Miracle-dz/UniFAR-FTUs huggingface dataset:
Value-added processes in information systems

In this volume, the author develops a new approach for the analysis of differing types of informations systems, called the Value-Added Model. This approach is based on the anlaysis of information-use environments and on the system responses to the needs of those environments. The model is applied to a variety of information systems. Document-based systems, academic, public, and special libraries, abstracting and indexing services, and book publishing are among those analyzed. Within decision systems, the author looks at management information systems and decision support systems within the value-added framework.
The Development of Hot-deck Corrected Item Mean (HDD-CIM) for Estimating Missing Data

Abstract The purpose of this study was to develop the hot-deck corrected item mean (HDD-CIM) for missing data estimation and compare its under missing complete at random (MCAR) and simple random sampling with two methods, namely; Corrected item mean (CIM) and hot-deck (HDD). The secondary data from a survey of public information about the prevalence of drugs, 2011, a survey by the Bureau of Statistics Mahasarakham Province were used and the comparisons were made with three sample sizes (100, 200 and 500) and four levels of percentage of missing data (5%, 10%,15% and 20%). It appears that the HDD-CIM method has most efficiency in estimating missing data. Keywords: Missing data, MCAR, Simple random sampling, Corrected item mean (CIM) , hot-deck (HDD)
Constructing a heterogeneous real-time system

The construction of a real-time system on heterogeneous hardware platforms, forces one to make choices on which programming language, operating system, development process and application programmers interface to use. The application (a micro-satellite) requirements state that the system must be dependable in a remote and harsh environment such as space. This paper details the choices made and the experience gained from living with the choices made in the development of a micro-satellite and its associated ground support. The emphasis is on simple solutions throughout. The simple solution is important for the verification and validation of the complete system. <<ETX>>
Bigfoot: An Earth Science Computing Environment for the Sequoia 2000 Project

The Sequoia 2000 Project is a large-scale collaboration between the Digital Equipment Corporation, the University of California, and several industrial partners and government agencies, for developing new computing environments for global change research. The primary focus of the Project is to develop solutions for massive data storage, data access, data analysis and visualization, and wide-area networking. These solutions are embodied in ‘Bigfoot,’ a computing environment the Project is building at the Berkeley campus. Bigfoot comprises 10 terabytes of tertiary storage supporting commercial and experimental file systems, managed by an extended relational database management system supporting Earth science data types and operations. Bigfoot is linked to a complex of private high-speed local and wide area networks, running both standard and experimental protocols. Research projects are extending Bigfoot to couple directly to general circulation models, and to provide visualization, full-text retrieval, and geographic information system capabilities. Introduction This chapter discusses ‘Bigfoot,’ the computing environment developed for the Sequoia 2000 Project at the University of California (UC). It begins with a very brief overview of the Sequoia 2000 Project, followed by a discussion of the computing problems that Bigfoot was designed to address. The architecture of the current implementation of Bigfoot is then described in some detail.
Comparison of architectures and robustness of model reference adaptive controllers and L1  adaptive controllers

The paper compares direct and indirect model reference adaptive controllers (MRAC) with the L1 adaptive controller. The architectures of the closed‐loop systems are first compared in a simple setting that clarifies the similarities and differences of the controllers. The indirect MRAC and the L1 controller have identical state predictors, but they differ in the computation of the control signal, which in L1 is carried out by solving a feedback loop. The special case, where the controllers only adapt to a parameter representing input disturbances, is discussed. In this case, the closed‐loop system is linear, so it may not be appropriate to call the controllers adaptive. The special case does, however, give good insight into similarities and differences of the controllers and the effects of various modifications. In particular, the analysis gives good understanding of the robustness properties.Copyright © 2013 John Wiley & Sons, Ltd.
Constrained-input/constrained-output stability for adaptive RLS lattice filters

