Miracle-dz/UniFAR-FTUs huggingface dataset:
Backstepping control of linear time-varying systems with known and unknown parameters

The backstepping control design procedure has been used to develop stabilizing controllers for time invariant plants that are linear or belong to some class of nonlinear systems. The use of such a procedure to design stabilizing controllers for plants with time varying parameters has been an open problem. In this paper we consider the backstepping design procedure for linear time varying (LTV) plants with known and unknown parameters. We first show that a backstepping controller can be designed for an LTV plant by following the same steps as in the linear time-invariant (LTI) case and treating the plant parameters as constants at each time t. Its stability properties however cannot be established by using the same Lyapunov function and techniques as in the LTI case. We then introduce a new parametrization and filter structure that takes into account the plant parameter variations leading to a new backstepping controller. The new control design guarantees exponential convergence of the tracking error to zero if the plant parameters are exactly known. If the parameters are not precisely known but the time variations of the parameters associated with the system zeros are known, the appropriate choice of certain design parameters, without using any adaptive law, leads to closed-loop stability and perfect regulation. This new control design is modified and supplemented with an update law to be applicable to LTV plants with unknown parameters. In the adaptive control design, the notion of structured parameter variations is used in order to include possible a priori information about the plant parameter variations. With this formulation, only the unstructured plant parameters are estimated and are required to be slowly time varying, and the structured plant parameters are allowed to have any finite speed of variation. The adaptive controller is shown to be robust with respect to the unknown but slow parameter variations in the global sense. We derive performance bounds which can be used to select certain design parameters for performance improvement. The properties of the proposed control scheme are demonstrated using simulation results.
Forensic Identification Reporting Using A GMM Based Speaker Recognition System Dedicated to Algerian Arabic Dialect Speakers

Starting from the fact of the lack of Arabic databases dedicated to performance evaluation of speaker recognition and forensic reporting systems. We present in this paper our experience in constructing an Algerian dialect database and the motivation beyond this work. After that, the corpus based Bayesian framework for interpretation of evidence in forensic systems in terms of likelihood ratio (LR) is applied on this database under different recording conditions: microphone, fixed and cellular. Preliminary results in terms of Receiver Operating Characteristics (ROC) and TIPPET plots show higher accuracy in matched conditions of training and testing. However, the performance degrades significantly in mismatched conditions.
A Framework for Aerial Video Stabilization

Surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting them. Aerial surveillance is now a day's very popular for broadcasting, news, shooting or gathering data from air and providing large quantity of video data for many purposes, including search and rescue, military operations, commercial applications, counter terrorism, and border patrol. In video surveillance the major task is to trace the moving object as it is in small screen. So for this it is required to track the moving object until the object is visible. But the main challenge is to trace the moving object in mobile platform including moving camera. Therefore video stabilization is necessary for mobile video surveillance. Present paper gives the review of aerial video stabilization.
AGRICULTURAL FIELD EXTRACTION FROM AERIAL IMAGES USING A REGION COMPETITION ALGORITHM

The segmentation of agricultural fields on aerial images is still a manual activity in most Geographic Information Systems, requiring repetitive, tedious and time-consuming work. In this article we address the problem of semiautomatic segmenting of agricultural fields by region competition techniques that integrate region growing and deformable models. The deformable models dynamically adapt their contour, starting from a rough seed, and homogeneous parcels are analyzed in an energy-minimizing framework. The algorithm is integrated into an edition menu that also allows parcels to be drawn from scratch and set operations to be applied between parcels that have already been extracted. To achieve the optimum image segmentation, we study different aspects: user interaction, parameterization and convergence criteria.
Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.
A Global Optimization Algorithm ( GOP ) For Certain Classes ofNonconvex NLPs :

A large number of nonlinear optimization problems involve bilinear, quadratic and/or polynomial functions in their objective function and/or constraints. In this paper, a theoretical approach is proposed for global optimization in constrained nonconvex NLP problems. The original nonconvex problem is decomposed into primal and relaxed dual subproblems by introducing new transformation variables if necessary and partitioning of the resulting variable set. The decomposition is designed to provide valid upper and lower bounds on the global optimum through the solutions of the primal and relaxed dual subproblems respectively. New theoretical results are presented that enable the rigorous solution of the relaxed dual problem. The approach is used in the development of a Global OPtimization algorithm (GOP). The algorithm is proved to attain nite-convergence and-global optimality. An example problem is used to illustrate the GOP algorithm both computationally and geometrically. In an accompanying paper (Visweswaran and Floudas, 1990), application of the theory and the GOP algorithm to various classes of optimization problems, as well as computational results of the approach are provided.
Draft Genome Sequences of Three Capnocytophaga cynodegmi Strains Isolated from the Oral Cavity of Healthy Dogs

