A symbolic summation approach to Feynman integral calculus

Given a Feynman parameter integral, depending on a single discrete variable N$N$ and a real parameter ε$\epsilon$, we discuss a new algorithmic framework to compute the first coefficients of its Laurent series expansion in ε$\epsilon$. In a first step, the integrals are expressed by hypergeometric multi-sums by means of symbolic transformations. Given this sum format, we develop new summation tools to extract the first coefficients of its series expansion whenever they are expressible in terms of indefinite nested product–sum expressions. In particular, we enhance the known multi-sum algorithms to derive recurrences for sums with complicated boundary conditions, and we present new algorithms to find formal Laurent series solutions of a given recurrence relation.     

An approach to automatic generation of dynamic equations of elastic joint manipulators in symbolic language

This paper describes an approach for automatic generation of the equations of motion of elastic joint manipulators in symbolic language. It is based on a vector-parametrization of the Lie groupSO(3) and uses the Lagrange's formalism to derive the dynamical equations, the final forms of which are like the equations generated by a recursive Newton-Eulerian algorithm. These characteristics together increase the computational efficiency of the algorithm and give a very good insight into the dynamical structure of the system. In addition to this, the inertia matrix is explicitly given in the final equations, which is very important for the applicability of a mathematical model in different fields of control and simulation. The suggested algorithm is therefore quite appropriate for the purposes of robot modeling, identification as well as for the applications in real time simulations and control.     

A synergistic approach to improving symbolic execution using test ranges

Innovations in Systems and Software Engineering - Symbolic execution is a systematic technique for checking programs, which has received a lot of research attention during the last decade. In...     

A new approach to the symbolic factorization of multivariate polynomials

A heuristic factorization scheme that uses learning and other heuristic programming techniques to improve the efficiency of determining the symbolic factorization of multivariate polynomials with integer coefficients and an arbitrary number of variables and terms is described. The learning program, POLYFACT, in which the factorization scheme is implemented is also described. POLYFACT uses learning through the dynamic construction and manipulation of first-order predicate calculus heuristics to reduce the amount of searching for the irreducible factors of a polynomial.Tables containing the results of factoring randomly generated multivariate polynomials are presented: (1) to demonstrate that learning does improve considerably the efficiency of factoring polynomials, and (2) to show that POLYFACT does learn from previous experience.The factorization times of polynomials factored by both the scheme implemented in POLYFACT and Wang's implementation of Berlekamp's algorithm are given. The two algorithms are compared, and two situations where POLYFACT'S algorithm can be used to improve the efficiency of Wang's algorithm are discussed.     

A symbolic model checking approach to verifying satellite onboard software

This paper discusses the use of symbolic model checking technology to verify the design of an embedded satellite software control system called the attitude and orbit control system (AOCS). This system is mission critical because it is responsible for maintaining the attitude of the satellite and for performing fault detection, isolation, and recovery decisions. An executable AOCS implementation by Space Systems Finland has been provided in Ada source code form, and we use the input language of the symbolic model checker NuSMV 2 to model the implementation at a detailed level. We describe the modeling techniques and abstractions used to alleviate the state space explosion due to the handling of timers and the large number of system components controlled by the AOCS. The required behavior has been specified as extended state machine diagrams and translated to temporal logic properties. Besides well-known LTL and CTL model checking algorithms, we adapt a previously unexplored form of the liveness-to-safety approach to the problem. The latter new technique turns out to successfully prove all desired properties of the system, outperforming both the LTL and CTL implementations of NuSMV 2.     

