Gender differences in digital music distribution methods

This study examines gender differences among Greek Internet users in using digital music distribution methods. First, a Web-based questionnaire was developed and placed on most popular Greek forums. Then, 300 Greek Internet users stated their opinions regarding digital music and the following downloading methods: 1) Http downloading, 2) P2P downloading, 3) Torrent downloading, and 4) Combined iTunes method. The results revealed that P2P programs are the first choice for downloading mp3. The responders were satisfied with the P2P’s easiness of use, the variety of songs and the no need to pay. However, they worried about P2P’s safety. Gender differences were identified in several areas. Music industry managers could consider these findings in their strategies to reach the consumers. Advertising companies could use the results to target differently male or female consumers.     

Inverse lighting design using a coverage optimization strategy

Lighting design is an essential process in computer cinematography, games, architectural design and various other applications for correctly illuminating or highlighting parts of a scene and enhancing storytelling. When targeting specific illumination goals and constraints, this process can be tedious and counter-intuitive even for experienced users and thus automatic, goal-driven methods have emerged for the estimation of a lighting configuration to match the desired result. We present a general automatic approach to such an inverse lighting design problem, where the number of light sources along with their position and emittance are computed given a set of user-specified lighting goals. To this end, we employ a special hierarchical light clustering that operates in the lighting goal coverage domain and overcomes limitations of previous approaches in environments with high occlusion or structural complexity. Our approach is independent of the underlying light transport model and can quickly converge to usable solutions. We validate our results and provide comparative evaluation with the current state of the art.     

Accessibility-based reranking in multimedia search engines

Multimedia Tools and Applications - Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide...     

Visual understanding industrial workflows under uncertainty on distributed service oriented architectures

Service Oriented Architecture (SOA) is an evolution of distributed computing based on the request/reply design paradigm for synchronous and asynchronous applications. In such architectures, application developers or system integrators can build scenarios by composing one or more services without knowing the services’ underlying implementations. In this paper, we adopt a SOA framework for on-line scheduling complex industrial workflows. The proposed SOA consists of three main layers; the input layer, the benchmarking layer, the prediction layer and the scheduler. The main innovation of our SOA architecture is the prediction layer that supports computer vision tools able to visually observe how an industrial workflow is actually executed. This way, we can automatically estimate the actual execution time for a process. In particular a new self-initialized visual tracker algorithm is proposed in this paper to robustly trace workers’ trajectory in a plant via visual observations. Then, part-to-whole curve matching is presented so as to find correspondences among the traced curve and the ideal one and thus improving scheduler efficiency. The input layer interoperably describes industrial operations using the XPDL (an XML-based) format. The benchmarker evaluates much faster than real-time and in an off-line mode how long it takes for an industrial workflow to be executed on a given resource guaranteeing an almost real-time implementation of the video processing algorithms on the plant. The last component of the proposed SOA is the scheduler with the goal to assign the workflows to the available resources. In this paper, a Maximum Benefit First (MBF) scheduler is presented which maximizes the total gain received by the industry when completing execution of all the beneficial operations with minimum violations of their delivery deadlines (that is with the minimum compensations). Experiments have been conducted on a real-world industrial plant of Nissan Iberica automobile construction indicating the efficiency of the proposed system.     

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.     

Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis

In this paper, a novel multi-view human movement recognition method is presented. A novel representation of multi-view human movement videos is proposed that is based on learning basic multi-view human movement primitives, called multi-view dynemes. The movement video is represented in a new feature space (called dyneme space) using these multi-view dynemes, thus producing a time invariant multi-view movement representation. Fuzzy distances from the multi-view dynemes are used to represent the human body postures in the dyneme space. Three variants of Linear Discriminant Analysis (LDA) are evaluated to achieve a discriminant movement representation in a low dimensionality space. The view identification problem is solved either by using a circular block shift procedure followed by the evaluation of the minimum Euclidean distance from any dyneme, or by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). The discriminant movement representation combined with camera viewpoint identification and a nearest centroid classification step leads to a high human movement classification accuracy.     

SMART-Reactors: Tailoring Gas Holdup Distribution by Additively Manufactured Lattice Structures

In chemical process engineering, fast gas-liquid reactions often suffer from an inefficient distribution of gas and therefore mixing and mass transfer performance. This study deals with the possibility of influencing the local gas holdup and bubble size distribution in a gas-liquid process using additively manufactured lattice structures (AMLS). The used measuring technique to study bubble size, velocity, and the local gas holdup is a photo-optical needle probe. By using AMLS, a significant radial homogenization of the local gas holdup and the mean bubble size is achieved. Furthermore, it can be demonstrated that the bubble size can be tailored by the geometry of the inserted structure. It is illustrated that the mean bubble velocities are lowered within the lattice resulting in a higher residence time of the dispersed phase with an impact on the mass transfer performance within the AMLS.     

Application of biological processes for the removal of arsenic from groundwaters

Bacteria are widespread, abundant, geochemically reactive components of aquatic environments. In particular, iron-oxidizing bacteria, are involved in the oxidation and subsequent precipitation of ferrous ions. Due to this property, they have been applied in drinking water treatment processes, in order to accelerate the removal of ferrous iron from groundwaters. Iron also exerts a strong influence on arsenic concentrations in groundwater sources, while iron oxides are efficient adsorbents in arsenic removal processes. In the present study, the removal of arsenic (III and V), during biological iron oxidation has been investigated. The results showed that both inorganic forms of arsenic could be efficiently treated, for the concentration range of interest in drinking water (50-200microg/L). In addition, the oxidation of trivalent arsenic was found to be catalyzed by bacteria, leading to enhanced overall arsenic removal, because arsenic in the form of arsenites cannot be efficiently sorbed onto iron oxides. This method comprises a cost competitive technology, which can find application in treatment of groundwaters with elevated concentrations of iron and arsenic.