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Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, machine learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms is proposed. In particular, genetic programming is used, which allows solving a symbolic regression problem through the use of genetic algorithms. The identification of these correlations is a typical objective of the diagnostic approach and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The mathematical model obtained highlights the importance of the complex relationship existing between the different gene's mutations present in the tumor tissue of the group of patients considered. [ABSTRACT FROM AUTHOR]

In wireless sensor networks applications involving a huge number of sensors, some of the sensor devices may result to be redundant. As a consequence, the simultaneous usage of all the sensors may lead to a faster depletion of the available energy and to a shorter network lifetime. In this context, one of the well-known and most important problems is Maximum Network Lifetime Problem (MLP). MLP consists in finding non-necessarily disjoint subsets of sensors (covers), which are autonomously able to surveil specific locations (targets) in an area of interest, and activating each cover, one at a time, in order to guarantee the network activity as long as possible. MLP is a challenging optimization problem and several approaches have been proposed to address it in the last years. A recently proposed variant of the MLP is the Maximum Lifetime Problem with Time Slots (MLPTS), where the sensors belonging to a cover must be operational for a fixed amount of time, called operating time slot, whenever the cover is activated. In this paper, we generalize MLPTS by taking into account the possibility, for each subset of active sensors, to neglect the coverage of a small percentage of the whole set of targets. We define such new problem as α c -MLPTS, where α c defines the percentage of targets that each cover has to monitor. For this new scenario we propose three approaches: a classical Greedy algorithm, a Carousel Greedy algorithm and a modified version of the genetic algorithm already proposed for MLPTS. The comparison of the three heuristic approaches is carried out through extensive computational experiments. The computational results show that the Carousel Greedy represents the best trade-off between the proposed approaches and confirm that the network lifetime can be considerably improved by omitting the coverage of a percentage of the targets. [ABSTRACT FROM AUTHOR]

The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein–Uhlenbeck processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory dataset collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system. [ABSTRACT FROM AUTHOR]

Cloud platforms encompass a large number of storage services that can be used to manage the needs of customers. Each of these services, offered by a different provider, is characterized by specific features, limitations and prices. In presence of multiple options, it is crucial to select the best solution fitting the customer requirements in terms of quality of service and costs. Most of the available approaches are not able to handle uncertainty in the expression of subjective preferences from customers, and can result in wrong (or sub-optimal) service selections in presence of rational/selfish providers, exposing untrustworthy indications concerning the quality of service levels and prices associated to their offers. In addition, due to its multi-objective nature, the optimal service selection process results in a very complex task to be managed, when possible, in a distributed way, for well-known scalability reasons. In this work, we aim at facing the above challenges by proposing three novel contributions. The fuzzy sets theory is used to express vagueness in the subjective preferences of the customers. The service selection is resolved with the distributed application of fuzzy inference or Dempster-Shafer theory of evidence. The selection strategy is also complemented by the adoption of a game theoretic approach for promoting truth-telling ones among service providers. We present empirical evidence of the proposed solution effectiveness through properly crafted simulation experiments. [ABSTRACT FROM PUBLISHER]

Presents the authors' response to Yong Gao, following his comments on their paper `Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space, Part I: Basic Properties of Selection and Mutation. Author's agreement that the full potential of the genetic algorithm approach can be appreciated if it is supported by a sound mathematical formulation.

Security is a very complex and challenging problem in Cloud organizations. Ensuring the security of operations within the cloud by also enforcing the users’ own security requirements, usually results in a complex tradeoff with the efficiency of the overall system. In this paper, we developed a novel architectural model enforcing cloud security, based on a multi-agent scheme and a security aware non-deterministic Meta Scheduler driven by genetic heuristics. Such model is explicitly designed to prevent Denial of Service and Timing Attacks over the cloud and has been demonstrated to be integrable within the well-known OpenStack platform. Additionally, we proposed two different models for assuring users security demands. The first is a scoring model that allows scheduling tasks only on the Virtual Machines offering proper security level. The second model takes into account the time spent on the necessary cryptographic operations dedicated to particular task. The above scheduling system has been simulated in order to assess the effectiveness of the proposed security architecture, resulting in an increased system safety and resiliency against attacks, without sensibly impacting the performance of the whole cloud environment. [ABSTRACT FROM AUTHOR]