This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.

In this paper, we present a new dependency parsing method for languages which have very small annotated corpus and for which methods of segmentation and morphological analysis producing a unique (automatically disambiguated) result are very unreliable. Our method works on a morphosyntactic lattice factorizing all possible segmentation and part-of-speech tagging results. The quality of the input to syntactic analysis is hence much better than that of an unreliable unique sequence of lemmatized and tagged words. We propose an adaptation of Eisner's algorithm for finding the k-best dependency trees in a morphosyntactic lattice structure encoding multiple results of morphosyntactic analysis. Moreover, we present how to use Dependency Insertion Grammar in order to adjust the scores and filter out invalid trees, the use of language model to rescore the parse trees and the k-best extension of our parsing model. The highest parsing accuracy reported in this paper is 74.32% which represents a 6.31% improvement compared to the model taking the input from the unreliable morphosyntactic analysis tools.

Transmission delay in audio communications is a well-known obstacle to achieving smooth communication. However, it is not known what kinds of effects are caused by small delays. We hypothesized that the small delay in the listener's responses disturbs the speaker's "verbal conditioning," where the verbal behavior of the speaker varies in accordance with the listener's responses. We examined whether the small delays in the listener's responses disturb the speaker's verbal conditioning using an artificial-grammar learning task. The results suggested that a 300-ms delay disturbed the participants' verbal conditioning although they were not adequately aware of the delay.

In software design and development, program diagrams are often used for good visualization. Many kinds of program diagrams have been proposed and used. To process such diagrams automatically and efficiently, the program diagram structure needs to be formalized. We aim to construct a diagram processing system with an efficient parser for our program diagram Hichart. In this paper, we give a precedence graph grammar for Hichart that can parse in linear time. We also describe a parsing method and processing system incorporating the Hichart graphical editor that is based on the precedence graph grammar.

Trust negotiation is an authorizing technique based on digital credentials, in which both a user and a server gradually establish trust in each other by repeatedly exchanging their credentials. A trust negotiation strategy is a function that answers a set of credentials to disclose to the other party, depending on policies and the set of already disclosed credentials. The disclosure tree strategy (DTS), proposed by Yu et al., is one of the strategies that satisfies preferable properties. DTS in a simple implementation requires exponential time and space; however, neither an efficient algorithm nor the lower-bound of its complexity was known. In this paper, we investigate the computational complexity of DTS. We formulate subproblems of DTS as problems on derivation trees of a context-free grammar (CFG), and analyze the computational complexity of the subproblems using the concepts of CFGs. As a result, we show that two subproblems EVL and MSET of DTS are NP-complete and NP-hard, respectively, while both are solvable in polynomial time if we modify EVL not to require non-redundancy and MSET not to answer any subset useless for leading the negotiation to success.

In this paper, we study deterministic linear pushdown tree automata (deterministic L-PDTAs) and some variations. Since recognition of an input tree by a deterministic L-PDTA can be done in linear time, deterministic L-PDTAs are applicable to many kinds of applications. A strict hierarchy will be shown among the classes of tree languages defined by a variety of deterministic L-PDTAs. It will be also shown that deterministic L-PDTAs are weakly equivalent to nondeterministic L-PDTAs.

A space-efficient approximation algorithm for the grammar-based compression problem, which requests for a given string to find a smallest context-free grammar deriving the string, is presented. For the input length n and an optimum CFG size g, the algorithm consumes only O(g log g) space and O(n log*n) time to achieve O((log*n)log n) approximation ratio to the optimum compression, where log*n is the maximum number of logarithms satisfying log log log n > 1. This ratio is thus regarded to almost O(log n), which is the currently best approximation ratio. While g depends on the string, it is known that g=Ω(log n) and for strings from k-letter alphabet [12].

During software requirements analysis, developers and stakeholders have many alternatives of requirements to be achieved and should make decisions to select an alternative out of them. There are two significant points to be considered for supporting these decision making processes in requirements analysis; 1) dependencies among alternatives and 2) evaluation based on multi-criteria and their trade-off. This paper proposes the technique to address the above two issues by using an extended version of goal-oriented analysis. In goal-oriented analysis, elicited goals and their dependencies are represented with an AND-OR acyclic directed graph. We use this technique to model the dependencies of the alternatives. Furthermore we associate attribute values and their propagation rules with nodes and edges in a goal graph in order to evaluate the alternatives with them. The attributes and their calculation rules greatly depend on the characteristics of a development project. Thus, in our approach, we select and use the attributes and their rules that can be appropriate for the project. TOPSIS method is adopted to show alternatives and their resulting attribute values.

