Partial 3D Shape Retrieval by Reeb Pattern Unfolding

This paper presents a novel approach for fast and efficient partial shape retrieval on a collection of 3D shapes. Each shape is represented by a Reeb graph associated with geometrical signatures. Partial similarity between two shapes is evaluated by computing a variant of their maximum common sub-graph.
By investigating Reeb graph theory, we take advantage of its intrinsic properties at two levels. First, we show that the segmentation of a shape by a Reeb graph provides charts with disk or annulus topology only. This topology control enables the computation of concise and efficient sub-part geometrical signatures based on parameterisation techniques. Secondly, we introduce the notion of Reeb pattern on a Reeb graph along with its structural signature. We show this information discards Reeb graph structural distortion and still depicts the topology of the related sub-parts. The number of combinations to evaluate in the matching process is then dramatically reduced by only considering the combinations of topology equivalent Reeb patterns.
The proposed framework is invariant against rigid transformations and robust against non-rigid transformations and surface noise. It queries the collection in interactive time (from 4 to 30 seconds for the largest queries). It outperforms the competing methods of the SHREC 2007 contest in term of NDCG vector and provides, respectively, a gain of 14.1% and 40.9% on the approaches by Biasotti et al. [ BMSF06 ] and Cornea et al. [ CDS*05 ].
As an application, we present an intelligent modelling-by-example system which enables a novice user to rapidly create new 3D shapes by composing shapes of a collection having similar sub-parts.     

Instructional animations can be superior to statics when learning human motor skills

Based on the assumption of a working memory processor devoted to human movement, cognitive load theory is used to explore some conditions under which animated instructions are hypothesised to be more effective for learning than equivalent static graphics. Using paper-folding tasks dealing with human movement, results from three experiments confirmed our hypothesis, indicating a superiority of animation over static graphics. These results are discussed in terms of a working memory processor that may be facilitated by our mirror-neuron system and may explain why animated instructional animations are superior to static graphics for cognitively based tasks that involve human movement.     

On the decoding of binary cyclic codes with the Newton identities

We revisit in this paper the concept of decoding binary cyclic codes with Gröbner bases. These ideas were first introduced by Cooper, then Chen, Reed, Helleseth and Truong, and eventually by Orsini and Sala. We discuss here another way of putting the decoding problem into equations: the Newton identities. Although these identities have been extensively used for decoding, the work was done manually, to provide formulas for the coefficients of the locator polynomial. This was achieved by Reed, Chen, Truong and others in a long series of papers, for decoding quadratic residue codes, on a case-by-case basis. It is tempting to automate these computations, using elimination theory and Gröbner bases.Thus, we study in this paper the properties of the system defined by the Newton identities, for decoding binary cyclic codes. This is done in two steps, first we prove some facts about the variety associated with this system, then we prove that the ideal itself contains relevant equations for decoding, which lead to formulas.Then we consider the so-called online Gröbner basis decoding, where the work of computing a Gröbner basis is done for each received word. It is much more efficient for practical purposes than preprocessing and substituting into the formulas. Finally, we conclude with some computational results, for codes of interesting length (about one hundred).     

Improved Interface Conditions for 2<Emphasis Type="Italic">D</Emphasis> Domain Decomposition with Corners: Numerical Applications

This article deals with a local improvement of domain decomposition methods for 2-dimensional elliptic problems for which either the geometry or the domain decomposition presents conical singularities. After explaining the main results of the theoretical analysis carried out in Chniti et al. (Calcolo 45, 2008), the numerical experiments presented in this article confirm the optimality properties of the new interface conditions.     

Local and Global Interactions in an Evolutionary Resource Game

Conditions for the emergence of cooperation in a spatial common-pool resource game are studied. This combines in a unique way local and global interactions. A fixed number of harvesters are located on a spatial grid. Harvesters choose among three strategies: defection, cooperation, and enforcement. Individual payoffs are affected by both global factors, namely, aggregate harvest and resource stock level, and local factors, such as the imposition of sanctions on neighbors by enforcers. The evolution of strategies in the population is driven by social learning through imitation, based on local interaction or locally available information. Numerous types of equilibria exist in these settings. An important new finding is that clusters of cooperators and enforcers can survive among large groups of defectors. We discuss how the results contrast with the non-spatial, but otherwise similar, game of Sethi and Somanathan (American Economic Review 86(4):766–789, 1996).      

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.     

