Miracle-dz/UniFAR-FTUs huggingface dataset:
A geometric method to obtain error-correcting classification by neural networks with fewer hidden units

Coding the output of a neural network can reduce the output dimension but may also increase the requirements for approximation accuracy tolerated by error-correcting. Using a generalization of orthogonality, we propose a method for error-correcting classification by feedforward neural networks that reduces the number of hidden units for a variety of classes of patterns.
Kernel Fisher Analysis Based FeatureExtraction for Face Recognition UsingEuclidean Classifier

Face recognition is one of the important factors in this real situation.The kernel approach has been proposed to solve face recognition problem (by mapping input space to high dimensional feature space). Algorithm such as KFA (Kernel Fisher Analysis), preprocessing and training the images and classify using classifier for the images taken from ORL dataset. Euclidean Classifier is used for recognition. The advantage of MATLAB software is chosen to implement the analysis.
Heuristics for QoS maintenance: adaptive policies in differentiated services wireless networks

The fluctuation in resource availability demands for adaptive behavior in wireless environments. The problem is exacerbated by the differentiated services with different quality demands. We present the MATS+ scheme, an adaptive bandwidth allocation and admission control algorithm for mobile integrated services networks. This extends the recently proposed MATS scheme [11] to include non-real time classes and a per-class utilization control. We define an analytical model and performance metrics to evaluate the proposed scheme. The efficiency and flexibility of the analytical model allows conducting several experiments in a real word scenario by changing different system parameters. From the obtained results we define an interesting heuristics to initialize the scheme guaranteeing QoS requirements and to maintain the QoS while adapting to environment changing conditions.
Categorization of Musical Patterns by Self-Organizing Neuronlike Networks

Simulations of self-organizing neuronlike networks are used to demonstrate how untrained listeners might be able to sort their perceptions of dozens of diverse musical features into stable, meaningful schemata. A presentation is first made of the salient characteristics of such networks, especially the adaptive-resonance-theory (ART) networks proposed by Stephen Grossberg. Then a discussion follows of how a computer simulation of a four-level ART network - a simulation dubbed L'ART pour l'art - independently categorized musical events in Mozart's six earliest compositions. The ability of the network to abstract significant voiceleading combinations from these pieces (and in fact to detect a possible error in the New Mozart Edition) suggests that this approach holds promise for the study of how ordinary listeners process music's multidimensional complexity. In addition, the categorizations produced by the network are suggestive of alternative conceptualizations of music's hierarchical structure. a prim household, tidiness is a paramount virtue. Dishes line cupboards like soldiers on parade. Books march across shelves in close-order drill. There is a place for everything, and everything is in its place. A visitor senses
Towards symbolic strategy synthesis for <<A>>-LTL

We provide a symbolic algorithm for synthesizing winning strategies in Alternating Time Temporal Logic. ATL has game semantics, which makes it suitable for modeling open systems where coalitions of agents work together to achieve their goals. A typical use of the algorithm would begin with a highly non-deterministic set of agents, A, for which we wish to synthesize behavior. These may be composed with a set of opponent agents, which provide an environment. The desired behavior is then written in <<A>>-LTL, and a strategy for A to implement this is synthesized. If A implements this strategy, then the system will be guaranteed to satisfy the desired property. The algorithm presented here is part of a work in progress and updates can be found at http://www.cs. bham.ac. uk/ /spl sim/ ath/atl/spl I.bar/synthesis.
Multiple Description Transform Coding Comparison for Multiresolution Image Representation

Compression is widely used in image processing to improve efficiency transmission over erasure networks, so the design of the compression algorithm depends on the performance of transform. Many approaches have been applied, such that Discret Cosine Transform (DCT) adopted by JPEG standard and Discret Wavelet Transform (DWT) leaded to JPEG2000 which has remarkable success in still signal and image processing, but it fails to capture discontinuities to represent edges directionality, therefore new schemes have been developed to compensate this limitation, such as Contourlet Transform (CT) and it’s over-complete version Nonsubsampled Contourlet Transform (NSCT) to overcome the lacked shift invariant property in CT. In this paper, in the suggested scheme of multiple description transform coding (MDTC), experimental results demonstrate that our method leads to perform image reconstruction for practical MDC design in term of peak signal to noise ratio (PSNR) and visual quality assessment.
The Xyce Parallel Electronic Simulator - An Overview

The Xyce{trademark} Parallel Electronic Simulator has been written to support the simulation needs of the Sandia National Laboratories electrical designers. As such, the development has focused on providing the capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). In addition, they are providing improved performance for numerical kernels using state-of-the-art algorithms, support for modeling circuit phenomena at a variety of abstraction levels and using object-oriented and modern coding-practices that ensure the code will be maintainable and extensible far into the future. The code is a parallel code in the most general sense of the phrase--a message passing parallel implementation--which allows it to run efficiently on the widest possible number of computing platforms. These include serial, shared-memory and distributed-memory parallel as well as heterogeneous platforms. Furthermore, careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved even as the number of processors grows.
Two New Hybrid-type Data Dissemination Algorithms forLarge-Scale Wireless Sensor Networks

How to get a better balance between the push and pull of the event data is the common goal of the various data dissemination algorithms in wireless sensor networks (WSNs). At this basis, the load of the system hotspot should be further decreased and the number of the event data replicas should be reduced. In the paper, two well-known typical structured and unstructured data dissemination algorithms (DCS and CN) are analyzed and their advantages and disadvantages are figured out. At the same time, by combining the push- pull strategies of the two algorithms, two new data dissemination algorithms are proposed based on the hybrid structured and unstructured data push-pull strategies for the ALL-type queries in the different application situation in WSNs. The two new algorithms are hybrid-Dcs-Cn1 (HDC1) and hybrid-Dcs-Cn2 (HDC2). The theoretical analyses and simulations show that they resolve the problems of the high load of the hotspot and the large number of the event data replicas and the low performance of the query on the premise that ensuring the balance between the push and pull. The total system communication cost of the two new algorithms are close to or better than the existing algorithms, so the two algorithms are more appropriate for the WSNs , especially when there are a large volume of data to be dealt with, and they are two energy-efficient data dissemination algorithms.
Office Document Search by Semantic Relationship Approach

Office applications are becoming a major pillar of today’s organizations since they are used to edit a vast amount of digital documents. Finding these office documents from disparate data sources that fit users’ need is an essential task in conducting organization activities. Traditional search tools that employ keyword and phrase matching between the query and search index alone tend to offer high recall and low precision. As the result, search users are faced with too many irrelevant results. In order to solve this problem, we propose a novel search system that effectively searches documents of some office applications by the search query whose definition is based on document type, search terms and semantic relationship between the search terms and the documents. We present a technique that collects search terms and their semantic relationship from the documents to generate the XML-based search indices. Furthermore, our query optimization algorithm is also presented.
Croatian HMM-based Speech Synthesis

The paper describes the development of a trainable speech synthesis system, based on hidden Markov models. An approach to speech signal generation using a source-filter model is presented. Inputs into the synthesis system are speech utterances and their phone level transcriptions. A method using context dependent acoustic models and Croatian phonetic rules for speech synthesis is proposed. Croatian HMM based speech synthesis experiments are presented and generated speech results are discussed
Whirlpool: Structuring Mesh-Based Protocol for QoS-Aware Peer-to-Peer Streaming

Membership service is an essential problem in peer-to-peer (P2P) networks since many p2p applications such as media broadcasting and content distribution require a high performance overlay structure to deliver satisfying quality of service (QoS). The speed a peer locating a good neighbor can greatly affect the QoS of the application running on that peer. In this paper, we present a new mesh-based P2P multicast protocol called Whirlpool. The core idea in Whirlpool is the General Tree-Based Streaming Model, which organizes the peers into different levels based on their end-to-end latencies from the streaming source. The level calculation algorithm is totally distributed and can also provide the peers with biased direction in terms of end-to-end latency in selections of neighbor peers. The experiment results show that: (1) Whirlpool is scalable and robust to highly dynamic environment; (2) With the help of Whirlpool, peers can quickly locate good neighbors based on their QoS characteristics.
Embedding of Pathology Reports in Pathology Images

A pathology report contains Gross or Macroscopic description, Microscopic description and Diagnosis determined by examining cells and tissues under a microscope. In this paper, we propose a method to embed pathology reports in pathology images. In proposed method, adjacent pixel difference is used to embed data. The proposed algorithm is completely appropriate for pathology images. This algorithm has low computational complexity and high embedding capacity. The proposed method is reversible and the original image is restored after extracting data without any loss. The embedding of pathology reports in pathology images can be used in pathology exams, assessment of pathology residents, self-assessment programs, and constructing pathology archives and databases.
Modulation transfer function and noise assessment

