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Due to their open nature and popularity, Android-based devices represent one of the main targets for malware attacks that may adversely affect the privacy of their users. Considering the huge Android market share, it is necessary to build effective tools able to reliably detect zero-day malware on these platforms. Therefore, several static and dynamic analysis methods based on Neural Networks and Deep Learning have been proposed in the literature. Despite machine learning can be considered the most promising approach for classifying applications into malware or legitimate ones, its success strongly depends on the choice of the right features used for building the detection model. This is definitely not an easy task that requires a systematic solution. Accordingly, this work represents the sequences of API calls invoked by apps during their execution as sparse matrices looking like images (API-images), which can be used as fingerprints of the apps’ behavior over time. We also used autoencoders to autonomously extract the most representative and discriminating features from these matrices, that, once provided to an artificial neural network-based classifier have shown to be effective in detecting malware, also when the network is trained on a reduced number of samples. Experimental results show that the resulting framework is able to outperform more complex and sophisticated machine learning approaches in malware classification.

Smart personal devices are assuming a fundamental role in the ubiquitous communication and computing arena. They provide new sophisticated cameras and new visual search interfaces and facilities that can drastically improve their presence and role in complex IoT-based critical infrastructures, such as healthcare monitoring and emergency systems, or remote access control facilities and smart authentication services. This new scenario calls for strong secure and resilient visual query mechanisms for these devices. In this work we propose an innovative secure visual search system, which is well-suited for ubiquitous computing scenarios empowered by modern smart personal devices. More precisely, we show how to insert, at the visual data acquisition time, a watermark inside the already compressed descriptor characterizing an MPEG-CDVS data stream used in visual queries, to make it possible to decode the watermark on the server side in order to improve the robustness against image-based identity spoofing. Such a security enforcement solution may be practical in several real-life applications involving visual queries performed from personal trusted devices, and it is particularly suitable in all those application domains that require performing visual queries with a high degree of security. It has been extensively tested and achieved satisfactory results: the presence of such a watermark does not affect the image matching performance and functionality.

In non-destructive testing of aerospace structures’ defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems.

Link prediction has steadily become an important research topic in the area of complex networks. However, the current link prediction algorithms typi cally neglect the network evolution and tend to exhibit low accuracy and scal ability when applied to large-scale organisations. In this article, we propose a novel distributed temporal link prediction algorithm based on label propagation (DTLPLP), governed by the dynamical properties of the interactions between nodes. In particular, nodes are associated with labels, which include details of their sources and the corresponding similarity value. When such labels are propagated across neighbouring nodes, they are updated based on the weights of the incident links, and the values from same source nodes are aggregated to evaluate the scores of links in the predicted network. Furthermore, DTLPLP has been designed to be distributed and parallelised, and thus is suitable for large-scale network analysis. As part of the validation process, we have de signed a prototype system developed in Pregel, which is a distributed network analysis framework. Experiments are conducted on the Enron e-mail network and the General Relativity and Quantum Cosmology Scientific Collaboration network. The experimental results show that when compared to the most of link prediction algorithms, DTLPLP offers enhanced accuracy, stability and scalability.

In the last years, the number of frauds in credit card-based online payments has grown dramatically, pushing banks and e-commerce organizations to implement automatic fraud detection systems, performing data mining on huge transaction logs. Machine learning seems to be one of the most promising solutions for spotting illicit transactions, by distinguishing fraudulent and non-fraudulent instances through the use of supervised binary classification systems properly trained from pre-screened sample datasets. However, in such a specific application domain, datasets available for training are strongly imbalanced, with the class of interest considerably less represented than the other. This significantly reduces the effectiveness of binary classifiers, undesirably biasing the results toward the prevailing class, while we are interested in the minority class. Oversampling the minority class has been adopted to alleviate this problem, but this method still has some drawbacks. Generative Adversarial Networks are general, flexible, and powerful generative deep learning models that have achieved success in producing convincingly real-looking images. We trained a GAN to output mimicked minority class examples, which were then merged with training data into an augmented training set so that the effectiveness of a classifier can be improved. Experiments show that a classifier trained on the augmented set outperforms the same classifier trained on the original data, especially as far the sensitivity is concerned, resulting in an effective fraud detection mechanism.