Classification rule discovery for the aviation incidents resulted in fatality

Data mining methods have been successfully applied to different fields. Aviation industry is one of them. There is a large amount of knowledge and data accumulation in aviation industry. These data could be stored in the form of pilot reports, maintenance reports, incident reports or delay reports. This paper explains the data mining application on the incident reports of the Federal Aviation Administration (FAA) Accident/Incident Data System database, contains incident data records for all categories of civil aviation between the years of 2000 and 2006. In this study, we applied data mining methods on the incident reports. Moreover rough sets concept is used to reduce the attributes of data set. The purpose of this application is to find out the effective attributes in order to reduce the number of the fatality in the incidents. The categorization tools and decision trees are used to find the relations and rules about the incidents resulted in fatality. For this purpose data-mining analysis is conducted. As a result some rules about the fatality are obtained and also the parameters that affect the fatality in the incident have determined. The rules found are tested in terms of their accuracy and reliability, and these results are seen to be meaningful.     

Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection

This study proposes a technique based upon Fuzzy C-Means (FCM) classification theory and related fuzzy theories for choosing an appropriate value of the Variable Precision Rough Set (VPRS) threshold parameter (β) when applied to the classification of continuous information systems. The VPRS model is then combined with a moving Average Autoregressive Exogenous (ARX) prediction model and Grey Systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed mechanism, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data are then reduced using a GM(1, N) model, classified using a FCM clustering algorithm, and then supplied to a VPRS classification module which selects appropriate investment stocks in accordance with a pre-determined set of decision-making rules. Finally, a grey relational analysis technique is employed to weight the selected stocks in such a way as to maximize the rate of return of the stock portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio results obtained using the proposed hybrid model are compared with those obtained using a Rough Set (RS) selection model. The effects of the number of attributes of the RS lower approximation set and VPRS β-lower approximation set on the classification are systematically examined and compared. Overall, the results show that the proposed stock forecasting and stock selection mechanism not only yields a greater number of selected stocks in the β-lower approximation set than in the RS approximation set, but also yields a greater rate of return.     

Fault-tree analysis for liquefied natural gas terminal emergency shutdown system

Natural gas, one of the cleanest, most efficient and useful of all energy sources, is a vital component of the world’s supply of energy. To make natural gas more convenient for storage and transportation, it is refined and condensed into a liquid called liquefied natural gas (LNG). In a LNG site, safety is a long-team and critical issue. The emergency shutdown (ESD) system in the LNG receiving terminal is used to automatically stop the pumps and isolate the leakage section. Fault-tree analysis (FTA) has been widely used for providing logical functional relationships among subsystems and components of a system and identifying the root causes of the undesired failures in a system. In the conventional FTA for the ESD system, we usually assume that exact failure probabilities of events are collected. However, in most real applications, first, the FTA for the ESD system needs to be made at a early design or manufacturing stage, certain new components normally used without failure data; secondly, sometimes the environmental change in the system during the operation periods. This makes more difficult to gather past exact failures data for the FTA. To complete the FTA of the ESD system under these uncertain situations, we apply the intuitionistic fuzzy sets (IFS) theory to the FTA. We generate the intuitionistic fuzzy fault-tree interval, and the intuitionistic fuzzy reliability interval for the ESD system. We also present an algorithm to find the critical components in the system based on IFS–FTA and determine weak paths in the ESD system, where the key improvement must be made.     

Should we try to measure software quality attributes directly?

Most external software quality attributes are conceptually subjective. For example, maintainability is an external software quality attribute, and it is subjective because interpersonally agreed definitions for the attribute include the phrase ‘the ease with which maintenance tasks can be performed’. Subjectivity clearly makes measurement of the attributes and validation of prediction systems for the attributes problematic. In fact, in spite of the definitions, few statistically valid attempts at determining the predictive capability of prediction systems for external quality attributes have been published. When validations have been attempted, one approach used is to ask experts to indicate if the values provided by the prediction system informally agree with the experts’ intuition. These attempts are undertaken without determining, independently of the prediction system, whether the experts are capable of direct consistent measurement of the attribute. Hence, a statistically valid and unbiased estimate of the predictive capability of the prediction system cannot be obtained (because the experts’ measurement process is not independent of the prediction system’s values). In this paper, it is argued that the problem of subjective measurement of quality attributes should not be ignored if quality is to be introduced into software in a controlled way. Further, it is argued that direct measurement of quality attributes should be encouraged and that in fact such measurement can be quantified to establish consistency using an existing approach. However, the approach needs to be made more accessible to promote its use. In so doing, it would be possible to decide whether consistent independent estimates of the true values of software quality attributes can be assigned and prediction systems for quality attributes developed.