Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable optical switch structure based on tunable wavelength converters and arrayed waveguide grating routers

An optical packet switch designed for asynchronous operation is analyzed and simulated. The switch design is cost optimized, with only two stages of tunable wavelength converters, standard demultiplexers designed for wavelength-division multiplexing (WDM), and one single arrayed waveguide grating router used. The switch can be rearranged to be nonblocking, but performs without buffers almost like a strictly nonblocking switch, where the allowed traffic load is limited primarily by output contention. The switch is scalable and upgradeable in the sense that any number of input and output fibers is allowed, up to the number of WDM channels available on a fiber.
Communicating Insights from Complex Simulation Models

to understand their possibilities and limitations. A possible answer to this problem is to use experiential learning exercises to help nonspecialists in the understanding of these models. Interactive (or man-machine) simulation is considered a &dquo;hands-on&dquo; exercise for communicating insights from complex simulation models via a gaming approach. In a previous article (Geurts, van Griensven, Gubbles, and Vennix, 1985) we reported on the use of an interactive simulation of the Dutch social security system. Exploratory research on the use of this interactive simulation with groups of persons with little or no formal mathematical training indicated that participants consider the simulation useful, fascinating, and stimulating. At the same time, however, most participants reported that it is very complicated to realize certain social security policy goals (i.e., to
A Decentralised Architecture for Group Key Management

In recent years many different proposals have been presented to solve the problem of multicast communication security. There are proposals that employ a central entity, which is responsible for managing the whole group, and thus is not scalable for large groups. Other proposals distribute the group key generation among all members of the group. This also does not scale to large groups because every single member of a group participates in the key generation. Yet, other proposals divide large groups into smaller ones, employing a controller for each subgroup. Although these proposals solve the problem of scalability, other issues are raised. For example, some of these schemes employ a central controller for the subgroup controllers, and thus, if the central (subgroup) controller is compromised the whole group will be disrupted. On the other hand, the proposals, which have solved this issue by removing the subgroup central controller, have introduced new problems such as interference in the data path or leaving the key generation vulnerable to attacks. In this report, we analyse a large number of different approaches to group key management and present a distributed group key management scheme called Hydra. Hydra is based on the idea of subgroups, but it does not use a central controller in order to avoid one single point of failure. Also, Hydra employs a simple protocol to select the next key generator among the subgroup controllers, and in doing so, avoids the creation of other limitations to the group communication.
Linear logic as a tool for reasoning on a Petri net model

The objective of this paper is to explore what could be the basis of a new approach for a model based diagnosis of discrete event dynamic systems such as manufacturing plants. The principle of this approach is to use linear logic in order to elaborate abductive reasonings above a Petri net based deep model of the behavior of the manufacturing system. In this paper we assume that the basis of a linear logic representation of a Petri net is known and we focus on the fact that a good utilization of the linear logic structural rules allows the direct characterization of sets of transition sequences. In other words the structural concurrency of a Petri net can be used at the reasoning level to generate sequences which only differ with respect to the firing order of parallel transitions. As a direct consequence of this result, the differences between the two multiplicative connectives /spl otimes/ and ? of linear logic are stated. On the right part of the linear implication connective, the first one express the production of resources which have to be simultaneously consumed whereas the second one describes resources which can be independently consumed although they participate in a unique proof.
Hierarchical dynamic state estimator using ANN-based dynamic load prediction

The paper presents a new scheme for dynamic state estimation in power systems. The dynamics of the power system are modelled more realistically using artificial-neural-network-based bus-load prediction and load flow for state prediction. At the filtering step a hierarchical model which incorporates the measurement function nonlinearities is used. This considerably reduces the computational effort, making the proposed scheme suitable for on-line application. Test results for an IEEE 118-bus test system are presented to show the characteristics of the present method.
A new dependency model based testability analyzer

This paper describes the development of a testability analysis tool called ADMA. The approach used in ADMA is based on use of dependency models. Dependency models are the basis of several important products for testability analysis. What makes ADMA different from existing testability analysis tools is its capability to use multiple analysis algorithms and its user-friendly report format. The rationale behind the decision to support multiple algorithms is that an algorithm may perform better for certain circuits than other algorithms ADMA allows users to compare the results of different analysis side by side. ADMA provides concise reports of different formats for different types of users, such as executives, system designers, test engineers, and dependency modelers. Thus, users can focus on the aspects that are of interest to them. The subject paper gives an overview of the ADMA, emphasizing the fundamental advances represented by ADMA. The paper also describes how this tool integrates with the Automatic Dependency Model Generator, and with the Automatic Built-in Test generator. The paper does not focus on ADMA as a product, but more on how it generally extends current dependency mode-based tools.
A hybrid coder for hidden Markov models using a recurrent neural networks

