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This paper presents our recent attempt to make a super-large scale spoken-term detection system, which can detect any keyword uttered in a 2,000-hour speech database within a few seconds. There are three problems to achieve such a system. The system must be able to detect out-of-vocabulary (OOV) terms (OOV problem
Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may
and there is a need to develop a testing framework which is application independent and scalable with the increased requirements of each application. An automated testing framework has been proposed here for testing web applications. The proposed framework is based on keyword driven framework in which a excel file
Result integrating is a key component for keyword querying across heterogeneous databases. Once the results from various search engines are collected, the search engine merges them into a single ranked list. In this paper, firstly, we present a novel model of searching, which the database is an undirected graph and
patterns. However, this implies an expensive computation or communication cost of all the data on the server. Existing solutions are not efficient due to their impractical communication and computation cost. Besides, most of them do not support keyword search. In this paper, we introduce the mechanism of pricing to solve the
This work demonstrates the development of Keyword Spotting (KWS) system using Vowel Onset Point (VOP), Vector Quantization (VQ) and Hidden Markov Model(HMM) based techniques. The goal of KWS system is to spot the keywords present in the test speech signal, while neglecting rest of the words. In this work, first
This paper proposes an extended vector space model (VSM), which is called M2VSM (meta keyword-based modified VSM). When conventional VSM is applied to document clustering, it is difficult to adjust the granularity of cluster in terms of topic. In order to solve the problem, M2VSM considers meta keywords such as
By borrowing ideas from a cryptographic algorithm of low key authentic degree, a novel steganographic method based on keyword shift is presented. The master key of the method is to shift the sensitive keywords in the text. The conditions to guarantee the reversibility of the method are analyzed and found out, the
In this paper, we study the problem of the data redundancy in XML Keyword Search by SLCA and propose a new mode to resolve it. We begin by introducing the notion of SLCA and analyzing its faults. Then we propose the concept of Indirect-SLCA (ISLCA) to reduce the redundancy basing on the notion of Heterogeneous node
Being able to search for words or phrases in historic handwritten documents is of paramount importance when preserving cultural heritage. Storing scanned pages of written text can save the information from degradation, but it does not make the textual information readily available. Automatic keyword spotting systems
Due to its considerable ease of use, relational keyword search (R-KWS) has become increasingly popular. Its simplicity, however, comes at the cost of intensive query processing. Specifically, R-KWS explores a vast search space, comprised of all possible combinations of keyword occurrences in any attribute of every
A common approach to performing keyword search over relational databases is to find the minimum Steiner trees in database graphs. These methods, however, are rather expensive as the minimum Steiner tree problem is known to be NP-hard. Further, these methods cannot benefit from DBMS capabilities. We propose a new
How to find the teaching resources according to users' demand quickly and accurately on the Internet is urgent to be solved. This paper proposes a design of pretreatment for keyword-based search over network teaching resource database based on ontology. Firstly, the teaching ontology is created according to the
Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers
In this paper we propose a new technique for robust keyword spotting that uses bidirectional long short-term memory (BLSTM) recurrent neural nets to incorporate contextual information in speech decoding. Our approach overcomes the drawbacks of generative HMM modeling by applying a discriminative learning procedure
This paper introduces a novel keyword searching paradigm in relational databases (DBs), where the result of a search is a ranked set of object summaries (OSs). An OS summarizes all data held about a data subject (DS) in the database. More precisely, it is a tree with a tuple containing the keyword as a root and
In the last three decades, engineering education research (EER) has made remarkable progress towards a field of interdisciplinary scholarship. This paper defines EER by developing a keyword-based scheme for exploring EER-related scientific publications and collaboration. The keyword-based scheme refers to a conceptual
integrated feature set is obtained after normalization of both sets of features thus obtained. This integrated feature set is used in a Hidden Markov Modeling (HMM) framework along with a novel sliding syllable protocol for keyword spotting. Keyword spotting experiments are conducted on the Hindi language database developed for
Conventional top-k spatial keyword queries require users to explicitly specify their preferences between spatial proximity and keyword relevance. In this work we investigate how to eliminate this requirement by enhancing the conventional queries with interaction, resulting in Interactive Top-k Spatial Keyword (ITkSK
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