An extended access control mechanism exploiting data dependencies

In general, access control mechanisms in DBMSs ensure that users access only those portions of data for which they have authorizations, according to a predefined set of access control policies. However, it has been shown that access control mechanisms might be not enough. A clear example is the inference problem due to functional dependencies, which might allow a user to discover unauthorized data by exploiting authorized data. In this paper, we wish to investigate data dependencies (e.g., functional dependencies, foreign key constraints, and knowledge-based implications) from a different perspective. In particular, the aim was to investigate data dependencies as a mean for increasing the DBMS utility, that is, the number of queries that can be safely answered, rather than as channels for releasing sensitive data. We believe that, under given circumstances, this unauthorized release may give more benefits than issues. As such, we present a query rewriting technique capable of extending defined access control policies by exploiting data dependencies, in order to authorize unauthorized but inferable data.     

Ambiguity and tacit knowledge in requirements elicitation interviews

Interviews are the most common and effective means to perform requirements elicitation and support knowledge transfer between a customer and a requirements analyst. Ambiguity in communication is often perceived as a major obstacle for knowledge transfer, which could lead to unclear and incomplete requirements documents. In this paper, we analyze the role of ambiguity in requirements elicitation interviews, when requirements are still tacit ideas to be surfaced. To study the phenomenon, we performed a set of 34 customer–analyst interviews. This experience was used as a baseline to define a framework to categorize ambiguity. The framework presents the notion of ambiguity as a class of four main sub-phenomena, namely unclarity, multiple understanding, incorrect disambiguation and correct disambiguation. We present examples of ambiguities from our interviews to illustrate the different categories, and we highlight the pragmatic components that determine the occurrence of ambiguity. Along the study, we discovered a peculiar relation between ambiguity and tacit knowledge in interviews. Tacit knowledge is the knowledge that a customer has but does not pass to the analyst for any reason. From our experience, we have discovered that, rather than an obstacle, the occurrence of an ambiguity is often a resource for discovering tacit knowledge. Again, examples are presented from our interviews to support this vision.     

Detecting attack signatures in the real network traffic with ANNIDA

In this paper, an improved version of ANNIDA for detecting attack signatures in the payload of network packets is presented. The Hamming Net artificial neural network methodology was used with good results. A review of the application’s development is followed by a summary of the modifications made in the application in order to classify real data. Application improvements are reported, solving the problems of time delays in writing/reading data in the files and data collision effects when generating numeric keys used to model data for the neural network. Test results highlight the increased accuracy and efficiency of the new application when submitted to real data from HTTP network traffic containing actual traces of attacks and legitimate data. Finally, an evaluation of the application to detect signatures in real network traffic data is presented.     

A new 3D parallel SPH scheme for free surface flows

We propose a new robust and accurate SPH scheme, able to track correctly complex three-dimensional non-hydrostatic free surface flows and, even more important, also able to compute an accurate and little oscillatory pressure field. It uses the explicit third order TVD Runge-Kutta scheme in time, following Shu and Osher [Shu C-W, Osher S. Efficient implementation of essentially non-oscillatory shock-capturing schemes. J Comput Phys 1988;89:439-71], together with the new key idea of introducing a monotone upwind flux for the density equation, thus removing any artificial viscosity term. For the discretization of the velocity equation, the non-diffusive central flux has been used. A new flexible approach to impose the boundary conditions at solid walls is also proposed. It can handle any moving rigid body with arbitrarily irregular geometry. It does neither produce oscillations in the fluid pressure in proximity of the interfaces, nor does it have a restrictive impact on the stability condition of the explicit time stepping method, unlike the repellent boundary forces of Monaghan [Monaghan JJ. Simulating free surface flows with SPH. J Comput Phys 1994;110:399-406]. To asses the accuracy of the new SPH scheme, a 3D mesh-convergence study is performed for the strongly deforming free surface in a 3D dam-break and impact-wave test problem providing very good results.Moreover, the parallelization of the new 3D SPH scheme has been carried out using the message passing interface (MPI) standard, together with a dynamic load balancing strategy to improve the computational efficiency of the scheme. Thus, simulations involving millions of particles can be run on modern massively parallel supercomputers, obtaining a very good performance, as confirmed by a speed-up analysis. The 3D applications consist of environmental flow problems, such as dam-break flows and impact flows against a wall. The numerical solutions obtained with our new 3D SPH code have been compared with either experimental results or with other numerical reference solutions, obtaining in all cases a very satisfactory agreement.     

