Tuple Structures Research Articles

A New Unit Selection Optimisation Algorithm for Corpus-Based TTS Systems Using the RBF-Based Data Compression Technique

A major drawback of corpus-based speech synthesis systems is the use of large acoustic inventories, and currently one of the main challenges is the optimal representation of concatenation costs associated with units in the acoustic inventory. These concatenation costs are used to evaluate spectral mismatches between the acoustic units to be concatenated. The combinatorics of costs grows exponentially with the size of the acoustic inventories and can result in hundreds of millions or even billions of concatenation costs to be processed. Therefore, in this paper, we represent a novel unit selection optimization algorithm, which minimizes the size of concatenation costs through the vector quantization-based compression technique and tuple structures. Furthermore, the proposed optimization algorithm is designed to be used as an objective measure to optimize the performance of the unit selection cost function regarding the quality of the speech output, and to evaluate the effect of the vector quantization-based compression technique on its performance. The results obtained show that even when data compression is above 50%, the effect on the quality of the synthesized speech is negligible.

Top-k keyword search with recursive semantics in relational databases

Existing solutions for keyword search over relational databases focused on finding joined tuple structures from a data graph. We observe that such a graph using tuples as nodes and foreign-key references as edges cannot describe the joining connections between tuples within a single relation, and thus cannot support recursive query semantics over a relational database. To solve this problem, in our approach, we firstly model a weighted data graph considering both foreign key references and tuple joining connections within a single relation. Secondly, we discuss the ranking strategy for both nodes and edges supporting the recursive semantics by incorporating PageRank methods. Finally, an approximation algorithm as well as a top-k enumeration algorithm is presented by running Dijkstra algorithm based on dynamic programming strategy to enumerate result tuple trees. At the end of this paper, we conduct an experimental study and report the findings.

Novel LVCSR Decoder Based on Perfect Hash Automata and Tuple Structures – SPREAD –

The paper presents the novel design of a one-pass large vocabulary continuous-speech recognition decoder engine, named SPREAD. The decoder is based on a time-synchronous beam-search approach, including statically expanded cross-word triphone contexts. An approach using efficient tuple structures is proposed for the construction of the complete search-network. The foremost benefits are the important space savings and higher processing speed, and the compact and reduced size of the tuple structure, especially when exploiting the structure of the key. In this way, the time needed to load the ASR search-network into the memory is also significantly reduced. Further, the paper proposes and presents the complete methodology for compiling general ASR knowledge sources into a tuple structures. Additionally, the beam search is enhanced with the novel implementation of a bigram language model Look-Ahead technique, by using tuple structures and a caching scheme. The SPREAD LVCSR decoder is based on a token-passing algorithm, capable of restricting its search-space by several types of token pruning. By using the presented language model Look-Ahead technique, it is possible to increase the number of tokens that can be pruned without decoding precision loss.

Ten thousand SQLs

Keyword search in relational databases has been extensively studied. Given a relational database, a keyword query finds a set of interconnected tuple structures connected by foreign key references. On rdbms, a keyword query is processed in two steps, namely, candidate networks ( CN s) generation and CN s evaluation, where a CN is an sql. In common, a keyword query needs to be processed using over 10,000 sqls. There are several approaches to process a keyword query on rdbms, but there is a limit to achieve high performance on a uniprocessor architecture. In this paper, we study parallel computing keyword queries on a multicore architecture. We give three observations on keyword query computing, namely, a large number of sqls that needs to be processed, high sharing possibility among sqls, and large intermediate results with small number of final results. All make it challenging for parallel keyword queries computing. We investigate three approaches. We first study the query level parallelism, where each sql is processed by one core. We distribute the sqls into different cores based on three objectives, regarding minimizing workload skew, minimizing intercore sharing and maximizing intra-core sharing respectively. Such an approach has the potential risk of load unbalancing through accumulating errors of cost estimation. We then study the operation level parallelism, where each operation of an sql is processed by one core. All operations are processed in stages, where in each stage the costs of operations are re-estimated to reduce the accumulated error. Such operation level parallelism still has drawbacks of workload skew when large operations are involved and a large number of cores are used. Finally, we propose a new algorithm that partitions relations adaptively in order to minimize the extra cost of partitioning and at the same time reduce workload skew. We conducted extensive performance studies using two large real datasets, DBLP and IMDB , and we report the efficiency of our approaches in this paper.

