The Role of Duration Models and Symbolic Representation for Timing in Synthetic Speech

In order to determine priorities for the improvement of timing in synthetic speech this study looks at the role of segmental duration prediction and the role of phonological symbolic representation in the perceptual quality of a text-to-speech system. In perception experiments using German speech synthesis, two standard duration models (Klatt rules and CART) were tested. The input to these models consisted of a symbolic representation which was either derived from a database or a text-to-speech system. Results of the perception experiments show that different duration models can only be distinguished when the symbolic representation is appropriate. Considering the relative importance of the symbolic representation, post-lexical segmental rules were investigated with the outcome that listeners differ in their preferences regarding the degree of segmental reduction. As a conclusion, before fine-tuning the duration prediction, it is important to derive an appropriate phonological symbolic representation in order to improve timing in synthetic speech.     

STRUCTURED REPRESENTATION OF FMS INTEGRATING SI-NETS AND HIGH LEVEL PETRI NETS

In this paper a structured knowledge-based approach to the representation and scheduling of flexible manufactoring systems (FMSs) is described. Our approach is based on a structured conceptual representation (a KL-ONE-like Si-net representation formalism), extended with an instant-based temporal reasoning formalism. Furthermore, the approach integrates a particular extension to high-level Petri nets (PNs), structured timed colored Petri nets (STCPNs), for the modeling and simulation of the FMS. Such a representation scheme allows us to use SI-nets' good properties related to inference (classification and inheritance), which are lacking in PNs, and at the same time provides an extension toward an explicit representation for time. The integration of Si-nets with PNs is necessary because simulation and low-level coordination of FMSs require a procedural approach that is not within the aims of Si-nets. Therefore, procedural and symbolic levels, corresponding to the different hierarchical levels of the representation and control system of the FMS, coexist in the system. Using a qualitative terminology, we may also call them analog and symbolic knowledge. We assume that such a hybrid representation system may be useful, since a procedural representation, integrated within a logic formalism, can increase the expressive power without complicating the notation or the representation itself. The paper describes both the representational aspects and the modeling of the control system of the FMS, focusing on the interaction mechanisms among the different levels of representation. In particular, we show how an STCPN-based model can be automatically derived starting from the symbolic component of the system. A particular FMS case study, regarding a class of problems of resource-constrained multiproject scheduling (where projects are sets of tasks temporally related), is discussed.     

Heterogeneous knowledge representation: integrating connectionist and symbolic computation

Heterogeneous knowledge representation allows combination of several knowledge representation techniques. For instance, connectionist and symbolic systems are two different computational paradigms and knowledge representations. Unfortunately, the integration of different paradigms and knowledge representations is not easy and very often is informal. In this paper, we propose a formal approach to integrate these two paradigms where as a symbolic system we consider a (logic) rule-based system. The integration is operated at language level between neural networks and rule languages. The formal model that allows the integration is based on constraint logic programming and provides an integrated framework to represent and process heterogeneous knowledge. In order to achieve this we define a new language that allows expression and modelling in a natural and intuitive way the above issues together with the operational semantics.     

一种基于系统总误码率的协同伙伴选择算法

提出了一种基于系统总误码率的协同伙伴选择算法。当系统总发送功率一定时,通过系统误码率公式,采用带CRC校验的DF协同通信的协同增益,来降低系统总的误码率。仿真表明,此算法有效地降低了系统总误码率,并且系统SNR越大,协同通信对系统数据传输的帮助越明显。     

A unified symbolic evaluation framework for parallelizing compilers

The quality of many optimizations and analyses of parallelizing compilers depends significantly on the ability to evaluate symbolic expressions and on the amount of information available about program variables at arbitrary program points. In this paper, we describe an effective and unified symbolic evaluation framework that statically determines the values of variables and symbolic expressions, assumptions about and constraints between variable values, and the condition under which control flow reaches a program statement. We introduce the program context, a novel representation for comprehensive and compact control and data flow analysis information. Program contexts are described as first order logic formulas, which allows us to use public domain software for standard symbolic manipulation. Computations are represented as algebraic expressions defined over a program's problem size. Our symbolic evaluation techniques comprise accurate modeling of assignment and input/output statements, branches, loops, recurrences, arrays, and procedures. All of our techniques target both linear, as well as nonlinear, expressions and constraints. Efficiency of symbolic evaluation is highly improved by aggressive simplification techniques. A variety of examples, including program verification, dependence analysis, array privatization, communication vectorization, and elimination of redundant communication, are used to illustrate the effectiveness of our approach. We present results from a preliminary implementation of our framework, which is used as part of a parallelizing compiler that demonstrates the potential performance gains achievable by employing symbolic evaluation to support program parallelization.     

