Search

Online banking relies on user-owned home computers and mobile devices, all vulnerable to man-in-the-middle attacks which are used to steal money from bank accounts. Banks mitigate this by letting users verify information that originates from these untrusted devices. This is not user-friendly since the user has to process the same information twice. It also makes the user an unnecessary critical factor and risk in the security process. This paper concerns a case study of an information scheme which allows the user to enter critical information in a trusted device, which adds data necessary for the recipient to verify its integrity and authenticity. The output of the device is a code that contains the information and the additional verification data, which the user enters in the computer used for online banking. With this, the bank receives the information in a secure manner without requiring an additional check by the user, since the data is protected from the moment the user entered it in the trusted device. This proposal shows that mundane tasks for the user in online banking can be automated, which improves both security and usability.

International audience; Small satellites for earth observation are an important ongoing development in space research. Small satellites are usually powered from a solar energy system. In order to maximize the solar energy captured on small satellites, orbit path selection becomes an important parameter. In this work, we have conducted some numerical studies for future small satellites (nanosatellites) developed within the PERSEUS project. We have determined the optimal variation of the orbit angle (the minimum angle between the satellitepsilas orbit plane and solar vector), in order to obtain an eclipse duration as short as possible, and to maintain the angle value as long as possible thus maximizing the solar energy captured by its photovoltaic panels.

In today’s big data era, data and information are the main capital. High Energy Physics (HEP) as a data-intensive field of research drives significant demands for managing large datasets. Physics data need to be stored, accessed, and properly understood. This paper deals with the increasing resource requirements of the Large Hadron Collider (LHC) experiments at European Laboratory for Particle Physics (CERN). We analyze the unique requirements of the A Large Ion Collider Experiment (ALICE). This paper proposes a software-defined storage (SDS) model based on cloud and edge computing to optimize and organize the data storage of the current ALICE grid computing model. Also, it analyses how software-defined storage and distributed computing technologies could potentially be used to address the challenges and unique requirements faced by the ALICE experiment. The proposed model represents the basis for future research in which the parameters will be further examined, with an emphasis on the scalability of storage and computing resources.

Shielded fabrics are fabrics with conductive properties which are used for attenuating electromagnetic fields. One of the manufacturing methods for shielded fabrics is interweaving common textiles with metal threads, thus giving the fabric conductive properties. This paper examines the required density of metal threads interwoven in a fabric to achieve the desired properties.

Reducing data center (DC) power consumption is one of green Information and Communication Technology (ICT) challenges, which can be realised through improvement of DC energy-efficiency. To accomplish this improvement, each component of DC including those of power supply system such as uninterruptible power supplies (UPSs), has to operate at its maximum power efficiency. Since the broadly accepted concept based on server consolidation approach demonstrates significant DC power saving at the level of server racks, the contribution of constantly active UPSs to the overall DC power inefficiency become non-negligible. It is shown that a possible technique for reducing UPS power inefficiency in DCs can be based on dynamic merging of UPS activity state with server consolidation, where the neighbouring connectivity between one UPSs and two neighbour server racks have been established on the backup power supply paths. In this paper enhancement of neighbouring connectivity concept based on a new approach for establishing connectivity between UPSs and server racks on DC backup power delivery paths are introduced. More specifically, the algorithm which enables serial connectivity of active UPSs with DC server racks during merged UPS and server consolidation process is proposed. Through the extensive simulations of real DC having a large number of rack servers and UPSs, the proposed concept based on serial connectivity has been tested. Results show that proposed concept improves the energy-efficiency of DC when compared with neighbouring connectivity approach or existing legacy DC power supply approach lacking any UPS consolidation. Proposed concept can be used in practice as a solution which can reduce the impact of power consumption inefficiency of UPSs as main DC power supply elements.

This paper presents preliminary results of the questionnaire (QR) that was conducted during April and May 2018 in three countries: Bulgaria, Greece, and Croatia. The QR is part of the activities within a project funded by Danube Strategic Project Fund (DSPF): Increasing the well being of the population by RObotic and ICT based iNNovative education (RONNI). The QR has been delivered to schools in each of the participating countries. Two sets of questions were delivered to target groups: teachers/experts and parents. The analysis of the results will be used in proposing innovative teaching strategies and methodologies, transferable across the regions to support effective learning.

Online social networks (OSNs) are platforms which facilitate social interactions between their users through message exchange, photo and video sharing, status updates, etc. One of the most popular OSNs is Facebook. Connections between users on Facebook are modeled through concept of friendship. Each connection between users is binary – two users either are or aren't "friends". Information about of the actual intensity or nature of their connection is not available although in real life it can vary significantly. A majority of observed network friends are acquaintances in real-life while close friends are in the minority. The goal of this paper is to demonstrate and evaluate how user interaction statistics can be utilized for effective assessment of the nature of users' real-life relationship. Using an ensemble of popular classification algorithms, we will classify ego-user’s network friends into 3 groups: close friends, friends and acquaintances. As our main contribution, we will compare the efficiency of chosen algorithms and suggest the best approach for conducting this type of analysis on similar OSN communication data.

