Query processing issues in region-based image databases

Many modern image database systems adopt a region-based paradigm, in which images are segmented into homogeneous regions in order to improve the retrieval accuracy. With respect to the case where images are dealt with as a whole, this leads to some peculiar query processing issues that have not been investigated so far in an integrated way. Thus, it is currently hard to understand how the different alternatives for implementing the region-based image retrieval model might impact on performance. In this paper, we analyze in detail such issues, in particular the type of matching between regions (either one-to-one or many-to-many). Then, we propose a novel ranking model, based on the concept of Skyline, as an alternative to the usual one based on aggregation functions and k-Nearest Neighbors queries. We also discuss how different query types can be efficiently supported. For all the considered scenarios we detail efficient index-based algorithms that are provably correct. Extensive experimental analysis shows, among other things, that: (1) the 1–1 matching type has to be preferred to the N–M one in terms of efficiency, whereas the two have comparable effectiveness, (2) indexing regions rather than images performs much better, and (3) the novel Skyline ranking model is consistently the most efficient one, even if this sometimes comes at the price of a reduced effectiveness.     

Multidisciplinary Design Optimization Framework for the Pre Design Stage

Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows:

The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously.

The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front.

The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step.

The work demonstrated for the first time that such capabilities can be used in a coordinated way to enhance the efficiency of the computational process and the effectiveness of the decision making at preliminary design stage.     

Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin,India

In this work, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) signatures were analysed over some critical sites in Lakhimpur District in Brahmaputra basin, India, characterized by a high frequency of flooding events. The site is mostly covered by paddy fields. Results obtained were compared with water level measurements in three stations close to the main channel of the river. Information about surface temperature, which allowed us to estimate the emissivity, was also available. Investigations were carried out at the C, X, and K_a bands of the AMSR-E channel. A multi-frequency analysis indicated that the X band would represent a good compromise between resolution and sensitivity requirements, while at the C band the resolution was too coarse and at the K_a band the signatures were affected by raindrops. Samples collected during rain were eliminated using techniques based on the 89.0 GHz channel. However, even after this correction, the K_a band showed poor sensitivity due to higher attenuation by vegetation. The correlations between different pairs of variables, viz. polarization index (PI), water level (WL), and fractional water surface area (F _WS), were also investigated. At the X band, the water level was better correlated with the PI than with emissivity and other parameters defined in the literature. The correlation was good in cases of slow variation in WL. In cases of sudden variation in the river, the PI followed the variations with some time delay related to the propagation of water within the covered AMSR-E pixel.     

Bellaso’s 1552 cipher recovered in Venice

Abstract

In his 1553 paper Giovan Battista Bellaso presented his first polyalphabetic cipher, writing that this was an improved reprint of a previous cipher of 1552 that had been printed in Venice on a loose leaflet without instructions. There was much speculation about this early cipher, since no print of that cipher was reported to have been found. Now a printed copy of that leaflet has been found in Venice by the author, and this paper is a report of that finding.     

Authoring vs. Configuring Affective Agents for Interactive Storytelling

Autonomous characters in interactive storytelling can be supported by using affective agent architectures. The configuration of most current tools for controlling agents is, however, implementation specific and not tailored to the needs of authors. Based on literature review, a questionnaire evaluation of authors’ preferences for character creation, and a case study of an author’s conceptualization of this process, we investigate the different methods of configuration available in current agent architectures, reviewing discrepancies and matches. Given these relations, promising approaches to configuration are identified, based on initial inner states, “global” parameters of characters, libraries of stock characters, and selections of backstory experiences.     