A new approach to characterizing the stability of adaptive filters is presented. The notion of constrained-input/constrained-output (CICO) stability is introduced as a generalization of the standard notion of bounded-input/bounded-output (BIBO) stability. This new notion involves a set of constraints on the filter data (i.e., signals and parameters) that, unlike boundedness, are specific to the filter in consideration. The set of all data that satisfy the constraints is the feasibility domain of the adaptive filter. The feasibility domain of two particular adaptive filters is established: (i) the normalized recursive-least-squares (RLS) lattice, and (ii) the unnormalized RLS lattice/. >
Improving the visual comfort of stereoscopic images

We compared the visual comfort and apparent depth of stereoscopic images for three camera configurations: parallel (without image shift), image-shifted and converged. In the parallel and image-shifted configurations, the stereo cameras were pointed straight ahead. In the converged configuration the cameras were toed-in. In the image-shifted configuration the image frame was shifted perpendicularly with respect to the line of sight of the camera. The parallel configuration produces images with uncomfortably large disparities for objects near the camera. By converging the cameras or by shifting the image, these large disparities can be reduced and visual comfort can be improved. However, the converged configuration introduces keystone distortions into the image, which can produce visual discomfort. The image-shifted configuration does not introduce keystone distortions, but affects the width of the image frame. It also requires unusual camera hardware or computer post-processing to shift the images. We found that converged and image-shifted configurations improved the visual comfort of stereoscopic images by an equivalent amount, without affecting the apparent depth. Keystone distortions in the converged configuration had no appreciable negative effect on visual comfort.
Uncovering information centres in requirements traceability networks

Non-trivial software project are likely to result in the generation of a dynamic and complex requirements traceability network of numerous artefacts, use cases and collaborating developers. Such complexity makes the process of creating and maintaining requirements traceability links expensive. Also, obtaining useful project related information from existing traceability links is difficult. This is because understanding vital information about a software process requires insight into where information centres exist within the software project. For instance, which of the entities within the traceability network are potential single points of failure and where are the bottlenecks within the software project. This paper presents an approach that enables an automated generation of requirements traceability network of use cases, code artefacts and developers. These networks are further combined with information centrality measures that can be used to provide useful insight into the potential entities that can hold important information about the software project. An initial study suggests that requirements traceability networks combined with such centrality measure can provide vital cues during software development processes. Lessons learnt during this research are also presented. KeywordsRequirements Traceability; Information centres; Network Analysis; Collaborative Software Development
Edge-following strategies using the parallel control formulation

Different strategies to deal with edge-following tasks for robotic manipulators are analyzed and compared in the common framework provided by the parallel control technique, which proves a flexible tool for handling interaction tasks. Simulation results show that the use of force-based logic in the task planning results in helpful support for the operation of the robot control system. <<ETX>>
A diagnostic method using wavelets networks application to engine knock detection

The paper proposes to use wavelet networks in order to realize a diagnostic system. First, they constitute the models of the operating modes of the system. Then they allow extraction of relevant features from acquired data. The goal of the method is to partition the feature space into classes representing the operating modes of the system. The whole process that belongs to pattern recognition, leads to diagnostic fusion. It is applied to engine knock detection. Experimental results are reported.
Comparison of Protein Complexes Predicted from PPI Networks by DPClus and Newman Clustering Algorithms

A Protein-Protein Interaction network, what we call a PPI network is considered as an important source of information for prediction of protein functions. However, it is quite difficult to analyze such networks for their complexity. We expected that if we could develop a good visualizing method for PPI networks, we could predict protein functions visually because of the close relation between protein functions and protein interactions. Previously, we proposed one, which is based on clustering concepts, by extracting clusters defined as relatively densely connected group of nodes. But the results of visualization of a network differ very much depending on the clustering algorithm. Therefore, in this paper, we compare the outcome of two different clustering algorithms, namely DPClus and Newman algorithms, by applying them to a PPI network, and point out some advantages and limitations of both.
Extração de Dados do Twitter para aplicação na Análise de Sentimentos

This work has as objective collect data from the social network Twitter, with the idea to make a Portuguese database to be applied in Sentiment Analysis. Besides the data extracting, a classification algorithm was created based on the emoticons found. This database will be used in future Sentiment Analysis works like the application of others kinds of classification algorithms such as machine learning algorithms.
A dynamic node architecture scheme for backpropagation neural networks

