Página 1 dos resultados de 259 itens digitais encontrados em 0.111 segundos

Taking Notes on PDAs with Shared Text Input

Denoue, Laurent; Singh, Gurminder; Das, Arijit
Fonte: Escola de Pós-Graduação Naval Publicador: Escola de Pós-Graduação Naval
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.64%
This paper presents a system designed to support note taking on a wirelessly connected PDA in a classroom. The system leverages the devices’ wireless connectivity to allow students to share their notes in real time, allowing individuals to quickly reuse words from their fellow note takers. In addition, presentation material such as Powerpoint slides are also extracted when presented by the teacher, giving students further means to reusing words. We describe the system and report on the findings of an initial user study where the system has been used for four months during a graduate level course on wireless mobile computing with 20 students.

Memórias associativas L-fuzzy com ênfase em memórias associativas fuzzy intervalares; L-fuzzy associative memories with an emphasis on interval-valued fuzzy associative memories

Tiago Schuster
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/01/2015 PT
Relevância na Pesquisa
45.7%
As últimas décadas têm testemunhado a emergência de uma variedade de abordagens à resolução de problemas com base na computação em reticulados como, por exemplo, as redes neurais morfológicas e os modelos neurocomputação e de raciocínio fuzzy em reticulados. Usamos aqui o termo "reticulado'' no sentido dado no trabalho seminal de Birkhoff. A teoria dos reticulados nasceu da álgebra booleana e tem um grande leque de aplicações como a análise de conceitos formais, a inteligência computacional, a teoria dos conjuntos fuzzy e a morfologia matemática (MM). A MM em reticulados completos representa a base teórica para uma série de modelos de inteligência computacional conhecidos como redes neurais morfológicas (MNNs), que incluem as memórias associativas morfológicas em tons de cinza e as memórias associativas morfológicas fuzzy (FMAMs). As últimas décadas têm testemunhado a emergência de uma variedade de abordagens à resolução de problemas com base na computação em reticulados como, por exemplo, as redes neurais morfológicas e os modelos neurocomputação e de raciocínio fuzzy em reticulados. Usamos aqui o termo "reticulado'' no sentido dado no trabalho seminal de Birkhoff. A teoria dos reticulados nasceu da álgebra booleana e tem um grande leque de aplicações como a análise de conceitos formais...

On evaluation of XML documents using fuzzy linguistic techniques

Peis, E.; Herrera-Viedma, E.; Herrera, J.C.
Fonte: ISKO Conference Publicador: ISKO Conference
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.74%
Recommender systems evaluate and filter the great amount of information available on the Web to assist people in their search processes. A fuzzy evaluation method of XML documents based on computing with words is presented. Given an XML document type (e.g. scientific article), we consider that its elements are not equally informative. This is indicated by the use of a DTD and defining linguistic importance attributes to the more meaningful elements of the DTD designed. Then, the evaluation method generates linguistic recommendations from linguistic evaluation judgements provided by different recommenders on meaningful elements of DTD.

Evaluating the informative quality of web sites by fuzzy computing with words

Peis, E.; Herrera-Viedma, E.; Olvera Lobo, Mar??a Dolores; Herrera, J.C.; Hassan-Montero, Yusef
Fonte: Atlantic Web Intelligence Conference Publicador: Atlantic Web Intelligence Conference
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
95.85%
In this paper we present a method based on fuzzy computing with words to measure the informative quality of Web sites used to publish information stored in XML documents. This method generates linguistic recommendations on the informative quality of Web sites. This method is made up of both an evaluation scheme to analyze the informative quality of such Web sites and a generation method of linguistic recommendations. The evaluation scheme presents both technical criteria of Web site design and criteria related to the content of information of Web sites. It is oriented to the user because the chosen criteria are user friendly, in such a way that visitors to a Web site can assess them by means of linguistic evaluation judgements. The generation method generates linguistic recommendations of Web sites based on those linguistic evaluation judgements using the LOWA and LWA operators. Then, when a user looks for information on the Web we can help him/her with both recommendations on Web sites which store the retrieved documents and also recommendations on other Web sites which store other documents of interest related to his/her information needs. With this proposal information filtering and evaluation possibilities on the Web are increased.

