An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Exports, Imports and Wages:Evidence from Matched Firm-Worker-Product Panels

The analysis of the effects of firm-level international trade on wages has so far focused on the role of exports, which are also typically treated as a composite good. However, we show in this paper that firm-level imports can actually be a wage determinant as important as exports. Furthermore, we also find significant differences in the relationship between trade and wages across types of products. In particular, firms that increase their exports (imports) of high- (intermediate-) technology products tend to increase their salaries. Our analysis is based on unique data from Portugal, obtained by merging a matched firm-worker panel and a matched firm-transaction panel. Our data set follows the population of manufacturing firms and all their workers from 1995 to 2005 and allows for several control variables, including jobspell fixed effects.

ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given

The Cross Sectional Dynamics of Heterogenous Trade Models

In this paper we propose a framework for studying export dynamics and market specific flows in a multicountry model of trade with heterogenous firms. Countries are asymmetric in terms of their size, the size distribution of potential entrants, properties of firms idiosyncratic shocks, and trade barriers. The model has predictions in terms of cross-sectional moments and exporters dynamics. We show that persistent productivity shocks are enough to account for, qualitatively, many features of the data. In particular, the model is consistent with observed patterns of entry and exit across markets, export sales distribution, and the life cycle of new exporters.

Monetary policy in low income countries: the case of Uganda

This thesis addresses interrelated issues that influence the implementation of monetary policy in low income countries (LICs). These include the role of inflation persistence, financial frictions and the potential impact of regime-changes or large shocks. The analysis is applied to data for the Ugandan economy. Chapter 3 extends the quantile regression approach to investigate inflation persistence in LICs. The results suggest mean-reversion for the whole sample, however, there is evidence of asymmetric mean-reversion within specific quantiles. In addition, it is noted that the level of persistence increased after 2006 and during the inflation-targeting period. The study also suggests that a measure of core inflation that is derived from wavelet techniques appears to provide a useful measure of this variable. Chapter 4 considers the role of financial frictions in Uganda. It makes use of a dynamic stochastic general equilibrium (DSGE) model that incorporates several small open-economy features. The model parameters are estimated with the aid of Bayesian techniques using quarterly macroeconomic data. The results suggest that the central bank currently responds to changes in the interest rate spread and that it may be possible to derive a more favourable sacrifice ratio by making use of a slightly more aggressive response to macroeconomic developments. Chapter 5 employs a Markov-switching DSGE model to consider the possibility of regime-switching behaviour. Two variants of regime-switching models are considered: One that incorporates regime-switching features in the monetary policy rule (only) and another that incorporates regime-switching features in both the monetary policy rule and in the volatility of the shock processes. Most of the parameters are again estimated with the aid of Bayesian techniques. The results suggest that the model parameters do not remain constant over the two regimes and the transition probabilities appear to capture important economic events. In addition, the out-of-sample evaluation suggests that the regime-switching models may provide a more accurate description of the data generating processes

Automated Detection of Pipe Bursts and other Events in Water Distribution Systems

Copyright 2012 by the American Society of Civil EngineersThis paper presents a new methodology for the automated near real-time detection of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions) at the District Metered Area (DMA) level. The new methodology makes synergistic use of several self-learning Artificial Intelligence (AI) techniques and statistical data analysis tools including wavelets for de-noising of the recorded pressure/flow signals, Artificial Neural Networks (ANNs) for the short-term forecasting of pressure/flow signal values, Statistical Process Control (SPC) techniques for short and long term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian Inference Systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (UK) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate

EuroMInd-D : a density estimate of monthly gross domestic product for the Euro area

EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottomup approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and raggededged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules