Analysis of multi-agent activity using petri nets

This paper presents the use of place/transition petri nets (PNs) for the recognition and evaluation of complex multi-agent activities. The PNs were built automatically from the activity templates that are routinely used by experts to encode domain-specific knowledge. The PNs were built in such a way that they encoded the complex temporal relations between the individual activity actions. We extended the original PN formalism to handle the propagation of evidence using net tokens. The evaluation of the spatial and temporal properties of the actions was carried out using trajectory-based action detectors and probabilistic models of the action durations. The presented approach was evaluated using several examples of real basketball activities. The obtained experimental results suggest that this approach can be used to determine the type of activity that a team has performed as well as the stage at which the activity ended.     

Disjoint essential sets of implicates of a CQ Horn function

In this paper we study a class of CQ Horn functions introduced in Boros et al. (Ann Math Artif Intell 57(3–4):249–291, 2010). We prove that given a CQ Horn function f, the maximal number of pairwise disjoint essential sets of implicates of f equals the minimum number of clauses in a CNF representing f. In other words, we prove that the maximum number of pairwise disjoint essential sets of implicates of f constitutes a tight lower bound on the size (the number of clauses) of any CNF representation of f.     

Explanation and reliability of prediction models: the case of breast cancer recurrence

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.     

Numerical solutions of unsteady flows with low inlet Mach numbers

This study deals with a numerical solution of a 2D unsteady flow of a compressible viscous fluid in a channel for low inlet airflow velocity. The unsteadiness of the flow is caused by a prescribed periodic motion of a part of the channel wall with large amplitudes, nearly closing the channel during oscillations. The channel is a simplified model of the glottal space in the human vocal tract and the flow can represent a model of airflow coming from the trachea, through the glottal region with periodically vibrating vocal folds, and to the human vocal tract.     

Multi-view Superpixel Stereo in Urban Environments

Urban environments possess many regularities which can be efficiently exploited for 3D dense reconstruction from multiple widely separated views. We present an approach utilizing properties of piecewise planarity and restricted number of plane orientations to suppress reconstruction and matching ambiguities causing failures of standard dense stereo methods. We formulate the problem of the 3D reconstruction in MRF framework built on an image pre-segmented into superpixels. Using this representation, we propose novel photometric and superpixel boundary consistency terms explicitly derived from superpixels and show that they overcome many difficulties of standard pixel-based formulations and handle favorably problematic scenarios containing many repetitive structures and no or low textured regions. We demonstrate our approach on several wide-baseline scenes demonstrating superior performance compared to previously proposed methods.     

Adsorption–desorption noise in plasmonic chemical/biological sensors for multiple analyte environment

We investigated intrinsic noise in plasmonic sensors caused by adsorption and desorption of gaseous analytes on the sensor surface. We analyzed a general situation when there is a larger number of different analyte species. We applied our model to calculate various analyte mixtures, including some environmental pollutants, toxic and dangerous substances. The spectral density of mean square refractive index fluctuations follows a dependence similar to that of generation-recombination noise in photodetectors, flat at lower frequencies and sharply decreasing at higher. Some of the calculated noise levels are well within the detection range of conventional surface plasmon resonance sensors. An AD noise peak is observed in temperature dependence of mean square refractive index fluctuations, thus sensor operating temperature may be optimized to obtain larger signal to noise ratio. A significant property of AD noise is its rise with the decreasing plasmon sensor area, which means that it will be even more pronounced in modern nanoplasmonic devices. Our consideration is valid both for conventional surface plasmon resonance devices and for general nanoplasmonic devices.     

Adaptive content-based music retrieval system

This paper presents a tunable content-based music retrieval (CBMR) system suitable the for retrieval of music audio clips. The audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The main goal of tuning the feature space is to improve retrieval performance, since some features may have more impact on perceived similarity than others. The tuning process utilizes our genetic algorithm. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently performed because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(logn) time complexity. Different objective functions based on cluster similarity and dissimilarity measures are used for the genetic algorithm. We have found that all of them have similar impact on the retrieval performance in terms of precision and recall. The paper includes experimental results in measuring retrieval performance, reporting significant improvement over the untuned feature space.     

Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment

Sustainable supply chain management (SSCM) provides economic, social end environmental requirements in material and service flows occurring between suppliers, manufacturers and customers. SSCM structure is considered as a prerequisite for a sustainable success. Thus designing an effective SCM structure provides competitive advantages for the companies. In order to achieve an effective design of this structure, it is possible to apply quality function deployment (QFD) approach which is successfully applied as an effective product and system development tool. This study presents a decision framework where analytic network process (ANP) integrated QFD and zero-one goal programming (ZOGP) models are used in order to determine the design requirements which are more effective in achieving a sustainable supply chain (SSC). The first phase of the QFD is the house of quality (HOQ) which transforms customer requirements into product design requirements. In this study, after determining the sustainability requirements named customer requirements (CRs) and design requirements (DRs) of a SSC, ANP is employed to determine the importance levels in the HOQ considering the interrelationships among the DRs and CRs. Furthermore ZOGP approach is used to take into account different objectives of the problem. The proposed method is applied through a case study and obtained results are discussed.