A new approach to automatic speech summarization

This paper proposes a new automatic speech summarization method. In this method, a set of words maximizing a summarization score is extracted from automatically transcribed speech. This extraction is performed according to a target compression ratio using a dynamic programming (DP) technique. The extracted set of words is then connected to build a summarization sentence. The summarization score consists of a word significance measure, a confidence measure, linguistic likelihood, and a word concatenation probability. The word concatenation score is determined by a dependency structure in the original speech given by stochastic dependency context free grammar (SDCFG). Japanese broadcast news speech transcribed using a large-vocabulary continuous-speech recognition (LVCSR) system is summarized using our proposed method and compared with manual summarization by human subjects. The manual summarization results are combined to build a word network. This word network is used to calculate the word accuracy of each automatic summarization result using the most similar word string in the network. Experimental results show that the proposed method effectively extracts relatively important information by removing redundant and irrelevant information.     

A template-based approach to automatic face enhancement

This paper presents Visual ENhancement of USers (VENUS), a system able to automatically enhance male and female frontal facial images exploiting a database of celebrities as reference patterns for attractiveness. Each face is represented by a set of landmark points that can be manually selected or automatically localized using active shape models. The faces can be compared remapping the landmarks by means of Catmull–Rom splines, a class of interpolating splines particularly useful to extract shape-based representations. Given the input image, its landmarks are compared against the known beauty templates and moved towards the K-nearest ones by 2D image warping. The VENUS performances have been evaluated by 20 volunteers on a set of images collected during the Festival of Creativity, held in Florence, Italy, on October 2007. The experiments show that the 73.9% of the beautified faces are more attractive than the original pictures.     

A New Approach to Fully Automatic Mesh Generation

Automatic mesh generation is one of the most important parts in CIMS (Computer Integrated Manufacturing System).A method based on mesh grading propagation which automatically produces a triangular mesh in a multiply connected planar region is presented in this paper.The method decomposes the planar region into convex subregions,using algorithms which run in linear time.For every subregion,an algorithm is used to generate shrinking polygons according to boundary gradings and form delaunay triangulation between two adjacent shrinking polygons,both in linear time.It automatically propagates boundary gradings into the interior of the region and produces satisfactory quasi-uniform mesh.     

A feature-based approach to better automatic treebank conversion

In the field of constituency parsing, there exist multiple human-labeled treebanks which are built on non-overlapping text samples and follow different annotation standards. Due to the extreme cost of annotating parse trees by human, it is desirable to automatically convert one treebank (called source treebank) to the standard of another treebank (called target treebank) which we are interested in. Conversion results can be manually corrected to obtain higher-quality annotations or can be directly used as additional training data for building syntactic parsers. To perform automatic treebank conversion, we divide constituency parses into two separate levels: the part-of-speech (POS) and syntactic structure (bracketing structures and constituent labels), and conduct conversion on these two levels respectively with a feature-based approach. The basic idea of the approach is to encode original annotations in a source treebank as guide features during the conversion process. Experiments on two Chinese treebanks show that our approach can convert POS tags and syntactic structures with the accuracy of 96.6 and 84.8 %, respectively, which are the best reported results on this task.     

A multi-objective evolutionary approach to automatic melody generation

Existing evolutionary approaches to automatic composition generate only a few melodies in a certain style that is specified by the setting of parameters or the design of fitness functions. Thus, their composition results cannot cover the various tastes of music. In addition, they are not able to deal with the multidimensional nature of music. This paper presents a novel multi-objective evolutionary approach to automatic melody composition in order to produce a variety of melodies at once. To this end, two conflicting fitness measures are investigated to evaluate the fitness of melody; (1) stability and (2) tension. Resorting to music theory, genetic operators (i.e., crossover and mutation) are newly designed to improve search capability in the multi-objective fitness space of music composition. The experimental results demonstrate the validity and effectiveness of the proposed approach. Moreover, the analysis of composition results proves that the proposed approach generates a set of pleasant and diverse melodies.     

