IP Multimedia Subsystem (IMS) network presents a new generation in the telecommunication infrastructure by providing a variety of services whatever the used technologies. Therefore, to make the customers satisfied, their telecom operator provides them several services according to their attached user profile (subscription type). However, some services are not hosted by the home operator; the service providers are responsible to present these services to the customers. In this case, the operator contacts the concerned provider for requesting the access to the desired services. In fact, this access may arise some security issues which affect the customer privacy and the services security. In this paper,we project our approach, Tr-OrBAC, in the context of collaboration between service providers and telecom operators.We discuss the case when the customer wishes to access to a service hosted by an external service provider. We present the encountered security problems. Then, we depict and we detail the functioning of our approach to resolve these problems.
Smartphones have become a new means of communication and a major method of obtaining information. Dependence on or the excessive use of smartphones may affect health in the long term. However, little attention has been focused on smartphone use and dependence among undergraduates. Therefore, this study investigated the smartphone use behavior of Taiwanese undergraduates and their dependence on smartphones. A cross-sectional study design with a structured questionnaire was used for undergraduates aged≥20 years from four universities. The results suggested that the majority of participants, particularly women, experienced physical discomfort caused by smartphone use. Moreover, the participants exhibited a moderate to high dependence on smartphones. Smartphone use and dependence were both affected by family economic status, monthly allowance, and major. The findings indicated that health care providers, educators, and parents can play critical roles in encouraging the healthy use of smartphones among undergraduates.
Source routing (SR) minimum cost forwarding (MCF) – SRMCF – is a reactive, energy-efficient routing protocol proposed to improve the existent MCF methods utilized in heterogeneous wireless sensor networks (WSN). This paper presents an analytical analysis with experimental support that demonstrates the effectiveness of the proposed protocol. SRMCF stems from SR concepts and MCF methods exploited in ad hoc WSNs, where all unicast communications (between sensor nodes and the base station, or vice versa) use minimum cost paths. The protocol utilized in the present work was updated and now also handles link and node failures. Theoretical analysis and simulations show that the final protocol exhibits better throughput and energy consumption than MCF. Memory requirements for the routing table in the base station are also analyzed. Experimental results in a real scenario were obtained for implementations of both protocols, MCF and SRMCF, deployed in a small network of TelosB motes. Results show that SRMCF presents a 33% higher throughput and 24% less energy consumption than MCF. Extensive simulations for larger networks of MICAz and TelosB motes confirm the theoretical analysis. The impact of using SRMCF with two different MAC protocols, Berkeley-MAC and ContikiMac, is also evaluated by simulation, and the latter setup was also verified experimentally.
finding a trade-off between trained sales people maximization and training cost minimization) is best compared to the current optimization method applied by the company (only maximizing the number of trained sales people) or indeed linear programming to solely minimize the training cost.The application of MOLP provides 3% less trained sales people compared to the current optimization method applied by the company and reduces the training cost by up to 19.8%.
The rapid development of services based on distributed architectures is now emerging as important items that transform mode of communication, and the exponential growth of the Web makes a strong pressure on technologies, for a regular improvement of performance, so it's irresistible to use distributed architectures and techniques for the search and information retrieval on the Web, to provide more relevant search result, in minimum possible time. This paper discuss some solutions researchers are working on, to make search engines more faster and more intelligent, specifically by considering the semantic context of users and documents, and the use of distributed architectures. This paper also presents the overall architecture of GENAUM; the collaborative, semantic and distributed search engine, based on a network of agents, which is the core part of the system. The functionality of GENAUM is spread across multiple agents, to fulfill user's performance expectations. At the end of this paper, some preliminary experimental results are presented, that attempts to test the user modeling process of GENAUM, using reference ontology.
In recent years there has been an increasing interest in cross-device interaction research involving mobile computing. We contribute to this research with a comparative study of four interaction techniques for moving information from a mobile device to a large display. The four techniques (Pinch, Swipe, Throw, and Tilt) were compared through a laboratory experiment with 53 participants, measuring their effectiveness, efficiency and error size. Findings from the experiment revealed that the Swipe technique performed best on all measures. In terms of effectiveness, the Tilt technique performed the worst, and especially so with small targets. In terms of efficiency and error size, the Pinch technique was the slowest and also the most imprecise. We also found that target size mattered considerably for all techniques, confirming previous research. Based on our findings we discuss why the individual techniques performed as observed, and discuss implications for using mobile devices in cross-device interaction design.
Rate Control plays an important role in video compression for transportation over heterogeneous network bandwidth varying conditions. A combined spatial-temporal rate control scheme is proposed for scalable video coding. Introductory quantization parameter estimation is determined based on complete variance distortion method for I frame of first GOP and estimates buffer occupancy level. With the estimated buffer level, target bits are determined considering the coding complexity is proposed in the rate control scheme. In addition, a proportional integral and derivative (PID) controller that calculates the error and minimize fluctuation between the actual buffer fullness and target buffer fullness for competent buffer utilization. As a result, the proposed scheme exploits entire buffer exclusive of crossing overflow and underflow level. The investigational results are compared with other two benchmark schemes and the proposed scheme can able to achieve better target bit adjustment with condensed fluctuations and competent buffer utilization.
