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Nonparametric Problem-Space Clustering: Learning Efficient Codes for Cognitive Control Tasks
No full textWe present an information-theoretic method permitting one to find structure in a problem space (here, in a spatial navigation domain) and cluster it in ways that are convenient to solve different classes of control problems, which include planning a path to a goal from a known or an unknown location, achieving multiple goals and exploring a novel environment. Our generative nonparametric approach, called the generative embedded Chinese restaurant process (geCRP), extends the family of Chinese restaurant process (CRP) models by introducing a parameterizable notion of distance (or kernel) between the states to be clustered together. By using different kernels, such as the the conditional probability or joint probability of two states, the same geCRP method clusters the environment in ways that are more sensitive to different control-related information, such as goal, sub-goal and path information. We perform a series of simulations in three scenarios-an open space, a grid world with four rooms and a maze having the same structure as the Hanoi Tower-in order to illustrate the characteristics of the different clusters (obtained using different kernels) and their relative benefits for solving planning and control problems
Learning programs is better than learning dynamics: a Programmable Neural Network Hierarchical Architecture in a multi-task Scenario
No full textDistributed and hierarchical models of control are nowadays popular in computational modeling and robotics. In the artificial neural network literature, complex behaviors can be produced by composing elementary building blocks or motor primitives, possibly organized in a layered structure. However, it is still unknown how the brain learns and encodes multiple motor primitives, and how it rapidly reassembles, sequences and switches them by exerting cognitive control. In this paper we advance a novel proposal, a hierarchical programmable neural network architecture, based on the notion of programmability and an interpreter-programmer computational scheme. In this approach, complex (and novel) behaviors can be acquired by embedding multiple modules (motor primitives) in a single, multi-purpose neural network. This is supported by recent theories of brain functioning in which skilled behaviors can be generated by combining functional different primitives embedded in "reusable" areas of "recycled" neurons. Such neuronal substrate supports flexible cognitive control, too. Modules are seen as interpreters of behaviors having controlling input parameters, or programs that encode structures of networks to be interpreted. Flexible cognitive control can be exerted by a programmer module feeding the interpreters with appropriate input parameters, without modifying connectivity. Our results in a multiple T -maze robotic scenario show how this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers permitting to learn, encode and control multiple qualitatively different behaviors
Neurobiological Impact of EMDR in Cancer
No full textThe exposure to a life-threatening disease such as cancer may constitute a traumatic experience that in some cases may lead to the development of posttraumatic stress disorder (PTSD). In recent years, several studies investigated this syndrome in patients with cancer, but few focused on the underlying neurobiology. The aim of this work was to review the current literature of neurobiology of PTSD in oncological diseases, focusing on a comparison with the results of neurobiological studies on PTSD in nononcological patients and on treatments resulted effective for such disorder. Brain structures having a role in the appearance of PTSD in psycho-oncology, and in particular, in intrusive symptoms, seem to be the same involved in non-oncologic PTSD. These findings may have important implications also at clinical level, suggesting that psychotherapies found to be effective to treat PTSD in different populations may be offered also to patients with cancer-induced posttraumatic symptoms. Further studies are needed to deepen our knowledge about cancer-related PTSD neurobiology and its treatment, aiming at transferring the results into clinical practice
Design di una rete dati geograficamente distribuita: use case della rete dati regionale toscana della Pubblica Amministrazione
No full textLa Pubblica Amministrazione ? composta da diversi enti quali i comuni, le universit?, le scuole e gli ospedali i cui uffici sono tipicamente sparsi sul territorio. La necessit? sempre maggiore di scambiare informazioni porta ciascuna sede ad avvalersi dei servizi di un Service Provider che, oltre a fornire un accesso diretto ad Internet, pu? offrire un collegamento tra le varie sedi distaccate allo scopo di costituire la rete VPN della Pubblica Amministrazione. Una tecnologia legacy, robusta e scalabile, usata da molti Service Providers per la creazione di reti VPN ? basata sulla struttura ATM/SDH che tuttavia non ? in grado di gestire efficacemente la banda. Molte altre soluzioni tradizionali per la creazione di VPN quali le reti a circuito, GRE e IPSEC risultano difficilmente scalabili,e non consentono la facile introduzione di nuovi servizi. Questo Technical Report illustra come alcune tecnologie innovative quali L3 MPLS VPN, VPLS e VRF-Lite (Multi-VRF) con Tunneling IP possano essere adottate dai Service Providers allo scopo di creare delle reti VPN geografiche multipunto in maniera ottima e scalabile. Come esempio illustriamo l\u27use case della rete dati regionale toscana della Pubblica Amministrazione
MalProfiler: Automatic and Effective Classification of Android Malicious Apps in Behavioral Classes