ABSTRACT Here, we present the draft genome sequences of three strains of Capnocytophaga cynodegmi. In contrast to the very close relationship among them, C. cynodegmi and Capnocytophaga canimorsus differ dramatically in terms of virulence in humans. Comparative genomics provided some understanding on how Capnocytophaga species may switch from being dog commensals to human pathogens.
Running head : HEBBIAN LEARNING How Far Can You Go with Hebbian Learning , and When Does it Lead you Astray ?

This paper considers the use of Hebbian learning rules to model aspects of development and learning, including the emergence of structure in the visual system in early life. There is considerable physiological evidence that a Hebb-like learning rule applies to the strengthening of synaptic efficacy seen in neurophysiological investigations of Long-Term Potentiation, and similar learning rules are often used to show how various properties of visual neurons and their organization into ocular dominance stripes and orientation columns could arise without being otherwise pre-programmed. Some of the plusses and minuses of Hebbian learning are considered. Hebbian learning can strengthen the neural response that is elicited by an input; this can be good if the response made is appropriate to the situation but it can also be counterproductive if a different response would be more appropriate. Cases in which this outcome-independent Hebbian type of strengthening might account at least in part for cases in which humans fail to learn are considered, and computational models embodying the Hebbian approach are described that can account for the findings. At a systems level, Hebbian learning cannot be the whole story. From a computational point of view, Hebbian learning can certainly lead one in the wrong direction, and some form of control over this is necessary. Also, experimental findings clearly show that human learning can be affected by feedback. Several ways in which sensitivity to feedback might be incorporated to guide learning within a fundamentally Hebbian framework for learning are considered.
Active Dictionary Learning in Sparse Representation Based Classification

Sparse representation, which uses dictionary atoms to reconstruct input vectors, has been studied intensively in recent years. A proper dictionary is a key for the success of sparse representation. In this paper, an active dictionary learning (ADL) method is introduced, in which classification error and reconstruction error are considered as the active learning criteria in selection of the atoms for dictionary construction. The learned dictionaries are caculated in sparse representation based classification (SRC). The classification accuracy and reconstruction error are used to evaluate the proposed dictionary learning method. The performance of the proposed dictionary learning method is compared with other methods, including unsupervised dictionary learning and whole-training-data dictionary. The experimental results based on the UCI data sets and face data set demonstrate the efficiency of the proposed method.
Proceedings of the 1st International Workshop on Model-driven Approaches in Software Product Line Engineering Organisation Editors Contents 1. Tracking Evolution in Model-based Product Lines 2. Enabling End-users Participation in an Mdd-spl Approach 3. a Model-based Framework for Automated Product D

Many of the benefits expected from software product lines are based on the assumption that the additional investment in setting up a product line pays off later when products created. However, to fully exploit this we need to optimize application engineering processes and handle SPL artifacts in a systematic and efficient manner. This workshop explores how model-driven approaches can help to achieve these goals. In particular the workshop revolves around three themes: 1. Efficient product derivation. The true return on investment in product line engineering, is achievable when the product lines can be efficiently used for product derivation. How can application engineering benefit from model-driven and aspect-oriented approaches? 2. Link PLE research and industry practice. We have to overcome the gap between research and industrial practice so that both sides can learn from each other. Hence, we are particularly interested in experience reports that discuss the use of models in real world PLE projects. 3. SPL models with a meaning. If we want to improve product derivation, we require models that are more than just vehicles for documentation and discussions on the whiteboard – models that are precise and expressive enough to be used in for automation and in advanced interactive tools. However, if the existing models are documentary and ambiguous, how do we come to more precise models? ii Abstract— Software product lines are complex and need to be maintained and evolved over many years. New customer requirements, new products derived, technology changes, and internal enhancements lead to continuous changes of the artifacts and models constituting a product line. Managing such changes therefore becomes a key issue during a product line's evolution. We propose an approach that supports multi-level monitoring of product line artifacts and models and continuous tracking of changes. We present tool support for evolution tracking in Eclipse workspaces and illustrate our approach with examples from DOPLER, an existing Eclipse-based product line environment.
Efficient decoding algorithm using triangularity of $\mbf{R}$ matrix of QR-decomposition

