Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Relations And The Lexicon Antonymy Synonymy And Other Paradigms

Thank you very much for downloading semantic relations and the lexicon antonymy synonymy and other paradigms. As you may know, people have search numerous times for their favorite novels like this semantic relations and the lexicon antonymy synonymy and other paradigms, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their desktop computer.
Septic B-spline collocation method for numerical solution of the coupled Burgers’ equations

Abstract In this paper, a numerical solution of the coupled Burgers' equations based on septic B-spline collocation method is presented. The scheme is based on the Crank–Nicolson formulation for time integration and septic B-spline functions for space integration. The method has been showed unconditionally stable by using Von-Neumann technique. The efficiency of this method is demonstrated by applying two test problems. The obtained numerical results are found in a good agreement with the exact solution. This method is efficient, powerful, and economical. It can also applicable to other linear and nonlinear partial differential equations.
Content Coverage and Redundancy Removal in Video Summarization

Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.
A review of segmentation and edge detection methods for real time image processing used to detect brain tumour

The most complicated structure of the human body is the brain and the segmentation of the brain needs to be precise and accurate But to do this it is very difficult because the shape is not in regular and investigation is difficult. Also segmentation is not easy which will consume lots of time and skilled manpower is required. Real time image processing requires processing on large data of image pixels in a stipulated time. Reconfigurable device such as FPGAs can be program to process on large image data and required processing time on image can be reduced by deploying parallelism techniques in algorithm. Different segmentation based approaches have been reviewed and compared over here to extract the tumor from set of brain images. Also different edge detection approaches have been reviewed and compared. Detecting Edge is principal means used in many algorithm for image processing.
Ant Colony Optimization and Multiple Knapsack Problem

The ant colony optimization algorithms and their applications on the multiple knapsack problem (MKP) are introduced. The MKP is a hard combinatorial optimization problem with wide application. Problems from different industrial fields can be interpreted as a knapsack problem including financial and other management. The MKP is represented by a graph, and solutions are represented by paths through the graph. Two pheromone models are compared: pheromone on nodes and pheromone on arcs of the graph. The MKP is a constraint problem which provides possibilities to use varied heuristic information. The purpose of the chapter is to compare a variety of heuristic and pheromone models and different variants of ACO algorithms on MKP.
Saliency Detection for Small Maritime Target Using Singular Value Decomposition of Amplitude Spectrum

ABSTRACT Spectrum analysis based saliency detection approaches have been studied and applied in computer vision. They are either generalized filtering or spectrum whitening of amplitude spectrum of image. However, as resultant salient regions may be inconsistent, a better result can be achieved by combining several scales. This paper proposes a novel saliency detection method for small maritime target using singular value decomposition (SVD) of amplitude spectrum. Color and intensity features of image are extracted and Fourier transform is employed on them. SVD is conducted on amplitude spectrums of each feature and their saliency maps are defined as the inverse Fourier transform of non-dominant components, whose amplitude spectrum is measured by difference between original amplitude spectrums and their main components. Taking into account the characteristics of maritime visual scene, the dominant parts of amplitude spectrum are selected according to threshold which is the ratio of singular values. Color saliency map and intensity saliency map are linearly combined to achieve the saliency map. The proposed method is conducted on the original image size, thus maintaining information of small maritime target. Experiments are made to show the validity and effectiveness. Comparison between proposed method and related approaches demonstrates that the new method is more suitable for saliency detection of small maritime target. Effect of threshold is also analyzed, and 50% is best for saliency detection of small maritime targets. The new saliency detection approach can be used for small maritime target detection and tracking.
A resampling-based method for class discovery and visualization of gene expression microarray data

In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data. The method can best be thought of as an analysis approach, to guide and assist in the use of any of a wide range of available clustering algorithms. We call the new methodology consensus clustering, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a vi- sualization tool to inspect cluster number, membership, and boundaries. We present the results of our experiments on both simulated data and real gene expression data aimed at evaluating the eectiveness of the methodology in discovering biologically meaningful clusters.
CONCLUDING YOUR MASTER'S LEVEL THESIS

Students writing a Master’s level thesis often seek advice not only on their research but also on how to approach the writing of such a document. This is not surprising as for many writing a thesis is a completely new experience and one that many have to complete in a foreign language. The advice available is often generic and not necessarily appropriate. To help such students we need to better understand the structure of these documents. This paper looks at one section of the thesis, that of the concluding chapter to determine its rhetoric structure and compare it with the advice available to the students within the Polish context in which they are writing.
The Psychological Nature of Text Comprehension in terms of Text Learning Processes.

