Miracle-dz/UniFAR-FTUs huggingface dataset:
A Focused Crawler in order to Get Semantic Web Resources (CSR)

This paper presents a Focused Crawler in order to Get Semantic Web Resources (CSR). Structured data web are available in formats such as Extensible Markup Language (XML), Resource Description Framework (RDF) and Ontology Web Language (OWL) that can be used for processing. One of the main challenges for performing a manual search and download semantic web resources is that this task consumes a lot of time. Our research work propose a focused crawler which allow to download these resources automatically and store them on disk in order to have a collection that will be used for data processing. CRS consists of three layers: (a) The User Interface Layer, (b) The Focus Crawler Layer and (c) The Base Crawler Layer. CSR uses as a selection policie the Shark-Search method. CSR was conducted with two experiments. The first one starts on December 15 2012 at 7:11 am and ends on December 16 2012 at 4:01 were obtained 448,123,537 bytes of data. The CSR ends by itself after to analyze 80,4375 seeds with an unlimited depth. CSR got 16,576 semantic resources files where the 89 % was RDF, the 10 % was XML and the 1% was OWL. The second one was based on the Web Data Commons work of the Research Group Data and Web Science at the University of Mannheim and the Institute AIFB at the Karlsruhe Institute of Technology. This began at 4:46 am of June 2 2013 and 1:37 am June 9 2013. After 162.51 hours of execution the result was 285,279 semantic resources where predominated the XML resources with 99 % and OWL and RDF with 1 % each one.
Adaptive edge focusing

Edge focusing, an efficient implementation method of continuous scale space filtering, has achieved better result in edge detection. Yet, the edge focusing algorithm smooths all edges with the filter of same scale, that generates ragged edges in the following areas: (1) area with diffuse edge, (2) texture edges. An optimal scale of edge detector is derived. With this optimal scale, the original edge focusing algorithm is modified. During the process of the revised edge focusing, if the scale of the edge detector has reached the optimal scale at any edge point, then the edge at that point will not be changed any more with a decreasing scale of the filter. The benefit of this modification is that it can avoid ragged edge and false edge points. The performance of the modified edge focusing algorithm is better than the original one. The experimental results support this conclusion. <<ETX>>
An Agenda for the Study of Intertextuality

summary:The study of intertextuality has been a central pursuit of scholars of Greek and especially Latin literature. It promises to reveal the meaning of texts for original audiences, trace authorial influence, and illuminate an aspect of literary artistry. Yet inconsistent standards and the scattering of insights across publications have hindered progress. This article proposes restoring momentum toward the goals of intertextual study through an agenda of representing intertexts in a standard digital form susceptible to complex and systematic analysis.
The formation of abstracts by the selection of sentences

Auto-abstracting techniques based on high-frequency words show an extremely small variation among themselves in the selection of sentences to form abstracts. Human selection of sentences, although less variable than chance expectancy, is considerably more variable than the machine methods. There was very little agreement between the subjects and machine methods in their selection of representative sentences.
Developing a derivative-enhanced object-oriented toolkit for scientific computations.

We describe the development of a differentiated version of PETSC, an object-oriented toolkit for the parallel solution of scientific problems modeled by partial differential equations. Traditionally, automatic differentiation tools are applied to scientific applications to produce derivative-augmented code, which can then be used for sensitivity analysis, optimization, or parameter estimation. Scientific toolkits play an increasingly important role in developing large-scale scientific applications. By differentiating PETSC, we provide accurate derivative computations in applications implemented using the toolkit. In addition to using automatic differentiation to generate a derivative enhanced version of PETSC, we exploit the component-based organization of the toolkit, applying high-level mathematical insight to increase the accuracy and efficiency of derivative computations.
A neural network based time series forecasting system

The neural network (NN) arena has suffered in the past years a remarkable development as one of the novel fields for artificial intelligence (AI). Time series analysis based on models of the variability of observations by postulating trends and cyclic effects, with a view to understand the cause of variation and to improve forecasting, suggests the use of NNs to deal with principal component analysis (PCA). The purpose of this work is to present a logical based NN system, along with: 1) the time series forecasting, with its characteristics of strong noise component and nonlinearity in data, showing itself as a field in which the use of NNs is particularly advisable; and 2) the PCA rules, organized in a default hierarchy as logical theories competing with one another for the right to represent a particular situation or to predict its successors, i.e. assisting in the process of choosing the best network to forecast each series. Some trials are conducted and the basic performance measures are used as baselines for comparison with other methods.
Jaspis - A Spoken Dialogue Architecture and its Applications

