User-private information retrieval based on a peer-to-peer community

Private information retrieval (PIR) is normally modeled as a game between two players: a user and a database. The user wants to retrieve some item from the database without the latter learning which item is retrieved. Most current PIR protocols are ill-suited to provide PIR from a search engine or large database: (i) their computational complexity is linear in the size of the database; (ii) they assume active cooperation by the database server in the PIR protocol. If the database cannot be assumed to cooperate, a peer-to-peer (P2P) user community is a natural alternative to achieve some query anonymity: a user gets her queries submitted on her behalf by other users in the P2P community. In this way, the database still learns which item is being retrieved, but it cannot obtain the real query histories of users, which become diffused among the peer users. We name this relaxation of PIR user-private information retrieval (UPIR). A peer-to-peer UPIR system is described in this paper which relies on an underlying combinatorial structure to reduce the required key material and increase availability. Extensive simulation results are reported and a distributed key management version of the system is described.     

CMOS-MEMS resonant pressure sensors: optimization and validation through comparative analysis

An optimized CMOS-MEMS resonant pressure sensor with enhanced sensitivity at atmospheric pressure has been reported in this paper. The presented work reports modeling and characterization of a resonant pressure sensor, based on the variation of the quality factor with pressure. The relevant regimes of air flow have been determined by the Knudsen number, which is the ratio of the mean free path of the gas molecule to the characteristic length of the device. The sensitivity has been monitored for the resonator design from low vacuum to atmospheric levels of air pressure. This has been accomplished by reducing the characteristic length and optimization of other parameters for the device. While the existing analytical model has been adapted to simulate the squeeze film damping effectively and it is validated at higher values of air pressure, it fails to compute the structural damping mechanisms dominant in the molecular flow regime, i.e. at lower levels of air pressure. This discrepancy has been solved by finite element modeling that has incorporated both structural and film damping effects. The sensor has been designed with an optimal geometry of 140 × 140 × 8 µm having 6 × 6 perforations along the row and column of the plate, respectively, for maximum Q, with an effective mass of 0.4 µg. An enhanced quality factor of 60 and reduced damping coefficient of 4.34 µNs/m have been obtained for the reported device at atmospheric pressure. The sensitivity of the manufactured device is approximately −0.09 at atmospheric pressure and increases to −0.3 at 40 kPa i.e. in the lower pressures of slip flow regime. The experimental measurements of the manufactured resonant pressure sensor have been compared with that of the analytical and finite element modeling to validate the optimization procedure. The device has been manufactured using standard 250 nm CMOS technology followed by an in-house BEOL metal-layer release through wet etching.

     

Variable selection procedures and efficient suboptimal mask search algorithms in fuzzy inductive reasoning

This paper describes two new suboptimal mask search algorithms for Fuzzy inductive reasoning (FIR), a technique for modelling dynamic systems from observations of their input/output behaviour. Inductive modelling is by its very nature an optimisation problem. Modelling large-scale systems in this fashion involves solving a high-dimensional optimisation problem, a task that invariably carries a high computational cost. Suboptimal search algorithms are therefore important. One of the two proposed algorithms is a new variant of a directed hill-climbing method. The other algorithm is a statistical technique based on spectral coherence functions. The utility of the two techniques is demonstrated by means of an industrial example. A garbage incinerator process is inductively modelled from observations of 20 variable trajectories. Both suboptimal search algorithms lead to similarly good models. Each of the algorithms carries a computational cost that is in the order of a few percent of the cost of solving the complete optimisation problem. Both algorithms can also be used to filter out variables of lesser importance, i.e. they can be used as variable selection tools.     

Piagetian Liquid Conservation in the Great Apes (Pan paniscus, Pan troglodytes, and Pongo pygmaeus).

An understanding of Piagetian liquid conservation was investigated in 4 bonobos (Pan paniscus), 5 chimpanzees (Pan troglodytes), and 5 orangutans (Pongo pygmaeus). The apes were tested in the ability to track the larger of 2 quantities of juice that had undergone various kinds of transformations. The accuracy of the apes' judgment depended on the shape or number of containers into which the larger quantity was transferred. The apes made their choice mainly on the basis of visual estimation but showed modest success when the quantities were occluded. The results suggest that the apes rely to a greater extent on visual information, although they might have some appreciation of the constancy of liquid quantities. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

MIGA, A Software Tool for Nonlinear System Modelling with Modular Neural Networks

This paper presents a software tool suitable for dynamic system modelling. The models generated by this tool are modular neural networks, see [1]. Each module behaves like a functional block and is connected to the other modules like in classical block diagrams. This tool allows the inclusion of a priori knowledge and, furthermore, to extract physical information from the models, once the system has learned. The modelling tool is capable of automatic model generation, parameter estimation and model validation.     

Complementary Resistive Switching Using Metal–Ferroelectric–Metal Tunnel Junctions

Complementary resistive switching (CRS) devices are receiving attention because they can potentially solve the current‐sneak and current‐leakage problems of memory arrays based on resistive switching (RS) elements. It is shown here that a simple anti‐serial connection of two ferroelectric tunnel junctions, based on BaTiO₃, with symmetric top metallic electrodes and a common, floating bottom nanometric film electrode, constitute a CRS memory element. It allows nonvolatile storage of binary states (“1” = “HRS+LRS” and “0” = “LRS+HRS”), where HRS (LRS) indicate the high (low) resistance state of each ferroelectric tunnel junction. Remarkably, these states have an identical and large resistance in the remanent state, characteristic of CRS. Here, protocols for writing information are reported and it is shown that non‐destructive or destructive reading schemes can be chosen by selecting the appropriate reading voltage amplitude. Moreover, this dual‐tunnel device has a significantly lower power consumption than a single ferroelectric tunnel junction to perform writing/reading functions, as is experimentally demonstrated. These findings illustrate that the recent impressive development of ferroelectric tunnel junctions can be further exploited to contribute to solving critical bottlenecks in data storage and logic functions implemented using RS elements.     

Evaluation of word spotting under improper segmentation scenario

International Journal on Document Analysis and Recognition (IJDAR) - Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods...     

Competing agents in agent-mediated institutions

Social processes and agent interaction always take place in a specific context. A school of thought in social studies analyses them in the framework of institutions. We present in this paper the notion ofagentmediated institutions and show how it is relevant for multi-agent systems (MAS) in general and, more specifically, for MAS that include human agents and software agents involved in socioeconomic interactions. We show how the social interactions of human and software agents taking place in the Cohabited Mixed-Reality Information Spaces (COMRIS) project can be described as such an institution, the Conference Centre institution.     

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.