A hybrid coder is introduced for obtaining descriptions of speech patterns. This coder uses vector quantization (VQ) techniques on mel-scale cepstral coefficients and their derivatives together with a recurrent network (RN) for describing suprasegmental features of speech. The purpose of these features is to focus the search when hidden Markov models (HMMs) are used for speech unit or word models. Preliminary experiments of speaker-independent connected digit recognition show that using a hybrid coder based on a RN improves recognition performance. <<ETX>>
Annotating Argumentation Acts in Spoken Dialog

This manual describes a method for annotating rhetorical relations, adjacency pairs, and other argumentation acts found in task-oriented spoken dialog [Traum, 1993; Traum and Hinkelman, 1992]. It is largely aimed at the novice annotator rather than the computational linguist, and therefore in choosing terminology we have valued intuitiveness over precision. This work came out of an exploration of how to mark structure above the speech act in the Monroe corpus [Stent, 2000 (TN 99-2)]. For more information about the development of this manual, see [Stent, 2000 (INLG)]. This tool is designed for use with ArgumentationTool, a tool for marking argumentation acts in dialog that is available from http://www.cs. rochester.edu/research/cisd/resources/aad/.
Adaptive Approximation Based Control-Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches-[Book review; J. A. Farrell and M. M. Polycarpou]

This book, by two of the key people who have developed rigorous methods in the arena of Intelligent Control, provides a long-awaited overall perspective that unifies nonlinear network approximators and adaptive control techniques. The focus of the book is continuous-time adaptive systems. The text consists of eight chapters. Some of the topics discussed include: approximation theory; approximation structures used in Intelligent Control; methods for formulating parametric models; extensions to nonlinear control architectures; control of nonlinear scalar systems; an all-purpose theory of adaptive approximation-based control for general nonlinear systems; and applications to controls design for fixed-wing aircraft. Exercises and design problems at the end of each chapter magnify the usefulness of this book. Intuitive clarity of exposition and mathematical rigor are extremely well-balanced to provide a book that can be used to introduce these concepts to a graduate student class or to industry engineers, and yet also satisfy the most demanding of theoreticians.
SOFT COMPUTING TOOLS IN RAINFALL-RUNOFF MODELING

ABSTRACT The use of rainfall-runoff models in the decision making process of water resources planning and management has become increasingly indispensable. Rainfall-runoff modeling in the broad sense started at the end of 19th century and till today there are various types of models based on their mechanism, input data and other modeling requirements. These type of models range from physical, conceptual, empirical models and more sophisticated models like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP), Model Tree (MT), Support Vector Machine (SVM) and recently Chaos theory. The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.
Token-passing communication protocol in hardware-based real-time spiking neural networks

Biological neural networks are based upon axonal point-to-point connections which inspire connectionist architecture. As we attempt to engineer ever larger analogues of these neural networks we are forced to multiplex neural signals over time shared paths. This can alter timing of neural information, which is critical in real-time oscillatory networks. Because shared paths induce extra delay due to multiplexing signals, traveling on the channel and passing through routing devices, guaranteeing event arrival deadlines across the communication process becomes crucial. This paper addresses issues related to the guarantee of event timings with arbitrary deadline constraints in real-time distributed spiking neural network systems based on token-ring architecture. To achieve this objective, we propose an integrated method in selecting key system parameters. We show that several parameters must be set carefully if event deadlines are to be satisfied. The token holding time (THT) parameter controls the bandwidth allocation for each node in the token-ring network, and must be set properly to avoid deadline misses. The target token rotation time (TTRT) determines both the speed of token circulation and the network utilization available to nodes. TTRT should also be chosen carefully to ensure that the token circulates fast enough while maintaining a high available utilization. As prove of concept, the proposed method is applied to a multi-board spiking neural network system hosting up to 140 analog neurons spread across 7 circuit-boards. Experimental analysis shows that deadline constraints are guaranteed along with bandwidth allocation fairness when applying the proposed method.
Effects of learning to program a computer in BASIC or Logo on problem-solving abilities