A genetic approach to automatic neural network architecture optimization

The use of artificial neural networks for various problems has provided many benefits in various fields of research and engineering. Yet, depending on the problem, different architectures need to be developed and most of the time the design decision relies on a trial and error basis as well as on the experience of the developer. Many approaches have been investigated concerning the topology modelling, training algorithms, data processing. This paper proposes a novel automatic method for the search of a neural network architecture given a specific task. When selecting the best topology, our method allows the exploration of a multidimensional space of possible structures, including the choice of the number of neurons, the number of hidden layers, the types of synaptic connections, and the use of transfer functions. Whereas the backpropagation algorithm is being conventionally used in the field of neural networks, one of the known disadvantages of the technique represents the possibility of the method to reach saddle points or local minima, hence overfitting the output data. In this work, we introduce a novel strategy which is capable to generate a network topology with overfitting being avoided in the majority of the cases at affordable computational cost. In order to validate our method, we provide several numerical experiments and discuss the outcomes.

     

A hybrid framework to extract bilingual multiword expression from free text

Bilingual multiword expression extraction is always a significant problem in extracting meaning from free text. This involves analyzing large amounts of textual information. In this paper we propose a text mining approach to extract bilingual multiword expression. Both statistic and rule-based methods are employed into the system. There are two phases in the extraction process. In the first phase, lots of candidates are extracted from the corpus by statistic methods. The algorithm of multiple sequence alignment is sensitive to the flexible multiword. In the second phase, error-driven rules and patterns are extracted from corpus. For acquired high qualified instances, the manual work with active learning is also performed in sample selection. These trained rules are used to filter the candidates. Bilingual comparisons are used in a parallel corpus. Parts of bilingual syntactic patterns are obtained from the bilingual phrase dictionary. Some related experiments are designed for achieving the best performance because there are lots of parameters in this system. Experimental results showed our approach gains good performance.     

A novel logic-based automatic approach to constructing compliant security policies

It is significant to automatically detect and resolve the incompliance in security policy.Most existing works in this field focus on compliance verification,and few of them provide approaches to automatically correct the incompliant security policies.This paper proposes a novel approach to automatically transform a given security policy into a compliant one.Given security policy Π and delegation policy M declared by logic programs,the approach automatically rewrites Π into a new one ΠM which is compliant with M and is readable by the humans.We prove that the algorithm is sound and complete under noninterference assumption.Formally,we show that the security policy query evaluation algorithm with conflict and unsettlement resolution still works very well on ΠM.The approach is automatic,so it doesn’t require a administrator with excess abilities.In this sense,our proposal can help us to save much manpower resource in security management and improves the security assurance abilities.     

A relaxation approach to splitting in an automatic theorem prover

The splitting of a problem into subproblems often involves the same variable appearing in more than one of the subproblems. This makes these subproblems dependent upon one another since a solution to one may not qualify as a solution to another. A two stage method of splitting is described which first obtains solutions by relaxing the dependency requirement and then attempts to reconcile solutions to different subproblems. The method has been realized as part of an automatic theorem proper programmed in lisp which takes advantage of the procedural power that lisp provides. The program has had success with cryptarithmetic problems, problems from the blocks world, and has been used as a subroutine in a plane geometry theorem prover.     

A topic modeling based approach to novel document automatic summarization

Most of existing text automatic summarization algorithms are targeted for multi-documents of relatively short length, thus difficult to be applied immediately to novel documents of structure freedom and long length. In this paper, aiming at novel documents, we propose a topic modeling based approach to extractive automatic summarization, so as to achieve a good balance among compression ratio, summarization quality and machine readability. First, based on topic modeling, we extract the candidate sentences associated with topic words from a preprocessed novel document. Second, with the goals of compression ratio and topic diversity, we design an importance evaluation function to select the most important sentences from the candidate sentences and thus generate an initial novel summary. Finally, we smooth the initial summary to overcome the semantic confusion caused by ambiguous or synonymous words, so as to improve the summary readability. We evaluate experimentally our proposed approach on a real novel dataset. The experiment results show that compared to those from other candidate algorithms, each automatic summary generated by our approach has not only a higher compression ratio, but also better summarization quality.     

A hybrid automatic image annotation approach

Multimedia Tools and Applications - Automated image annotation (AIA) is an important issue in computer vision and pattern recognition, and plays an extremely important role in retrieving...