Experiencing SAX: a novel symbolic representation of time series

Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. While many symbolic representations of time series have been introduced over the past decades, they all suffer from two fatal flaws. First, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Second, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. In this work we formulate a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. In particular, we will demonstrate the utility of our representation on various data mining tasks of clustering, classification, query by content, anomaly detection, motif discovery, and visualization.     

Iconic representation of visual data and models

Knowledge based image analysis is a combination of digital signal processing and symbolic reasoning. In this paper, we will look at some problems connected to the symbolic reasoning approach to image interpretation and see how an iconic representation can help to solve some of them. We will show that many of the features and problems connected with both symbolic and iconic representation are complementary.     

Modelling timed reactive systems from natural-language requirements

At the very beginning of system development, typically only natural-language requirements are documented. As an informal source of information, however, natural-language specifications may be ambiguous and incomplete; this can be hard to detect by means of manual inspection. In this work, we present a formal model, named data-flow reactive system (DFRS), which can be automatically obtained from natural-language requirements that describe functional, reactive and temporal properties. A DFRS can also be used to assess whether the requirements are consistent and complete. We define two variations of DFRS: a symbolic and an expanded version. A symbolic DFRS (s-DFRS) is a concise representation that inherently avoids an explicit representation of (possibly infinite) sets of states and, thus, the state space-explosion problem. We use s-DFRS as part of a technique for test-case generation from natural-language requirements. In our approach, an expanded DFRS (e-DFRS) is built dynamically from a symbolic one, possibly limited to some bound; in this way, bounded analysis (e.g., reachability, determinism, completeness) can be performed. We adopt the s-DFRS as an intermediary representation from which models, for instance, SCR and CSP, are obtained for the purpose of test generation. An e-DFRS can also be viewed as the semantics of the s-DFRS from which it is generated. In order to connect such a semantic representation to established ones in the literature, we show that an e-DFRS can be encoded as a TIOTS: an alternative timed model based on the widely used IOLTS and ioco. To validate our overall approach, we consider two toy examples and two examples from the aerospace and automotive industry. Test cases are independently created and we verify that they are all compatible with the corresponding e-DFRS models generated from symbolic ones. This verification is performed mechanically with the aid of the NAT2TEST tool, which supports the manipulation of such models.     

Symbolic systems,explicit properties: on hybrid approaches for LTL symbolic model checking

In this work we study hybrid approaches to LTL symbolic model checking; that is, approaches that use explicit representations of the property automaton, whose state space is often quite manageable, and symbolic representations of the system, whose state space is typically exceedingly large. We compare the effects of using, respectively, (i) a purely symbolic representation of the property automaton, (ii) a symbolic representation, using logarithmic encoding, of explicitly compiled property automaton, and (iii) a partitioning of the symbolic state space according to an explicitly compiled property automaton. We apply this comparison to three model-checking algorithms: the doubly-nested fixpoint algorithm of Emerson and Lei, the reduction of emptiness to reachability of Biere et al., and the singly-nested fixpoint algorithm of Bloem et al. for weak automata. The emerging picture from our study is quite clear, hybrid approaches outperform pure symbolic model checking, while partitioning generally performs better than logarithmic encoding. The conclusion is that the hybrid approaches benefit from state-of-the-art techniques in semantic compilation of LTL properties. Partitioning gains further from the fact that the image computation is applied to smaller sets of states.     

A symbolic manipulator for automated verification of reactive systems with heterogeneous data types

In this paper, we present the design and implementation of the Composite Symbolic Library, a symbolic manipulator for model checking systems with heterogeneous data types. Our tool provides a common interface for different symbolic representations, such as BDDs, for representing Boolean logic formulas and polyhedral representations for linear arithmetic formulas. Based on this common interface, these data structures are combined using a disjunctive composite representation. We propose several heuristics for efficient manipulation of this composite representation and present experimental results that demonstrate their performance. We used an object-oriented design to implement the Composite Symbolic Library. We imported the CUDD library (a BDD library) and the Omega Library (a linear arithmetic constraint manipulator that uses polyhedral representations) to our tool by writing wrappers around them which conform to our symbolic representation interface. Our tool supports polymorphic verification procedures which dynamically select symbolic representations based on the input specification. Our symbolic representation library can be used as an interface between different symbolic libraries, model checkers, and specification languages. We expect our tool to be useful in integrating different tools and techniques for symbolic model checking, and in comparing their performance.     