In the context of Earth observation satellites such as SPOT or IKONOS, it is important to measure the Modulation Transfer Function (MTF) and the noise in order to quantify the quality of the imaging system. This measurement is useful to decide to focus the telescope or to make a deconvolution filter whose purpose is to enhance image contrast. This paper presents a univariant MTF and noise measurement method using non specific views. It is a particular application of a general approach of image quality assessment. The method presented in this paper is based on artificial neural network (ANN) use. The ANN learns how to recognize MTF and noise from known images, and the neural network is able, after a learning step, to assess the MTF and the noise from unknown images.
Wireless Sensor Networks: A Perspective to Time Synchronization

Wireless Sensor Networks is getting lot of attention by the research community. It consists of small devices distributed over geographical areas and each device has sensing, computing and communicating components. Time synchronization targets at equalizing the local times for all nodes in the network. It has been observed from the literature survey that all the nodes suffer from a problem named clock drift. This problem causes clock difference among nodes as time goes because the processors do not run exactly at the same speed. To overcome this problem a number of techniques have been proposed. TPSN (Timing-Sync Protocol for Sensor Networks) is one of the effective protocols proposed to synchronize sensor networks. In this paper, we propose improvements over TPSN to synchronize nodes in a wireless sensor network more effectively with a lower message complexity and higher precision.
Live video object tracking and segmentation using graph cuts

Graph cuts have proven to be powerful tools in image segmentation. Previous graph cut research has proposed methods for cutting across large graphs constructed from multiple layered video frames, resulting in an object being tracked across multiple frames. However, this research focuses on cutting graphs constructed from a prerecorded video sequence. In live video scenarios, frames cannot be layered to construct 3D volumes, since the contents of the subsequent frames are unknown. Instead, new graphs must be created and cut for each frame on demand. Resource limitations make this unfeasible on high-resolution videos. In addition, object tracking requires a method for incorporating the previous frame's object position and shape into the current graph. We propose a method for tracking and segmenting objects in live video that utilizes regional graph cuts and object pixel probability maps. The regionalization of the cuts around the tracked object will increase the speed of the tracker, and the object pixel probability maps will enable more flexible tracking.
Evaluation of rehabilitation robots for eating

Many rehabilitation robotic systems have been developed to aid people with disabilities but few are used in practice. A new method to evaluate the performance of a rehabilitation robot that aids quadriplegics in eating is described in this paper. Seven objective indices are proposed to evaluate the performance of this rehabilitation robotic system. Each index is discussed by putting the evaluator in the position of the users, which has proved effective.
Learning Corrections for Hyperelastic Models From Data

Unveiling physical laws from data is seen as the ultimate sign of human intelligence. While there is a growing interest in this sense around the machine learning community, some recent works have attempted to simply substitute physical laws by data. We believe that getting rid of centuries of scientific knowledge is simply nonsense. There are models whose validity and usefulness is out of any doubt, so try to substitute them by data seems to be a waste of knowledge. While it is true that fitting well-known physical laws to experimental data is sometimes a painful process, a good theory continues to be practical and provide useful insights to interpret the phenomena taking place. That is why we present here a method to construct, based on data, automatic corrections to existing models. Emphasis is put in the correct thermodynamic character of these corrections, so as to avoid violations of first principles such as the laws of thermodynamics. These corrections are sought under the umbrella of the GENERIC framework (Grmela and Oettinger, 1997), a generalization of Hamiltonian mechanics to non-equilibrium thermodynamics. This framework ensures the satisfaction of the first and second laws of thermodynamics, while providing a very appealing context for the proposed automated correction of existing laws. In this work we focus on solid mechanics, particularly large strain (visco-)hyperelasticity.
An analytical evaluation of tiling for stencil codes with time loop

Stencil codes such as the Jacobi, Gaus-Seidel, and red-black Gaus-Seidel kernels are among the most time-consuming routines in many scientific and engineering applications. The performance of these codes critically depends on an efficient usage of caches, and can be improved by tiling. Several tiling schemes have been suggested in the literature; this paper gives an overview and comparison. Then, in the main part, we prove a lower bound on the number of cold and capacity misses. Finally, we analyze a particular tiling scheme, and show that it is off the lower bound by a factor of at most six. Our results show up limitations to the speedup that can be gained by future research.
Phases-based dynamic genetic strategies for genetic algorithms

