers were developed. The Proportional-Integral (PI) controller with an anti-windup feature was designed to overcome the drawbacks of the traditional PI controller. Model Reference Adaptive Control (MRAC) was introduced into this thesis to deal with the uncertainties of the system. At last, the hybrid PI-MRAC controller was implemented to maintain the advantages of both PI and MRAC approaches. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot. Advanced Control Design of an Autonomous Line Painting Robot Mincan Cao GENERAL AUDIENCE ABSTRACT Painting still plays a fundamental role in communication nowadays. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, a Painting Robot with a two-camera system was designed. Computer vision techniques and advanced control theory were introduced into this thesis. Three different controllers were developed, including Proportional-Integral (PI) with an anti-windup feature, Model Reference Adaptive Control (MRAC) and the hybrid PI-MRAC. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot.
Classification of Two Comic Books based on Convolutional Neural Networks

Unphotographic images are the powerful representations described various situations. Thus, understanding intellectual products such as comics and picture books is one of the important topics in the field of artificial intelligence. Hence, stepwise analysis of a comic story, i.e., features of a part of the image, information features, features relating to continuous scene etc., was pursued. Especially, the length and each scene of four-scene comics are limited so as to ensure a clear interpretation of the contents. In this study, as the first step in this direction, the problem to classify two four-scene comics by the same artists were focused as the example. Several classifiers were constructed by utilizing a Convolutional Neural Network(CNN), and the results of classification by a human annotator and by a computational method were compared. From these experiments, we have clearly shown that CNN is efficient way to classify unphotographic gray scaled images and found that characteristic features of images to classify incorrectly.
OGC City Geography Markup Language (CityGML) Encoding Standard

CityGML is an open data model and XML-based format for the storage and exchange of virtual 3D city models. It is an application schema for the Geography Markup Language version 3.1. 1 (GML3), the extendible international standard for spatial data exchange issued by the Open Geospatial Consortium (OGC) and the ISO TC211. The aim of the development of CityGML is to reach a common definition of the basic entities, attributes, and relations of a 3D city model. This is especially important with respect to the cost-effective sustainable maintenance of 3D city models, allowing the reuse of the same data in different application fields.
Cost-Sensitive Ensemble via Adaptive Weighted Cost Proportionate Sampling

Cost Proportionate Sampling (CPS) has proved to be a feasible and highly efficient cost-sensitive meta-learning technique, however, current cost-sensitive meta-learning techniques based on CPS suffer the problem of stationary sampling probability which will decrease the diversity between samples and result in low efficiency for ensemble. This paper presents a novel CPS technique named Adaptive Weighted Cost Proportionate Sampling (AWCPS), which works by introducing an adaptive selection weight as a balance factor during CPS process to diversify the samples as well as maintaining the cost-sensitivity. Based on AWCPS, we further propose a novel cost-sensitive metalearning algorithm named Cost Sensitive Ensemble via AWCPS (CSEA). The experiments on public large, real datasets demonstrate that compared to current cost-sensitive algorithms based on CPS, CSEA can obtain significant advantage in degrading the cumulative costs as well as improve the classification accuracy.
LARY_CR: Software package for the Analysis of Cardio Vascularand Respiratory Rhythms, in the SCILAB_SCICOS environment

LARY\_CR is a software package dedicated to the study of cardiovascular and respiratory rhythms, developed in the SCILAB\_SCICOS scientific environment. It presents signal processing methods, from events detection on raw signals to the variability analysis of the resulting time series. The events detection concerns the heart beat recognition on the electrocardio- gram, defining the RR time series, the maxima and minima on the arterial blood pressure defining the systolic and diastolic time series. These detections are followed by the resampling of the time series then their analyse. This analyse uses temporal and time frequency methods: Fourier Transform, spectral gain between the cardiac and blood pressure series, Smooth Pseudo Wigner\_Ville Distribution, Complex DeModulation, temporal method of the cardiovascular Sequences. This software is aiming to give some tools for studying the autonomic nervous system, acting in particular in the baroreflex loop; its functionnin- g is reflected by the cardiovascular variabilities and their relationships with the other physiological signals, especially the respiratory activity.
Performance Analysis of Cache Consistency Maintenance in Mobile Environment using Agent Technique

This paper illustrates three Agents Based Techniques namely, Thread Based Cache Consistency Model, Multiple Server Cache Consistency Model, and Cluster Based Cache Consistency Models used to solve the problems of cache consistency maintenance and also compared with existing method. The drawbacks of existing method are latency, excessive bandwidth wastage, query delay, server overloads as well as network traffic. In the Thread Based Cache Consistency Model, the log is maintained in both server and client, it is easy for server as well as client to make their data object reliable. Agents at server and client make an internal request response operation(s) to access or update data object. It is easy to update the cached data and as well to maintain consistency between client and server. The Multiple Server Cache Consistency Models where the node is designed as a middle server (master client cache) between server and clients, some of the client data cache is placed in a middle server. The middle server is near the client, as result the communication cost and energy consumption is comparatively less. It reduces server’s workload. The Cluster Based Cache Consistency Model designed as a Cluster head node between the server and the client using agent technique, effectively reduces the query latency. These three Agent Based Models are implemented in Ns2 and their performance was analysed and compared with existing method. Agent Based Technique resulted in better performance in relation to update delay, through various numbers of nodes and update rate.
Pre-processing, Registration and Quality Assessment of Adaptive Optics Assisted Retinal Images

