POPULARITY AND AUDIENCE MEASUREMENT OF MOBILE APPS: ENABLING EFFECTIVE MOBILE ADVERTISEMENT

Ph.DDOCTOR OF PHILOSOPH

Customized risk assessment in military shipbuilding

This paper describes a customized risk assessment framework to be applied in military shipbuilding projects. The framework incorporates the Delphi method with visual diagrams, Bayesian Networks (BN) and the expression of expert opinions through linguistic variables. Noisy-OR and Leak Canonical models are used to determine the conditional probabilities of the BN model. The approach can easily be adapted for other shipbuilding construction projects. The visual diagrams that support the Delphi questionnaire favor the comprehensive visualization of the interdependencies between risks, causes, risks and causes, and risks and effects. The applicability of the framework is illustrated through the assessment of risk of two real military shipbuilding projects. This assessment includes a sensitivity analysis that is useful to prioritize mitigation actions. In the two cases studies, the risks with higher probability of occurrence were failures or errors in production, of the contracted, in the requirements, and in planning. The results of the sensitivity analysis showed that a set of mitigation actions directed at relatively easily controllable causes would have achieved important reductions in risk probabilities.- (undefined

Applied orthogonal design for filtrating conditions of ultrasonic-assisted extraction from plant-chicory

The objective of the current study is to achieve global optimization of ultrasound-assisted extraction from chicory roots using a mixed orthogonal array design. Eight conditional factors were examined in the mixed orthogonal array (L16 (43×26)). The results showed that the importance of the eight factors, in decreasing order, was ethanol content, impregnation repetitions, ultrasonic input power, sonication temperature, sonication repetitions, solvent-to-solid ratio, impregnation time and sonication time. The optimum extraction conditions included a frequency of 40 kHz, an impregnation time of 24 h with two rounds of impregnation, a sonication period of 30 min and an ultrasonic input power of 400 W with two rounds of sonication. Importantly, these conditions were independent of alcohol content, solvent-tosolid ratio and sonication temperature. At frequency of 40 kHz, the alcohol content, solvent-to-solid ratio and sonication temperature were optimized in the range of 50 to 75% (v/v), 32:1 and 50°C, respectively.Key words: Chicory roots, ultrasound-assisted extraction, mixed orthogonal design, select factors, global optimization

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method

Latent class approaches for modelling multiple ordinal items

The modelling of the latent class structure of multiple Likert items is reviewd. The standard latent class approach is to model the absolute Likert ratings. Commonly, an ordinal latent class model is used where the logits of the profile probabilities for each item have an adjacent category formulation (DeSantis et al., 2008). an alternative developed in this paper is to model the relative orderings, using a mixture model of the relative differences between pairs of Likert items. This produces a paired comparison adjacent category log-linear model (Dittrich et al., 2007; Francis and Dittrich, 2017), with item estimates placed on a (0,1) “worth” scale for each latent class. The two approaches are compared using data on environmental risk from the International Social Survey Programme, and conclusions are presented

ASMOD 2018: Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data

[English]:This volume collects the peer-reviewed contributions presented at the 2nd International Conference on “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 - held at the Department of Political Sciences of the University of Naples Federico II (24-26 October 2018). The Conference brought together theoretical and applied statisticians to share the latest studies and developments in the field. In addition to the fundamental topic of latent structure analysis and modelling, the contributions in this volume cover a broad range of topics including measuring dissimilarity, clustering, robustness, CUB models, multivariate models, and permutation tests. The Conference featured six distinguished keynote speakers: Alan Agresti (University of Florida, USA), Brian Francis (Lancaster University, UK), Bettina Gruen (Johannes Kepler University Linz, Austria), Maria Kateri (RWTH Aachen, Germany), Elvezio Ronchetti (University of Geneva, Switzerland), Gerhard Tutz (Ludwig-Maximilians University of Munich, Germany). The volume includes 22 contributions from scholars that were accepted as full papers for inclusion in this edited volume after a blind review process of two anonymous referees./ [Italiano]: Il volume raccoglie i contributi presentati alla seconda Conferenza Internazionale “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 – che si è svolta presso il Dipartimento di Scienze Politiche, Università di Napoli Federico II, nei giorni 24-26 ottobre 2018. La Conferenza ha visto la presentazione di studi sia teorici che applicati al fine di condividere i più recenti sviluppi scientifici nel campo. Oltre al tema fondamentale dell'analisi delle strutture latenti e dei modelli, i contributi richiamano una vasta gamma di argomenti, tra cui misure di dissimilarità, metodi di clustering, analisi di robustezza, modelli CUB, modelli multivariati e test di permutazione. In particolare, questa pubblicazione contiene le relazioni invitate di studiosi riconosciuti a livello internazionale: Alan Agresti (Università della Florida, USA), Brian Francis (Università Lancaster, Regno Unito), Bettina Gruen (Johannes Kepler University Linz, Austria), Maria Kateri (RWTH Aachen, Germania), Elvezio Ronchetti (Università di Ginevra, Svizzera), Gerhard Tutz (Università Ludwig-Maximilians di Monaco, Germania). Il volume include, inoltre, 22 contributi di studiosi che sono stati accettati dopo un processo di revisione anonima

Spatial Dependence and Heterogeneity in Empirical Analyses of Regional Labour Market Dynamics

Are regions within a country really independent islands? Do economic relations and effects really have a homogenous, unique size across an entire country? These two assumptions are often imposed implicitly in empirical economic and social research. In his doctoral thesis, the author discusses how statistical methods can deviate from this unrealistic model structure through employing spatial patterns in both observable variables and presumed relations. Opportunities to improve our understanding of the economy as well as chances and perils in the application of such methods are demonstrated in a number of studies on aspects of regional labour market dynamics.Warum sollen Regionen innerhalb eines Landes unabhängige Inseln sein? Und warum sollen, über das gesamte Land hinweg, einheitlich starke ökonomische oder soziale Wirkungszusammenhänge bestehen? Diese zwei Annahmen werden in der angewandten empirischen Wirtschafts- und Sozialforschung üblicherweise implizit unterstellt. Wie in statistischen Verfahren von dieser unrealistischen Modellstruktur unter Ausnutzung der räumlichen Strukturen in beobachteten Variablen und unterstellten Zusammenhängen abgewichen werden kann, diskutiert der Autor im vorliegenden Band. Möglichkeiten, unser Verständnis der Ökonomie zu vertiefen, werden ebenso verdeutlicht, wie Chancen und Tücken beim Einsatz der Methoden in Studien zu verschiedenen Aspekten der Arbeitsmarktdynamik