IoT deployments and then related experiments tend to be highly heterogeneous leading to fragmented and non-interoperable silo solutions. Yet there is a growing need to interconnect such experiments to create rich infrastructures that will underpin the next generation of cross sector IoT applications in particular as using massive number of data. While research have been carried out for IoT test beds and interoperability for some infrastructures less has been done on the data. In this paper, we present the first step of the FIESTA certification method for federated semantic IoT test bed, which provides stakeholders with the means of assessing the interoperability of a given IoT testbed and how it can be federated with other ones to create large facility for experimenter. Focus is given on data and semantic context of the test beds and how they can interoperate together for larger experiments with data.

This paper presents an offline approach to analyzing feature interactions in embedded systems. The approach consists of a systematic process to gather the necessary information about system components and their models. The model is first specified in terms of predicates, before being refined to timed automata. The consistency of the model is verified at different development stages, and the correct linkage between the predicates and their semantic model is checked. The approach is illustrated on a use case from home automation.

Recently, John R. Anderson proposed a correspondence between the modules of his cognitive architecture ACT-R and specific brain regions. This Brain Mapping Hypothesis allows the prediction of Blood-Oxygen-Level Dependent curves for these regions using cognitive models. These predictions may be compared to actual data from functional Magnetic Resonance Imaging experiments. While the Brain Mapping Hypothesis has been tested with very simple tasks mostly from algebraic problem solving, we conducted experiments with a more complex task to study the robustness of the Brain Mapping Hypothesis against different domains, multidimensional strategy spaces, and modeling errors. The ACTR model in this paper is a synthesis of our prior models, providing a better fit imaging data. Our results show that the Brain Mapping Hypothesis is not to be dismissed, yet there still remain assumptions in the model that do cause inexact predictions for some modules. We discuss how models of complex problem solvers can achieve a better fit to data by adaptations to their symbolic structure.

The contribution of this paper is a Generic Communication Middleware (GCM) concept and architecture definition for application messaging in heterogeneous distributed computing environments. The GCM is targeted to facilitate the development of distributed applications into heterogeneous computing environments, with special attention given to applicability for both wireless and wired communication and variable capability devices. The requirements are gathered from the literature and initial prototype implementations. The GCM concept and architecture are presented in the paper. The novelty of the GCM middleware is that it provides both application and transport independent messaging system architecture that can be widely applied in different applications and services as middleware. Functional validation of the GCM concept and architecture is provided via prototypes overviewed and empirically analyzed in the paper.

Neural networks are becoming wide-spread, including applications in mobile robotics and related fields. Most state - of-the-art approaches to training neural networks use video cameras for generating training datasets. However, these data are hard and time-consuming to collect resulting in a bottleneck for neural network training procedure. Thus, the paper briefly presents simulation-based LiDAR data collection for the training of neural networks for obstacle avoidance. The influence of two data collection parameters in simulation (distance to obstacles and number of LiDAR points) on the performance of the realworld mobile robot is analysed in more depth. Experimental testing was performed in a narrow corridor (augmented with additional obstacles) in order to fully test the neural networks and detect possible limitations. For a better understanding of proposed algorithms and analysis of their performance in reallife scenarios, a simple test-bed was devised with Turtbebot 2 as a test vehicle. Based on obtained results, and with safety in mind, conclusions are drawn and possible future improvements proposed.

In this paper, a methodology is presented to identify business impact indicators from the use case requirements in an early phase of the development stage. It is based on the Quality Function Deployment method and the business model design framework. This methodology is applied to the design for a unified management framework to facilitate autonomics in complex, ubiquitous and large-scale networks. The analysis concludes that this innovation will be particularly disruptive in the functional architecture and value proposition aspects of the business model.

Effective management of projects is becoming increasingly important for any type of organisation to remain competitive in today’s dynamic business environment due to pressure of globalisation. Planning a project with proper considerations of all necessary factors and managing a project to ensure its successful implementation are facing a lot challenges. Initial stage in planning a project is costly, time consuming and usually with poor accuracy on cost and effort predictions. On the other hand, detailed information for previous projects may be buried in piles of archived documents, which make it increasingly difficult to learn from the previous experiences. Project portfolio has been brought into this field aiming to improve the information sharing and management among different projects. However, the amount of information that could be shared is still limited to generic information. In this paper, we design and implement a novel software system iPAS, which automatically generate a project plan with effort estimation of time and cost based on data collected from previous completed projects in standardised industries. To maximise the data sharing and management among different projects, a method of using product-based planning from PRINCE2 methodology is proposed. iPAS has been trialed with cases in two organisations, which clearly shows the business benefits of autonomic project management. It reduced effort to plan new projects and manage project portfolio and decreased estimation bias thereby reducing operational risk. It also automatically benchmarked performance against company best practices.