Texts are important tools for learning. Thus the attempt to make texts more understandable is a reflection of a purpose-function related necessity for learning from text. Besides the idea of preserving and correcting teaching materials is frequently explored by contemporary researchers. So it is evident that more advanced proficiency is need to make the efforts to prepare educational materials more scientific. So that this paper starts with an overall description of theories of textual organization and learning from text, continuing theoretical background which may correspond to a pragmatic representation in order to account for some phenomena directly related to language processing in working memory, learning from text, and individual differences. The reason for this is the ideas about modern theories of memory such as working memory is responsible for not only recalling the stored information but also for storing the results of partial processes such as successive processes like language comprehension. The other reason is the generalizations about the interaction between the processes of physical representation and pattern of a text put forward in accordance with these ideas. As a result, due to the nature of factors which affect learner's level of recalling and understanding from text, this study aims to test this kind of assumption.
Example Based Dialogue System Based on Satisfaction Prediction

In dialogue systems, dialogue modeling is one of the most important factors contributing to user satisfaction. Especially in example-based dialogue modeling (EBDM), effective methods for dialog example databases and selecting response utterances from examples improve dialogue quality. Conventional EBDM-based systems use example database consisting of pair of user query and system response. However, the best responses for the same user query are different depending on the user’s preference. We propose an EBDM framework that predicts user satisfaction to select the best system response for the user from multiple response candidates. We define two methods for user satisfaction prediction; prediction using user query and system response pairs, and prediction using user feedback for the system response. Prediction using query/response pairs allows for evaluation of examples themselves, while prediction using user feedback can be used to adapt the system responses to user feedback. We also propose two response selection methods for example-based dialog, one static and one user adaptive, based on these satisfaction prediction methods. Experimental results showed that the proposed methods can estimate user satisfaction and adapt to user preference, improving user satisfaction score.
complexity scalable video decoding scheme for H.264/AVC

Recent proliferation of portable devices requires video contents playable on virtually any portable devices. However, their limited computing resources pose significant restriction to accomplish real-time decoding of high resolution or high quality video contents. To solve the problem, we propose a complexity scalable video decoding scheme for realtime playback on portable devices. In this paper, we analyze the complexity of H.264/AVC video decoding elements and develop a complexity scalable decoding scheme by simplifying motion compensation and deblocking filtering. Experimental results with the H.264/AVC main profile coded bitstream show that its decoding complexity can be reduced up to 26% without significant loss in subjective quality as compared to the conventional H.264/AVC decoder. Keywords-H.264/AVC; video decoder; complexity scalable decoding
A guaranteed-throughput switch for network-on-chip

Today's systems on a chip (SoC) contain numerous complex functional blocks integrated by an elaborate network of interconnects and buses. As systems grow in complexity, the on-chip interconnect network is expected to become critical for overall system-level metrics, such as performance, power consumption, reliability etc. However, present day's dedicated channels and shared buses do not scale and therefore do not meet these requirements. The emerging network-on-chip approach, based on on-chip communication network, might solve the problems by A. Jantsch and H. Tenhunen (2003). In this paper, a guaranteed-throughput switch designed for NoC is described. This switch provides in-order delivery and supports multicast operation. It is implemented with random access memory at the input and output. The input and output are then connected by a fully connected interconnect network.
Modal analysis and dynamic responses of a rotating Cartesian manipulator with generic payload and asymmetric load

Abstract Here, investigation to explore the effect of generic payload and externally applied asymmetric load on the calculation of modal parameters and dynamic performance of a rotating flexible manipulator under prismatic motion has been established. We thus have developed a dynamic model of a rotating Cartesian manipulator with a payload whose center of gravity doesn’t coincide with the point of attachment, to determine the modal parameters i.e., natural frequency and corresponding mode-shape. These modal parameters are then illustrated graphically upon varying parameters like offset parameters (i.e., offset mass, offset inertia, offset length), mass and stiffness of rotary actuator, and amplitude and frequency of asymmetric load. An investigation into the nonlinear dynamics of the system accounting of geometric nonlinearity has been executed while obtained results have been validated numerically within the permissible error at the assorted critical points in frequency characteristic curves. Current research further investigates the influences of offset parameters, mass and stiffness of the actuator, frequency and amplitude of axial force on the steady state responses for the primary and sub-harmonic resonance conditions to reveal the built-in saddle-node and pitchfork bifurcation due to which the system losses its structural stability. This work enables an insight into the modal characteristics and nonlinear behavior of a rotating-Cartesian manipulator with a generic payload under asymmetric axial force and prismatic motion.
Wavelet-based feature extraction for EEG classification

