A scalable algorithm for extraction and clustering of event-related pictures

The event detection problem, which is closely related to clustering, has gained a lot of attentions within event detection for textual documents. However, although image clustering is a problem that has been treated extensively in both Content-Based Image Retrieval (CBIR) and Text-Based Image Retrieval (TBIR) systems, event detection within image management is a relatively new area. Having this in mind, we propose a novel approach for event extraction and clustering of images, taking into account textual annotations, time and geographical positions. Our goal is to develop a clustering method based on the fact that an image may belong to an event cluster. Here, we stress the necessity of having an event clustering and cluster extraction algorithm that are both scalable and allow online applications. To achieve this, we extend a well-known clustering algorithm called Suffix Tree Clustering (STC), originally developed to cluster text documents using document snippets. The idea is that we consider an image along with its annotation as a document. Further, we extend it to also include time and geographical position so that we can capture the contextual information from each image during the clustering process. This has appeared to be particularly useful on images gathered from online photo-sharing applications such as Flickr. Hence, our STC-based approach is aimed at dealing with the challenges induced by capturing contextual information from Flickr images and extracting related events. We evaluate our algorithm using different annotated datasets mainly gathered from Flickr. As part of this evaluation we investigate the effects of using different parameters, such as time and space granularities, and compare these effects. In addition, we evaluate the performance of our algorithm with respect to mining events from image collections. Our experimental results clearly demonstrate the effectiveness of our STC-based algorithm in extracting and clustering events.     

An empirical examination of application frameworks success based on technology acceptance model

Framework-based development is currently regarded as one of the most promising software development approaches when it comes to improvements in lead time, productivity and quality. However, many frameworks and projects based on frameworks still report failures, which indicate that there are problems related to both frameworks technology and frameworks usage. The objective of our research was to examine the major drivers that have an impact on a framework’s acceptance and a framework’s success. We used the technology acceptance model (TAM) and Seddon’s information systems success model as our underlying theory. Data collected from an online survey of 389 active framework users was used to test hypothesized models. Data analysis was performed via structural equation modeling. Our findings support the post-adoption version of TAM and the relationship between continuous use and the successful use of systems, where more use also means more net benefits. We found that the successful use of frameworks is mainly dependent on two factors: continuous framework usage intention and the perceived usefulness of the framework. Several practical and theoretical implications can be foreseen including advances in framework development guidelines and insight into the relationship between the acceptance and success of frameworks.     

Application of Optimal Homotopy Asymptotic Method for solving nonlinear equations arising in heat transfer

We consider one of the newest analytical methods, the Optimal Homotopy Asymptotic Method (OHAM), to solve nonlinear equations arising in heat transfer. Two specific applications are considered: cooling of a lumped system with variable specific heat and the temperature distribution equation in a thick rectangular fin radiation to free space. Results obtained by OHAM, which does not need small parameters are compared with numerical results and a very good agreement was found. This method provides us with a convenient way to control the convergence of approximation series and adjust convergence regions when necessary. The results reveal that the proposed method is explicit, effective and easy to use.     

Applying temporal dependence to detect changes in streaming data

Detection of changes in streaming data is an important mining task, with a wide range of real-life applications. Numerous algorithms have been proposed to efficiently detect changes in streaming data. However, the limitation of existing algorithms is that they assume that data are generated independently. In particular, temporal dependencies of data in a stream are still not thoroughly studied. Motivated by this, in this work we propose a new efficient method to detect changes in streaming data by exploring the temporal dependencies of data in the stream. As part of this, we introduce a new statistical model called the Candidate Change Point (CCP) model, with which the main idea is to compute the probabilities of finding change points in the stream. The computed probabilities are used to generate a distribution, which is, in turn, used in statistical hypothesis tests to determine the candidate changes. We use the CCP model to develop a new algorithm called Candidate Change Point Detector (CCPD), which detects change points in linear time, and is thus applicable for real-time applications. Our extensive experimental evaluation demonstrates the efficiency and the feasibility of our approach.     

Nonlinear dynamic analysis of an electrical machine rotor–bearing system by the optimal homotopy perturbation method

In this paper an analytical technique, called the optimal homotopy perturbation method (OHPM), is employed to study the nonlinear behaviour of an electrical machine modelled as a rotor supported by two journal bearings with nonlinear suspension. The dynamics of the rotor centre and bearing centre are studied and the spatial displacements in the horizontal and vertical directions are obtained. It is shown that the main strength of the OHPM is its fast convergence, since after only one iteration we obtain very accurate results for a complicated nonlinear problem, which proves that this method is very efficient in practice.     

Model-driven engineering of process control software – beyond device-centric abstractions

This paper presents a new, two-level, model-driven engineering approach to industrial process control software. The first level (infrastructure engineering) is concerned with the following: the definition of the development process and guidelines, the definition of a domain-specific modeling language, the specification of the model transformations, and the development of a tool suite. This tool suite enables modeling of the process control software and the automatic code generation for programmable logic controllers. In the second level (application engineering), the process control software is engineered using the results of the infrastructure level. The approach is demonstrated on excerpts from an industrial project.     

Crosslinking of cis-1,4-polyisoprene rubber by electron beam irradiation

Various unsaturated polyfunctional monomers were kneaded into cis-1,4-polyisoprene containing fillers under 80°C and then irradiated using an electron beam accelerator to prepare vulcanized rubber with good quality. Results showed that 2G (diethyleneglycol dimethacrylate) is the best sensitizer. The optimal tensile strength of vulcanized IR was obtained by using 14 phr 2G and irradiated to 180 kGy. Compared with the sulfur vulcanizate, greater values of tensile strength, elongation at break, and 100% stress were found with the radiation-cured IR. Their Young's modulus and tan σ were similar. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 66: 113–116, 1997     

Detection of potential inconsistencies in knowledge bases

We present a new computational approach to the problem of detection of potential inconsistencies in knowledge bases. For such inconsistencies, we characterize the sets of possible input facts that will allow the knowledge based system to derive the contradiction. the state-of-the-art approach to a solution of this problem is represented by the COVADIS system which checks simple rule bases. the COVADIS approach relies on forward chaining and is strongly related to the way ATMS computes labels for deducible facts. Here, we present an alternative computation method that employs backward chaining in a kind of abductive reasoning. This approach gives a more focused reasoning, thus requiring much less computation and memory than COVADIS. Further, since our method is very similar to SLD-resolution, it is suitable for handling the more powerful knowledge base form represented by Horn claause bases. Finally, our method is easily extended to uncertain knowledge bases, assuming that the uncertainty calculus is modeled by possibilistic logic. This extension allows us to model the effect of user defined belief thresholds for inference chains.