Crossing textual and visual content in different application scenarios

This paper deals with multimedia information access. We propose two new approaches for hybrid text-image information processing that can be straightforwardly generalized to the more general multimodal scenario. Both approaches fall in the trans-media pseudo-relevance feedback category. Our first method proposes using a mixture model of the aggregate components, considering them as a single relevance concept. In our second approach, we define trans-media similarities as an aggregation of monomodal similarities between the elements of the aggregate and the new multimodal object. We also introduce the monomodal similarity measures for text and images that serve as basic components for both proposed trans-media similarities. We show how one can frame a large variety of problem in order to address them with the proposed techniques: image annotation or captioning, text illustration and multimedia retrieval and clustering. Finally, we present how these methods can be integrated in two applications: a travel blog assistant system and a tool for browsing the Wikipedia taking into account the multimedia nature of its content.

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

Filamentary Superconductivity in Semiconducting Policrystalline ZrSe<Subscript>2</Subscript> Compound with Zr Vacancies

Z r S e ₂ is a band semiconductor studied long time ago. It has interesting electronic properties, and because its layer structure can be intercalated with different atoms to change some of the physical properties. In this investigation, we found that Zr deficiencies alter the semiconducting behavior and the compound can be turned into a superconductor. In this paper, we report our studies related to this discovery. The decreasing of the number of Zr atoms in small proportion according to the formula Zr _x Se₂, where x is varied from about 8.1 to 8.6 K, changing the semiconducting behavior to a superconductor with transition temperatures ranging between 7.8 and 8.5 K, is depending on the deficiencies. Outside of those ranges, the compound behaves as semiconducting with the properties already known. In our experiments, we found that this new superconductor has only a very small fraction of superconducting material determined by magnetic measurements with applied magnetic field of 10 Oe. Our conclusions is that superconductivity is filamentary. However, in one studied sample, the fraction was about 10.2 %, whereas in others is only about 1% or less. We determined the superconducting characteristics; the critical fieldsthat indicate a type 2 superonductor with Ginzburg-Landau κ parameter of the order about 2.7. The synthesis procedure is quite normal following the conventional solid state reaction. In this paper, included are the electronic characteristics, transition temperature, and evolution with temperature of the critical fields.     

A PDE method for patchwise approximation of large polygon meshes

Three-dimensional (3D) representations of complex geometric shapes, especially when they are reconstructed from magnetic resonance imaging (MRI) and computed tomography (CT) data, often result in large polygon meshes which require substantial storage for their handling, and normally have only one fixed level of detail (LOD). This can often be an obstacle for efficient data exchange and interactive work with such objects. We propose to replace such large polygon meshes with a relatively small set of coefficients of the patchwise partial differential equation (PDE) function representation. With this model, the approximations of the original shapes can be rendered with any desired resolution at interactive rates. Our approach can directly work with any common 3D reconstruction pipeline, which we demonstrate by applying it to a large reconstructed medical data set with irregular geometry.     

On the minimum of a positive polynomial over the standard simplex

We present a new positive lower bound for the minimum value taken by a polynomial P$P$ with integer coefficients in k$k$ variables over the standard simplex of R^k${\mathbb{R}}^k$, assuming that P$P$ is positive on the simplex. This bound depends only on the number of variables k$k$, the degree d$d$ and the bitsize τ$\tau$ of the coefficients of P$P$ and improves all the previous bounds for arbitrary polynomials which are positive over the simplex.     

Predicting user’s preferences using neural networks and psychology models

In this paper we describe a new model suitable for optimization problems with explicitly unknown optimization functions using user’s preferences. The model addresses an ability to learn not known optimization functions thus perform also a learning of user’s preferences. The model consists of neural networks using fuzzy membership functions and interactive evolutionary algorithms in the process of learning. Fuzzy membership functions of basic human values and their priorities were prepared by utilizing Schwartz’s model of basic human values (achievement, benevolence, conformity, hedonism, power, security, self-direction, stimulation, tradition and universalism). The quality of the model was tested on “the most attractive font face problem” and it was evaluated using the following criteria: a speed of optimal parameters computation, a precision of achieved results, Wilcoxon signed rank test and a similarity of letter images. The results qualify the developed model as very usable in user’s preference modeling.     

A novel model for arc territory design: promoting Eulerian districts

The problem of district design for the implementation of arc routing activities is addressed. The aim is to partition a road network into a given number of sectors to facilitate the organization of the operations to be implemented within the region. This problem arises in numerous applications such as postal delivery, meter readings, winter gritting, road maintenance, and municipal solid waste collection. An integer linear programming model is proposed where a novel set of node parity constraints to favor Eulerian districts is introduced. Series of instances were solved to assess the impact of these parity constraints on the objective function and deadhead distance. Networks with up to 401 nodes and 764 edges were successfully solved. The model is useful at a tactical level as it can be used to promote workload balance, compactness, deadhead distance reduction and parity in districts.     

Delivering public value through open government data initiatives in a Smart City context

By using ICT in an innovative way, governments can improve the delivery of services and interaction with stakeholders. Open data is a way to help public organizations became more open and improve interaction with stakeholders. This paper aims to identify what are the public values enhancements acquired on smart city environment that discloses open data. We propose a conceptual model to analyze the smart city initiative. We contextualized the model taking a smart city domain by analyzing three related-initiatives that comprises open data in a smart city case carried at Rio de Janeiro Operations Center (COR) in Brazil by seven deep-interviewees directly involved - from inside and outside – in this case. The findings reveal evidences that open data initiatives contribute to enhance the delivery of public value in smart city contexts.     

Replacing dichromate with hydrogen peroxide in the chemical oxygen demand (COD) test

The widely used standard method for chemical oxygen demand (COD) involves hazardous chromium species, and its two-hour heating protocol entails a substantial amount of energy expenditure. In the present work we report a proof of concept for a major modification of this method in the range 10-800 mgCOD/L, whereby H2O2 is proposed as a replacement oxidizer. This modification not only reduces the use of unsafe chromium species but also allows for the use of milder conditions that decrease the total energy outlay. The results are comparable with those obtained either with the standard method or with a commercial Hach? kit.     

Decoding Speech Prosody: Do Music Lessons Help?

Three experiments revealed that music lessons promote sensitivity to emotions conveyed by speech prosody. After hearing semantically neutral utterances spoken with emotional (i.e., happy, sad, fearful, or angry) prosody, or tone sequences that mimicked the utterances' prosody, participants identified the emotion conveyed. In Experiment 1 (n=20), musically trained adults performed better than untrained adults. In Experiment 2 (n=56), musically trained adults outperformed untrained adults at identifying sadness, fear, or neutral emotion. In Experiment 3 (n=43), 6-year-olds were tested after being randomly assigned to 1 year of keyboard, vocal, drama, or no lessons. The keyboard group performed equivalently to the drama group and better than the no-lessons group at identifying anger or fear. (PsycINFO Database Record (c) 2010 APA, all rights reserved)