In selective contents broadcasting, i.e. watching contents users selected themselves, the server can deliver several contents to many users. However, when users watch the data continuously, waiting time occurs by decreasing the available bandwidth and increasing the number of contents. Therefore, many researchers have proposed scheduling methods to reduce the waiting time. Although the conventional method reduces waiting time by producing the broadcast schedule in fast-forwarding, that for playing contents in normal playback becomes lengthened. In this paper, we propose a scheduling method for switching the playback speed in selective contents broadcasting. Our proposed method can make the broadcast schedule based on the configuration of the program and the available bandwidth. In addition, waiting time can be reduced by dividing each content into two types of data for fast-forwarding and normal playback and scheduling them.
Increasing cyber-security presents an ongoing challenge to security professionals. Research continuously suggests that online users are a weak link in information security. This research explores the relationship between cyber-security and cultural, personality and demographic variables. This study was conducted in four different countries and presents a multi-cultural view of cyber-security. In particular, it looks at how behavior, self-efficacy and privacy attitude are affected by culture compared to other psychological and demographics variables (such as gender and computer expertise). It also examines what kind of data people tend to share online and how culture affects these choices. This work supports the idea of developing personality based UI design to increase users' cyber-security. Its results show that certain personality traits affect the user cyber-security related behavior across different cultures, which further reinforces their contribution compared to cultural effects.
Practical works have a fundamental role in the curriculum of any scientist, engineer, and technician. It helps learners to face the real world and put in practice what they have learned to judge their operability. Moreover, due to some limiting factors and due to the growth number of learners, universities and institutes have become inapt to give efficient learning. Distance education presents a future key to reduce these restrictions. Currently, remote experiments together with web-based courses approach significantly contribute to many aspects of education for learners. In this context, the main question addressed is how we ensure that an educational system evolves to better serve the needs of learners? The present work proposes a solution based on student’s Personal Learning Environments ‘PLEs’. PLEs are educational platforms that help learners take control and manage their own learning process, learning modules with remote experiments, for reaching a specific goal. In order to response these criteria we use the Learning Management System (LMS) Moodle, the e-portfolio Mahara, the Remote Laboratory Management System (RLMS) iLab Shared Architecture (ISA) with additional tools and plug-ins to implement the learning by doing environment.
Mining Big Data is the capability of finding new useful information in complex massive datasets, that may be continuously changing and may have varied data types. Big data is helpful only when it is transformed into knowledge or useful information. Data Intelligence is about transforming data into information, information into knowledge, and knowledge into value. It refers to the intelligent interaction with data in a rich, semantically meaningful ways, where data is used to learn and to obtain knowledge. However, extracting valuable information from this data by following the classical Knowledge Discovery process reveals new previously unknown challenges, due to Big Data properties. These challenges have received a lot of attention in recent years, and still need more and more contribution and research. A large number of publications have yielded a plethora of proposed methods and algorithms. In this paper, we provide a comprehensive literature review on Big Data current status. We present the Data Intelligence framework in the context of Big Data from data acquisition until insight extraction, we highlight its main issues, and identify its progress in both technological and algorithmic perspectives. We summarize and analyse relevant research papers in the field, collected from different scientific databases. This investigation will help researchers to understand the current status of Data Intelligence, discover new research opportunities, and gain information about this field.
Mobile phones are becoming a great necessity for elderly people; the features they provide supported by rich functionality made them one of the indispensable gadgets used in their daily life. However, as mobile phones get more advanced and their interfaces become more complicated, new design recommendations and guidelines need to be developed to serve the elderly needs. In this project we distilled guidelines and design recommendations targeting elderly users' needs. Then, we used these guidelines to implement a prototype user interface that takes Arab elderly requirements into considerations. We then evaluated the developed interface on a set of Arab elderly people to determine its appropriateness for the target audience.
Social media platforms have proven to be a powerful source of opinion sharing. Thus, mining and analyzing these opinions has an important role in decision-making and product benchmarking. However, the manual processing of the huge amount of content that these web-based applications host is an arduous task. This has led to the emergence of a new field of research known as Sentiment Analysis. In this respect, our objective in this work is to investigate sentiment classification in Arabic tweets using machine learning. Three classifiers namely Naïve Bayes, Support Vector Machine and K-Nearest Neighbor were evaluated on an in-house developed dataset using different features. A comparison of these classifiers has revealed that Support Vector Machine outperforms others classifiers and achieves a 78% accuracy rate.
