Software engineering courses are characterized by heavy theoretical and complex concepts that might be challenging to teach while keeping students engaged in the classroom, which might impact them from pursuing software engineering careers later. Software engineering is well known to be a male dominated field even though the first software engineer was a woman. Research has shown that storytelling can be used to improve students and teachers engagement in primary and secondary education. However, there is a lack of research on storytelling application and efficiency in higher education to teach complex concepts. This study explored the impact of combining a story with lecture on undergraduate female students in a software engineering course. We investigated the following hypothesis: Combining storytelling with lectures can improve the female students interest in software engineering. A questionnaire study has been conducted with 24 female undergraduate students in the classroom. Students had no prior knowledge on software engineering before the study. The questionnaire was designed to be simple to motivate students to participate in the study. It contained three questions to capture the interest of female students in pursuing a career in software engineering: prior to the lecture, after the lecture, and after the story. The results showed that the story incorporated in the lecture had great impact on female students perception about software engineering. Combining storytelling with software engineering lectures seems a promising tool to engage female students in higher education and increase their interest in pursuing a career in software engineering.

Internet of things (IoT) aims to connect billions of devices to the Internet. These devices generate an enormous amount of traffic. A Wireless Body Area Network (WBAN) aggregates the data generated from the body sensors deployed on the human body or inside it. Due to resource constraints of the deployed sensors, aggregated data is forwarded to a server at an edge node or a cloud for further processing and decision making. With the wide acceptance of IoT deployments, WBANs can also be integrated with IoT to provide a clear contextual environment for smart healthcare applications' users. Software Defined Networks (SDN) is a new networking paradigm providing centralized control and programmability of the networks. These features of SDN open new vistas for WBAN and IoT applications. In this paper, a personal digital assistant (PDA) is configured as an SDN switch, and we emulate the SDN functionality with Mininet to verify that this approach can be used in WBANs with minimum deployment complexity and network overhead, while efficiently manage the connected devices and the connections between them.

The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.

Abstract In a wireless network, mutual secure neighbor verification (MSN) is defined as the capability of a node (verifier) to verify the claim by another node (claimer) that it exists within a certain physical distance from the verifier. This problem has received great attention because it has numerous practical applications. Current state‐of‐the‐art approaches to solve this problem, such as the use of time of flight, signal strength, and angle of arrival, suffer from impracticality in terms of application and computation cost. In this work, we propose two algorithms to mitigate the MSN problem during the incremental deployment phase of static senor networks. Each node should announce its location and the power level it uses for transmission. Cooperative and base station verifications are used to detect nodes that lie about their locations. The simulation results show that we can achieve high detection (>90%) of nodes that forge their location information using either high power transmission or by colluding with other malicious nodes. Copyright © 2011 John Wiley & Sons, Ltd.

Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.

Sharing data among organizations plays an important role in security and data mining. In this paper, the authors describe a Data Sharing Miner and Analyzer (DASMA) system that simulates data sharing among N organizations. Each organization has its own enforced policy. The N organizations share their data based on trusted third party. The system collects the released data from each organization, processes it, mines it, and analyzes the results. Sharing in DASMA is based on trusted third parties. However, organizations may encode some attributes, for example. Each organization has its own policy represented in XML format. This policy states what attributes can be released, encoded, and randomized. DASMA processes the data set and collects the data, combines it, and prepares it for mining. After mining, a statistical report is produced stating the similarities between mining with data sharing and mining without sharing. The authors test, apply data sharing, enforce policy, and analyze the results of two separate datasets in different domains. The results indicate a fluctuation on the amount of information loss using different releasing factors.

We propose an uplink non-cooperative game-theoretic power control algorithm for wireless data in direct sequence spread spectrum code division multiple access (DS-CDMA) single cell communication systems. We put forward a target function composed of the difference between a utility function and a pricing function. In this proposed game-theoretic algorithm, each user unilaterally and based on local information decides its transmit power level that maximizes its target function. The pricing function used is a linear function of the transmit power with a slope (pricing factor) chosen in a market-based mechanism, where the base station (BS) decides the pricing factor and communicates it to the users in the cell. Finally, an S-modular games framework is exploited to establish the existence and uniqueness of a Nash equilibrium (NE) operating point of the proposed game. This NE operating game exhibits Pareto dominance over that of NE operating point of other existing Algorithms in the literature as we show in the simulations.

Secure verification of neighborhood membership (SVNM) is a relatively recently stated problem that has an increasing number of practical applications. The problem can be stated as the capability of a wireless network node (verifier) to verify the claim by another node (claimer) that it exists within a certain physical distance from the verifier. Several physical properties of the received signal are used for onehop location estimation - signal strength, time of flight, and angle of arrival, etc. However, many of these approaches are either not practically applicable or prohibitively expensive in many wireless network scenarios. In this work, we propose algorithms to detect and isolate nodes that lie about their location in the incremental addition phase of static sensor networks. Every node should announce its position and the power level it uses for transmission. Cooperative and base station verifications are used to detect nodes that lie about their locations. Our results show that we can achieve perfect detection (100%) of nodes that falsify their location information using either high power transmission or with the help of other nodes.

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.

The training time for SVMs to compute the maximal marginal hyper-plane is at least O(N/sup 2/) with the data set size N, which makes it nonfavorable for large data sets. This work presents a study for enhancing the training time of SVMs, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the dynamically growing self-organizing tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms. Clustering analysis helps find the boundary points, which are the most qualified data points to train SVMs, between two classes. We present a new approach of combination of SVMs and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique in terms of accuracy loss and training time gain using two benchmark real data sets.