An efficient decoding algorithm named `divided decoder' is proposed in this paper. Divided decoding can be combined with any decoder using QR-decomposition and offers different pairs of performance and complexity. Divided decoding provides various combinations of two or more different searching algorithms. Hence it makes flexibility in error rate and complexity for the algorithms using it. We calculate diversity orders and upper bounds of error rates for typical models when these models are solved by divided decodings with sphere decoder, and discuss about the effects of divided decoding on complexity. Simulation results of divided decodings combined with a sphere decoder according to different splitting indices correspond to the theoretical analysis.
Composition of the lunar highland crust from near‐infrared spectroscopy

Near-infrared reflectance spectra of the lunar highlands are synthesized to obtain global information about the composition of the lunar near-side crust. Immature exposed surfaces from small fresh craters, mountains and massifs, and large craters with central peaks are included in a survey of rock types across the lunar near side. Compositions recognized in these data include anorthosite, noritic, gabbric, and troctolitic mineral assemblages. Three quarters of the areas studied that represent samples from the upper 1–2 km of lunar crust exhibit noritic compositions with different amounts of pyroxene and/or brecciation alteration. Norites are ubiquitous across the near side and show no spatial clustering associated with any of the major basins. In contrast to this noritic megaregolith of the upper crust, material representing stratigraphically deeper zones (5–15 km) of the lunar crust is dominated by gabbros, anorthosites, and troctolites, with less than a quarter of the areas studied exhibiting a noritic composition. The origin of the noritic megaregolith and its relation to deeper crustal material is thus somewhat of an enigma. The current limited statistics for areas identified as anorthosites and troctolites are insufficient to derive global spatial distributions. Gabbroic areas, however, are notably concentrated in the western hemisphere. Although incomplete for a global assessment of crustal materials, these data demonstrate that the lunar near-side crust is clearly mineralogically heterogeneous, both laterally and vertically, and not well mixed below 1–2 km.
Voluntary Association in Public Goods Experiments: Reciprocity, Mimicry and Efficiency

We find that a process of voluntary association where individuals express a preference about whom they want to be associated with can create strong incentives to increase efficiency and contributions in provision of a public good. This process of endogenous group formation perfectly sorted contributions by the order of group formation. Comparison of middle and last period behaviour suggests that a majority of the subject population are conditional cooperators, with a minority of monetary payoff maximisers. The experiment illustrates that under favourable conditions, where the opportunities of entry and exit are symmetrically balanced, a process of voluntary association can mitigate the free-rider problem.
Optimizing a GPU Algorithm through Hardware Profiling Analysis

Usage of GPU-based architectures for scientific computing has been steadily increasing in the last years. This new paradigm for both programming and execution has been applied to solve several classic problems much faster than using the conventional multiprocessor and/or multicomputer approach. These architectures allow an increase in performance -- compared to conventional CPU processors -- for specific types of algorithms that are particularly suitable for its greater number of simpler cores which execute one single instruction at a time, each one for different sets of data. Since this is still a relative new technology, GPU device manufacturers as well as independent researchers have published several experiences (success stories), best practices, and optimization guides to aid developers for obtaining the maximum program performance. However, there is still little information about the possible optimizations that can only be harnessed by analyzing the specific device's hardware performance counters. In this paper, we discuss several optimizations based on hardware profiling and share our learned lessons about how such data can be used to optimize a scientific algorithm on a GPU using CUDA.
Identifying ultra-cool dwarfs at low Galactic latitudes: a southern candidate catalogue