FORMALIZATION OF TEXTUAL USE CASES BASED ON PETRI NETS

A use case is a specification of interactions involving a system and external actors of that system. The intuitive, user centered nature of textual use cases is one of the reasons for the success of the use case approach. A certain level of formalization is however needed to automate use case based system development, including tasks such as design synthesis, verification and validation. In this paper, a mapping from textual use cases to a formal model (Petri nets) is proposed. Use cases are described in a restricted-form of natural language. The abstract syntax of the language is formally defined using a tuple structure. The mapping from use cases to Petri nets considers use cases sequencing constraints defined at the syntactic-level, and provides a definition of execution semantics to use cases.

Scalable keyword search on large data streams

It is widely recognized that the integration of information retrieval (IR) and database (DB) techniques provides users with a broad range of high quality services. Along this direction, IR-styled m-keyword query processing over a relational database in an rdbms framework has been well studied. It finds all hidden interconnected tuple structures, for example connected trees that contain keywords and are interconnected by sequences of primary/foreign key relationships among tuples. A new challenging issue is how to monitor events that are implicitly interrelated over an open-ended relational data stream for a user-given m-keyword query. Such a relational data stream is a sequence of tuple insertion/deletion operations. The difficulty of the problem is related to the number of costly joins to be processed over time when tuples are inserted and/or deleted. Such cost is mainly affected by three parameters, namely, the number of keywords, the maximum size of interconnected tuple structures, and the complexity of the database schema when it is viewed as a schema graph. In this paper, we propose new approaches. First, we propose a novel algorithm to efficiently determine all the joins that need to be processed for answering an m-keyword query. Second, we propose a new demand-driven approach to process such a query over a high speed relational data stream. We show that we can achieve high efficiency by significantly reducing the number of intermediate results when processing joins over a relational data stream. The proposed new techniques allow us to achieve high scalability in terms of both query plan generation and query plan execution. We conducted extensive experimental studies using synthetic data and real data to simulate a relational data stream. Our approach significantly outperforms existing algorithms.

Modeling software testing costs and risks using fuzzy logic paradigm

The overall lifecycle cost associated with product failures exceeds 10% of yearly corporations’ turnover. A major factor contributing to this loss is ineffective performance of software and systems Verification, Validation and Testing (VVT). Given these realities, we proposed a set of quantitative probabilistic models for estimating costs and risks stemming from carrying out any given VVT strategy [Engel, A., Barad, M., 2003. A methodology for modeling VVT risks and costs. Systems Engineering Journal 6 (3), 135–151, Wiley InterScience, Online ISSN: 1520-6858, Print ISSN: 1098-1241]. We also demonstrated that quality costs in software-intensive projects are likely to consume as much as 60% of the development budget. Finally, we showed that project cost and duration could be reduced by optimizing the VVT strategy, yielding about 10–15% reduction in development costs and project schedule [Engel, A., Shachar, S., 2006. Measuring and optimizing systems’ quality costs and project duration. Systems Engineering Journal 9 (3), 259–280]. A key problem associated with such cost and time estimates is that input data are imprecise by nature. Certain parameters are better captured using tuple structures (e.g. Minimum, Most-likely and Maximum values). Other parameters can be better encapsulated using linguistic terms such as “High” or “Low”. This paper extends the above research by modeling the problem using the fuzzy logic paradigm. We estimate the quality cost occurring during the development of software for an avionic suite in a fighter aircraft and demonstrate that applying fuzzy logic methodology yields results comparable to estimations based on models using the probabilistic paradigm (less than 4% differences in each of the five VVT cost categories).