Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2011-2015)

The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered by Google Scholar Metrics and ranked according to their h-index. Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2010-2014). The searches were conducted between the 7th and 12th of December, 2016. A total of 1634 proceedings have been identified.Martín-Martín, A.; Ayllón, JM.; Orduña Malea, E.; Delgado López-Cózar, E. (2016). Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2011-2015). http://hdl.handle.net/10251/11237

Improving The Fault Tolerance of Ad Hoc Routing Protocols using Aspect-oriented Programming

[ES] Las redes ad hoc son redes inalámbricas distribuidas formadas por nodos móviles que se ubican libremente y dinámicamente, capaces de organizarse de manera propia en topologías arbitrarias y temporales, a través de la actuación de los protocolos de encaminamiento. Estas redes permiten a las personas y dispositivos conectarse sin problemas rápidamente, en áreas sin una infraestructura de comunicaciones previa y con un bajo coste. Muchos estudios demuestran que los protocolos de encaminamiento ad hoc se ven amenazados por una variedad de fallos accidentales y maliciosos, como la saturación de vecinos, que puede afectar a cualquier tipo de red ad hoc, y el ruido ambiental, que puede afectar en general a todas las redes inalámbricas. Por lo tanto, el desarrollo y la implementación de estrategias de tolerancia a fallos para mitigar el efecto de las fallos, es esencial para el uso práctico de este tipo de redes. Sin embargo, los mecanismos de tolerancia a fallos suelen estar implementados de manera específica, dentro del código fuente de los protocolos de encaminamiento que hace que i) ser reescrito y reorganizado cada vez que una nueva versión de un protocolo se libera, y ii) tener un carácter completamente remodelado y adaptado a las nuevas versiones de los protocolos. Esta tesis de máster explora la viabilidad de utilizar programación orientada a aspectos (AOP), para desarrollar e implementar los mecanismos de tolerancia a fallos adecuados para toda una familia de protocolos de encaminamiento, es decir, las versiones actuales y futuras de un protocolo determinado (OLSR en este caso). Por otra parte, se propone una nueva metodología para ampliar estos mecanismos a diferentes familias de protocolos proactivos (OLSR, BATMAN y Babel) con un nuevo concepto de AOP, el metaaspecto. La viabilidad y efectividad de la propuesta se ha evaluado experimentalmente, estableciendo así un nuevo método para mejorar la implementación de la portabilidad y facilidad de mantenimiento de los mecanismos de tolerancia a fallos en los protocolos de enrutamiento ad hoc y, por lo tanto, la fiabilidad de las redes ad hoc.[EN] Ad hoc networks are distributed networks consisting of wireless mobile nodes that can freely and dynamically self-organize into arbitrary and temporary topologies, through the operation of routing protocols. These networks allow people and devices to seamlessly interconnect rapidly in areas with no pre-existing communication infrastructure and with a low cost. Many studies show that ad hoc routing protocols are threatened by a variety of accidental and malicious faults, like neighbour saturation, which may affect any kind of ad hoc network, and ambient noise, which may impact all wireless networks in general. Therefore, developing and deploying fault tolerance strategies to mitigate the effect of such faults is essential for the practical use of this kind of networks. However, those fault tolerance mechanisms are usually embedded into the source code of routing protocols which causes that i) they must be rewritten and redeployed whenever a new version of a protocol is released, and ii) they must be completely redeveloped and adapted to new routing protocols. This master thesis explores the feasibility of using Aspect-Oriented Programming (AOP) to develop and deploy fault tolerance mechanisms suitable for a whole family of routing protocols, i.e. existing and future versions of a given protocol (OLSR in this case). Furthermore, a new methodology is proposed to extend these mechanisms to different families of proactive protocols (OLSR, B.A.T.M.A.N and Babel) using a new concept in AOP, the meta-aspect. The feasibility and effectiveness of the proposal is experimentally assessed, thus establishing a new method to improve the deployment, portability, and maintainability of fault tolerance mechanisms for ad hoc routing protocols and, therefore, the dependability of ad hoc networks.Bustos Rodríguez, AJ. (2012). Improving The Fault Tolerance of Ad Hoc Routing Protocols using Aspect-oriented Programming. http://hdl.handle.net/10251/18421Archivo delegad

Energy efficient assignment and deployment of tasks in structurally variable infrastructures