With the proliferation of mobile devices characterizing modern cyber-physical systems, service switching and handoff over large coverage areas become key aspects of the Internet of Things (IoT), mainly when remotely controlling and interacting with mission-critical autonomous vehicles that potentially may cover quite large distances such as driverless cars and Unmanned Aerial Vehicles (UAVs). These requirements can now be fully satisfied by the widespread Fast handover for Proxy Mobile IPv6 (F-PMIPv6) technology, that can be yet considered as a cornerstone in emerging 5G communications, but, unfortunately, such an approach only supports homogeneous handover, that may result in a nontrivial problem due to the heterogeneity in mobile communications technologies characterizing the available cyber-physical solutions and IoT network access devices. Recently, many researchers developed efficient solutions for the integration of F-PMIPv6 and Media Independent Handover (MIH) to allow fast handover in a highly heterogeneous mobile network. However, these models lack the security features which are necessary to protect IoT devices during handoffs. In this paper, a new security protocol, MIH-based secure cross-layer handover protocol for Fast Proxy Mobile IPv6 networks (MIH-SPFP), is proposed, incorporating the features of Secure Protocol for Fast-PMIPv6 (SPFP) into F-PMIPv6-MIH and reducing the security risks during the handover. The proposed solution also provides low latency by reducing the re-authentication path during the inter-Mobile Access Gateway (MAG) handovers. The security of the proposed protocol has been analyzed by using Burrows–Abadi–Needham (BAN) logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and its performance has been evaluated through numerical simulation by selecting “Marathon Broadcasting” as a case study. Results show that the proposed protocol not only effectively secures the handover process but is also more efficient compared with the standard MIH handover solution.

Nowadays, there is a growing demand of middleware solutions for reliable event notification over the Internet due to several key industrial projects aiming at integrating existing legacy systems and enhancing their functionalities. Reliable event notification is mainly realized by means of retransmissions, with the consequent worsening of the performance and traffic load. Traditionally, the opposite approach of spatial redundancy is not used due to the tunability, scalability and flexibility issues of its implementation schemes. In this paper, we propose a game theoretic formulation of Forward Error Correction (one of the mean schemes to introduce spatial redundancy), in order to resolve these mentioned issues. Moreover, we introduce distributed strategic learning for the optimal formulation of the payoff functions in the game, and for the effective adaptivity in response to the possible variations in the experienced loss patterns. We prove the quality of this solution by using a series of simulations run on OMNET++.

Stackelberg games may reveal to be extremely useful in supporting decisions in attack-defense scenarios. We call such games Security Stackelberg games. They are characterized by two kinds of players: the defender, who defines his strategy in advance, and the attacker, who follows the defender's decisions. Security Stackelberg games may be used to model a typical cloud security scenario, where the provider has to expose its defense strategy and the attackers act by following the provider's actions. In this work we define a model based on Stackelberg games that enables the automatic selection of provider-level security decisions in Cloud Computing environments. Potential Cloud attack scenarios are modeled as nonzero-sum Security Stackelberg games between the attacker and the Cloud provider. This allows the estimation of the strategy that minimizes the reward for the attacker and maximizes the gain of the defender. The model has been experimentally verified to be effective in automatically determining defense strategies from the cloud provider perspective.

Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of data samples in classes, especially when the number of data representing the larger class (majority) is much greater than that of the smaller class (minority). In order to solve this problem, various types of undersampling or oversampling techniques have been proposed to create a dataset with equal number of samples in each class by reducing or increasing the number of samples in majority or minority classes, respectively. Ensemble classifiers use multiple learning algorithms to enhance the accuracy of classification. Based on the results, combining undersampling or oversampling methods with ensemble classifiers can result in models with better performance. By using both clustering and new undersampling methods, the present study aimed to propose a novel clustering-based undersampling method to create a balanced dataset. This method uses k-means clustering algorithm for clustering the data, Mahalanobis distance to analyze samples distance in each cluster to centroid, and a selection method that preserves the pattern of data distribution in each cluster. Regarding the experimental results obtained by 44 benchmark datasets from KEEL repository, the proposed approach performed better than that of seven state-of-the-art approaches.

Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by Big Data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered. In this article we propose and analyse a novel method to extract and build fragments of Bayesian networks (BNs) from unstructured large data sources. The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources. The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios.

The efficiency of broadcast propagation schemes can significantly condition the performance and effectiveness of mobile ad-hoc networks. Probabilistic forwarding strategies are considered between the most promising optimization approaches due to their inherent simplicity that makes them easy to implement on any platform, very fast and computationally inexpensive. In this work, we present a novel adaptive probabilistic approach for containing broadcast propagation in reactive network discovery based on modeling the broadcast propagation behavior as harmonic spherical waves generated by a single source, and hence determining the forwarding probability by considering how the intensity of a wave attenuates with its distance from the source. We used discrete event simulation to demonstrate how the proposed strategy can introduce appreciable benefits on the overall performance of modern broadcast-based reactive routing protocols, resulting in a real breakthrough in ad-hoc environments characterized by an high degree of mobility.

Traffic matrices, abstract representations of demand, are essential for network operators endeavoring to model, measure, maintain, and improve the efficiency of their complex and heterogeneous architectures. Traffic matrix estimation consists in inferring a traffic matrix from link-level measurements. Provoked by the need to enable agile deployment of new services while, at the same time, slashing operating expenditure and energy consumption, the trend in telecommunications is to shift functionality from physical appliances to virtualized services. We analyze the effects of this landscape change on traffic matrices, their dynamics, and their estimation, indicating some new challenges and problems that will arise in all the associated modeling, analysis and evaluation activities.

Gossiping is a widely known and successful approach to reliable communications, tolerating packet losses and link crashes. It has been extensively used in several middleware kinds, such as event notification services and application domains, like infrastructures for air traffic management, power grid control, health information exchange, just to cite some of them. Despite achieving a high loss-tolerance and scalability degrees, gossiping is affected by degraded performances and heavy traffic loads on the network. For this reason, it may be not optimal in applications where reliability must be provided jointly with timeliness and/or in congestion-prone networks. The crucial aspect for improving a gossiping scheme is deciding which nodes should receive a gossiping message, and our driving idea is to adopt a distributed strategic learning logic to determine such nodes in an efficient manner. This is able to resolve gossiping's weakness points and to achieve better performance and reduced traffic loads. This paper describes how to introduced strategic learning in a gossip scheme so as to determine the best set of nodes that can be used to send gossip messages and to optimize their utility. Such a solution has been experimentally assessed through a set of simulations demonstrating the effectiveness of the proposal.

Nowadays, several advanced ICT infrastructures demand multicast services able to jointly provide a high degree of reliability and good performances despite of failure occurrences. The current practice to tolerate failures in such services mainly consists in the adoption of methods based on temporal redundancy, where messages are successfully delivered to all the interested destinations by means of retransmissions. However, in such methods, a high degree of reliability is typically obtained at the expenses of severe performance fluctuations and instability. On the contrary, approaches that make use of forward error correction imply a more performance-friendly behavior; however, the effective tuning of such approaches is a critical and challenging task, which has limited their applicability in large-scale multicast services. This paper is aimed at presenting a solution to resolve this issue by effectively tuning the redundancy degree to be applied, so as to be able to tolerate the losses affecting the multicast communications without an excessive, and un-needed, workload imposed over the network. To this aim, we drawn from the experience of layered multicast, commonly applied to multimedia content delivery, for addressing such tunability problem. Specifically, we detail how to apply layered multicast within the context of a well known multicast service and how to determine the correct redundancy degree to be applied. We also make use of extensive simulations to evaluate the quality of the proposed approach in terms of reliability, performance and overhead, and to compare it with the best retransmission-based approach available in the literature.

