On behavior strategy solutions in finite extended decision processes

Techniques for finding best behavior strategies on arbitrary information collection scheme

Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A Compromise between Neutrino Masses and Collider Signatures in the Type-II Seesaw Model

A natural extension of the standard $SU(2)_{\rm L} \times U(1)_{\rm Y}$ gauge model to accommodate massive neutrinos is to introduce one Higgs triplet and three right-handed Majorana neutrinos, leading to a $6\times 6$ neutrino mass matrix which contains three $3\times 3$ sub-matrices $M_{\rm L}$, $M_{\rm D}$ and $M_{\rm R}$. We show that three light Majorana neutrinos (i.e., the mass eigenstates of $\nu_e$, $\nu_\mu$ and $\nu_\tau$) are exactly massless in this model, if and only if $M_{\rm L} = M_{\rm D} M_{\rm R}^{-1} M_{\rm D}^T$ exactly holds. This no-go theorem implies that small but non-vanishing neutrino masses may result from a significant but incomplete cancellation between $M_{\rm L}$ and $M_{\rm D} M_{\rm R}^{-1} M_{\rm D}^T$ terms in the Type-II seesaw formula, provided three right-handed Majorana neutrinos are of ${\cal O}(1)$ TeV and experimentally detectable at the LHC. We propose three simple Type-II seesaw scenarios with the $A_4 \times U(1)_{\rm X}$ flavor symmetry to interpret the observed neutrino mass spectrum and neutrino mixing pattern. Such a TeV-scale neutrino model can be tested in two complementary ways: (1) searching for possible collider signatures of lepton number violation induced by the right-handed Majorana neutrinos and doubly-charged Higgs particles; and (2) searching for possible consequences of unitarity violation of the $3\times 3$ neutrino mixing matrix in the future long-baseline neutrino oscillation experiments.Comment: RevTeX 19 pages, no figure

RAPID : research on automated plankton identification

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.This material is based upon work supported by the National Science Foundation under Grants OCE-0325018, OCE-0324937, OCE-0325167 and OCE-9423471, and the European Union under grants Q5CR-2002-71699, MAS3-ct98-0188, and MAS2-ct92-0015

Explanation Based Generalisation = Partial Evaluation

We argue that explanation-based generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well known technique in the functional and logic programming literature. We show this equivalence by analysing the definitions and underlying algorithms of both techniques, and by giving a Prolog program which can be interpreted as doing either explanation-based generalisation or partial evaluation

Essays on Matching Markets and Their Equilibria

Matching theory and matching markets are a core component of modern economic theory and market design. This dissertation presents three original contributions to this area. The first essay constructs a matching mechanism in an incomplete information matching market in which the positive assortative match is the unique efficient and unique stable match. The mechanism asks each agent in the matching market to reveal her privately known type. Through its novel payment rule, truthful revelation forms an ex post Nash equilibrium in this setting. This mechanism works in one-, two- and many-sided matching markets, thus offering the first mechanism to unify these matching markets under a single mechanism design framework. The second essay confronts a problem of matching in an environment in which no efficient and incentive compatible matching mechanism exists due to matching externalities. I develop a two-stage matching game in which a contracting stage facilitates subsequent conditionally efficient and incentive compatible Vickrey auction stage. Infinite repetition of this two-stage matching game enforces the contract in every period. This mechanism produces inequitably distributed social improvement: parties to the contract receive all of the gains and then some. The final essay demonstrates the existence of prices which stably and efficiently partition a single set of agents into firms and workers, and match those two sets to each other. This pricing system extends Kelso and Crawford's general equilibrium results in a labor market matching model and links one- and two-sided matching markets as well

On behavior strategy solutions of finite two- person constant-sum extended games

Recall-sensitivity and behavior strategy solutions in finite two-person constant-sum extended game

Are future psychologists willing to accept and use a humanoid robot in their practice? Italian and English students' perspective.

Despite general scepticism from care professionals, social robotics research is providing evidence of successful application in education and rehabilitation in clinical psychology practice. In this article, we investigate the cultural influences of English and Italian psychology students in the perception of usefulness and intention to use a robot as an instrument for future clinical practice and, secondly, the modality of presentation of the robot by comparing oral versus video presentation. To this end, we surveyed 158 Italian and British-English psychology students after an interactive demonstration using a humanoid robot to evaluate the social robot’s acceptance and use. The Italians were positive, while the English were negative towards the perceived usefulness and intention to use the robot in psychological practice in the near future. However, most English and Italian respondents felt they did not have the necessary abilities to make good use of the robot. We concluded that it is necessary to provide psychology students with further knowledge and practical skills regarding social robotics, which could facilitate the adoption and use of this technology in clinical settings