QoS management middleware solutions for Bluetooth audio distribution

A primary challenge in the mobile Internet scenario is the dynamic differentiation of provided levels of Quality of Service (QoS) depending on client characteristics and current resource availability. In this context, the paper presents how the application-level ubiQoS middleware supports the provisioning of audio applications with different QoS levels over the last Bluetooth segment to wireless devices. To this purpose, ubiQoS dynamically deploys mobile proxies, acting as masters in Bluetooth piconets, to properly configure and manage Bluetooth connections at runtime. Audio applications working on top of ubiQoS can easily exploit differentiated QoS by simply specifying different user classes and terminal profiles. ubiQoS proxies exploit JSR82ext, a newly developed Java library that extends the JSR82 specification for Java-based Bluetooth communication facilities with richer QoS management functions for all types of Bluetooth connections. The reported experimental results show that our Java-based implementation can dynamically support differentiated QoS levels for audio traffic, with an efficient usage of the available Bluetooth bandwidth.     

Abnormal cytoskeletal protein expression in cultured skin fibroblasts from type 1 diabetes mellitus patients with nephropathy: A proteomic approach

Diabetic nephropathy (DN) develops in about 40% of insulin-dependent type 1 diabetes mellitus (T1DM) patients, and is associated not only with diabetes duration and metabolic control, but also with a genetic predisposition. Constitutive alterations of cytoskeletal proteins may play a role in the development of DN. We investigated the expression of these proteins in cultured skin fibroblasts, obtained from long-term T1DM patients with and without DN but comparable metabolic control, and from matched healthy subjects, by means of 2-DE electrophoresis and MS-MALDI analyses. In T1DM with DN, compared to the other two groups, quantitative analyses revealed an altered expression of 17 spots (p<0.05-p<0.01), corresponding to 12 unique proteins. In T1DM with DN, beta-actin and three isoforms of tubulin beta-2 chain, tropomodulin-3, and LASP-1 were decreased, whereas two tubulin beta-4 chain isoforms, one alpha actinin-4 isoform, membrane-organizing extension spike protein (MOESIN), FLJ00279 (corresponding to a fragment of myosin heavy chain, non-muscle type A), vinculin, a tropomyosin isoform, and the macrophage capping protein were increased. A shift in caldesmon isoforms was also detected. These results demonstrate an association between DN and the constitutive expression of cytoskeleton proteins in cultured skin fibroblasts from T1DM with DN, which may retain pathophysiologycal implications.     

Combining supervised and unsupervised learning for data clustering

Clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approach, however, is not viable when dissimilarity is conceptual rather than metric. In this paper, we propose to extract the dissimilarity relation directly from the available data. To this aim, we train a feedforward neural network with some pairs of points with known dissimilarity. Then, we use the dissimilarity measure generated by the network to guide a new unsupervised fuzzy relational clustering algorithm. An artificial data set and a real data set are used to show how the clustering algorithm based on the neural dissimilarity outperforms some widely used (possibly partially supervised) clustering algorithms based on spatial dissimilarity.     

Enhancing grid security by fine-grained behavioral control and negotiation-based authorization

Nowadays, Grid has become a leading technology in distributed computing. Grid poses a seamless sharing of heterogeneous computational resources belonging to different domains and conducts efficient collaborations between Grid users. The core Grid functionality defines computational services which allocate computational resources and execute applications submitted by Grid users. The vast models of collaborations and openness of Grid system require a secure, scalable, flexible and expressive authorization model to protect these computational services and Grid resources. Most of the existing authorization models for Grid have granularity to manage access to service invocations while behavioral monitoring of applications executed by these services remains a responsibility of a resource provider. The resource provider executes an application under a local account, and acknowledges all permissions granted to this account to the application. Such approach poses serious security threats to breach system functionality since applications submitted by users could be malicious. We propose a flexible and expressive policy-driven credential-based authorization system to protect Grid computational services against a malicious behavior of applications submitted for the execution. We split an authorization process into two levels: a coarse-grained level that manages access to a computational service; and a fine-grained level that monitors the behavior of applications executed by the computational service. Our framework guarantees that users authorized on a coarse-grained level behave as expected on the fine-grained level. Credentials obtained on the coarse-grained level reflect on fine-grained access decisions. The framework defines trust negotiations on coarse-grained level to overcome scalability problem, and preserves privacy of credentials and security policies of, both, Grid users and providers. Our authorization system was implemented to control access to the Globus Computational GRAM service. A comprehensive performance evaluation shows the practical scope of the proposed system.