In the last years, the power consumption of telecommunication networks has attracted the attention of both researchers and field experts in order to contain the associated energy bills and reduce their ecological impact. Many of the proposed solutions have been focused exclusively on the reduction of the power consumption, without adequately considering more traditional network engineering objectives such as balancing resource utilization, routing policy, or resilience schemes. As a consequence, network control plane strategies passed from one extreme to the other, from being totally energy-unaware to exclusively energy-efficient at the expenses of load-balancing, with obvious impacts on the power consumption in the former case and on the blocking rate in the latter one. In this paper, we present a hybrid routing and wavelength assignment algorithm that, when the network is lightly loaded, operates in an energy-efficient way, by routing the connections on the paths requiring the lowest amount of energy, while, when the network load increases, it dynamically switches to a pure load-balancing scheme in order to best allocate the available communication resources. The switching decision among load-balancing and energy-awareness is taken dynamically, driven by a threshold on the number of new connections requests reaching the network during a prefixed time window. Simulation results show the effectiveness of the hybrid algorithm, which achieves lower energy consumption than a pure load-balancing algorithm while keeping the network load fairly distributed on the available resources.

Federated cloud organizations, spanning across multiple networked sites that provide both computing and storage resources, can be considered the state-of-the-art solutions for providing multi-tenant runtime services in modern distributed processing environments. In these scenarios, by re-optimizing the communication paths between virtual machines and big data sources, at evenly spaced interval or when required by circumstances, the overall communication and runtime resource utilization on the cloud infrastructure is re-balanced, so that more virtual machines can be allowed to access the needed big data sources with adequate bandwidth, thereby significantly improving the perceived performance and quality of service. The problem of re-optimization is tackled with a powerful meta-heuristic, the greedy randomized adaptive search procedure (GRASP), augmented by path re-linking. In order to evaluate the proposed approach, extensive simulations have been performed, leading to very interesting results, demonstrating the effectiveness and validity of the underlying ideas and their applicability to real large-scale federated cloud scenarios. Efficient re-optimization strategy for big data access in multi-tenant cloud infrastructures.Based on a Greedy Randomized Adaptive Search meta-heuristic working on a flexible federated cloud model.Performs end-to-end path rerouting and Virtual Machine migration to improve access to big data.Rebalances cloud resource usage so that more virtual machines can effectively access data sources.

Mobile terminals support multiple and heterogeneous communication technologies.We study energy-related dynamics of secure communications among mobile terminals.We formulate an energy model which considers communication and security activities.The model is useful to minimize the overall energy consumption of involved terminals.The model has been validated through simulation. Despite the wide deployment of advanced wireless coverage infrastructures, finding the best way for achieving secure mobile communication in every-day's life activities is still an open question. Indeed, a large number of mobile terminals, supporting multiple networking technologies, may be used to manage data from everywhere and at anytime. However, the effort required for achieving security, given the complexity of cryptographic algorithms, heavily affects the power consumption of terminals. Such energy demand, together with the one required to manage communication activities, makes energy-efficient secure communication among hardware-constrained handheld devices a challenging topic.In this work, we introduce an analytic energy model for secure communication among multi-mode terminals. This model describes the energy consumption of mobile terminals operating within a dynamic network scenario, considering both their interconnection and secure data exchange issues, in order to develop adaptive strategies for energy-efficient secure communications. Finally, the model has been validated through simulation.

This paper will describe Transformative Computing technologies as a new area of modern information sciences, which plays crucial role in development future IT technologies. Transformative computing allow to join communication technologies, and data processing techniques with advanced AI solutions, which allow to analytically process and manage acquired data. It enhances possibilities of efficient data analysis, and thanks to the application of AI, opens a new areas of data exploration towards planning, decision supporting, and advanced secure information management in distributed systems. In this paper we'll focus on new possible areas of transformative computing applications, especially for semantic information processing, as well as cognitive data reasoning.