We present an ultra-cool dwarf (UCD) catalogue compiled from low southern Galactic latitudes and mid-plane, from a cross-correlation of the Two Micron All-Sky Survey (2MASS) and the SuperCOSMOS survey. The catalogue contains 246 members identified from 5042 deg2 within 220° ≤ l ≤ 360° and 0° < l ≤ 30°, for |b| ≤ 15°. Sixteen candidates are spectroscopically confirmed in the near-infrared as UCDs with spectral types from M7.5V to L9, the latest being the unusual blue L dwarf 2MASS J11263991−5003550. Our catalogue selection method is presented enabling UCDs from ∼M8V to the L–T transition to be selected down to a 2MASS limiting magnitude of (for S/N ≥ 10). This method does not require candidates to have optical detections for catalogue inclusion. An optimal set of optical/near-IR and reduced proper-motion selection criteria have been defined that includes: an RF and IN photometric surface gravity test; a dual RF-band variability check and an additional photometric classification scheme to selectively limit numbers of potential contaminants in regions of severe overcrowding. We identify four candidates as possible companions to nearby Hipparcos stars – observations are needed to identify these as potential benchmark UCD companions. We also identify 12 UCDs within a possible distance 20 pc, three are previously unknown of which two are estimated within 10 pc, complementing the nearby volume-limited census of UCDs. An analysis of the catalogue spatial completeness provides estimates for distance completeness over three UCD MJ ranges, while Monte Carlo simulations provide an estimate of catalogue areal completeness at the 75 per cent level. We estimate a UCD space density of ρ(total) = (6.41 ± 3.01) × 10−3 pc−3 over the range of , similar to values measured at higher Galactic latitudes () in the field population and obtained from more robust spectroscopically confirmed UCD samples.
Evaluating Information Systems Effectiveness - Part I: Comparing Evaluation Approaches

While the sue and costs of Management Information Systems (MIS) have become highly visible, little attention has been paid to assessing and communicating system effectiveness. Evaluation of system effectiveness is difficult due to its multidimensionality, its qualitative and quantitative aspects, and the multiple, and often conflicting, evaluator viewpoints. This article provides an overview of what system effectiveness means and how it should be measured. It is the first of two articles to appear in consecutive issues of the MIS Quarterly. Starting with a definition of system effectiveness, this article examines evaluation of system effectiveness in terms of a conceptual hierarchy of system objectives. The hierarchy is used to discuss problems in, and recommendations for, evaluating system effectiveness, and to compare MIS evaluation approaches. The second article characterizes and compares the evaluator viewpoints on system effectiveness for decision makers in several functional groups involved in MIS implementation ᾢ user, MIS, internal audit, and management. The second article recommends several MIS evaluation approaches for incorporating multiple dimensions and multiple evaluator viewpoints into evaluations of information system effectiveness.
Tissue segmentation and classification of MRSI data using canonical correlation analysis

In this article an accurate and efficient technique for tissue typing is presented. The proposed technique is based on Canonical Correlation Analysis, a statistical method able to simultaneously exploit the spectral and spatial information characterizing the Magnetic Resonance Spectroscopic Imaging (MRSI) data. Recently, Canonical Correlation Analysis has been successfully applied to other types of biomedical data, such as functional MRI data. Here, Canonical Correlation Analysis is adapted for MRSI data processing in order to retrieve in an accurate and efficient way the possible tissue types that characterize the organ under investigation. The potential and limitations of the new technique have been investigated by using simulated as well as in vivo prostate MRSI data, and extensive studies demonstrate a high accuracy, robustness, and efficiency. Moreover, the performance of Canonical Correlation Analysis has been compared to that of ordinary correlation analysis. The test results show that Canonical Correlation Analysis performs best in terms of accuracy and robustness. Magn Reson Med, 2005. © 2005 Wiley‐Liss, Inc.
Customer Relationship Management And Organizational Performance: A Conceptual Framework Based On The Balanced Scorecard (Study Of Iranian Banks)

Customers have become crucial for every organization. Due to the competitive environment, customer relationship management (CRM) is one of the important business strategy for companies. Banks can benefit from focusing and concentrating on Customer Relationship Management . There is a stiff competition in the banking industry in Iran . So Iranian banks try to reform their strategies and process to achieve competitive advantage. The customers are an important element that helps the bank to achieve this objective . Banks try to notice the customers requirements and needs for satisfying their customers . Iranian Banks went through reform from product orientation to customer orientation. More importantly, the banks realized that building long-term relationship with their customer is crucial for achieving sustainable competitive advantage and improving performance. This study examined the link between customer relationship management and different measures of performance on the banking sector. Data were collected from the bank sectors in Iran. Based on extensive review of literature, the sub processes in implementing CRM were extracted and also four measures of Balanced scorecard applied to performance. The results indicated that CRM processes are associated with bank performance.