In recent decades, adaptive optics (AO) technology has been embedded into retinal imaging devices, producing a new generation of instrument, which can provide retinal images with cellular resolution. This new technological advancement allows viewing of retinal microscopic structure, which is of great significance for the diagnosis, and subsequent treatment monitoring, of retinal pathologies that can result in visual loss. Developing compact and simplified AO assisted retinal imaging devices with an automated feature analysis is the current focus of interest for transferring AO technology to clinical use. In this study, we present an enhanced processing of sequences of retinal images obtained using an AO flood illumination system. We aim to provide image processing techniques for pre-processing, assessing the quality and image registration of cone photoreceptor and retinal nerve fiber layer (RNFL) images. Our results demonstrate the effectiveness of a wavelet based approach to correcting uneven illumination and automatic evaluation of image quality in terms of the results of image registration. In particular, we present the significance of image quality analysis while selecting a certain percentage of the sharpest images in a sequence for image registration. In order to register the images, we include methods that are specifically developed to measure tiny rotations in addition with correlation based techniques to correct for translational motion. We show that correcting for small rotations exhibits a significant improvement, especially at the edges of the image, which is important for creating larger mosaics. We then present the methods of investigating the characteristics of retinal nerve fiber bundles to discriminate RNFL images with good and poor striation. This enables feature comparison between healthy and glaucoma eyes. Finally, we discuss the implications of our results and possible future studies.
Path planning in a 2-D known space using neural networks and skeletonization

A neural network and a skeletonization based path planning in a 2D known space is presented. For the neural network path planning approach a Kohonen self-organizing net has been chosen, while for the skeletonization Kwok's method (1988) was used. The output of the network represents a reduced representation of the free space available for robotic movement in a 2D known environment.
A SOFTWARE PROTECTION METHOD BASED ON TIME-SENSITIVE CODE AND SELF-MODIFICATION MECHANISM

This paper proposes a systematic method for protecting software against malicious reverse engineering attacks. Our method aims to increase the cost of obtaining secret information in a program on the assumption that the adversaries have the ability to perform dynamic analysis as much as static analysis. A program protected by our method contains many time-sensitive codes, which are overwritten with fake (dummy) codes. Each time-sensitive code is modified during execution via self-modification according to the time taken to execute a designated block of the program. If the execution time of the block is within the predetermined range, the time-sensitive code becomes the original one. On the other hand, if the execution time is out of the range, the time-sensitive code becomes the other fake one. In order to obtain the secret information by static analysis, the adversary must find the routines that modify timesensitive codes which are scattered over the program, and must guess the predetermined valid execution time of the target blocks. In order to obtain the secret information by dynamic analysis, the adversary must make the execution reach the restricted points of the program without stopping the execution. As a result, our method helps to construct highly invulnerable software.
Location Sensing Using Bluetooth for GPS Suppression

LOCATION SENSING USING BLUETOOTH FOR GPS SUPPRESSION Nicholas Mair Advisor: University of Guelph, 2012 Professor Qusay H. Mahmoud With the ubiquity of mobile devices, there has been increased interest in determining how they can be used with location-based services. These types of services work best when the device has the ability to sense its location frequently, while still maintaining enough battery life to carry out its normal daily functions. Since the life of the battery on a mobile device is already so limited, ways of preserving that energy has become an important issue. The goal of this thesis is to demonstrate that Bluetooth can assist in providing energy efficient mobile device localization. This goal is achieved through a proposed Bluetooth Location Service Discovery framework which provides an API that can be incorporated into third party applications. The API allows BlackBerry devices to use surrounding Bluetooth devices in order to make a prediction about its current location. Predictions are completed with the assistance of the K-Nearest Neighbour data mining algorithm, and can be used as an alternative to invoking the GPS. The results obtained through experiments demonstrate that the results are comparable to those obtained with GPS.
Multi-Objective Aerodynamic Optimization of Supercritical Wing with Substantial Pressure Constraints

A multi-objective aerodynamic optimization of supercritical wing of a wide-body aircraft based on genetic algorithm and CST parameterization method is presented in this paper. Favorable pressure distributions of a “supercritical type” wing are considered as a lot of constraints in the optimization process. Geometry constraints and aerodynamic constraints are also employed. A full potential solver is adopted as a low fidelity aerodynamic analysis tool for preliminary optimization. A Reynolds Averaged NavierStokes solver is used as high fidelity tool for detailed optimization, and design validation. With more than 150 constraints, the optimized design has a typical supercritical pressure distribution. The CFD results also show that the design has a superior aerodynamic performance.
Watershed data aggregation for mean-shift video segmentation