A Fuzzy Petri Nets Model for Computing With Words

Cao, Yongzhi; Chen, Guoqing
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/10/2009
Relevância na Pesquisa
66.05%
Motivated by Zadeh's paradigm of computing with words rather than numbers, several formal models of computing with words have recently been proposed. These models are based on automata and thus are not well-suited for concurrent computing. In this paper, we incorporate the well-known model of concurrent computing, Petri nets, together with fuzzy set theory and thereby establish a concurrency model of computing with words--fuzzy Petri nets for computing with words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of transitions are some special words modeled by fuzzy sets. By employing the methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which makes it possible for computing with more words. The language expressiveness of the two formal models of computing with words, fuzzy automata for computing with words and FPNCWs, is compared as well. A few small examples are provided to illustrate the theoretical development.; Comment: double columns 14 pages, 8 figures

Algebraic synchronization criterion and computing reset words

Berlinkov, Mikhail; Szykuła, Marek
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.64%
We refine a uniform algebraic approach for deriving upper bounds on reset thresholds of synchronizing automata. We express the condition that an automaton is synchronizing in terms of linear algebra, and obtain upper bounds for the reset thresholds of automata with a short word of a small rank. The results are applied to make several improvements in the area. We improve the best general upper bound for reset thresholds of finite prefix codes (Huffman codes): we show that an $n$-state synchronizing decoder has a reset word of length at most $O(n \log^3 n)$. In addition to that, we prove that the expected reset threshold of a uniformly random synchronizing binary $n$-state decoder is at most $O(n \log n)$. We also show that for any non-unary alphabet there exist decoders whose reset threshold is in $\varTheta(n)$. We prove the \v{C}ern\'{y} conjecture for $n$-state automata with a letter of rank at most $\sqrt[3]{6n-6}$. In another corollary, based on the recent results of Nicaud, we show that the probability that the \v{C}ern\'y conjecture does not hold for a random synchronizing binary automaton is exponentially small in terms of the number of states, and also that the expected value of the reset threshold of an $n$-state random synchronizing binary automaton is at most $n^{3/2+o(1)}$. Moreover...

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

Li, Teng; Narayana, Vikram K.; El-Ghazawi, Tarek
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/11/2015
Relevância na Pesquisa
35.69%
The High Performance Computing (HPC) field is witnessing a widespread adoption of Graphics Processing Units (GPUs) as co-processors for conventional homogeneous clusters. The adoption of prevalent Single- Program Multiple-Data (SPMD) programming paradigm for GPU-based parallel processing brings in the challenge of resource underutilization, with the asymmetrical processor/co-processor distribution. In other words, under SPMD, balanced CPU/GPU distribution is required to ensure full resource utilization. In this paper, we propose a GPU resource virtualization approach to allow underutilized microprocessors to effi- ciently share the GPUs. We propose an efficient GPU sharing scenario achieved through GPU virtualization and analyze the performance potentials through execution models. We further present the implementation details of the virtualization infrastructure, followed by the experimental analyses. The results demonstrate considerable performance gains with GPU virtualization. Furthermore, the proposed solution enables full utilization of asymmetrical resources, through efficient GPU sharing among microprocessors, while incurring low overhead due to the added virtualization layer.; Comment: 21 pages