Domain-specific regular acceleration

The regular model-checking approach is a set of techniques aimed at exploring symbolically infinite state spaces. These techniques proceed by representing sets of configurations of the system under analysis by regular languages, and the transition relation between these configurations by a transformation over such languages. The set of reachable configurations can then be computed by repeatedly applying the transition relation, starting from a representation of the initial set of configurations, until a fixed point is reached. In order for this computation to terminate, it is generally needed to introduce so-called acceleration operators, the purpose of which is to explore in one computation step infinitely many paths in the transition graph of the system. A simple form of acceleration operator is one that is associated to a cycle in the transition graph, computing the set of states that can be obtained by following this cycle arbitrarily many times. The computation of acceleration operators is strongly dependent on the type of the data values that are manipulated by the system, and on the symbolic representation chosen for handling sets of such values. In this survey, we describe acceleration operators suited for the regular state-space exploration of systems relying on FIFO communication channels, as well as those based on integer and real variables.     

A hybrid scheme for action representation

Strong deficiencies are present in symbolic models for action representation and planning, regarding mainly the difficulty of coping with real, complex environments. These deficiencies can be attributed to several problems, such as the inadequacy in coping with incompletely structured situations, the difficulty of interacting with visual and motorial aspects, the difficulty in representing low-level knowledge, the need to specify the problem at a high level of detail, and so on. Besides the purely symbolic approaches, several nonsymbolic models have been developed, such as the recent class of subsym-bolic techniques. A promising paradigm for the modeling of reasoning, which combines features of both symbolic and analogical approaches, is based on the construction of analogical models of the reference for the internal representations, as introduced by Johnson-Laird. In this work, we propose a similar approach to the problem of knowledge representation and reasoning about actions and plans. We propose a hybrid approach, symbolic and analogical, in which the inferences are partially devolved to measurements on analogical models generated starting from the symbolic representation. the interaction between the symbolic and the analogical level is due to the fact that procedures are connected to some symbols, allowing generating, updating, and verifying the mental model. the hybrid model utilizes, for the symbolic component, a representation system based on the distinction between terminological and assertional knowledge. the terminological component adopts a SI-Net formalism, extended by temporal primitives. the assertional component is a subset of first-order logics. the analogical representation is a set of concurrent procedures modeling parts of the world, action processes, simulations, and metaphors based on force fields concepts. A particular case study, regarding the problem of the assembly of a complex object from parts, is taken as an experimental paradigm. © 1993 John Wiley Sons, Inc.     

Towards a Symbolic Interpretation of Approximate Reasoning

We study knowledge-based systems using symbolic many-valued logic and we focus on the management of knowledge through linguistic concepts characterized by vague terms or labels. In previous papers we have proposed a symbolic representation of nuanced statements. In this representation, we have interpreated some nuances of natural language as linguistic modifiers and we have defined them within a multiset context. In this paper, we continue the presentation of our symbolic model and we propose new deduction rules dealing with nuanced statements. We limit ourself to present new generalizations of the Modus Ponens rules dealing with nuanced statements.     

Action Language verifier: an infinite-state model checker for reactive software specifications

Action Language is a specification language for reactive software systems. In this paper, we present the syntax and the semantics of the Action Language and we also present an infinite-state symbolic model checker called Action Language Verifier (ALV) that verifies (or falsifies) CTL properties of Action Language specifications. ALV is built on top of the Composite Symbolic Library, which is a symbolic manipulator that combines multiple symbolic representations. ALV is a polymorphic model checker that can use different combinations of the symbolic representations implemented in the Composite Symbolic Library. We describe the heuristics implemented in ALV for computing fixpoints using the composite symbolic representation. Since Action Language specifications allow declaration of unbounded integer variables and parameterized integer constants, verification of Action Language specifications is undecidable. ALV uses several heuristics to conservatively approximate the fixpoint computations. ALV also implements an automated abstraction technique that enables parameterized verification of a concurrent system with an arbitrary number of identical processes.     