The importance of cyber-physical systems is growing very fast, being part of the Internet of Things vision. These devices generate data that could collapse the network and can not be assumed by the cloud. New technologies like Mobile Cloud Computing and Mobile Edge Computing are taking importance as solution for this issue. The idea is offloading some tasks to devices situated closer to the user device, reducing network congestion and improving applications performance (e.g., in terms of latency and energy). However, the variability of the target devices’ features and processing tasks’ requirements is very diverse, being difficult to decide which device is more adequate to deploy and run such processing tasks. Once decided, task offloading used to be done manually. Then, it is necessary a method to automatize the task assignation and deployment process. In this thesis we propose to model the structural variability of the deployment infrastructure and applications using feature models, on the basis of a SPL engineering process. Combining SPL methodology with Edge Computing, the deployment of applications is addressed as the derivation of a product. The data of the valid configurations is used by a task assignment framework, which determines the optimal tasks offloading solution in different network devices, and the resources of them that should be assigned to each task/user. Our solution provides the most energy and latency efficient deployment solution, accomplishing the QoS requirements of the application in the process.Plan Propio de Investigación de la UMA Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario. We analyse the extent to which a tweet's country of origin can be determined by making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart from each other, to analyse the extent to which a model trained from historical tweets can still be leveraged for classification of new tweets. With classification experiments on all 217 countries in our datasets, as well as on the top 25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level classification of tweets. We find that the use of a single feature, such as the use of tweet content alone -- the most widely used feature in previous work -- leaves much to be desired. Choosing an appropriate combination of both tweet content and metadata can actually lead to substantial improvements of between 20\% and 50\%. We observe that tweet content, the user's self-reported location and the user's real name, all of which are inherent in a tweet and available in a real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of a model trained on historical tweets to classify new tweets, finding that the choice of a particular combination of features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain. However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities, especially for English and Spanish speaking countries.Comment: Accepted for publication in IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE

Situation inference and context recognition for intelligent mobile sensing applications

The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are limitless for future intelligent systems. Situation inference is a non-trivial issue in the domain of ubiquitous computing research due to the challenges of mobile sensing in unrestricted environments. There are various advantages to having robust and intelligent situation inference from data streamed by mobile sensors. For instance, we would be able to gain a deeper understanding of human behaviours in certain situations via a mobile sensing paradigm. It can then be used to recommend resources or actions for enhanced cognitive augmentation, such as improved productivity and better human decision making. Sensor data can be streamed continuously from heterogeneous sources with different frequencies in a pervasive sensing environment (e.g., smart home). It is difficult and time-consuming to build a model that is capable of recognising multiple activities. These activities can be performed simultaneously with different granularities. We investigate the separability aspect of multiple activities in time-series data and develop OPTWIN as a technique to determine the optimal time window size to be used in a segmentation process. As a result, this novel technique reduces need for sensitivity analysis, which is an inherently time consuming task. To achieve an effective outcome, OPTWIN leverages multi-objective optimisation by minimising the impurity (the number of overlapped windows of human activity labels on one label space over time series data) while maximising class separability. The next issue is to effectively model and recognise multiple activities based on the user's contexts. Hence, an intelligent system should address the problem of multi-activity and context recognition prior to the situation inference process in mobile sensing applications. The performance of simultaneous recognition of human activities and contexts can be easily affected by the choices of modelling approaches to build an intelligent model. We investigate the associations of these activities and contexts at multiple levels of mobile sensing perspectives to reveal the dependency property in multi-context recognition problem. We design a Mobile Context Recognition System, which incorporates a Context-based Activity Recognition (CBAR) modelling approach to produce effective outcome from both multi-stage and multi-target inference processes to recognise human activities and their contexts simultaneously. Upon our empirical evaluation on real-world datasets, the CBAR modelling approach has significantly improved the overall accuracy of simultaneous inference on transportation mode and human activity of mobile users. The accuracy of activity and context recognition can also be influenced progressively by how reliable user annotations are. Essentially, reliable user annotation is required for activity and context recognition. These annotations are usually acquired during data capture in the world. We research the needs of reducing user burden effectively during mobile sensor data collection, through experience sampling of these annotations in-the-wild. To this end, we design CoAct-nnotate --- a technique that aims to improve the sampling of human activities and contexts by providing accurate annotation prediction and facilitates interactive user feedback acquisition for ubiquitous sensing. CoAct-nnotate incorporates a novel multi-view multi-instance learning mechanism to perform more accurate annotation prediction. It also includes a progressive learning process (i.e., model retraining based on co-training and active learning) to improve its predictive performance over time. Moving beyond context recognition of mobile users, human activities can be related to essential tasks that the users perform in daily life. Conversely, the boundaries between the types of tasks are inherently difficult to establish, as they can be defined differently from the individuals' perspectives. Consequently, we investigate the implication of contextual signals for user tasks in mobile sensing applications. To define the boundary of tasks and hence recognise them, we incorporate such situation inference process (i.e., task recognition) into the proposed Intelligent Task Recognition (ITR) framework to learn users' Cyber-Physical-Social activities from their mobile sensing data. By recognising the engaged tasks accurately at a given time via mobile sensing, an intelligent system can then offer proactive supports to its user to progress and complete their tasks. Finally, for robust and effective learning of mobile sensing data from heterogeneous sources (e.g., Internet-of-Things in a mobile crowdsensing scenario), we investigate the utility of sensor data in provisioning their storage and design QDaS --- an application agnostic framework for quality-driven data summarisation. This allows an effective data summarisation by performing density-based clustering on multivariate time series data from a selected source (i.e., data provider). Thus, the source selection process is determined by the measure of data quality. Nevertheless, this framework allows intelligent systems to retain comparable predictive results by its effective learning on the compact representations of mobile sensing data, while having a higher space saving ratio. This thesis contains novel contributions in terms of the techniques that can be employed for mobile situation inference and context recognition, especially in the domain of ubiquitous computing and intelligent assistive technologies. This research implements and extends the capabilities of machine learning techniques to solve real-world problems on multi-context recognition, mobile data summarisation and situation inference from mobile sensing. We firmly believe that the contributions in this research will help the future study to move forward in building more intelligent systems and applications