Network interfaces for programmable NICs and multicore platforms

The availability of multicore processors and programmable NICs, such as TOEs (TCP/IP Offloading Engines), provides new opportunities for designing efficient network interfaces to cope with the gap between the improvement rates of link bandwidths and microprocessor performance. This gap poses important challenges related with the high computational requirements associated to the traffic volumes and wider functionality that the network interface has to support. This way, taking into account the rate of link bandwidth improvement and the ever changing and increasing application demands, efficient network interface architectures require scalability and flexibility. An opportunity to reach these goals comes from the exploitation of the parallelism in the communication path by distributing the protocol processing work across processors which are available in the computer, i.e. multicore microprocessors and programmable NICs.Thus, after a brief review of the different solutions that have been previously proposed for speeding up network interfaces, this paper analyzes the onloading and offloading alternatives. Both strategies try to release host CPU cycles by taking advantage of the communication workload execution in other processors present in the node. Nevertheless, whereas onloading uses another general-purpose processor, either included in a chip multiprocessor (CMP) or in a symmetric multiprocessor (SMP), offloading takes advantage of processors in programmable network interface cards (NICs). From our experiments, implemented by using a full-system simulator, we provide a fair and more complete comparison between onloading and offloading. Thus, it is shown that the relative improvement on peak throughput offered by offloading and onloading depends on the rate of application workload to communication overhead, the message sizes, and on the characteristics of the system architecture, more specifically the bandwidth of the buses and the way the NIC is connected to the system processor and memory. In our implementations, offloading provides lower latencies than onloading, although the CPU utilization and interrupts are lower for onloading. Taking into account the conclusions of our experimental results, we propose a hybrid network interface that can take advantage of both, programmable NICs and multicore processors.     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.     

Parallel Approach for Ensemble Learning with Locally Coupled Neural Networks

Ensemble learning has gained considerable attention in different tasks including regression, classification and clustering. Adaboost and Bagging are two popular approaches used to train these models. The former provides accurate estimations in regression settings but is computationally expensive because of its inherently sequential structure, while the latter is less accurate but highly efficient. One of the drawbacks of the ensemble algorithms is the high computational cost of the training stage. To address this issue, we propose a parallel implementation of the Resampling Local Negative Correlation (RLNC) algorithm for training a neural network ensemble in order to acquire a competitive accuracy like that of Adaboost and an efficiency comparable to that of Bagging. We test our approach on both synthetic and real datasets from the UCI and Statlib repositories for the regression task. In particular, our fine-grained parallel approach allows us to achieve a satisfactory balance between accuracy and parallel efficiency.     

Co-constructing with stakeholders a role-playing game to initiate collective management of erosive runoff risks at the watershed scale

Erosive runoff is a recurring problem and is a source of sometimes deadly muddy floods in the Pays de Caux (France). The risk results from a conjunction of natural factors and human activity. Efficient actions against runoff in agricultural watersheds are well known. However they are still difficult to implement as they require co-operation between stakeholders. Local actors thus need tools to help them understand the collective consequences of their individual decisions and help to initiate a process of negotiation between them. We decided to use a participatory approach called companion modelling (ComMod), and, in close collaboration with one of the first group of local stakeholders, to create a role-playing game (RPG) to facilitate negotiations on the future management of erosive runoff. This paper describes and discusses the development of the RPG and its use with other groups of local stakeholders within the framework of two game sessions organized by two different watershed management committees. During the joint construction step, stakeholders shared their viewpoints about the environment, agents, rules, and how to model runoff in preparation for the creation of the RPG. During the RPG sessions, two groups of eight players, including farmers, mayors and watershed advisors, were confronted with disastrous runoff in a fictive agricultural watershed. Results showed that they managed to reduce runoff by 20–50% by engaging a dialogue about grass strips, storage ponds and management of the intercrop period. However, further progress is still needed to better control runoff through the implementation of better agricultural practices because, during the RPG sessions, the watershed advisors did not encourage farmers to do so. Because of the complexity of management problems, results of jointly constructing the game and the RPG sessions showed that modelling and simulation can be a very useful way of accompanying the collective learning process. This new way of working was welcomed by the participants who expressed their interest in organizing further RPG sessions.     