Several grammars of which generative power is between context-free grammar and context-sensitive grammar were proposed. Among them are macro grammar and tree adjoining grammar. Multiple context-free grammar is also a natural extension of context-free grammars, and is known to be stronger in its generative power than tree adjoining grammar and yet to be recognizable in polynomial time. In this paper, the generative power of several subclasses of variable-linear macro grammars and that of multiple context-free grammars are compared in details.

Two ways of introducing alternation for context-free grammars and pushdown automata are compared. One is the usual way which combines "states" with alternation [1], [4], [7], and the other is the way used in [6] to define the alternating context-free grammar, i.e., alternation is governed by the variables of the grammar. In this paper the latter way is taken over to define a new type of alternating pushdown automaton by combining the "pushdown symbols" of the pushdown automaton with alternation. We have derived a characterization of the original alternating context-free grammars in terms of such a new type of alternating pushdown automaton without states. It is also shown that, if (non-alternating) states are introduced as an additional feature for this type of pushdown automaton, then the resulting alternating pushdown automaton has exactly the same expressive power as the original alternating pushdown automaton.

In this paper, a recognition algorithm for the class of tree languages generated by linear, monadic context-free tree grammars (LM-CFTGs) is proposed. LM-CFTGs define an important class of tree languages because LM-CFTGs are weakly equivalent to tree adjoining grammars (TAGs). The algorithm uses the CKY algorithm as a subprogram and recognizes whether an input tree can be derived from a given LM-CFTG in O(n4) time, where n is the number of nodes of the input tree.

This paper presents the analogical conception of Chomsky normal form and Greibach normal form for linear, monadic context-free tree grammars (LM-CFTGs). LM-CFTGs generate the same class of languages as four well-known mildly context-sensitive grammars. It will be shown that any LM-CFTG can be transformed into equivalent ones in both normal forms. As Chomsky normal form and Greibach normal form for context-free grammars (CFGs) play a very important role in the study of formal properties of CFGs, it is expected that the Chomsky-like normal form and the Greibach-like normal form for LM-CFTGs will provide deeper analyses of the class of languages generated by mildly context-sensitive grammars.

Vigna and Ghezzi showed that the language of grid graphs could not be constructed by their context-free graph grammars [1]. In this paper, we construct a context-sensitive graph grammar for the two-dimensional grid graphs.

In this paper, we are concerned with a structural ambiguity problem of tree adjoining grammars (TAGs), which is an essential problem when we try to model consensus structures of given set of ribonucleic acid (RNA) secondary structures by TAGs. RNA secondary structures can be represented as strings with structural information, and TAGs have a descriptive capability of this kind of strings, what we call structure-annotated strings. Thus, we can model RNA secondary structures by TAGs. It is sufficient to use existing alignment methods for just computing the optimal alignment between RNA secondary structures. However, when we also want to model the resulting alignment by grammars, if we adopt these existing methods, then we may fail in modeling the alignment result by grammars. Therefore, it is important to introduce a new alignment method whose alignment results can be appropriately modeled by grammars. In this paper, we will propose an alignment method based on TAG's derivations each corresponding to a given RNA secondary structure. For an RNA secondary structure, there exist a number of derivations of TAGs which correspond to the structure. From the grammatical point of view, the property of TAGs drives us to the question how we should choose a derivation from these candidates in order to obtain an optimal alignment. This is the structural ambiguity problem of TAGs, which will be mainly discussed in this paper. For dealing with this problem appropriately, we will propose an edit distance between two structure-annotated strings, and then present an algorithm which computes an optimal alignment based on the edit distance.