All-optical quantum computing with a hybrid solid-state processing unit

Pei, Pei; Zhang, Feng-Yang; Li, Chong; Song, He-Shan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.68%
We develop an architecture of hybrid quantum solid-state processing unit for universal quantum computing. The architecture allows distant and nonidentical solid-state qubits in distinct physical systems to interact and work collaboratively. All the quantum computing procedures are controlled by optical methods using classical fields and cavity QED. Our methods have prominent advantage of the insensitivity to dissipation process benefiting from the virtual excitation of subsystems. Moreover, the QND measurements and state transfer for the solid-state qubits are proposed. The architecture opens promising perspectives for implementing scalable quantum computation in a broader sense that different solid-state systems can merge and be integrated into one quantum processor afterwards.; Comment: 9 pages, 4 figures, supplement the demonstration for the efficiency and practicability of the proposal, supplement figure 4, and amend some words and phrases

A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network

Zhu, Chenxi; Qiu, Xipeng; Chen, Xinchi; Huang, Xuanjing
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/05/2015
Relevância na Pesquisa
35.65%
In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets.

Approximate Robotic Mapping from sonar data by modeling Perceptions with Antonyms

Guadarrama, Sergio; Ruiz-Mayor, Antonio
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/06/2010
Relevância na Pesquisa
45.55%
This work, inspired by the idea of "Computing with Words and Perceptions" proposed by Zadeh in 2001, focuses on how to transform measurements into perceptions for the problem of map building by Autonomous Mobile Robots. We propose to model the perceptions obtained from sonar-sensors as two grid maps: one for obstacles and another for empty spaces. The rules used to build and integrate these maps are expressed by linguistic descriptions and modeled by fuzzy rules. The main difference of this approach from other studies reported in the literature is that the method presented here is based on the hypothesis that the concepts "occupied" and "empty" are antonyms rather than complementary (as it happens in probabilistic approaches), or independent (as it happens in the previous fuzzy models). Controlled experimentation with a real robot in three representative indoor environments has been performed and the results presented. We offer a qualitative and quantitative comparison of the estimated maps obtained by the probabilistic approach, the previous fuzzy method and the new antonyms-based fuzzy approach. It is shown that the maps obtained with the antonyms-based approach are better defined, capture better the shape of the walls and of the empty-spaces...

Research On Mobile Cloud Computing: Review, Trend, And Perspectives

Qi, Han; Gani, Abdullah
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/06/2012
Relevância na Pesquisa
35.79%
Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry buzz words and a major discussion thread in the IT world since 2009. As MCC is still at the early stage of development, it is necessary to grasp a thorough understanding of the technology in order to point out the direction of future research. With the latter aim, this paper presents a review on the background and principle of MCC, characteristics, recent research work, and future research trends. A brief account on the background of MCC: from mobile computing to cloud computing is presented and then followed with a discussion on characteristics and recent research work. It then analyses the features and infrastructure of mobile cloud computing. The rest of the paper analyses the challenges of mobile cloud computing, summary of some research projects related to this area, and points out promising future research directions.; Comment: 8 pages, 7 figures, The Second International Conference on Digital Information and Communication Technology and its Applications

Retraction and Generalized Extension of Computing with Words

Cao, Yongzhi; Ying, Mingsheng; Chen, Guoqing
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.06%
Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.; Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy Systems

Probabilistic Automata for Computing with Words

Cao, Yongzhi; Xia, Lirong; Ying, Mingsheng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/04/2006
Relevância na Pesquisa
66.05%
Usually, probabilistic automata and probabilistic grammars have crisp symbols as inputs, which can be viewed as the formal models of computing with values. In this paper, we first introduce probabilistic automata and probabilistic grammars for computing with (some special) words in a probabilistic framework, where the words are interpreted as probabilistic distributions or possibility distributions over a set of crisp symbols. By probabilistic conditioning, we then establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling arbitrary inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with some special words. To compare the transition probabilities of two near inputs, we also examine some analytical properties of the transition probability functions of generalized extensions. Moreover, the retractions and the generalized extensions are shown to be equivalence-preserving. Finally, we clarify some relationships among the retractions, the generalized extensions, and the extensions studied recently by Qiu and Wang.; Comment: 35 pages; 3 figures