Thesaurus enrichment for query expansion in audiovisual archives

It is common practice in audiovisual archives to disclose documents using metadata from a structured vocabulary or thesaurus. Many of these thesauri have limited or no structure. The objective of this paper is to find out whether retrieval of audiovisual resources from a collection indexed with an in-house thesaurus can be improved by enriching the thesaurus structure. We propose a method to add structure to a thesaurus by anchoring it to an external, semantically richer thesaurus. We investigate the added value of this enrichment for retrieval purposes. We first anchor the thesaurus to an external resource, WordNet. From this anchoring we infer relations between pairs of terms in the thesaurus that were previously unrelated. We employ the enriched thesaurus in a retrieval experiment on a TRECVID 2007 dataset. The results are promising: with simple techniques we are able to enrich a thesaurus in such a way that it adds to retrieval performance.     

Aligned texture map creation for pose invariant face recognition

In last years, Face recognition based on 3D techniques is an emergent technology which has demonstrated better results than conventional 2D approaches. Using texture (180° multi-view image) and depth maps is supposed to increase the robustness towards the two main challenges in Face Recognition: Pose and illumination. Nevertheless, 3D data should be acquired under highly controlled conditions and in most cases depends on the collaboration of the subject to be recognized. Thus, in applications such as surveillance or control access points, this kind of 3D data may not be available during the recognition process. This leads to a new paradigm using some mixed 2D-3D face recognition systems where 3D data is used in the training but either 2D or 3D information can be used in the recognition depending on the scenario. Following this concept, where only part of the information (partial concept) is used in the recognition, a novel method is presented in this work. This has been called Partial Principal Component Analysis (P²CA) since they fuse the Partial concept with the fundamentals of the well known PCA algorithm. This strategy has been proven to be very robust in pose variation scenarios showing that the 3D training process retains all the spatial information of the face while the 2D picture effectively recovers the face information from the available data. Furthermore, in this work, a novel approach for the automatic creation of 180° aligned cylindrical projected face images using nine different views is presented. These face images are created by using a cylindrical approximation for the real object surface. The alignment is done by applying first a global 2D affine transformation of the image, and afterward a local transformation of the desired face features using a triangle mesh. This local alignment allows a closer look to the feature properties and not the differences. Finally, these aligned face images are used for training a pose invariant face recognition approach (P²CA).     

Toward a wider use of formal methods for aerospace systems design and verification

Nowadays, it is well accepted that the development of critical systems involves the use of formal methods. One of the major fields where these methods made a lot of progress are the avionics, aerospace and more generally transport areas. Several methods, tools and techniques have been applied for the development of such systems in different parts of the world, and they have been actually put into practice during the development of specific aircraft programs. The aim of this introductory article and of this STTT special issue is to propose a brief overview of the state-of-the-art of current researches in formal methods applied to the development of avionics and aerospace systems and more broadly to take stock of the integration of formal methods in the aerospace industry.     

A multi-granular linguistic model for management decision-making in performance appraisal

The performance appraisal is a relevant process to keep and improve the competitiveness of companies in nowadays. In spite of this relevance, the current performance appraisal models are not sufficiently well-defined either designed for the evaluation framework in which they are defined. This paper proposes a performance appraisal model where the assessments are modelled by means of linguistic information provided by different sets of reviewers in order to manage the uncertainty and subjectivity of such assessments. Therefore, the reviewers could express their assessments in different linguistic scales according to their knowledge about the evaluated employees, defining a multi-granular linguistic evaluation framework. Additionally, the proposed model will manage the multi-granular linguistic labels provided by appraisers in order to compute collective assessments about the employees that will be used by the management team to make the final decision about them.