Speech can be an efficient and natural way for communication between humans and computers. Many practical applications have been constructed, but the full potential of speech applications has not been utilized. In addition to technological shortcomings, the development of speech applications lacks suitable techniques, methodology and development tools. For example, mobile and multilingual communication needs flexible and adaptive interaction methods which take into account the needs of different users and different environments. This dissertation addresses the following question: what kind of a system architecture do advanced speech applications require? The following challenges are specifically addressed: How could the system architecture support advanced interaction techniques? How could application development be supported by suitable models, methodology and tools? This dissertation introduces the Jaspis speech application architecture that has been designed to support adaptive and flexible human-computer interaction techniques. This work also presents several applications constructed on the Jaspis architecture. Two multilingual e-mail applications, and two timetable applications are presented to provide concrete examples. The challenges of pervasive speech applications are introduced in the context of an ubiquitous computing application. The findings from the use of the Jaspis-based applications are reported. Several Jaspis-based interaction models and tools are introduced to facilitate the development of practical applications. The focus is on human-computer interaction issues, and solutions are presented for tasks such as error management, Wizard of Oz experiments and corpora collection. Finally, an enhanced version of the Jaspis architecture is presented. The Jaspis architecture focuses on distributed and concurrent spoken dialogues, which are needed in pervasive applications, and in general to provide more natural interaction models. The main contribution of this work, the Jaspis architecture, will be released simultaneously with this dissertation as an open source software for speech application researchers and developers.
Facial Expression Recognition Based on Gabor Histogram Feature and MVBoost

In this paper , the Gabor transform is combined with the hierarchical histogram to extract facial expressional features , which could hierarchically represent the change of texture in local area , and thus capture the intrinsic facial features involved in facial expression analysis1 In addition , Gabor transform is relatively less sensitive to the change of lighting conditions and can thus tolerates certain rotation and deformation of images1 All these properties make this representation scheme quite robust in various conditions and more powerful than traditional representation scheme using 12D Gabor coefficients1 With the histogram feature used , a weak classifier which uses vectors as input and multi2class real values as output is designed to classify facial expression1 This weak classifier has been embedded into the multi2class real AdaBoost algorithm to meet the request of multi2class classification of facial expression1 The resulted method (named MVBoost) directly assigns a multi2class label to every input image1 Thus it needs not to train many two2class classifiers to meet the request of multi2class classification1 The training process and classification process are both simplified1 Experiments conducted on common database show the efficiency and effectiveness of the proposed technique1 It is expected that this new technique will make contribution to fast and robust facial expression recognition1
Application of a modulated light beam for the determination of the charge pattern

Investigations of the charging and discharging processes of electrets and other objects require in many cases determination of surface distribution of charge density. For such investigations induction capacitive probes are frequently used. Such probes, usually equipped with a small area measuring electrode (or aperture) are placed above and very close to the surface of investigated objects and moved along a straight line. By analyzing the surface line by line one can obtain a map of the distribution of the surface charge density. As well as simple induction probes one can use a commercial one with a DC-AC converting and electric field compensation in the air gap between the investigated sample and the probe. Other method was suggested by Alquie et al. They suggest to apply local perturbation of the geometry of a measuring system, where pressure pulse (generated on a small surface by laser ablation of a target deposited on the back electrode of the investigated electret) was used for creation of the transcient perturbation. A similar method is proposed below. Periodical and local heating of the absorption layer deposited on the back electrode of an electret is suggested for local and periodical perturbation of the geometry of a sandwich containing the investigated electret. A laser generated light beam (modulated and focused on the absorption layer) was used for local heating.
Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems

Machine learning is a technology paramount to enhancing the adaptability of agent-based systems. Learning is a desirable aspect in synthetic characters, or ‘believable’ agents, as it offers a degree of realism to their interactions. However, the advantage of collaborative efforts in multi-agent learning systems can be overshadowed by concerns over system scalability and adaptive dynamics. The proposed Multi-Agent Learning through Distributed Artificial Consciousness (MALDAC) Architecture is proposed as a scalable approach to developing adaptable systems in complex, believable environments. To support MALDAC, a cognitive architecture is proposed which applies emotional models and artificial consciousness theory to cope with complex environments. Furthermore, the cloud computing paradigm is employed in the architecture’s design to enhance system scalability. A virtual environment implementing MALDAC is shown to enhance scalability in multi-agent learning systems, particularly in stochastic and dynamic environments.
Conversational robots: An approach to conversation protocol issues that utilizes the paralinguistic information available in a robot-human setting