Rule Base Reduction and Genetic Tuning of Fuzzy Systems Based on the Linguistic 3-tuples Representation

Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation model, that allows the symbolic translation of a label considering an unique parameter. It involves a reduction of the search space that eases the derivation of optimal models. This work presents a new symbolic representation with three values (s, α, β), respectively representing a label, the lateral displacement and the amplitude variation of the support of this label. Based on this new representation we propose a new method for fine tuning of membership functions that is combined with a rule base reduction method in order to extract the most useful tuned rules. This approach makes use of a modified inference system that consider non-covered inputs in order to improve the final fuzzy model generalization ability, specially in highly non-linear problems with noise points. Additionally, we analyze the proposed approach showing its behavior in two real-world applications. Supported by the Spanish Ministry of Science and Technology under Projects TIC-2002-04036-C05-01 and TIN-2005-08386-C05-01.     

符号序列的概率向量聚类方法*

针对符号序列聚类中表示模型及序列间距离度量定义的困难问题,提出一种基于概率向量的表示模型及基于该模型的符号序列聚类算法。该模型引入符号序列的概率分布表示法,定义了一种基于概率分布差异的符号序列距离度量及该模型的目标函数,最后给出了一种符号序列K-均值型聚类算法,并在来自不同领域的实际应用序列集上进行了实验验证。实验结果表明,与基于子序列表示模型的符号序列聚类算法相比,所提方法在DNA序列和语音序列等具有较多符号的实际数据上,有效提高聚类精度的同时降低聚类时间50%以上。     

多源网络通信监测数据协同调度仿真

多源通信网络作为信息传输的主流方式,具有数据量巨大特点。针对传统多源网络通信监测数据协同调度方法性能较差、且应用过程较为复杂的问题,提出一种新的监测数据协同调度方法。将多源网络通信系统的数据请求视为随机发生事件,采用基于事件方法,为数据的协同调度问题创建可观的马尔科夫决策模型;并运用迭代值的方法对创建的模型进行求解,最终得出最佳协同调度策略。仿真结果证明,所提的多源网络通信监测数据协同调度方法,具有较高的可行性和可信度。     

Decentralised cooperative attitude tracking using modified Rodriguez parameters based on relative attitude information

In this article, the problem of decentralised cooperative attitude tracking based on relative attitude information is considered in the context of deep space spacecraft formation flying. We use modified Rodriguez parameters for attitude representation due to its nonreduction. We first present a distributed sliding-mode estimator and a cooperative attitude tracking control law such that the attitudes and angular velocities of all spacecraft track their references. Then we extend the result to three different cases. In the first case, we use a passivity approach to derive a control law without angular velocity measurements, which is of significance for applications with low-cost configurations of spacecraft. In the second case, to relax the special connectivity condition given in the original control design, we obtain a locally asymptotical stability condition for any undirected connected communication topology. In the third case, we consider cooperative attitude tracking in the presence of a dynamic communication topology. Simulation results are presented to validate the effectiveness of these control laws.     

Handwritten Chinese character recognition by rule-embedded Neocognitron

This paper presents an attempt to integrate neural computation with a domain knowledge technique to resolve the problem of the wide variety in handwritten Chinese characters. Despite their complexity, Chinese characters can be seen as structured patterns. Therefore, we propose a symbolic representation to describe these structural formations. In particular, we consider the Fuzzy Attributed Production Rule (FAPR) as a possible symbolic representation. On the neural computational side, we study Fukushima's Neocognitron model, which has been successfully demonstrated to recognize handwritten alphanumerics. Despite its power and tolerance capabilities, the supervised training scheme used by Fukushima is impractical for a large character set such as Chinese characters. We thus propose a ruleembedded Neocognitron network which can be readily mapped with structure-knowledge of Chinese characters as represented in FAPRs. In this paper, we demonstrate how 50 Chinese characters are mapped onto the network, and that the system performance in tolerating character structure deviations is satisfactory.     

Symbolic Communication Set Generation for Irregular Parallel Applications

Communication set generation significantly influences the performance of parallel programs. However, studies seldom give attention to the problem of communication set generation for irregular applications. In this paper, we propose communication optimization techniques for the situation of irregular array references in nested loops. In our methods, the local array distribution schemes are determined so that the total number of communication messages is minimized. Then, we explain how to support communication set generation at compile-time by introducing some symbolic analysis techniques. In our symbolic analysis, symbolic solutions of a set of symbolic expression are obtained by using certain restrictions. We introduce symbolic analysis algorithms to obtain the solutions in terms of a set of equalities and inequalities. Finally, experimental results on a parallel computer CM-5 are presented to validate our approach.