Typically, artificial neural network (ANN) training schemes require network architectures to be set before training. However, the Teaming speed and generalization characteristics of ANNs are dependent on their architectures. Thus, the viability of a specific architecture can only be evaluated after training. This work seeks to reduce the dependence of ANN capabilities on the preselection of network architectures. The present work describes an ANN dynamic node architecture (DNA) scheme which determines the appropriate number of nodes for a given network by defining an importance function which assigns an importance to each node in the network. Optimizing the network architecture becomes part of the training objective. The backpropagation learning algorithm has been implemented with this new DNA scheme.
Sistema de visión global para fútbol de robots

La meta fundamental de este proyecto es el desarrollo e implementación de un sistema de visión por computadora para las diversas categorías de fútbol de robots de las competencias FIRA, Robocup, y CAFR. El sistema presentado es de visión global. Para su implementación se utilizó la biblioteca OpenCV de Intel para el procesamiento de imágenes y para la interface de usuario el framework QT. De igual manera el otro objetivo del proyecto es fomentar el estudio y experimentación de técnicas de visión por computadora mediante un framework con plugins. Palabras claves: visión por computadora, fútbol de robots, procesamiento de imágenes.
Interactive data exploration with targeted projection pursuit

Data exploration is a vital, but little considered, part of the scientific process; but few visualisation tools can cope with truly complex data. Targeted Projection Pursuit (TPP) is an interactive data exploration technique that provides an intuitive and transparent interface for data exploration. A prototype has been evaluated quantitatively and found to outperform algorithmic techniques on standard visual analysis tasks.
Combined colour and contour representation using anti-aliased histograms

Image databases require accurate colour image retrieval, minimal storage requirements, and fast query execution. Histograms have been used to compactly represent colour distributions providing low storage requirements and fast query execution. We propose the novel idea to also use histograms to represent contour distributions. Our contour distribution approach is shown to perform as well as an exhaustive contour comparison approach. In addition, anti-aliased histograms also known as fuzzy histograms are applied to reduce the storage requirements and query times by a factor of four. Colour and contour similarities are combined to provide more accurate query results than either individual technique.
Improving Action Classifiers with Depth

The detection and classification of human activities and gestures in video clips has been a popular research topic during the last years. Lately, 3D data has proven to facilitate this task. In addition, the recent availability of depth sensors in normal products has produced many interesting applications for that research. In this thesis, we use depth and video information from a Microsoft Kinect device in a Hough-voting framework designed for action recognition. The depth information appears to be an invaluable addition to the set of conventional image features, if available for training and testing. However, the goal of this thesis is to use 3D data during training of the classifier, but test it on 2D data only. For the purpose of experimentation and evaluation, different datasets have been recorded with the Kinect. We present several modifications to the original Hough framework. In a first scenario, an attempt to imitate the depth information is made. The results of two different approaches show that depth imitation fails to considerably improve the average classification performance. In the second scenario, a more refined approach to regularize the training process of random decision trees using depth is adopted. The effects of this balanced use of depth data and normal image data look quite promising. Further, the use of depth as a means of image segmentation is examined. Aside from that, reconstruction of depth images from 2D data is intended and partially realized. The evaluations of those modifications reach the conclusion that depth information is useful, even if only used for training the action classifier. However, it had to be recognized that the results are solely based on self-recorded datasets, making a fair comparison to other work impossible.
Real-Time Detection of Movement in Prohibited Direction for Video Surveillance System

Abstract Video surveillance systems are well established tools for monitoring important areas and detecting abnormal situations. In places such as one way road or tunnel, airport arrival gate, subway entry gate etc. it is important to monitor the direction of movement and to detect those which are prohibited. If the event is detected in the same time when the situation happens, a fast reaction can fix the problem (turning on the red light to prevent cars from entering the tunnel, sending security force to stop and search the suspect etc.). In the article a working system which is able to detect movement in prohibited direction is presented. The algorithm proved a very good detection rate for tested movie sequences. By optimizing various aspects of the algorithm a real-time efficiency (30fps) for 640×480 resolution frames is achieved.
New adaptive sidelobe canceller with compensator using adaptive linear predictor