There have been many arguments that the underlying structure of natural languages is beyond the descriptive capacity of context-free languages. A well-known example is tree adjoining grammars; less common are spine grammars, linear indexed grammars, head grammars, and combinatory categorial grammars. It is known that these models of grammars have the same generative power of string languages and fall into the class of mildly context-sensitive grammars. For an automaton, it is known that the class of languages accepted by transfer pushdown automata is exactly the class of linear indexed languages. In this paper, deterministic transfer pushdown automata is introduced. We will show that the language accepted by a deterministic transfer pushdown automaton is generated by an unambiguous spine grammar. Moreover, we will show that there exists an inherently ambiguous language.

Several grammars have been proposed for representing RNA secondary structure including pseudoknots such as simple linear tree adjoining grammar (sl-tag), extended sl-tag (esl-tag) and RNA pseudoknot grammar (rpg). The main purpose of this paper is to compare the generative power of these grammars by identifying them as subclasses of multiple context-free grammars (mcfg). Specifically, it is shown that the class of languages generated by esl-tag (ESL-TAL) properly includes the class of languages generated by sl-tag (SL-TAL) and the class of languages generated by cfg. Also, we show that the class of languages generated by rpg coincides with the class of languages generated by mcfg with dimension one or two and rank one or two. Furthermore, it is shown that SL-TAL is a full trio and ESL-TAL is a substitution closed full AFL.

Refactoring is one of the promising techniques for improving software design by means of behavior-preserving structural transformation, and is widely taken into practice. In particular, it is frequently applied to design models represented with UML such as class diagrams. However, since UML design models includes multiple diagrams which are closely related from various views, to get behavior-preserving property, we should get the other types of design information and should handle with the propagation of the change on a diagram to the other diagrams. For example, to refactor a class diagram, we need behavioral information of methods included in the class and should also refactor diagrams which represent the behavior, such as state diagrams, activity diagrams. In this paper, we introduce refactoring on design models as transformations of a graph described by UML class diagram and action semantics. First, we define basic transformations of design models that preserve the behavior of designed software, and compose them into refactoring operations. We use Object Constraint Language (OCL) to specify when we can apply a refactoring operation. Furthermore we implement our technique on a graph transformation system AGG to support the automation of refactoring, together with evaluation mechanism of OCL expressions. Some illustrations are presented to show its effectiveness. The work is the first step to handle with refactoring on UML design models in integrated way.

We propose a normalization scheme of syntactic structures using a binary phrase structure grammar with composite labels. The normalization adopts binary rules so that the dependency between two sub-trees can be represented in the label of the tree. The label of a tree is composed of two attributes, each of which is extracted from each sub-tree, so that it can represent the compositional information of the tree. The composite label is generated from part-of-speech tags using an automatic labelling algorithm. Since the proposed normalization scheme is binary and uses only part-of-speech information, it can readily be used to compare the results of different syntactic analyses independently of their syntactic description and can be applied to other languages as well. It can also be used for syntactic analysis, which performs higher than the previous syntactic description for Korean corpus. We implement a tool that transforms a syntactic description into normalized one based on this proposed scheme. It can help construct a unified syntactic corpus and extract syntactic information from various types of syntactic corpus in a uniform way.

We describe a multiresolution 3D active balloon model to trace the boundaries of moving object. This model is able to analyze a shape hierarchically using 3D scale-space. The 3D scale-space can be determined by changing the parameters of the active balloon. We extended 2D process-grammar to describe the deformation process between a shape and a sphere, based on topological scale-space analysis. The geometric invariant features were used to analyze the deformation of nonrigid shapes. We analyzed the motion of a heart by using MRI data.

Ordered attribute grammars (OAGs for short) are a useful class of attribute grammars (AGs). For some attribute grammars, even though they are not circular, OAG circularity test reports that they are not ordered and fails to generate attribute evaluators because some approximation introduces circularities (called type 3 circularities in this paper). First we discuss that it is sometimes difficult for programmers to eliminate type 3 circularities by hand. Second, to reduce this difficulty, we propose a new AG class called OAG* that produces less type 3 circularities than OAG while preserving the positive characteristic of OAG. OAG* uses a global dependency graph GDS that provides a new approximation algorithm. We obtained good results with our experimental implementation of OAG*. It is shown that OAG* is different from the existing GAG and Eli/Liga systems. Finally, two combinations of Eli/Liga and OAG* are provided.