Network anomaly detection has the essential goal of reliably identifying malicious activities within traffic observations collected at specific monitoring points, in order to raise alarms and timely trigger specific reactions and countermeasures. This, ideally, should be done also in presence of previously unknown phenomena, also known as zero-day attacks. However, distinguishing anomalous events due to attacks from normal spikes or sharp variations in traffic flows can become a classic “finding a needle in a haystack” problem, due to the very complex and unpredictable nature of Internet traffic, which is extremely affected by randomness and background noise effects. To face this challenge we leveraged machine learning for developing a novel network anomaly detection solution, based on the exploitation of nonlinear invariant properties of the Internet traffic. These properties, by capturing its chaotic and fractal features, are better suited to represent the more intrinsic and discriminative dynamics within an inductively learned model to be used for effectively classifying, through logistic regression, previously unseen traffic aggregates or individual flows into “normal” or “anomalous” ones. The results of the performance evaluation, obtained within a standard and reproducible experimental validation framework, show that the approach is able to effectively isolate very different kinds of volumetric Denial of Service attacks within the context of complex mixes of traffic flows, with really satisfactory accuracy and precision.

Flexgrid Optical Networks provide higher spectrum efficiency and flexibility in comparison to traditional wavelength switched optical networks (WSON) due to the finer granularity in managing communication channels at the WDM layer, thus allowing beyond 100 Gbps transmission capabilities. They also promise allocating part of the traffic aggregation function to the optical layer, thus reducing the use of energy-hungry electronic devices. Accordingly, in this work, we analyzed these architectures from the power consumption perspective by evaluating the energy efficiency derived from introducing the Flexgrid technology in order to allow the shift of grooming and bandwidth aggregation functions at the optical layer, by envisioning a next generation of IP/MPLS-over-flexgrid transport architectures. This work was partially supported by the Ministerio de Economía y Competitividad of the Spanish Government under the SUNSET project (TEC2014-59583-C2-2-R) and the Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (2014SGR-1427).

The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit antennas. We propose an enhanced version of the SD based on an additional cost function derived from conditions on worst case interference, that we call dominance conditions. The proposed detector, the king sphere decoder (KSD), has a computational complexity that results to be not larger than the complexity of the sphere decoder and numerical simulations show that the complexity reduction is usually quite significant.

Today, large-scale distributed Cloud computing infrastructures are often characterized by the fact that each participating organization, like a company in the free economy scenario, when making its resources available, will strive for reducing its own cost and for optimizing its own benefit, regardless of the consequences on the global Cloud’s welfare, in terms of effectiveness and performance losses. The selfish behavior of all these entities (end-users, Cloud providers, underlying enterprises/data centers), competing strategically for resources (distributed storage, processing power, bandwidth), motivates the use of game theory and autonomous agents for effective Multi-User Task Scheduling. Accordingly, we present a novel uncoordinated fully distributed scheduling scheme for federated cloud organizations, based on independent, competing, and self-interested job/task execution agents, driven by optimum social welfare criteria towards a Nash equilibrium solution. The agents’ behavior is also conditioned by marginal costs, to force some kind of implicit coordination between the (often conflicting) objectives of the various entities involved in the cloud. Due to its inherent parallel nature, such schema can provide a significantly better scalability in presence of a large number of tasks and resources involved into the scheduling system.

Lack of knowledge of the prior distribution in classification problems that operate on small data sets may make the application of Bayes' rule questionable. Uniform or arbitrary priors may provide classification answers that, even in simple examples, may end up contradicting our common sense about the problem. Entropic priors (EPs), via application of the maximum entropy (ME) principle, seem to provide good objective answers in practical cases leading to more conservative Bayesian inferences. EP are derived and applied to classification tasks when only the likelihood functions are available. In this paper, when inference is based only on one sample, we review the use of the EP also in comparison to priors that are obtained from maximization of the mutual information between observations and classes. This last criterion coincides with the maximization of the KL divergence between posteriors and priors that for large sample sets leads to the well-known reference (or Bernardo's) priors. Our comparison on single samples considers both approaches in prospective and clarifies differences and potentials. A combinatorial justification for EP, inspired by Wallis' combinatorial argument for entropy definition, is also included. The application of the EP to sequences (multiple samples) that may be affected by excessive domination of the class with the maximum entropy is also considered with a solution that guarantees posterior consistency. An explicit iterative algorithm is proposed for EP determination solely from knowledge of the likelihood functions. Simulations that compare EP with uniform priors on short sequences are also included.