Functions have been implemented in various robots to enable them to follow a conversation protocol. The paralinguistic information involved in prosody and posture expression is used to improve the transparency of the conversational states, especially the protocol, thereby effectively contributing to natural and efficient communication. Information is communicated incrementally to enable error handling. Various rules for selecting conversation participants, forming a communication group, and turn-taking are followed. Since all the actions of a conversational robot are explicitly controlled, such robots should be useful for revealing important heretofore unknown conversational functions.
CACHE COHERENCE PROTOCOLS FOR LARGE-SCALE MULTIPROCESSORS

Caches have the potential to provide multiprocessors with an automatic mechanism for reducing both network traffic and average memory access latency. However, cache-based systems must address the problem of cache coherence. This thesis presents the results of the search for a cache coherence solution for Alewife, a large-scale multiprocessor being built at MIT. The research focuses on coherence protocols that use a directory, a list of cached copies of data, to avoid the need for a system-wide broadcast mechanism. The structure and the implementation of a number of coherence schemes are evaluated with coupled and decoupled simulation techniques. In addition to comparing the protocols in terms of hardware overhead and performance, the thesis reports on the experience gained by implementing several different schemes in ASIM, the Alewife machine simulator. The protocol search reaches two major conclusions: First, by using system-level optimizations, it is possible to use caches to build large-scale shared-memory multiprocessors. Second, the Alewife machine should use the integrated systems approach - handling common cases in hardware and exceptional cases in software - to solve the cache coherence problem.
MPEG Unified Speech and Audio Coding-The ISO/MPEG Standard for High-Efficiency Audio Coding of All C

In early 2012 the ISO/IEC JTC1/SC29/WG11 (MPEG) nalized the new MPEG-D Unied Speech and Audio Coding standard. The new codec brings together the previously separated worlds of general audio coding and speech coding. It does so by integrating elements from audio coding and speech coding into a unied system. The present publication outlines all aspects of this standardization eort, starting with the history and motivation of the MPEG work item, describing all technical features of the nal system, and further discussing listening test results and performance numbers which show the advantages of the new system over current state-of-the-art codecs.
Redundant complex number arithmetic for high-speed signal processing

This paper presents a new complex number representation called the Redundant Complex Number System (RCNS) as a useful basis for designing VLSI signal processors with complex arithmetic capability. RCNS described in this paper is defined as a positional number system that has a complex radix 2j and a digit set {-3,. ,0,. ,3} or {-2,. ,0,. ,2}. The use of complex radix 2j with the redundant digit set makes possible to construct high-speed complex number arithmetic circuits with highly regular structures. This paper discusses the elementary arithmetic algorithms of RCNS and their implementations.
Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in the analysis of scientific data. However, this relative transparency may encourage their use in an uncritical, and therefore possibly unproductive, fashion. The geometry of a network is among the most crucial factors in the successful deployment of network tools; in this review, we cover methods that can be used to determine optimum or near‐optimum geometries. These methods of determining neural network architecture include the following: (i) trial and error, in which architectures chosen semirandomly are tested and modified by the user; (ii) empirical or statistical methods, in which an ANN's internal parameters are adjusted based on the model's performance; (iii) hybrid methods, such as fuzzy inference; (iv) constructive and/or pruning algorithms, that add and/or remove neurons or weights from an initial architecture, respectively, based on a predefined link between architecture and ANN performance; (v) evolutionary strategies, which search the topology space using genetic operators to vary the neural network parameters. Several case studies illustrate the development of neural network models for applications in chemistry and chemical engineering. Copyright © 2011 John Wiley & Sons, Ltd.
GAUSSIAN AND FAST FOURIER TRANSFORM FOR AUTOMATIC RETINAL OPTIC DISC DETECTION

