Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

Abstract Background: Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful. Results: In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium. Conclusions: To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.

The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

EEG-based functional networks in schizophrenia.

Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the network properties with the graph metrics, including mall-worldness, vulnerability, modularity, assortativity, and synchronizability. The schizophrenic patients showed method-specific and frequency-specific changes especially pronounced for modularity, assortativity, and synchronizability measures. However, the differences between schizophrenia patients and normal controls in terms of graph theory metrics were stronger for the unpartial correlation method.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Networks

Cognitive radio networks (CRN) sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to force a wrong decision. Providing security to the sensing process is also complex, as it usually involves introducing limitations to the CRN applications. The most common limitation is the need for a static trusted node that is able to authenticate and merge the reports of all CRN nodes. This paper overcomes this limitation by presenting a protocol that is suitable for fully distributed scenarios, where there is no static trusted node.

A Secure and Anonymous Cooperative Sensing Protocol for Cognitive Radio Networks

Spectrum is an essential resource for the provision of mobile services. In order to control and delimit its use, governmental agencies set up regulatory policies. Unfortunately, such policies have led to a deficiency of spectrum as only few frequency bands are left unlicensed, and these are used for the majority of new emerging wireless applications. One promising way to alleviate the spectrum shortage problem is adopting a spectrum sharing paradigm in which frequency bands are used opportunistically. Cognitive radio is the key technology to enable this shift of paradigm.Cognitive radio networks are self-organized systems in which devices cooperate to use those spectrum ranges that are not occupied by licensed users. They carry out spectrum sensing in order to detect vacant channels that can be used for communication. Even though spectrum sensing is an active area of research, an important issue remains unsolved: the secure authentication of sensing reports. Not providing security enables the input of false data in the system thus empowering false results. This paper presents a distributed protocol based on wireless physical layer security, symmetric cryptography and one-way functions that allows determining a final sensing decision from multiple sources in a quick and secure way, as well as it preserves users¿ privacy.

ZigBee/ZigBee PRO security assessment based on compromised cryptographic keys

Sensor networks have many applications in monitoring and controlling of environmental properties such as sound, acceleration, vibration and temperature. Due to limitedresources in computation capability, memory and energy, they are vulnerable to many kinds of attacks. The ZigBee specification based on the 802.15.4 standard, defines a set of layers specifically suited to sensor networks. These layers support secure messaging using symmetric cryptographic. This paper presents two different ways for grabbing the cryptographic key in ZigBee: remote attack and physical attack. It also surveys and categorizes some additional attacks which can be performed on ZigBee networks: eavesdropping, spoofing, replay and DoS attacks at different layers. From this analysis, it is shown that some vulnerabilities still in the existing security schema in ZigBee technology.

A Secure Cooperative Sensing Protocol for Cognitive Radio Networks

Cognitive radio networks sense spectrum occupancyand manage themselves to operate in unused bands without disturbing licensed users. Spectrum sensing is more accurate if jointly performed by several reliable nodes. Even though cooperative sensing is an active area of research, the secureauthentication of local sensing reports remains unsolved, thus empowering false results. This paper presents a distributed protocol based on digital signatures and hash functions, and ananalysis of its security features. The system allows determining a final sensing decision from multiple sources in a quick and secure way.

Secure Dynamic MANET On-demand (SEDYMO) Routing Protocol

This paper describes the state of the art of secure ad hoc routing protocols and presents SEDYMO, a mechanism to secure a dynamic multihop ad hoc routing protocol. The proposed solution defeats internal and external attacks usinga trustworthiness model based on a distributed certification authority. Digital signatures and hash chains are used to ensure the correctness of the protocol. The protocol is compared with other alternatives in terms of security strength, energy efficiency and time delay. Both computational and transmission costs are considered and it is shown that the secure protocol overhead is not a critical factor compared to the high network interface cost.

On an IDS Model for Mobile Ad Hoc Networks

Manet security has a lot of open issues. Due to its character-istics, this kind of network needs preventive and corrective protection. Inthis paper, we focus on corrective protection proposing an anomaly IDSmodel for Manet. The design and development of the IDS are consideredin our 3 main stages: normal behavior construction, anomaly detectionand model update. A parametrical mixture model is used for behav-ior modeling from reference data. The associated Bayesian classi¯cationleads to the detection algorithm. MIB variables are used to provide IDSneeded information. Experiments of DoS and scanner attacks validatingthe model are presented as well.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Service Management in IP Networks

IP-verkoissa tarjottavat palvelut ovat lisääntyneet, on tullut uusia kanavia, jotka tarjoavat IP-pohjaisia palveluja. Internet-palvelujen tarjonta on tullut mukaan kaapelitelevisio- ja matkapuhelinverkkoihin. Lisääntynyt palvelujen tarjonta ja kysyntä ovat lisänneet palvelujen hallinnan merkitystä. IP-verkoissa on ilmennyt turvallisuuteen, skaalattavuuteen ja palvelun laatuun liittyviä ongelmia. Palvelun laadun tärkeys painottuu reaaliaikaisuutta ja suurta kapasiteettia vaativissa sovelluksissa. Tulevaisuudessa IP-liikenteen on ennustettu kasvavan yli satakertaiseksi nykyisestä tasosta kolmen vuoden kuluessa. Tämän vuoksi on kehitetty uusi verkon ja palvelun hallintamenetelmiä, joista tässä työssä on tutkittu sääntöpohjaista verkon hallintaa.

Tietoturva käyttäjän kannalta langattomaan lähiverkkotekniikkaan perustuvassa kaupunkiverkossa

Langattomien lähiverkkotekniikoiden käyttö on yleistynyt nopeasti viime vuosina. Varsinkin IEEE:n 802.11b-standardi on ollut suosittu. Tätä tekniikkaa on käytetty myös alueellisten access-verkkojen rakentamiseen. Tämä työ on tehty hankkeeseen, jossa tutkitaan langattoman lähiverkkotekniikan käyttöä operaattoririippumattoman kaupunkiverkon toteuttamiseen. Työssä tutkittiin langattoman lähiverkkotekniikan vaikutusta verkon käyttäjän tietoturvaan ja pyrittiin löytämään avoimeen kaupunkiverkkoon sopiva ratkaisu, joka parantaa käyttäjän tietoturvaa. Työssä käsitellään aluksi tietoturvan teoriaa ja langattomuuden vaikutusta tietoturvaan. Hankkeessa käytetty langaton lähiverkkotekniikka IEEE 802.11b ja sen tietoturvaominaisuudet esitellään. Tutustutaan myös lyhyesti muutamiin julkisiin, 802.11b-tekniikkaa käyttäviin verkkoihin, sekä niiden tietoturvaratkaisuihin. Työssä esitellään tuote, jolla pyrittiin parantamaan käyttäjien tietoturvaa hankkeen verkossa. Lisäksi kuvaillaan tuotteen asennus testiverkkoon. Testiverkon käyttöperiaatteiden perusteella päädyttiin tulokseen olla ottamatta testattua tuotetta käyttöön, vaikka tuote sinällään oli teknisesti toimiva.