The uncontrollable growth of the Internet, breaking through meshing and multi-homing practices the existing topology-based prefix aggregation mechanisms, creates the necessity of revisiting some fundamental aspects in the inter-domain routing model due to severe scalability issues in routing table size. In this paper, we at first analyze the root causes of these problems and then exploit a promising solution based on on-demand routing and on a widely known uniform caching and searching algorithm. Such algorithm is based on bond percolation, a mathematical phase transition model well-suited for random walk searches in power law networks, automatically shielding nodes with limited connectivity from large traffic volumes and reducing the total traffic to scale sub-linearly with the network size. The proposed solution introduces limited modifications to the BGP protocol, ensuring backward compatibility and allowing gradual deployment throughout the Internet. It dramatically reduces the routing table size requirements in all the nodes participating to the search network while allowing reliable and efficient on-demand discovery of unknown routing information, as demonstrated through extensive simulation experiments.

Current Internet e-mail facilities are built onto the foundation of standard rules and protocols, which usually allow a considerable amount of “freedom” to their designers. Each of these standards has been defined based on a number of vendor specific implementations, in order to provide common inter-working procedures for cross-vendor communication. Thus, a lot of optional and redundant information is being exchanged during e-mail sessions, which is available to implement versatile covert channel mechanisms.This work exploits this possibility by presenting a simple but effective steganographic scheme that can be used to deploy robust secret communication through spam e-mails. This scheme can offer unidirectional asynchronous one-to-one or one-to-many covert channel facilities that are able to bypass the most sophisticated firewalls and traffic analyzers. Its implementation neither affects the involved transport protocols nor causes any perceivable performance degradation or data loss to the end-users. The proposed scheme allows one to manage possible filtering/loss of the e-mails being the vehicle of the secret information. A novel retransmission method based on the Raptor codes has been adopted. The use of Raptor codes is key to correctly and efficiently manage the difficulty or impossibility to retransmit e-mails in the case of a unidirectional secret communication starting from one sender and directed to many recipients. In order to evaluate the performance characteristics of the proposed scheme, an empirical estimation of the covert channel bandwidth has been performed.

Summary Conventional tillage may impose changes in soil physical properties that lead to a decrease in soil physical quality. Although plough pan formation is considered to be an important consequence of conventional tillage practices in Southeastern Brazil, few studies have focused on its hydrological consequences. Detailed investigations in two experimental plots located in the hilly landscape of Serra do Mar close to Rio de Janeiro city were carried out to characterize the changes in soil physical properties and in soil hydrology due to plough pan formation. Conventional (CT) and minimum tillage (MT) practices were implemented in two plots for 3 years and soil matric potential (SMP) was monitored in each plot via nests of tensiometers and Watermark® sensors installed at different depths. Undisturbed soil blocks were collected for micromorphological analyses to quantify the total pore space in soils under CT and MT systems, and in soils under natural tropical forest. Results suggest that soils under the CT system developed a plough pan layer at about 20 cm depth that had 44% less total porosity as compared to surface conditions. It is shown that soils under the CT system tended to stay saturated for longer periods of time after each rainfall event. Besides, during intense rainy periods soils under the CT system may develop hydrologic conditions that favor lateral flows while soils under the MT system were still draining. Such hydrological responses may explain why average soil erosion rates measured for individual rainfall events under the CT system were about 2.5 times greater than the ones observed at MT. The results attested that conventional tillage in this area generated modifications in soil fabric, especially in pore-size distribution and connectivity, which induced important changes in soil hydrology and soil erosion. The agricultural practices used in this area, associated with the local steep hillslopes and intense rainfall events, are definitely not adequate and require the introduction of soil and water conservation practices in order to become sustainable.