Optic Disc (OD) segmentation with a combination of Gaussian and Fast Fourier Transform is proposed in this research. Three stages are required in this proposed research, i.e., first, Gaussian and Fast Fourier Transform for the preprocessing stage, second, determination of region of interest of optic disc, and third is segmentation of OD using thresholding process. The objective of the first stage is to reduce the number of blood vessel pixels. Fast Fourier Transform combined with median filter makes the blood vessel pixels are reduced. Hence, the segmentation stages will focus only on the region of OD. The ROI from the second stage is segmented using the thresholding process. The threshold value is determined manually before the segmentation process. The best accuracy is obtained by the threshold value equal to 60. The average sensitivity, specificity, and balanced accuracy are 87.1495%, 91.2636%, and 89.2065%, respectively.
Design of rotational dielectric dome with linear array feed for wide‐angle multibeam antenna applications

This paper proposes a method for design of the shape and thickness of a dielectric dome by optimization, aiming at achieving multibeam emission with a uniform high gain for a wide range of angles. The dielectric dome has the features that it does not require a tremendous number of elements, as in the constrained lens, and that excellent multibeam characteristics can be realized over a wider range of angles than in the conventional dielectric lens. These features are demonstrated through an example of the design of a dielectric dome in which a decrease in gain is avoided over a wide range of angles up to 70°. The validity of the proposed method is verified by numerical analysis, and also by measurement and evaluation of the radiation pattern of the constructed dielectric dome antenna in the Ka band. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(5): 49– 57, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20254
Phase Noise and Frequency Stability in Oscillators

Foreword Lute Maleki Foreword David B. Leeson Preface List of symbols 1. Phase noise and frequency stability 2. Phase noise in semiconductors and amplifiers 3. Heuristic approach to the Leeson effect 4. Phase noise and linear feedback theory 5. Noise in delay-line oscillators and lasers 6. Oscillator hacking A Laplace transform Bibliography.
Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.
Representing Uncertainty in RTS Games

Real-time strategy (RTS) games are partially observable environments, requiring players to reason under uncertainty. The main source of uncertainty in RTS games is that players do not initially know the game map, including what units the opponent has created. This information gradually improves, in part by exploring, as the game progresses. To compensate for this uncertainty, human players use their experience and domain knowledge to estimate the combination of units that opponents control, and make decisions based on these estimates. For RTS game AI to mimic this behavior of human players, a suitable knowledge representation is required. The order in which units can be created in RTS games is conditioned by a game specific technology tree where units represented by parent nodes in the tree need to be created before units represented by child nodes can be created. We propose the use of a Bayesian Network (BN) to represent the beliefs that RTS game AI players have about the expansion of the technology tree of their opponents. We implement a BN for the RTS game StarCraft R © and give several examples of its use. In particular, we evaluate our design by improving strategy prediction under uncertainty from previously reported work [37]. Using our BN, we are able to increase the precision of strategy prediction up to 56%. These results show that the proposed BN can be used to infer creation time values for unobserved units in RTS games and that BNs are a promising approach for RTS game AI to represent and reason with uncertainty in RTS games. Meðhondlun ovissu i rauntima herkaenskuleikjum
A new approach for human identification using the eye

Thomas, N. Luke. M.S.E.C. E. , Purdue University, May 2010. A New Approach For Human Identification Using The Eye. Major Professor: Yingzi Du. The vein structure in the sclera, the white and opaque outer protective covering of the eye, is anecdotally stable over time and unique to each person. As a result, it is well suited for use as a biometric for human identification. A few researchers have performed sclera vein pattern recognition and have reported promising, but low accuracy, initial results. Sclera recognition poses several challenges: the vein structure moves and deforms with the movement of the eye and its surrounding tissues; images of sclera patterns are often defocused and/or saturated; and, most importantly, the vein structure in the sclera is multi-layered and has complex non-linear deformation. The previous approaches in sclera recognition have treated the sclera patterns as a one-layered vein structure, and, as a result, their sclera recognition accuracy is not high. In this thesis, we propose a new method for sclera recognition with the following contributions: First, we developed a color-based sclera region estimation scheme for sclera segmentation. Second, we designed a Gabor wavelet based sclera pattern enhancement method, and an adaptive thresholding method to emphasize and binarize the sclera vein patterns. Third, we proposed a line descriptor based feature extraction, registration, and matching method that is scale-, orientation-, and deformation-invariant, and can mitigate the multi-layered deformation effects and tolerate segmentation error. It is empirically verified using the UBIRIS and IUPUI multi-wavelength databases that the proposed method can perform accurate sclera recognition. In addition, the recognition results are compared to iris recognition algorithms, with very comparable results.