AbstractThe purposes of this study were (a) to determine if learning to program a computer in either BASIC or Logo improves the problem-solving skills of fifth grade students when compared to a control group that receives no programming instruction, and (b) to determine if learning to program a computer in Logo is more effective than learning to program in BASIC. Subjects were 132 fifth graders from two suburban elementary schools. Subjects in the BASIC treatment group received seven weeks (approximately 35 hours) of computer programming instruction using the Computer Challenge Guide. Subjects in the Logo group received the same amount of instruction using Logo in the Classroom. The New Jersey Test of Reasoning Skills was used as the pretest and posttest measure, and data were analyzed using the analysis of covariance procedure. Results indicated that learning to program in BASIC or Logo does not significantly improve problem-solving abilities of fifth grade students, as measured by the instrument used, w.
Service blueprinting and process-chain-network: an ontological comparison

Recently, Sampson (2012b) has introduced a new way to visualise service delivery processes which he calls process-chain-network (PCN) analysis. Since the 1980s, service marketers and managers have been using service blueprinting to visualise a service delivery process. We ask 'How well does PCN represent the concepts present in service blueprinting? ' To answer this question, we examine the similarities and differences between service blueprinting and PCN using a recognised method of ontological comparison. We have found PCN supports some but not all of the concepts in service blueprinting. The outcome is important because knowing the shortcomings of PCN compared with service blueprinting will allow modellers to exercise care when using PCN diagrams to represent service processes.
Continual Word Embedding Based for Matching Lightweight Ontologies

Ontology matching is the task of finding alignments between two different ontologies. It has become the key point of building knowledge base and integrating heterogeneous data. In this paper, a novel ontology matching approach that is based on continual word embedding is proposed. We describe in details how is skip-gram model adapted to capture the semantic of words to learn the word embedding. After computing the name similarity of concepts, similarity flooding algorithm is used to fix the initial similarity. Experiments on Ontology Alignment Evaluation Initiative (OAEI) benchmark without instances show that the proposed method significantly improves the quality of mappings.
Fuzzy Maximum Power Point Tracking Techniques Applied to a Grid-Connected Photovoltaic System

Issues such as the increasing worried about global warming caused by the use of energy resources based on fossil fuel such as oil, gas and coal, have pointed the target toward sustainable energy resources free of greenhouse gas emissions. The PhotoVoltaic (PV) energy plays an important role into the called green energies sources, reason why its use has been rapidly invigorated A. Yafaoui (2009). However of this, a PV generator has two significant problems; the mismatch between the load and the conductance of the PV generator . Since, the load must match PV conductance of the PV generator to ensure the maximum power transfer A. Yafaoui (2009), Hohm & Ropp (2000). The other point, is that the PV generation depends of the weather conditions, such as solar irradiance and temperature Mutoh et al. (2006). To ensure the maximum power transfer the load seen from the PV generator must be continuously adjusted. Therefore, a Maximum Power Point Tracking (MPPT) algorithm must be implemented to achieve the match between the PV generator (PVG) and the load in real time, taking into account that the maximum power point is not known a priori it is not an easy task to track the maximum power point Mutoh et al. (2006) Kim et al. (2006), Zeng & Liu (2009). Additionally, regarding the low efficiency of a PVG it would be desirable to obtain the maximum power under any weather condition Mutoh et al. (2006), Roman et al. (2006). Many MPPT algorithm methods have been proposed to deal with the problem of the power generation variations due to changes at the solar irradiance and at PV cell temperature. They range from simple algorithm based on perturbation and observation to more complex based on neural network, and fuzzy logic control A. Yafaoui (2009), Hohm & Ropp (2000), Zeng & Liu (2009). A PV system can be implemented as a stand-alone system or as a grid connected generator. A stand-alone system requires a battery bank to store the energy obtained from the PV generator. That kind of system is frequently used in low power system to support a local energy requirement. On the other hand, a grid-connected PV system (GCPVS) usually does not require the battery bank and has become the primarymethod for high-power applications Kim et al. (2006). A GCPVS takes an important part into distributed power generation systems such as low-voltage distribution grids Alonso-Martinez et al. (2009), Vandoorn et al. (2009), Xue et al. (2004). Where, the energy generated by the GCPVS is sent to the power grid and consumed by the nearest customers. This is accomplished through an efficient DC/AC conversion by means of a solid-state Inverter. The inverter becomes the interface between the Neson Diaz, Johann Hernández, Oscar Duarte Universidad Nacional, División de Investigación Bogotá, LIFAE, Universidad Distrital F.J.C. Colombia Fuzzy Maximum Power Point Tracking Techniques Applied to a Grid-Connected Photovoltaic System 9
A Locality based Resource Management Scheme for Hierarchical P2P Overlay Network in Ubiquitous Computing