A new sidelobe cancelling scheme and its adaptive algorithm are proposed. The proposed scheme employs an adaptive compensator at the output of the conventional generalized sidelobe canceller in order to reduce undesirable fluctuations in the adaptive filter coefficients after convergence. A linear prediction error filter is utilized as the adaptive compensator. According to computer simulations, the proposed scheme yields better performance than the conventional one.
Improving service level agreements for a job scheduler by visualizing simulations

Currently, job owners at Google do not have a good way to generate suitable Service Level Agreements (SLAs), which means that they cannot accurately communicate their intentions to the job scheduler. This means that the owner's job might not finish on time or at all. The solution described in this thesis helps users visualize design changes to SLAs and use simulation to explore the behavior resulting from the SLAs. I have designed and begun development of a visualization and simulation framework that allows users to see how the job scheduler's behavior might vary under different SLA parameters. This thesis describes the steps made towards designing and implementing a system that both helps users visualize SLAs and their reward functions, and allows users to create an SLA and gain an idea of the behavior of a job scheduler with the SLA as input.
A novel stereo-model retrieval method in manufacturing

With the development of computer vision technologies, manufacturing is now using stereo models for production, and the amount of stereo models generated recently has increased significantly. Thus, a fast and reliable model retrieval algorithm has become important. In this article, we propose a novel view-based stereo-model retrieval algorithm using a modified principal component analysis algorithm. Unlike a traditional principal component analysis that uses the origin of a two-dimensional image, we apply principal component analysis to extract eigenvectors for each stereo model. First, we extract a set of two-dimensional images from different directions of the stereo object. Because each two-dimensional image can be seen as one sample of the stereo object, we utilize principal component analysis to extract the eigenvectors as a dictionary for each stereo object. Then, those eigenvectors are used to rebuild the query. Finally, the reconstruction residual is applied to represent the similarity between the query and the candidate stereo object. Experimentally, the proposed retrieval algorithm has been evaluated using the ETH-80 and ALOI datasets. Experimental results and comparisons with other methods show the effectiveness of the proposed approach that can be used in engineering manufacturing and computer-aided design applications.
Literature Survey On Image Filtering Techniques

Filtering is an essential part of any signal processing system. This involves estimation of a signal degraded, in most cases, by additive random noise. Several filtering techniques have been proposed where linear processing techniques have been the method of choice for many years because of their simplicity. Most of these techniques, however, assume a Gaussian model for the statistical characteristics of the underlying process and try to optimize the parameters of a system for this model. Nonlinear techniques have recently assumed significance as they are able to suppress Gaussian noise to preserve important signal elements such as edges and fine details and eliminate degradations occurring during signal formation or transmission through nonlinear channels. Among nonlinear techniques, the fuzzy logic based approaches are important as they are capable of reasoning with vague and uncertain information. There are fuzzy filters available for removing additive noise. A detailed literature survey has been done here to compare these conventional image filters with a new fuzzy based approach. This paper includes an analysis about the significance of such a fuzzy based approach for image filtering.
Recent Techniques in Node Failure Recovery in Wireless Sensor-Actor Networks

Recent technological advances have lead to the emergence of distributed wireless sensor and actor networks (WSANs) which are capable of observing the physical world, processing the data, making decisions based on the observations and performing appropriate actions. These networks can be an integral part of systems such as battlefield surveillance and microclimate control in buildings, nuclear, biological and chemical attack detection, home automation and environmental monitoring. In most applications of Wireless sensor and actor network it is important to sustain connectivity among all actors at all times. When an actor fails the inter actor topology may get partitions into disjoint blocks and the application may be negatively impacted. Tolerating the actor failure and restoring the lost connectivity need to be performed while imposing the least overhead on the individual actors. In this paper a Least-Disruptive topology Repair (LeDiR) algorithm is proposed. LeDiR is to restore connectivity without extending the length of the shortest path among nodes compared to the prefailure topology. LeDiR is a localized and distributed algorithm that leverages existing route discovery activities in the network and imposes no additional prefailure communication overhead. The performance of LeDiR is analyzed and simulated in Network Simulator(NS2) Environment.