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Diffusion-Weighted MR imaging: Clinical applications of kurtosis analysis to prostate cancer
No full textAdaptive Symmetric NMF for graph clustering
No full textOrganizing data into clusters is a key task for data compression and classication. In this paper we consider the case where the data are points belonging to a linear space, whose distance is measured through the Euclidean norm. A symmetric modeling of the graph clustering problem is addressed and an algorithm is proposed, based on NMF (nonnegative matrix factorization) techniques applied to a penalized nonsymmetric minimization problem. The solution depends on several parameters, whose choice is crucial. To overcome this difficulty, we suggest a heuristic approach which detects the best parameter values in an adaptive way. Extensive experimentation shows that the proposed algorithm is effective
Tuttoscuola n.559: C\u27era una volta Internet
No full textL\u27articolo illustra le attivit? e il materiale proposto dalla Ludoteca del Registro .it per fare attivit? di divulgazione nelle scuole in merito alla storia della Rete
Efficient Wireless Power Transfer under Radiation Constraints in Wireless Distributed Systems
No full textIn this chapter, we follow a new approach in studying the problem of efficiently charging a set of rechargeable nodes using a set of wireless power chargers, under safety constraints on the electromagnetic radiation incurred. In particular, we define a new charging model that greatly differs from existing models in that it takes into account real technology restrictions of the chargers and nodes of the system, mainly regarding energy limitations. Our model also introduces nonlinear constraints (in the time domain), that radically change the nature of the computational problems we consider. In this charging model, we present and study the Low Radiation Efficient Charging Problem (LREC), in which we wish to optimize the amount of ?useful? energy transferred from chargers to nodes (under constraints on the maximum level of imposed radiation). We present several fundamental properties of this problem and provide indications of its hardness. Finally, we propose an iterative local improvement heuristic for LREC, which runs in polynomial time, and we evaluate its performance via simulation. Our algorithm decouples the computation of the objective function from the computation of the maximum radiation and also does not depend on the exact formula used for the computation of the electromagnetic radiation in each point of the network, achieving good trade-offs between charging efficiency and radiation control; it also exhibits good energy balance properties. We provide extensive simulation results supporting our claims and theoretical result
A proposed evolution for the Italian certified electronic mail system
No full textOne the main objective of the European Commission is to innovate and bring ICT to its full potential in any sector, including eGovernment, eCommerce, and eHealth services. Certified Electronic Mail (CEM) systems of Member States are currently not interoperable, thus impacting on economic growth and competitiveness. The paper investigates the use of the DNSSec technology as a technological evolution of the Italian CEM System and the first step towards interoperability and adherence to international standards
Enhancing Android Permission through Usage Control: A BYOD Use-Case
No full textThe Bring Your Own Device (BYOD) paradigm, where the employees of a company install an application on their mobile devices to access company privileged information, is becoming very popular in the business environment. In order to perform their tasks, BYOD applications typically require a large set of rights which, in Android mobile devices, must be statically granted in order to have the application installed. However, this access control model is too coarse grained for the BYOD scenario, because employees would like to have a finer control on the rights granted to such applications, for instance to protect their privacy when they are not on duty. To address this issue, we propose to enhance the Android permission system through a Usage Control-based framework enabling employees to write policies which are continuously enforced while BYOD applications are running. This framework acts as a dynamic permission manager, where usage control policies grants, revokes and restores permissions to running applications on the base of mutable attributes describing the current context. Context is observed by using Android device standard APIs to monitor attributes such as mobile device location, WiFi status, battery level, current date and time, and so on. External trusted attribute providers can also be exploited
Let\u27s Bit!: alla scoperta di Internet con la peer education
No full textL\u27articolo descrive il progetto di peer education Let\u27s Bit! a cura della Ludoteca del Registro.it.L\u27articolo descrive il progetto di peer education Let\u27s Bit! a cura della Ludoteca del Registro.it