Object segmentation is considered as an important step in video analysis and has a wide range of practical applications. In this paper we propose a novel video segmentation method, based on a combination of watershed segmentation and mean-shift clustering. The proposed method segments video by clustering spatio-temporal data in a six-dimensional feature space, where the features are spatio-temporal coordinates and spectral attributes. The main novelty is an efficient data aggregation method employing watershed segmentation and local feature averaging. The experimental results show that the proposed algorithm significantly reduces the processing time by mean-shift algorithm and results in superior video segmentation where video objects are well defined and tracked throughout the time.
Simulating the TRIBA NOC Architecture

TriBA(Triplet Based Architecture) is a Network on Chip processor(NoC) architecture which merges the core philosophy of Object Oriented Design with the hardware design of multicore processors[1]. We present TriBASim in this paper, a NoC simulator specifically designed for TriBA.In TriBA ,nodes are connected in recursive triplets . TriBA network topology performance analysis have been carried out from different perspectives [2] and routing algorithms have been developed [3][4] but the architecture still lacks a simulator that the researcher can use to run simple and fast behavioural analysis on the architecture based on common parameters in the Network On Chip arena. TriBASim is introduced in this paper ,a simulator for TriBA ,based on systemc[6] . TriBASim will lessen the burden on researchers on TriBA ,by giving them something to just plug in desired parameters and have nodes and topology set up ready for analysis.
Prior Polarity Dictionary Derived from SentiWordNet based on Random Forest Algorithm

The prior polarity of words, as a challenging problem, can make great contribution to the sentiment analysis task. In this paper, we propose a method to generate the prior polarity dictionary based on Random Forest (RF) learning algorithm. We compare the proposed approach with the previous methods. The experimental results show that it is better than the state-of-art Support Vector Regression (SVR) method and it can gain better performance.
CAMIA-Computer-Aided Medical Images Analyzer

The computer tool named CAMIA (Computer-Aided Medical Image Analyzer) for quantitative and qualitative analysis of different medical images is described The system CAMIA can handle different standard image formats such as: BMP, GIF, RAW, JPEG, Biorad PIC, and DICOM. After preprocessing steps the main procedure applied in the system CAMIA is segmentation which is based on the watershed algorithm; the simplified mathematical model of the human visual system. In this way very good subjective quality of the primary segmented image is obtained. By using the interactive semiautomatic method of region growing and selection the medical doctor is able to select easily and extract objects of interest for further diagnosis. This procedure permits high reproducibility of results and robustness in object area determination as prerequisites for precise and objective quantification of detected objects. The embedded modules for statistical and quantitative analysis of objects enables the application of CAMIA in diagnostic practices such as cytology, patho-hystology, radiology (MR, CT, etc.). The system is derived and tested in cooperation with doctor-specialists in the Military Medical Academy in Belgrade.
Object-Oriented Design Quality Adaptation: A System Dynamics Simulation

ii List of Tables . viii List of Illustrations . x Chapter 1: Introduction . 1 Statement of the problem and anticipated benefits . 2 Research approach . 4 Simulation-based Virtual Software Engineering Laboratories (VSEL) . 4 Chapter 2: Literature Survey . 9 Simulation techniques and tools . 9 Software Process Simulation Modeling (SPSM) . 11 SPSM of design phase . 21 SPSM of adaptive systems . 24 The verification and validation of SPSM simulations . 28 Chapter 3: Simulation development and verification . 31 Phase 0 of the SDM: Pre-study and research hypotheses definition . 32 Phase 1 of the SDM: initial model development . 33 Phase 2 of the SDM: Model enhancement . 48 Chapter 4: Simulation validation . 74 Design 1 (D1): Library Information System (LIS) . 75
Transmitter linearization using Cartesian feedback for linear TDMA modulation

A linearized power amplifier suitable for low-power portable radio applications is described. One narrow-bandwidth and one wide-bandwidth application are investigated. Up to 40% power-added efficiency can be achieved for a 20-dB reduction of the third-order intermodulation products at 1.5 MHz from the carrier frequency in the wide-bandwidth system. Less than -60 dB out-of-band emission and 38% power-added efficiency can be obtained for a narrow-bandwidth system. <<ETX>>
Recognizing Noisy Romanized Japanese Words in Learner English

This paper describes a method for recognizing romanized Japanese words in learner English. They become noise and problematic in a variety of tasks including Part-Of-Speech tagging, spell checking, and error detection because they are mostly unknown words. A problem one encounters when recognizing romanized Japanese words in learner English is that the spelling rules of romanized Japanese words are often violated by spelling errors. To address the problem, the described method uses a clustering algorithm reinforced by a small set of rules. Experiments show that it achieves an F-measure of 0.879 and outperforms other methods. They also show that it only requires the target text and a fair size of English word list.