Search results

Describes intuitively the fact that four types of formal languages can be generated by four types of grammars or can be recognized by four types of automata. Gives the relationships between context‐sensitive languages and computer programming languages. Defines and investigates parallel productions, parallel grammars, and context‐free parallel grammars. Shows that context‐sensitive languages exist which can be generated by context‐free parallel grammars. In addition, states the advantages of context‐free parallel grammars. Also shows that context‐free languages (CFL) are a proper subset of context‐free parallel languages (CFPL). Furthermore, CFPL is a more effective tool for modelling computer programming languages than CFL, especially for parallel computer programming languages, for example, the ADA programming language. Also illustrates context‐sensitive property of recognizing hand‐written characters. The results may have useful applications in artificial intelligence, model parallel computer programming languages, software engineering, expert systems and robotics.

Algorithms for the construction of the Chomsky and Greibach normal forms for a fuzzy context‐free grammar using the algebraic approach are presented and illustrated by examples. The results obtained in this paper may have useful applications in fuzzy languages, pattern recognition, information storage and retrieval, artificial intelligence, database and pictorial information systems.

Interchange of text and hypertext between various systems is vital in order to reuse text and hypertext, but the task of generating translators between different representations is often complex and tedious. The Integrated Chameleon Architecture (ICA) is a public domain toolset for generating translators. However, ica can only handle context‐free grammars while the grammar of hypertext is not context‐free. This paper presents an extended ICA (E‐ICA) which is based on ICA with extra pre‐ and post‐processors to handle the context‐sensitive and implicit information of hypertext. A system called SGML‐MUCH has been developed using E‐ICA. The development and use of the SGML‐MUCH system is presented as a case study with converters for the hypertext systems MUCH, Guide, Hyperties, and Toolbook described in detail.

Based on the concept of fuzzy sets of type 2 (or fuzzy‐fuzzy sets) defined by L. A. Zadeh, fuzzy‐fuzzy automata ate newly formulated and some properties of these automata are investigated. It is shown that fuzzy‐fuzzy languages characterized by fuzzy‐fuzzy automata are closed under the operations of union, intersection, concatenation, and Kleene closure in the sense of fuzzy sets of type 2, but are not closed under complement. The power of fuzzy‐fuzzy automata as an acceptor is the same as that of ordinary fuzzy automata and finite automata, though fuzzy‐fuzzy automata include fuzzy automata and finite automata as special cases. Finally, fuzzy‐fuzzy grammars are illustrated and it is shown that fuzzy‐fuzzy grammars with context‐free rules can generate context‐sensitive languages.

This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them.

The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed.

The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general.

The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation.

The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks.

The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.

This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap.

FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine.

The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating.

This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base.

This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system.

This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This research aims to propose an attack that de-obfuscates codes by exploiting the properties of context-free grammars since it is important to understand the strength of obfuscation provided by context-free grammar-based obfuscators. In addition, the possibility of automatically generated transformations is explored.

As part of our empirical investigation, a development environment for obfuscating transformations is built. The tool is used to simulate a context-free obfuscator and to devise ways of reversing such transformations. Furthermore, a theoretical investigation of subset grammars and subset languages is carried out.

It is concluded that context-free grammar-based obfuscators provide limited levels of protection. Nevertheless, their application is appropriate when combined with other obfuscating techniques.

The algorithms behave as expected on a limited number of test samples. Further work is required to increase their practicality and to establish their average reliability.

This research shows how a frequency analysis attack can threaten the security of code scrambled by context-free grammar-based obfuscators.

This paper proposes solutions to problems related to the maintenance and update of already existing e‐learning courseware.

A structured approach in form of a reference model for the re‐engineering of existing educational material is presented. In this context, concepts already established in the area of aspect‐oriented programming are applied to deal with crosscutting concerns in e‐learning material. Finally, a software product line approach is proposed for the creation of new courseware using re‐engineered components.

It turns out that some aspects of the methodology developed for aspect‐oriented programming can also be used to restructure the existing e‐learning material in such a way that maintenance is eased and redundancy is significantly reduced. In addition, software product lines for e‐learning material provide a global framework for coordinating the re‐engineering and reuse of components.

The advantage of the proposed approach is that existing e‐learning standards and systems do not have to be modified or adapted.

Usually, courseware evolves during a longer period of time and its development does not start every time from zero. There is a high incentive for re‐engineering of existing courseware, since it constitutes in many cases the competitive advantage of companies or universities. However, up to now, little attention is paid to the maintenance and the efficient update of e‐learning material which is already there.

Aiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar.

The paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar.

It is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks.

Using this strategy, the robot completes the fourth-order Hanoi tower task has been verified.

An imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.