This paper uses the wavelet transform to obtain descriptive features of EEG data from a prenatal drug exposure study. The interest is in analyzing the data to determine temporal and spectral information that can be used as a predictor of central nervous system dysfunction. While the FFT can be useful, it does not incorporate temporal information. The wavelet transform is applied because it provides a multiresolution decomposition in time and frequency and is therefore well suited for the analysis of transient nonstationary signals such as these. Results show that useful features are obtained from the histograms of the relative time durations of specific frequencies obtained via the wavelet transform.
Updating Thesaurus via Extracting Keywords from Metadata

The application of thesauri in digital libraries is seriously constrained by the manual nature of current thesaurus maintenance mechanism which cannot keep up with the rapid evolvement of knowledge.This paper proposes a statistical method of extracting new terms from titles of metadata and settling them into the thesaurus.The settlement is based on the subject indexing coded in the metadata records.An experiment was conducted on the Chinese Classification and Thesaurus and a corpus of 5 thousands bibliographic data of computing domain.The successful result demonstrates that the techniques proposed are effective and can be applied to the corpus of large size and foreign language.
Nanotechnology-based combinational drug delivery systems for breast cancer treatment

Abstract Modern chemotherapeutic anticancer treatments have come a long way in the fight against breast cancer, thus bringing science closer to a cure. However, the nature of these drugs is to attack both cancerous and non-cancerous cells at the same time. In our studies, we synthesized Magnetic Cyclodextrin-polyurethane based nano-composite (Fe3O4-ECA-PU-CD) which is a drug delivery system using a biocompatible magnetic polymer that directs chemotherapeutic drugs to cancerous regions in the body with an external magnet. This nano-composite was able to facilitate the loading and simultaneous release of the hydrophilic antitumor drug Doxorubicin (DOX) along with the hydrophobic antitumor drug docetaxel. The composite was made using a simple co-precipitation method with magnetic nano-particles (MNPs) followed by a silica coating process and an in situ polymerization process. Verification of synthesis for the drug carrier was carried out using techniques such as scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Thermogravimetric Analysis (TGA), and a vibrating sample magnetometer (VSM) to measure magnetic properties. Drug loading and release efficiencies were monitored using an HPLC-UV detector. Finally, an MTT assay was performed in order to evaluate the biocompatibility and toxicity of the synthetic nano-composite on MCF7 cell lines. Our findings present a new biocompatible drug delivery system with a high capacity for loading and directing two different chemotherapeutic drugs simultaneously to cancer sites with little to no toxicity to the surrounding non-cancerous cells. These studies present a viable tool for cancer treatment and research where the cancer is efficiently removed while the patient remains healthy. Graphical Abstract
Optimal investment strategy and liability ratio for insurer with Lévy risk process

We investigate an insurer's optimal investment and liability problem by maximizing the expected terminal wealth under different utility functions. The insurer's aggregate claim payments are modeled by a Levy risk process. We assume that the financial market consists of a riskless and a risky assets. It is also assumed that the insurer's liability is negatively correlated with the return of the risky asset. The closed-form solution for the optimal investment and liability ratio is obtained using Pontryagin's Maximum Principle. Moreover, the solutions of the optimal control problems are examined and compared to the findings where the jump sizes are assumed to be constant.
Document Collection Partition Evaluation in Distributed Information Retrieval

It is difficult to evaluate the document collection partition in distributed information retrieval. Recently, there is no clear evaluation criterion for the document collection partition problem. In this paper, two partition models are built to formulate the document collection partition problem from the essence of the problem itself and they can be used as the evaluation criterion of the document collection partition problem. A Huffman_encoding_like algorithm is introduced to compute the optimum partition solution given a test query set. The optimum partition solution is a good reference of other partition solution. The experimental results show that the two models are
LPQ and LBP based Gabor filter for face representation