Mobile augmented reality (MAR) applications assist users in navigating and exploring their actual surroundings, displaying virtual contents that correspond to objects and scenes in the real world. However, despite the growing popularity of these applications, some experiences can be frustrating when users are unable to correctly recognize Points of Interest (POI), objects, or places they want to visit or obtain more information. The misleading recognition can occur due to imprecise Global Positioning System (GPS) data or a lack of QR codes for interaction. Hence, this article presents a proposal that combines pattern recognition in images with geolocation information to improve the accuracy of the identification of POIs. The usage scenario is the identification of azulejos (tiles) on the facades of historic buildings in the city of Belém of Pará, Brazil. This issue is relevant based on similarities between azulejos and its huge amount of different types, whose variety of designs and colors of geometric forms can make the identification a hard task. The used methods to extract the azulejos' features were the co-occurrence matrix combined with color percentage, and the global positioning data to increase the accuracy of classification because similar azulejos can be geographically far apart. Tests were conducted using six machine learning algorithms (neural network, decision tree, k-nearest neighbors, naive Bayes, random forest, and support vector machine) of different paradigms. The first results show that the pattern recognition in images combined with geolocation information is a promising approach for better identification of the POIs in MAR applications.
In Global Navigation Satellite systems (GNSS), the performances of classical localization methods show a significant degradation in constrained environments (urban and indoor environments), due to Non-Line-of-Sight(NLOS) and Multipath phenomena affecting GNSS signal. In order to improve positioning accuracy in hard environment, this paper aims to propose an approach to compute and adapt the NLOS and Multipath error model to GNSS signal reception conditions. The approach aims firstly to propose a Map-Matching based-technique to compute Multipath and NLOS errors in real time positioning, secondly, to test adequacy of these errors with the most used models in the literature and finally to model the Multipath and NLOS errors using Gaussian mixture noise. As a result, we have shown that a Gaussian, Rayleigh and Uniform model were not be able to model effectively Multipath and NLOS errors and we have demonstrated that a Gaussian mixture model can approximate these errors and improve positioning accuracy in urban environment.
Development of personalized e-Learning Management Systems (PeLMS) using advance modelling techniques is crucial to achieve personalization and adaptation in content deliveries. This paper delves on some important issues related to the integration of PeLMS with Semantic Overlay Networks (SON). Developing a prototype for a Business Statistics course delivery, a Semantic Web based PeLMS using Topic Maps, Ontology, Classification rules, and ISA Algorithm has been prescribed. Considering classification as one of the important aspects in the content organisation in PeLMS, this study proposes a new classifier, based upon the maximum entropy principle. It is argued that the most similar items in a learning object repository space can be classified together, on the statistical basis, to build a PeLMS. The ISA algorithm has been proposed to enable this classification. The paper also presents three key Learning Object Models for the organization of contents and suggests how an optimal level of personalization that can be ensured by maintaining the entropy within the system. Mechanisms such as normalization and time complexity have also been suggested to ensure personalized and optimal content delivery.
This paper investigates how to best couple hand-annotated data with information extracted from an external lexical resource to improve part-of-speech tagging performance. Focusing mostly on Amazigh tagging, we introduce a decision tree and Markov model using TreeTagger system. This system gives 92.3 % accuracy on the Amazigh corpus, an error reduction of 15 % (18.45 % on unknown words) over the same tagger without lexical information. We perform a series of experiments that help understanding how this lexical information helps improving tagging accuracy. We also conduct experiments on datasets and lexicons of varying sizes in order to assess the best tradeoff between annotating data versus developing a lexicon. We find that the use of a lexicon improves the quality of the tagger at any stage of development of either resource, and that for fixed performance levels the availability of the full lexicon consistently reduces the need for supervised data.
Dynamic hand gestures have become increasingly popular as touch-free input modality for interactive systems. There exists a variety of arm-worn devices for the recognition of hand gestures, which differ not only in their capabilities, but also in their positioning on users' arms. These differences in positioning might influence how well gestures are recognized, leading to different gesture recognition accuracies. In this paper, we investigate the effect of device placement on dynamic hand gesture recognition accuracy. We consider devices being strapped to the forearm on two positions: the wrist and below the elbow. These positions represent smart watches being worn on the wrist and devices with EMG sensors for the additional detection of static hand gestures (e.g spreading the fingers) being worn right below the elbow. Our hypothesis is that wrist-worn devices will have better recognition accuracy, caused by higher acceleration values of a bigger action radius of dynamic hand gestures. We conducted a comparative study using an LG G Watch and Thalmic Labs' Myo armband, for which we recorded a total of 12960 gesture samples of eight simple dynamic gestures in three different variants with eight participants. We evaluated a potential difference in gesture recognition accuracies using different feature sets and classifiers. Although recognition accuracies for wrist-worn devices seem higher, the difference is not statistically significant due to substantial variations in accuracy across participants. We thus cannot conclude that different positions of gesture recording devices on the lower arm have significant influence on correctly recognizing arm gestures.