No full textAndroid malicious apps are currently the main security threat for mobile devices. Due to their exponential growth in number of samples, it is vital to timely recognize and classify any new threat, to identify and effectively apply specific countermeasures. In this paper we propose MalProfiler, a framework which performs fast and effective analysis of Android malicious apps, based on the analysis of a set of static app features. The proposed approach exploits an algorithm named Categorical Clustering Tree (CCTree), which can be used both as a divisive clustering algorithm, or as a trainable classifier for supervised learning classification. Hence, the CCTree has been exploited to perform both homogeneous clustering, grouping similar malicious apps for simplified analysis, and to classify them in predefined behavioral classes. The approach has been tested on a set of 3500 real malicious apps belonging to more than 200 families, showing both an high clustering capability, measured through internal and external evaluation, together with an accuracy of 97% in classifying malicious apps according to their behavior
Enforcement of U-XACML History-Based Usage Control Policy
No full textUsage Control policies have been introduced to overcome issues related to the usage of resources. Indeed, a Usage Control policy takes into account attributes of subjects and resources which change over time. Hence, the policy is continuously enforced while an action is performed on a resource, and it is re-evaluated at every context change. This permits to revoke the access to a resource as soon as the new context violates the policy. The Usage Control model is very flexible, and mutable attributes can be exploited also to make a decision based on the actions that have been previously authorized and executed. This paper presents a history-based variant of U-XACML policies composed via process algebra-like operators in order to take trace of past actions made on resources by the subjects. In particular, we present a formalization of our idea through a process algebra and the enhanced logical architecture to enforce such policies
Resistance to biocides in listeria monocytogenes collected in meat-processing environments
No full textThe emergence of microorganisms exerting resistance to biocides is a challenge to meat-processing environments. Bacteria can be intrinsically resistant to biocides but resistance can also be acquired by adaptation to their sub-lethal concentrations. Moreover, the presence of biocide resistance determinants, which is closely linked to antibiotic resistance determinants, could lead to co-selection during disinfection practices along the food chain, and select cross-resistant foodborne pathogens. The purpose of this work was to test the resistance of wild strains of Listeria monocytogenes, isolated from pork meat processing plants, toward benzalkonium chloride (BC), used as proxy of quaternary ammonium compounds. Furthermore, the expression of two non-specific efflux pumps genes (lde and mdrL) under biocide exposure was evaluated. L. monocytogenes were isolated from five processing plants located in the Veneto region (northeast of Italy) before and after cleaning and disinfection (C&D) procedures. A total of 45 strains were collected: 36 strains before and nine after the C&D procedures. Collected strains were typed according to MLST and ERIC profiles. Strains sampled in the same site, isolated before, and after the C&D procedures and displaying the same MLST and ERIC profiles were tested for their sensitivity to different concentrations of BC, in a time course assay. The expression of non-specific efflux pumps was evaluated at each time point by qPCR using tufA gene as housekeeping. A differential expression of the two investigated genes was observed: lde was found to be more expressed by the strains isolated before C&D procedures while its expression was dose-dependent in the case of the post C&D procedures strain. On the contrary, the expression of mdrL was inhibited under low biocidal stress (10 ppm BC) and enhanced in the presence of high stress (100 ppm BC). These findings suggests a possible role for C&D procedures to select L. monocytogenes persisters, pointing out the importance of dealing with the identification of risk factors in food plants sanification procedures that might select more tolerant strains
Learn PAd: Collaborative and Model-Based Learning in Public Administrations
No full textIn modern society public administrations (PAs) are undergoing a transformation of their perceived role from controllers to proactive service providers. PAs are often under pressure to constantly improve the quality of delivered services, while coping with quickly changing context (changes in law and regulations, societal globalization, fast technology evolution) and decreasing budgets. As a result civil servants supporting the delivery of such services to citizens are challenged to understand and put in action latest procedures and rules within tight time constraints. In Learn PAd we propose to organize knowledge, and base the learning activities, according to several model kinds permitting to describe the activities to perform by the civil servants and their working context. Models also constitute the starting point to shape a collaborative platform that civil servants will use to share their competences on the activities to perform to serve citizens requests
Hyperspectral sensors as a management tool to prevent the invasion of the exotic cordgrass Spartina densiflora in the Do?ana wetlands
No full textWe test the use of hyperspectral sensors for the early detection of the invasive denseflowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368-1052 nm) and an AHS (430-13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images
Business Process Feature Model: An Approach to Deal with Variability of Business Processes.
No full textIn order to help organizations in providing similar services without the need to structure each of them separately, this chapter presents a modeling notation that supports variability for Business Process modeling. Variability is particularly relevant for Public Administration institutions where different offices organize the provisioning of services to citizens following similar rules, and adapting them to the characteristics of the different offices. The notation and the approach are inspired to feature modeling techniques, whereas in this case features are used to represent activities of a process family that can be differently implemented and connected. The proposed approach facilitates the development of a partially specified process model in terms of a set of fragments that in a subsequent step can be connected in order to fully specify the desired control flow. The notation and the approach were implemented on the the ADOxx platform