Many peer-to-peer (p2p) systems have been studied in distributed, ubiquitous computing environments. Distributed hash table (DHT)-based p2p systems can improve load-balancing even though locality utilization and user mobility are not guaranteed. We propose a mobile locality-based hierarchical p2p overlay network (MLH-Net) to address locality problems without any other services. MLH-Net utilizes mobility features in a mobile environment. MLH-Net is constructed as two layers, an upper layer formed with super-nodes and a lower layer formed with normal-nodes. The simulation results demonstrate that MLH-Net can decrease discovery routing hops by 13% compared with JXTA and 69% compared with Chord. It can decrease the discovery routing distance by 17% compared with JXTA and 83% compared with Chord depending on the environment.
Using Distmesh as a mesh generating tool for EIT

We present a collection of automatic mesh generation software tools for electrical impedance tomography (EIT) finite element models (FEMs). Creating an image in EIT involves retrieving conductivity distribution from surface electrode measurements. The initial forward problem of this process requires estimates of electric potential according to the current sources. With non-trivial geometries, this task is difficult analytically and demands a numerical method such as FEM. The areas nearest to electrodes have the highest current density and are the most significant measurement points. For our FEM model, these areas should have the greatest mesh density for the purposes of usefulness and accuracy. The research focuses on Distmesh, an open source MATLAB mesh generator, for creating mesh shapes with non-uniform mesh densities for EIT software. The inherent simplicity of the Distmesh code was the rationale for its evaluation. A mesh shape is defined from specifying its signed distance function and its mesh size distribution is defined with an element length function. We created circular meshes with mesh refinement towards endpoints of distributed electrodes. In three dimensions, cylinders were produced with mesh refinement towards rings of points for each electrode. Finally, to accommodate organic shapes, meshes for closed contours were created using elliptic Fourier descriptors. Because Distmesh was not designed for efficiency, the generation of well-defined meshes is fairly slow. For the contours, Distmesh forms reasonable meshes but is unable to improve node locations upon successive iterations. Distmesh is not a suitable mesh generator for EIT software unless its code is optimized for speed.
Parallel implementation of simulated annealing using transaction processing

Simulated annealing is an effective method for solving large combinatorial optimisation problems. Because of its iterative nature the annealing process requires a substantial amount of computation time. A new parallel implementation based on the concurrency control theory of database systems is presented; the parallelised annealing process is serialisable, Concurrent updates to the base solution are allowed provided that they do not have data conflict. Using the travelling salesman problem as the example application, the parallel simulated annealing algorithm is implemented on a Motorola Delta 3000 shared-memory multiprocessor system with eight processors. With a moderate problem size of 400 cities, a speedup efficiency of over 90% is achieved at high annealing temperature and close to 100% at a low annealing temperature.
PAIRED PROGRAMMING FOR NON-COMPUTING STUDENTS

In this paper, we describe a method used to teach non-computer science students how to program. We examine why students may not be engaging with the material and present an alternative method for conducting laboratory sessions. We allow students to self select their perceived level of expertise and use paired programming techniques. This has proved to be effective on several levels, engaging students and reducing demonstrator loading.
Investigation Of Optimal Wavelet Family To Improve The Psnr Of Digital Image

Denoising is the process of removing noise from image.Additive noise can be easily removed using simple threshold methods.Denoising of natural images corrupted by Gaussian noise using wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transform values.DWT supports multiresolution property which is not supported by another techniques(fourier transform). In this paper the visushrink, normalshrink, neighshrink and revised neighshrink methods are compared to get the better value of PSNR.Observed result shows that the revised neighshrink gives better result in terms of PSNR.
Face detection for selective polygon reduction of humanoid meshes

Automatic mesh optimization algorithms suffer from the problem that humans are not uniformly sensitive to changes on different parts of the body. This is a problem because when a mesh optimization algorithm typically measures errors caused by triangle reductions, the errors are strictly geometrical, and an error of a certain magnitude on the thigh of a 3D model will be perceived by a human as less of an error than one of equal geometrical significance introduced on the face. The partial solution to this problem proposed in this paper consists of detecting the faces of the 3D assets to be optimized using conventional, existing 2D face detection algorithms, and then using this information to selectively and automatically preserve the faces of 3D assets that are to be optimized, leading to a smaller perceived error in the optimized model, albeit not necessarily a smaller geometrical error. This is done by generating a set of per-vertex weights that are used to scale the errors measured by the reduction algorithm, hence preserving areas with higher weights. The final optimized meshes produced by using this method is found to be subjectively closer to the original 3D asset than their non-weighed counterparts, and if the input meshes conform to certain criteria this method is well suited for inclusion in a fully automatic mesh decimation pipeline