Ebaa Fayyoumi

A fixed structure learning automaton micro-aggregation technique for secure statistical databases

We consider the problem of securing statistical databases and, more specifically, the micro-aggregation technique (MAT), which coalesces the individual records in the micro-data file into groups or classes, and on being queried, reports, for the all individual values, the aggregated means of the corresponding group. This problem is known to be NP-hard and has been tackled using many heuristic solutions. In this paper we present the first reported Learning Automaton (LA) based solution to the MAT. The LA modifies a fixed-structure solution to the Equi-Partitioning Problem (EPP) to solve the micro-aggregation problem. The scheme has been implemented, rigorously tested and evaluated for different real and simulated data sets. The results clearly demonstrate the applicability of LA to the micro-aggregation problem, and to yield a solution that obtains a lower information loss when compared to the best available heuristic methods for micro-aggregation.

On optimizing the k -ward micro-aggregation technique for secure statistical databases

We consider the problem of securing a statistical database by utilizing the well-known micro-aggregation strategy, and in particular, the k-Ward strategy introduced in [1] and utilized in [2]. The latter scheme, which represents the state-of-the-art, coalesces the sorted data attribute values into groups, and on being queried, reports the means of the corresponding groups. We demonstrate that such a scheme can be optimized on two fronts. First of all, we minimize the computations done in evaluating the between-class distance matrix, to require only a constant number of updating distance computations. Secondly, and more importantly, we propose that the data set be partitioned recursively before a k-Ward strategy is invoked, and that the latter be invoked on the “primitive” sub-groups which terminate the recursion. Our experimental results, done on two benchmark data sets, demonstrate a marked improvement. While the information loss is comparable to the k-Ward micro-aggregation technique proposed by Domingo-Ferrer et.al. [2], the computations required to achieve this loss is a fraction of the computations required in the latter – providing a computational advantage which sometimes exceeds 80% if one method is used by itself, and more than 90% if both enhancements are invoked simultaneously.

Assistive Technology for Deaf People Based on Android Platform

Abstract Social communication is one of the most important pillars that our society based on. It is well-known that the language is the only way to communicate and interact with each other verbally or non-verbal way. People with special needs are members of this society and have the right to enjoy the communication with the external environment in an easy and professional manner. This paper aims to provide an interesting application that guarantees ultimate communication with the disabled users and vice versa. The key feature of this application is employing the Arabic language as a medium of communication to learn all the sign language terms. The power of this application appears in two aspects: first of all, the ability of normal people to communicate with the targeted people without having any previous knowledge on signs language. This can be either achieved by voice recognition of words or by typing the words in the Arabic language. The application is then displays the appropriate image(s) in the sign language. Secondly, and more importantly, people with special needs communicate with normal people by choosing the signs images on their phones from the numerous categories stored in the databases which express their ideas and thoughts. Consequently, the set of images is transformed into a text paragraph. We evaluated our application by testing it on real deaf and dumb users. We carefully created scenarios on realistic situations. The early results are promising as all deaf found the proposed technology useful and 90% of them wanted to use it on daily basis.

Achieving extended displays prototype via Wi-Fi direct technology

Wi-Fi Direct is considered as the way to connect people by offering a wider connectivity to the authorized devices. This paper presents a prototype that connects more than two displays wirelessly to create a VGA host by integrating the Miracast Application Program Interface (API) with Wi-Fi direct technology. This achievement supports high-quality multimedia streaming, audio and video conferencing, texts, gaming and more other applications. The behavior of the proposed prototype is measured by assessing the minimum number of connected group of devices, as well as the expected throughput in real life scenario. The experimental results show a remarkable overall performances measured by throughput, significance and applicability of the proposed prototype.

Integrated model of photovoltaic solar system with the sound biometric techniques

Photovoltaic system (PVS) is used for generating electric power by using solar cells to convert energy from the sun light into a flow of direct current electricity, which can be used to power equipment or to recharge a battery. In addition, the Sound biometric techniques can enable PVS to listen and understand their surrounding auditory environment since turning the lights on all the nights will get through a lot of energy which it might be used in other significant concerns. This paper proposed a model of combination between PVS to generate power energy (electricity) from the sunlight controlled by a sound biometric technique to reduce the consumption of the generated power energy by turning the lights on for the highways only when there are cars on the highway and only for some period of time to make the driving out of harmful ways and trouble-free; followed by an implementation of the proposed combination model between the PVS and biometric sound chip and a presentation of a discussion of the proposed system results.

A Novel Approach to Build a Generic Model of Photovoltaic Solar System Using Sound Biometric Techniques

This chapter presents the proposed model of combination between Photovoltaic solar system resources and sound biometric techniques, to generate power energy from the sunlight using the PVS controlled by a sound biometric technique. This work contributes to research knowledge by proposing and validating a sound biometric technique for allowing to reduce the consumption of the generated power energy by turn the lights on for the public roads only when there are vehicles on the way and only for some period of time to make the driving out of harms way and trouble-free. The proposed and combination models between the PVS and biometric sound chip is used for generating electric power by using solar cells to convert energy from the sun light into a flow of direct current electricity, which can be used to power equipment or to recharge a battery. In addition the Sound biometric techniques can enable PVS to listen and understand their surrounding auditory environment since turning the lights on all the time will get through a lot of energy which it might be used in other significant concerns.