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The task of zero resource query-by-example keyword search has received much attention in recent years as the speech technology needs of the developing world grow. These systems traditionally rely upon dynamic time warping (DTW) based retrieval algorithms with runtimes that are linear in the size of the search
interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo
A problem of multi-keyword search in a structured peer-to-peer (P2P) distributed computing system is considered. Methods have been developed to employ term-set indexing in a P2P system. Such an approach is an attempt to avoid excessive communication cost incurred by intersection operations in a single-term indexing
Search with privacy-preserving guarantee has been a critical issue for commercial cloud storage. Data owners outsource a large number of encrypted sensitive data to cloud and allow data users to search and retrieve encrypted data in cloud. Recently, Cao et al. [1] proposed a scheme to enable multi-keyword ranked
paper, algorithm is defined to improve relevancy of result based on webpage keyword ratio. In result analysis, result of proposed method is compared with deferent algorithms such as PageRank and Topic Distillation with Query Dependent Link Connections and Page Characteristics result.
Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature extraction method used was Mel-Frequency Cepstral Coefficients (MFCC). The ANN is a 3-layer feedforward neural network using Multi-Layer Perceptron (MLP). In recognizing the words, an HMM decoder was used which implemented the Viterbi
Intel MIC to further accelerate their computation. In this study, we present performance and evaluation comparison of GPU and MIC by implementing Multi Text Keyword Search algorithms from our prior work into MIC and GPU. We use NVIDIA K20c and NVIDIA K40 for our GPUs and Intel® Xeon Phi™ 5100 for the MIC. In
a way that it is secure and simultaneously searchable. To this end, one of the state-of-the art encryption schemes secure and privacy preserving keyword searching (SPKS) has been employed. The encryption algorithm employs CSP for partially decryption of the cipher texts. Consequently, the client computational and
In this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature
single feature stream which is more beneficial to all languages than the unilingual features. In the case of balanced corpus sizes, the multilingual BN features improve the automatic speech recognition (ASR) performance by 3–5% and the keyword search (KWS) by 3–10% relative for both limited (LLP) and full
We propose strategies for a state-of-the-art Vietnamese keyword search (KWS) system developed at the Institute for Infocomm Research (I2R). The KWS system exploits acoustic features characterizing creaky voice quality peculiar to lexical tones in Vietnamese, a minimal-resource transliteration framework to
This paper deals with the contribution of Curvelet transform to generate more accurate word image descriptors for Arabic keyword spotting in ancient documents. Due to its properties, Curvelets can tolerate more scale distortions and more directional features in images. The process of Curvelet descriptor generation is
online collection and online analysis of Youtube videos together. Particularly, we focus on the statistics of user-specified keyword-indexed Youtube videos. Our system permits any user to capture user-interested videos and give online statistic with graphic views directly. One can also download the online statistic results
technical background they can query and explore multiple data sources. This is the main significance of MashQL. This work aims in introducing semantic keyword search to retrieve the structured data.
To extract temporal variations in the relation between two or more words in a large time-series script, we propose three procedures for adoption by the existing Associated Keyword Space system, as follows. First, we begin the calculations from a previous state. Second, we add a random seed if a new object was present
We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order
Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the features using Weighted Principal Component Analysis (PCA). The
social streams. A model called “keyword-based evolving graph sequences” (kEGS) is proposed to capture the characteristics of information propagation in social streams. The experimental results show the usefulness of our approach in identifying real-world events in social streams.
privacy. In order to solve this problem, we propose a two round searchable encryption (TRSE) scheme, supporting top-k multi-keyword search, in which novel technologies, i.e., homomorphic encryption and vector space model, are employed. Vector space model helps to provide sufficient search accuracy, and homomorphic encryption
propose a """"Hybrid Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions"""" (""""IoT-SVK Search Engine"""" for short). The experimental results
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