High performance communication support is a critical aspect for modern Grid environments where the deployment of data-intensive applications often requires moving Petabytes of data between geographically distant data repositories in specific time ranges. The best-effort delivery system of the Internet, often used as the underlying transport network, imposes severe constraints on the transfer of massive amounts of data, and thus restricts the deployment of the above applications on wide-area scales. Besides the lack of bandwidth, the inability to provide dedicated links makes the current network technology not well suited for performance critical Grid computing. A solution is needed to provide dedicated end-to-end connections, dynamically allocable on-demand or by scheduled reservation to critical data-intensive applications. Wavelength routing is at the state of the art the most promising solution to realize such connections. Accordingly, we propose an evolutionary Grid approach built on inter-domain end-to-end lightpath provisioning, controlled by a meta-scheduling logic on advance reservation basis, offering the required connectivity services to the involved Grid nodes by influencing job scheduling decisions with network-related information. We discuss opportunities and challenges in the network control plane and Grid middleware design, together with the required interfaces, adapters and algorithms critical to the provision of network-assisted extensible resource scheduling services. The proposed solution will result in a flexible Grid architecture that supports cooperation between different scheduling entities, based on a scalable framework for dynamic configuration and interconnection of multiple types of resources for high performance Grid applications over globally distributed optical network systems.

Packet losses and delays represent two of the main causes of impairment on packet-based communications. Recent proposals for robust and efficient transmission suggest joint design of coding and routing strategies. This paper introduces a general framework to analyze time diversity in packet-based communications. An algorithm for designing an efficient distribution of information in multiple packets that takes into account for both losses and delays is presented. Simulation results show in which cases time diversity may have a positive impact on various applications.

In the forthcoming new era of truly distributed computing, industry, businesses, and home users are placing complex and challenging demands on the transport network, now powered by the emerging photonic technologies, about Quality-of-Service (QoS) assurances that are required for new real-time computing and storage service applications geographically distributed worldwide according to the Grid model. There is the need to devise mechanisms for QoS provisioning in IP over WDM networks that must consider the physical characteristics and limitations of the optical domain to ensure the proper treatment of service classes when passing from the electrical switching to the optical domain and back. In addition, these mechanisms should be directly accessible to Grid applications to make them able to request and release network resources as they need. A (G)MPLS-based control plane combined with a wavelength-routed optical network is seen as a very promising approach for the realization of transport infrastructures for the future ''photonic empowered'' Grid computing paradigm, since it allows native user-controlled bandwidth resources and class-of-service provisioning, that is one of the strongest requirements for truly distributed computing. Considering this, we propose a general framework for providing differentiated services QoS to Grid applications in wavelength-routed photonic networks, built on the strengths of GMPLS for dynamic path selection and wavelength assignment. This framework makes it technically and economically viable to think of a set of computing, storage or combined computing storage nodes coupled through a high-performance optical network as one large computational and storage device.

Mobile Internet connectivity is the fastest growing business in the telecommunications market because of the evolution of digital cellular, portable computing and personal communication technologies, and is playing a vital role in shaping the 21st century communications paradigms. In this scenario, the deployment of innovative wireless data networks, the integration with the Internet and the interworking between different wireless technologies, typically 2.5/3G cellular and Wireless LAN (WLAN), will be challenging objectives for competitive service providers. These factors, combined with the impact that mobile-related traffic may have on the fixed infrastructure, and the convergence of mobile and fixed services, drive towards a rationalization of the resource allocation and management procedures and make it urgent to address quality of service and traffic engineering for mobile data communications at the core transport level. The Multi-Protocol Label Switching (MPLS) technology is a versatile solution to address the above problems which delivers a unified control mechanism with connectionless multiprotocol capabilities, running over mixed media infrastructures, and defining evolutionary signalling mechanisms to support both quality of service (QoS) and traffic engineering to allow fine control of traffic flows in the network. The purpose of this paper is to show how MPLS technology can be employed to address all the required functions of tomorrow's converged wireless/wired networks, from initial IP-level authentication and session control to sophisticated resource reservation, traffic distribution, quality of service and policy management.

The training of feedforward networks using the conventional back propagation algorithm is plagued by slow convergence and misadjustment. In this paper, we apply optimal filtering techniques to train feedforward networks in the standard supervised learning framework. We consider first the global problem of computing the synaptic weights all simultaneously, and then develop the idea of local linearization and partitioning. We present three algorithms which are computationally more expensive than standard back propagation, but local at the neuron level. These algorithms do not incorporate any tunable parameters and show excellent performance in comparison to the expensive approach of the global Extended Kalman Algorithm in terms of speed of convergence and quality of solution obtained on three benchmark problems.