Towards an Extension of the 2-tuple Linguistic Model to Deal With Unbalanced Linguistic Term sets

Abchir, Mohammed-Amine; Truck, Isis
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/04/2013
Relevância na Pesquisa
45.65%
In the domain of Computing with words (CW), fuzzy linguistic approaches are known to be relevant in many decision-making problems. Indeed, they allow us to model the human reasoning in replacing words, assessments, preferences, choices, wishes... by ad hoc variables, such as fuzzy sets or more sophisticated variables. This paper focuses on a particular model: Herrera & Martinez' 2-tuple linguistic model and their approach to deal with unbalanced linguistic term sets. It is interesting since the computations are accomplished without loss of information while the results of the decision-making processes always refer to the initial linguistic term set. They propose a fuzzy partition which distributes data on the axis by using linguistic hierarchies to manage the non-uniformity. However, the required input (especially the density around the terms) taken by their fuzzy partition algorithm may be considered as too much demanding in a real-world application, since density is not always easy to determine. Moreover, in some limit cases (especially when two terms are very closed semantically to each other), the partition doesn't comply with the data themselves, it isn't close to the reality. Therefore we propose to modify the required input...

Recurrent Neural Network Method in Arabic Words Recognition System

Perwej, Yusuf
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/01/2013
Relevância na Pesquisa
35.73%
The recognition of unconstrained handwriting continues to be a difficult task for computers despite active research for several decades. This is because handwritten text offers great challenges such as character and word segmentation, character recognition, variation between handwriting styles, different character size and no font constraints as well as the background clarity. In this paper primarily discussed Online Handwriting Recognition methods for Arabic words which being often used among then across the Middle East and North Africa people. Because of the characteristic of the whole body of the Arabic words, namely connectivity between the characters, thereby the segmentation of An Arabic word is very difficult. We introduced a recurrent neural network to online handwriting Arabic word recognition. The key innovation is a recently produce recurrent neural networks objective function known as connectionist temporal classification. The system consists of an advanced recurrent neural network with an output layer designed for sequence labeling, partially combined with a probabilistic language model. Experimental results show that unconstrained Arabic words achieve recognition rates about 79%, which is significantly higher than the about 70% using a previously developed hidden markov model based recognition system.; Comment: 6 Pages...

A Systematic Mapping Study on Cloud Computing

Carvalho, Jose Fernando S.; Neto, Paulo Anselmo da Mota Silveira; Garcia, Vincius Cardoso; Assad, Rodrigo Elia; Durao, Frederico
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/08/2013
Relevância na Pesquisa
35.76%
Cloud Computing emerges from the global economic crisis as an option to use computing resources from a more rational point of view. In other words, a cheaper way to have IT resources. However, issues as security and privacy, SLA (Service Layer Agreement), resource sharing, and billing has left open questions about the real gains of that model. This study aims to investigate state-of-the-art in Cloud Computing, identify gaps, challenges, synthesize available evidences both its use and development, and provides relevant information, clarifying open questions and common discussed issues about that model through literature. The good practices of systematic map- ping study methodology were adopted in order to reach those objectives. Al- though Cloud Computing is based on a business model with over 50 years of existence, evidences found in this study indicate that Cloud Computing still presents limitations that prevent the full use of the proposal on-demand.

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Vincent, Pascal; de Brébisson, Alexandre; Bouthillier, Xavier
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.73%
An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D (e.g. 200 000). Computing the equally large, but typically non-sparse D-dimensional output vector from a last hidden layer of reasonable dimension d (e.g. 500) incurs a prohibitive O(Dd) computational cost for each example, as does updating the D x d output weight matrix and computing the gradient needed for backpropagation to previous layers. While efficient handling of large sparse network inputs is trivial, the case of large sparse targets is not, and has thus so far been sidestepped with approximate alternatives such as hierarchical softmax or sampling-based approximations during training. In this work we develop an original algorithmic approach which, for a family of loss functions that includes squared error and spherical softmax, can compute the exact loss, gradient update for the output weights, and gradient for backpropagation, all in O(d^2) per example instead of O(Dd), remarkably without ever computing the D-dimensional output. The proposed algorithm yields a speedup of D/4d ...