An Information Roadmap Method for Robotic Sensor Path Planning

A new probabilistic roadmap method is presented for planning the path of a robotic sensor deployed in order to classify multiple fixed targets located in an obstacle-populated workspace. Existing roadmap methods have been successful at planning a robot path for the purpose of moving from an initial to a final configuration in a workspace by a minimum distance. But they are not directly applicable to robots whose primary objective is to gather target information with an on-board sensor. In this paper, a novel information roadmap method is developed in which obstacles, targets, sensor’s platform and field-of-view are represented as closed and bounded subsets of an Euclidean workspace. The information roadmap is sampled from a normalized information theoretic function that favors samples with a high expected value of information in configuration space. The method is applied to a landmine classification problem to plan the path of a robotic ground-penetrating radar, based on prior remote measurements and other geospatial data. Experiments show that paths obtained from the information roadmap exhibit a classification efficiency several times higher than that of existing search strategies. Also, the information roadmap can be used to deploy non-overpass capable robots that must avoid targets as well as obstacles.     

A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation

Accurate assessment of phytoplankton chlorophyll-a (chla) concentrations in turbid waters by means of remote sensing is challenging due to the optical complexity of case 2 waters. We have applied a recently developed model of the form [R_rs^? 1(λ₁) ? R_rs^? 1(λ₂)] × R_rs(λ₃) where R_rs(λ_i) is the remote-sensing reflectance at the wavelength λ_i, for the estimation of chla concentrations in turbid waters. The objectives of this paper are (a) to validate the three-band model as well as its special case, the two-band model R_rs^? 1(λ₁) × R_rs(λ₃), using datasets collected over a considerable range of optical properties, trophic status, and geographical locations in turbid lakes, reservoirs, estuaries, and coastal waters, and (b) to evaluate the extent to which the three-band model could be applied to the Medium Resolution Imaging Spectrometer (MERIS) and two-band model could be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate chla in turbid waters.The three-band model was calibrated and validated using three MERIS spectral bands (660–670 nm, 703.75–713.75 nm, and 750?757.5 nm), and the 2-band model was tested using two MODIS spectral bands (λ₁ = 662–672, λ₃ = 743–753 nm). We assessed the accuracy of chla prediction in four independent datasets without re-parameterization (adjustment of the coefficients) after initial calibration elsewhere. Although the validation data set contained widely variable chla (1.2 to 236 mg m^? 3), Secchi disk depth (0.18 to 4.1 m), and turbidity (1.3 to 78 NTU), chla predicted by the three-band algorithm was strongly correlated with observed chla (r² > 0.96), with a precision of 32% and average bias across data sets of ? 4.9% to 11%. Chla predicted by the two-band algorithm was also closely correlated with observed chla (r² > 0.92); however, the precision declined to 57%, and average bias across the data sets was 18% to 50.3%. These findings imply that, provided that an atmospheric correction scheme for the red and NIR bands is available, the extensive database of MERIS and MODIS imagery could be used for quantitative monitoring of chla in turbid waters.     

A theoretical analysis of bagging as a linear combination of classifiers

We apply an analytical framework for the analysis of linearly combined classifiers to ensembles generated by bagging. This provides an analytical model of bagging misclassification probability as a function of the ensemble size, which is a novel result in the literature. Experimental results on real data sets confirm the theoretical predictions. This allows us to derive a novel and theoretically grounded guideline for choosing bagging ensemble size. Furthermore, our results are consistent with explanations of bagging in terms of classifier instability and variance reduction, support the optimality of the simple average over the weighted average combining rule for ensembles generated by bagging, and apply to other randomization-based methods for constructing classifier ensembles. Although our results do not allow to compare bagging misclassification probability with the one of an individual classifier trained on the original training set, we discuss how the considered theoretical framework could be exploited to this aim.     

Microwave Energy Drives “On–Off–On” Spin‐Switch Behavior in Nitrogen‐Doped Graphene

The established application of graphene in organic/inorganic spin‐valve spintronic assemblies is as a spin‐transport channel for spin‐polarized electrons injected from ferromagnetic substrates. To generate and control spin injection without such substrates, the graphene backbone must be imprinted with spin‐polarized states and itinerant‐like spins. Computations suggest that such states should emerge in graphene derivatives incorporating pyridinic nitrogen. The synthesis and electronic properties of nitrogen‐doped graphene (N content: 9.8%), featuring both localized spin centers and spin‐containing sites with itinerant electron properties, are reported. This material exhibits spin‐switch behavior (on–off–on) controlled by microwave irradiation at X‐band frequency. This phenomenon may enable the creation of novel types of switches, filters, and spintronic devices using sp²‐only 2D systems.