This paper focuses on the study of dynamic genetic strategies for adaptive parameters control in genetic algorithms (GA). The parameter space of GA is defined, and different adaptation approaches are compared. The currently adopted adaptive strategies make use of the crossover and mutation probabilities at individual level or component level, which, in the total process of evolution operations, can not fully exploiting the genetic information. Considering the dynamic population searching in the evolution process, a three phases-based adaptive genetic strategy is formulated, so that GA can be equipped simultaneously with the capabilities in both exploration and exploitation. It is then applied to the optimization of test functions, and the results reveal its efficiency and effectiveness.
Motion Control of a Quadrotor Aircraft via Singular Perturbations

In this paper, a new motion controller for a quadrotor aircraft is introduced. A reformulation of the control inputs of the dynamic model is discussed and then the control algorithm is given in a constructive form. The stability proof of the state space origin of the overall closed-loop system relies on the theory of singularly perturbed systems. Numerical simulations corroborate the viability of the proposed control scheme and the conclusions concerning stability. A set of simulations under practical conditions is also presented, where the system is affected by different types of disturbances and nonlinearities such as noise and actuator saturation.
Benefits of SPOT 5 HR and VHR data for forest management and windfall damage mapping

An assessment of SPOT 5 products, i.e. VHR and derived products, for forest management and windfall damage mapping has been carried out over of the Alsatian Region in NE France, principally over the Forest of Haguenau. This area has been badly affected by the devastating storms, Lohart and Martin, that struck continental Western Europe in December 1999, destroying the equivalent of several years of normal forest exploitation in France.
A study of the identification of authorship for Chinese texts

Style-based text authorship identification extracts features from authorship-known texts, constructs classifier and then identifies disputed texts. Authorship identification belongs to the domain of style classification and is a branch of text classification. In contrast with text classification which deals with the content of texts, authorship identification focuses on the form property of texts. This paper deals with the identification of authorship for Chinese texts.
Modular Self-Reconfigurable Robot Systems

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing field
Overerase phenomena: an insight into flash memory reliability

The most important reliability issues related to the erasing operation in flash memories are, still today, caused by single bit failures. In particular, the overerase of tail and fast bits affects the threshold voltage distribution width, causing bit-line leakage that produces read/verify circuitry malfunctions, affects the programming efficiency due to voltage drop, and causes charge-pump circuitry failure. This brief overview explores the most important characteristics of these anomalous bits, their relation with the erratic erase phenomena and their impact on flash memory reliability. Identification techniques, experimental results, and physical models are also discussed.
Directory Enabled Smart Spaces

Significant research and development in the area of system management has been carried out over the last number of years. This research has led to a number of proven technologies that are currently being used to manage large-scale systems. This dissertation aims to build upon this research and development to examine how a smart space management system could be created. The dissertation examines how an existing information model can be extended to adequately model the information that is required to manage a smart space. Policies, which can control the behaviour of the smart space, are also modelled using existing policy models. The information model and the policies are then mapped onto a directory service that is used as a repository. A prototype application is described which can use the directory service to control and manipulate a smart space. This prototype application is used as a platform to demonstrate the design decisions that have been taken. Conclusions will be drawn as to the potential benefits of using the Directory Enabled Networks Initiative and the Common Information model for the representation of smart space management information. The suitability of using policy based management techniques in the management of a smart space environment will also be discussed.
Password Hardened Biometric: A Complete Solution of Online Security

In present epoch, secure online access to enterprises resources is very important for any organization. To protect valuable data become one of the big challenge for today's business as enterprise customers or clients involved in business-to-customer (B2C) and business-to-business (B2B) e-commerce need to feel that their transactions are secured from system hackers. Biometric technology provides a solution to this problem in enterprise network security. In this article we enhance the security of online transaction using secure and unique biometric template. The uniqueness of our process is that we store biometric template and password together in encrypted form both without the fusion of score level and decision level, which leads a successful way to combine multiple technologies, different from earlier methods. Password hardened biometric system helps to generate many different secure biometric templates for the same biometric system. It also generates unique biometric templates for multiple biometric systems from the same biometric trait.