Words are not Equal: Graded Weighting Model for building Composite Document Vectors

Singh, Pranjal; Mukerjee, Amitabha
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/12/2015
Relevância na Pesquisa
35.69%
Despite the success of distributional semantics, composing phrases from word vectors remains an important challenge. Several methods have been tried for benchmark tasks such as sentiment classification, including word vector averaging, matrix-vector approaches based on parsing, and on-the-fly learning of paragraph vectors. Most models usually omit stop words from the composition. Instead of such an yes-no decision, we consider several graded schemes where words are weighted according to their discriminatory relevance with respect to its use in the document (e.g., idf). Some of these methods (particularly tf-idf) are seen to result in a significant improvement in performance over prior state of the art. Further, combining such approaches into an ensemble based on alternate classifiers such as the RNN model, results in an 1.6% performance improvement on the standard IMDB movie review dataset, and a 7.01% improvement on Amazon product reviews. Since these are language free models and can be obtained in an unsupervised manner, they are of interest also for under-resourced languages such as Hindi as well and many more languages. We demonstrate the language free aspects by showing a gain of 12% for two review datasets over earlier results...

Topic words analysis based on LDA model

Qiu, Xi; Stewart, Christopher
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/05/2014
Relevância na Pesquisa
35.85%
Social network analysis (SNA), which is a research field describing and modeling the social connection of a certain group of people, is popular among network services. Our topic words analysis project is a SNA method to visualize the topic words among emails from Obama.com to accounts registered in Columbus, Ohio. Based on Latent Dirichlet Allocation (LDA) model, a popular topic model of SNA, our project characterizes the preference of senders for target group of receptors. Gibbs sampling is used to estimate topic and word distribution. Our training and testing data are emails from the carbon-free server Datagreening.com. We use parallel computing tool BashReduce for word processing and generate related words under each latent topic to discovers typical information of political news sending specially to local Columbus receptors. Running on two instances using paralleling tool BashReduce, our project contributes almost 30% speedup processing the raw contents, comparing with processing contents on one instance locally. Also, the experimental result shows that the LDA model applied in our project provides precision rate 53.96% higher than TF-IDF model finding target words, on the condition that appropriate size of topic words list is selected.

Bilateral correspondence model for words-and-pictures association in multimedia-rich microblogs

Wang, Zhiyu; Cui, Peng; Xie, Lexing; Zhu, Wenwu; Rui, Yong; Yang, Shiqiang
Fonte: Association for Computing Machinary, Inc. Publicador: Association for Computing Machinary, Inc.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.78%
Nowadays, the amount of multimedia contents in microblogs is growing significantly. More than 20% of microblogs link to a picture or video in certain large systems. The rich semantics in microblogs provides an opportunity to endow images with higher-level semantics beyond object labels. However, this raises new challenges for understanding the association between multimodal multimedia contents in multimedia-rich microblogs. Disobeying the fundamental assumptions of traditional annotation, tagging, and retrieval systems, pictures and words in multimedia-rich microblogs are loosely associated and a correspondence between pictures and words cannot be established. To address the aforementioned challenges, we present the first study analyzing and modeling the associations between multimodal contents in microblog streams, aiming to discover multimodal topics from microblogs by establishing correspondences between pictures and words in microblogs. We first use a data-driven approach to analyze the new characteristics of the words, pictures, and their association types in microblogs. We then propose a novel generative model called the Bilateral Correspondence Latent Dirichlet Allocation (BC-LDA) model. Our BC-LDA model can assign flexible associations between pictures and words and is able to not only allow picture-word co-occurrence with bilateral directions...