Sometimes realistic face representation is confronted with blur or low-resolution face images, as a result, existing classification methods are not powerful and robust enough. This paper proposes a novel face representation approach (GLL) which fuses Gabor filter, Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). In the process of Gabor filter, it uses Gabor wavelet functions with two scales and eight orientations to capture the salient visual properties in face image. On this basis of Gabor features, we acquire LBP features and LPQ features, respectively, so as to fully explore the blur invariant property and the information in the spatial domain and among different scales and orientations. Experiments on both CMU-PIE and Yale B demonstrate the effectiveness of our GLL when dealing with different condition face data sets. & 2012 Elsevier B.V. All rights reserved.
On Bird Species Diversity and Remote Sensing—Utilizing Lidar and Hyperspectral Data to Assess the Role of Vegetation Structure and Foliage Characteristics as Drivers of Avian Diversity

Avian diversity has long been used as a surrogate for overall diversity. In forest ecosystems, it has been assumed that vegetation structure, composition, and condition have a significant impact on avian diversity. Today, these features can be evaluated via remote sensing. This study examined how structure metrics from lidar data and narrowband indices from hyperspectral data relate with avian diversity. This was assessed in four deciduous-dominated woods with differing age and structure set in an agricultural matrix in eastern England. The woods were delineated into cells within which metrics of avian diversity and remote sensing based predictors were calculated. Best subset regression was used to obtain best lidar models, hyperspectral models, and finally, the best models combining variables from both data sets. The aims were not only to examine the drivers of avian diversity, but to assess the capabilities of the two remote sensing techniques for the task. The amount of understorey vegetation was the best single predictor, followed by foliage height diversity, reflectance at 830 nm, anthocyanin reflectance index 1, and Vogelmann red edge index 2. This showed the significance of the full vertical profile of vegetation, the condition of the upper canopy, and potentially tree species composition. The results thus agree with the role that vegetation structure, condition, and floristics are assumed to have for diversity. However, the inclusion of hyperspectral data resulted in such minor improvements to models that its collection for these purposes should be assessed critically.
Analyse und Entwicklung Neuronaler Netze für die Vorhersage von 3D-Strömungslösungen anhand eines Fandesigns

Mittels CFD-Stromungssimulationen kann ein Verdichter einer Turbomaschine aerodynamisch bewertet und ausgelegt werden. Im Rahmen einer Optimierung werden diese Stromungssimulationen fur verschiedene Designvariationen haufig durchgefuhrt und benotigen daher enorm viel Rechenzeit. Allerdings fuhren schon kleine Anderungen in der Parametrisierung oder Zielformulierung einer ¨ Optimierung dazu, dass zuvor gewonnene Erkenntnisse nur bedingt in weiteren Optimierungen verwendet werden konnen. Eine vollstandige Weiterverwendung aller berechneten Stromungslosungen konnte zukunftige Optimierungen beschleunigen. ¨ Es soll daher ein Verfahren untersucht werden, mit dessen Hilfe Stromungsinformationen aus bereits durchgefuhrten Optimierungen unabhangig der Parametrisierung extrahiert werden konnen. Aus diesem Grund wurde ein Verfahren zur Berechnung von Stromungssimulationen mittels Neuronaler Netze entwickelt und untersucht. Diese Modelle konnen anhand bereits berechneter Stromungslosungen trainiert werden, sodass diese Informationen auf andere Geometrien in kurzer Zeit ubertragen werden konnen, um eine moglichst schnelle Vorhersage zu erhalten. Zu diesem Zweck wurde zunachst eine Datenbasis von Stromungslosungen anhand einer Optimierung erzeugt, die fur das Training des Modells genutzt wurde. In einem zweiten Schritt wurde ein Verfahren ¨ entwickelt, um die Geometrieinformationen fur die Nutzung mittels Neuronaler Netze aufzubereiten. ¨ Anschliesend wurde ein Modell fur ein Neuronales Netz analysiert und aufgestellt, welches auf ¨ moderne Techniken der Bilderkennung im Bereich Deep Learning zuruckgreift. Die Validierung ¨ und Bewertung des Verfahrens erfolgte anhand einer Testmenge auf Basis statistischer und aerodynamischer Metriken. Es konnte so ein Modell trainiert werden, welches fur verschiedene Geometrievariationen einer ¨ Optimierung Stromungsvorhersagen treffen kann. Die aerodynamische Auswertung verschiedener Kenngrosen offenbarte allerdings noch Verbesserungspotential, sodass der Einsatz des Modells im Rahmen einer Optimierung nicht abschliesend evaluiert wurde und lediglich Moglichkeiten fur zukunftige Anwendungsfalle aufgezeigt werden. Es ist dennoch erkennbar, dass verschiedene Stromungseffekte von dem Modell erlernt und wiedergegeben werden, sodass dies ein vielversprechender Ansatz fur weitere Untersuchungen ist.