Sameh A. Salem

Edutronics: Gamification for introducing kids to electronics

In this paper we present the Edutronics project, a new game-based equipment to introduce kids to Electronics by means of a fun learning environment based on learning by doing. The equipment has been developed as a graduation project by a group of Egyptian students, enrolled in a double degree program in ICT Engineering, organized by Uninettuno University (Italy) and Helwan University (Egypt). The equipment is composed by a hardware platform and a controlling software. The hardware platform consists of a didactic kit designed to allow to build electric circuits in a amusing and safe way. The controlling software has been developed as a mobile application and provides the students with exercises to be solved. Todays ubiquity of social networks is enabled through a Facebook profile in order to publish ones results. An Arduino board with Bluetooth shield connects the hardware to the software and is able to check the correctness of each exercise solutions. Such an equipment is targeted to young students of primary to high schools in order to introduce them to Electronics in a safe way and with an affordable cost for schools.

C30. An Interpolation Based Technique for Sign Language Recognition

The objective of the research presented in this paper is to facilitate the communication between people with hearing impairment (i.e. deaf people) and normal people. In addition, an efficient and fast algorithm for automatic translation system for gestures of manual numbers in the sign Language is proposed. The proposed algorithm does not rely on the use of any gloves or visual markings to accomplish the recognition job. The proposed system uses the concept of boundary tracing and finger tip detection. As an alternative, it deals with images of bare hands, which allows the user to interact with the system in a natural way. The technique is based on interpolation of signature of hand image. Experiments revealed that satisfactory results can be obtained via the proposed algorithm.

A host-based framework for RAT bots detection

Cyber threats push the researchers towards developing detection frameworks for protecting Internet users. Remote administration tool (RAT) is one of the serious cyber tools used by the attackers to fully control the targeted victim machine. In this paper a host based detection framework is introduced for RAT detection. The proposed framework depends on fully analysis of the system behavior of the host machine for the detection of RAT bots using machine learning techniques. The proposed framework provides a detection accuracy of 95.23% with low false positive rates.

A network-based framework for RAT-bots detection

Remote access Trojans (RATs) are used by attackers to compromise and control the victim machine. In this work, a novel Network-based framework is introduced for detecting RAT bots based on data mining techniques. Several machine learning (ML) techniques are used to differentiate between benign and RAT infected machines. Various performance measurements are used to evaluate the performance of the proposed framework. Experimental results demonstrate that the proposed system can achieve accuracy of 97.2% with 3.8% false positive rate.

Energy Aware Mobile Cloud Computing Algorithm for Android Smartphones

Nowadays, smartphones and tablet computers have become progressively essential parts of our life. However, these devices are limited in their computational resources compared to other processing devices such as personal computers and laptops. To mitigate this problem, cloud computing can be a promising candidate to help resource-constrained devices by offloading the heavy applications onto the Cloud. In this paper, a novel energy aware mobile cloud computing algorithm is proposed. The proposed algorithm estimates the application computational time and uses weighted parameters to obtain a reliable offloading decision. This actually saves the energy and reduces applications’ execution time. Experimental results on different applications show that the proposed algorithm improves applications’ performance and effectively reduces the energy consumption through a robust estimation of applications’ execution time.

Design of a Prediction System for Hydrate Formation in Gas Pipelines using Wireless Sensor Network

Before the evolution of the Wireless Sensor Networks (WSN) technology, many production wells in the oil and gas industry were suffering from the gas hydration formation process, as most of them are remotely located away from the host location. By taking the advantage of the WSN technology, it is possible now to monitor and predict the critical conditions at which hydration will form by using any computerized model. In fact, most of the developed models are based on two well-known hand calculation methods which are the Specific gravity and K-Factor methods. In this research, the proposed work is divided into two phases; first, the development of a three prediction models using the Neural Network algorithm (ANN) based on the specific gravity charts, the K-Factor method and the production rates of the flowing gas mixture in the process pipelines. While in the second phase, two WSN prototype models are designed and implemented using National Instruments WSN hardware devices. Power analysis is carried out on the designed prototypes and regression models are developed to give a relation between the sensing nodes (SN) consumed current, Node-to-Gateway distance and the operating link quality. The prototypes controller is interfaced with a GSM module and connected to a web server to be monitored via mobile and internet networks.

Harmonic Segment-Based Semi-Partitioning Scheduling on Multi-Core Real-Time Systems

Nowadays, the issue of scheduling multi-core real-time systems has become the focus of such research in industrial, biomedical, military, and other fields. As a consequence, a new semi-partitioning algorithm that uses a static Rate-Monotonic criterion to schedule real-time tasks on multi-core platforms is proposed. The improvement in the performance of real-time systems is achieved by exploitingthe fact that the utilization boundary of a task set increases to fully utilize the processors if the periods of tasks have harmonic nature among each other. Experimental results on randomly generated datasets and real-world datasets show that the proposed algorithm inevitably outperforms other competitive algorithms.

C24. Harmonic semi-partitioning scheduling algorithm for multi-core real-time systems

In this paper, a new semi-partitioning scheduling algorithm for muli-core real time system is proposed. The proposed algorithm is called Harmonic Semi-Partitioning and Task Splitting (HSPTS) which uses the Rate-Monotonic approach to address the problem of scheduling periodic tasks with implicit deadlines on multi-core systems. Two challenges have been addressed and resolved by the proposed algorithm, the first is to find the set of tasks which have harmonic relations with each other. While the second challenge is to assign and split the appropriate tasks among different processors without overrun. In this context, the overall utilization for multi-core systems will be improved and exceed the Liu&Laylands boundary for N tasks. To this extent, the suitable task pairs are assigned to processors using hyperbolic boundary. Experimental results show that the developed algorithm enhances not only the overall system utilization but also the number of processors needed to schedule multi-core real-time systems compared with other competitive algorithms.

EMCC: Enhancement of Motion Chain Code for Arabic Sign Language Recognition

In this paper, an algorithm for Arabic sign language recognition is proposed. The proposed algorithm facilitates the communication between deaf and non-deaf people. A possible way to achieve this goal is to enable computer systems to visually recognize hand gestures from images. In this context, a proposed criterion which is called Enhancement Motion Chain Code (EMCC) that uses Hidden Markov Model (HMM) on word level for Arabic sign language recognition (ArSLR) is introduced. This paper focuses on recognizing Arabic sign language at word level used by the community of deaf people. Experiments on real-world datasets showed that the reliability and suitability of the proposed algorithm for Arabic sign language recognition. The experiment results introduce the gesture recognition error rate for a different sign is 1.2% compared to that of the competitive method.

A Real-Time Approach for Detecting Malicious Executables

In this paper, we develop a real-time algorithm to detect malicious portable executable (PE) files. The proposed algorithm consists of feature extraction, vector quantization, and a classifier named Attribute-Biased Classifier (ABC). We have collected a large data set of malicious PE files from the Honeynet project in the EG-CERT and VirusSign to train and test the proposed system. We first apply a feature extraction algorithm to remove redundant features. Then the most effective features are mapped into two vector quantizers. Finally, the output of the two quantizers are given to the proposed ABC classifier to identify a PE file. The results show that our algorithm is able to detect malicious PE file with 99.3% detection rate, 97% accuracy, 0.998 AUC, and less than 1% false positive rate. In addition, our algorithm consumes a fraction of seconds to test a portable executable file.