Accuracy vs. traffic trade-off of Learning IoT Data Patterns at the Edge with Hypothesis Transfer Learning
No full textRight now, the dominant paradigm to supportknowledge extraction from raw IoT data is through global cloudplatform, where data is collected from IoT devices, and analysed.However, with the ramping trend of the number of IoT devicesspread in the physical environment, this approach might simplynot scale. The data gravity concept, one of the basis of Fog andMobile Edge Computing, points towards a decentralisation ofcomputation for data analysis, whereby the latter is performedcloser to where data is generated. Along this trend, in this paperwe explore the accuracy vs. network traffic trade-off when usingHypothesis Transfer Learning (HTL) to learn patterns fromdata generated in a set of distributed physical locations. HTLis a standard machine learning technique used to train modelson separate disjoint training sets, and then transfer the partialmodels (instead of the data) to reach a unique learning model.We have previously applied HTL to the problem of learninghuman activities when data are available in different physicallocations (e.g., areas of a city). In our approach, data is not movedfrom where it is generated, while partial models are exchangedacross sites. The HTL-based approach achieves lower (thoughacceptable) accuracy with respect to a conventional solution basedon global cloud computing, but drastically cuts the networktraffic. In this paper we explore the trade-off between accuracyand traffic, by assuming that data are moved to a variablenumber of data collectors where partial learning is performed.Centralised cloud and completely decentralised HTL are thetwo extremes of the spectrum. Our results show that there isno significant advantage in terms of accuracy, in using fewercollectors, and that therefore a distributed HTL solutions, alongthe lines of a fog computing approach, is the most promising one
A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services
No full textCar pooling is expected to significantly help inreducing traffic congestion and pollution in cities by enablingdrivers to share their cars with travellers with similar itinerariesand time schedules. A number of car pooling matching serviceshave been designed in order to efficiently find successful ridematches in a given pool of drivers and potential passengers.However, it is now recognised that many non-monetary aspectsand social considerations, besides simple mobility needs, mayinfluence the individual willingness of sharing a ride, whichare difficult to predict. To address this problem, in this studywe propose GOTOGETHER, a recommender system for carpooling services that leverages on learning-to-rank techniquesto automatically derive the personalised ranking model of eachuser from the history of her choices (i.e., the type of acceptedor rejected shared rides). Then, GOTOGETHER builds the listof recommended rides in order to maximise the success rateof the offered matches. To test the performance of our schemewe use real data from Twitter and Foursquare sources in orderto generate a dataset of plausible mobility patterns and riderequests in a metropolitan area. The results show that theproposed solution quickly obtain an accurate prediction of thepersonalised user?s choice model both in static and dynamicconditions
Modelli neuro-computazionali di sistema per studiare il tremore nel morbo di Parkinson e testare terapie alternative
No full textNo abstract availableI disturbi motori nel morbo di Parkinson sono tradizionalmente associati ad una progressiva morte dei neuroni della substantia nigra, una regione del cervello che, attraverso il neurotrasmettitore dopamina, influenza un\u27altra area cardine per il controllo dei movimenti, i gangli della base. Dati recenti suggeriscono che anche cervelletto, talamo e corteccia sono coinvolti nel Parkinson, lavorando in sinergia con i gangli della base. Questa nuova prospettiva di sistema e\u27 tuttavia ancora poco studiata, in parte perche\u27 gli strumenti di ricerca tradizionali (principalmente brain imaging e test comportamentali), non consentono lo studio degli effetti dell\u27interazione dinamica tra le aree del cervello coinvolte. I modelli neuro-computazionali di sistema potrebbero essere lo strumento adatto per affrontare questo problema poiche\u27 riescono a simulare tale interazione. Questo lavoro propone un modello neuro-computazionale del sistema gangli-della-basetalamo-corteccia per studiare il tremore parkinsoniano. Il modello riproduce dati sull\u27attivita\u27 neurale registrata su pazienti reali e consente di studiare come gli squilibri nei gangli della base si ripercuotono nel circuito gangli-della-base-talamo-corteccia influenzando il tremore. Questo cambio radicale di prospettiva - da singola area a multi-area - supporta l\u27ideazione di tecniche innovative per il trattamento del tremore che agiscono sull\u27intero sistema gangli-della-base-talamocorteccia piuttosto che, come spesso accade, solo sui gangli della base