A Recurrent Deep Neural Network Model to measure Sentence Complexity for the Italian Language

Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS. We have also provided a comparison of our model with a state of the art method used for the same purpos

Non-Blocking Signature of very large SOAP Messages

Data transfer and staging services are common components in Grid-based, or more generally, in service-oriented applications. Security mechanisms play a central role in such services, especially when they are deployed in sensitive application fields like e-health. The adoption of WS-Security and related standards to SOAP-based transfer services is, however, problematic as a straightforward adoption of SOAP with MTOM introduces considerable inefficiencies in the signature generation process when large data sets are involved. This paper proposes a non-blocking, signature generation approach enabling a stream-like processing with considerable performance enhancements.Comment: 13 pages, 5 figure

Real-time food intake classification and energy expenditure estimation on a mobile device

© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment

Non-Blocking Signature of very large SOAP Messages

Data transfer and staging services are common components in Grid-based, or more generally, in service-oriented applications. Security mechanisms play a central role in such services, especially when they are deployed in sensitive application fields like e-health. The adoption of WS-Security and related standards to SOAP-based transfer services is, however, problematic as a straightforward adoption of SOAP with MTOM introduces considerable inefficiencies in the signature generation process when large data sets are involved. This paper proposes a non-blocking, signature generation approach enabling a stream-like processing with considerable performance enhancements.Comment: 13 pages, 5 figure

Order Submission: The Choice between Limit and Market Orders

Most financial markets allow investors to submit both limit and market orders, but it is not always clear what affects the choice of order type. The authors empirically investigate how the time between order submissions, changes in the state of the order book, and price uncertainty influence the rate of submission of limit and market orders. The authors measure the expected time (duration) between the submissions of orders of each type using an asymmetric autoregressive conditional duration model. They find that the execution of market orders, as well as changes in the level of price uncertainty and market depth, impact the submissions of both best limit orders and market orders. After correcting for these factors, the authors also find differences in behaviour around market openings, closings, and unexpected events that may be related to changes in information flows at these times. In general, traders use more market (limit) orders at times when execution risk for limit orders is highest or the risk of unexpected price movements is highest.Exchange rate; Financial institution; Market structure and pricing

A Structural Error-Correction Model of Best Prices and Depths in the Foreign Exchange Limit Order Market

Traders using the electronic limit order book in the foreign exchange market can watch the posted price and depth of the best quotes change over the day. The authors use a structural errorcorrection model to examine the dynamics of the relationship between the best bid price, the best ask price, and their associated depths. They incorporate measures of the market depth behind the best quotes as regressors. They report four main findings. First, best prices and their associated depths are contemporaneously related to each other. More specifically, an increase in the ask (bid) price is associated with a drop (rise) in the ask (bid) depth. This suggests that sell traders avoid the adverse-selection risk of selling in a rising market. Second, when the spread-the error-correction term-widens, the bid price rises and the ask price drops, returning the spread to its long-term equilibrium value. Further, the best depth on both sides of the market drops, due to increased market uncertainty. Third, the lagged best depth impacts the price discovery on both sides of the market, with the effect being strongest on the same side of the market. Fourth, changes in the depth behind the best quotes impact both the best prices and quantities, even though those changes are unobservable to market participants.Exchange rates; Financial markets

Order Aggressiveness and Quantity: How Are They Determined in a Limit Order Market?

Dealers trading in a limit order market must choose both the order aggressiveness and the quantity for their orders. We empirically investigate how dealers jointly make these decisions in the foreign exchange market using a unique simultaneous equations model. The model uses an ordered probit model to account for the discrete nature of order aggressiveness and a censored regression model to capture the clustering of orders placed at the smallest available quantity, $1 million. We find evidence of a clear trade-off between order aggressiveness and quantity: more aggressive orders tend to be smaller in size. The increased competition (demand) suggested by increased depth on the same (opposite) side of the market leads to less (more) aggressive orders in smaller (larger) size. This holds for the depths at both the best and off-best prices, even though off-best depths are not observable to dealers.Exchange rates; Financial markets

Only reasoned action? An interorganizational study of energy-saving behaviors in office buildings

Substantial energy savings can be achieved by reducing energy use in office buildings. The reported study used a Theory of Planned Behavior (TPB) model extended with perceived habit to explain office energy-saving behaviors. One aim was to examine if organizational contextual variability independently predicted office energy-saving behaviors over and above TPB variables and self-reported habit. Another aim was to examine the relative predictive value of TPB variables and habit for energy-saving behaviors between organizational contexts. Survey data on energy-saving behaviors, TPB variables, and habit and number of office mates were collected from office workers of four organizations in the Netherlands. The results indicate that intention was the strongest direct predictor of the behaviors printing smaller and not printing e-mails, whereas habit was the strongest predictor of the behaviors switching off lights and switching off monitors. Of the social-cognitive factors, attitude was the strongest predictor of intentions overall. The effect of perceived norm varied widely between behaviors and subgroups. Number of office mates had a direct, unmediated effect on the behavior switching off lights and a mediated effect via attitude and perceived control. The effect of organizational contextual variability on behavior was entirely mediated through the psychosocial factors for the two ‘printing behaviors’, but only partially for the two ‘switching behaviors’. The relative predictive value of habit and intention differed between organizations. The findings suggest that organizational contextual variability has unconscious influences on some office energy-saving behaviors. Interventions should take variation in the relative importance of cognitive factors and habit between behaviors, and to a lesser extent between organizational contexts, into account

The sources and nature of long-term memory in aggregate output

This article examines the stochastic properties of aggregate macroeconomic time series from the standpoint of fractionally integrated models, focusing on the persistence of economic shocks. The authors develop a simple macroeconomic model that exhibits long-range dependence, a consequence of aggregation in the presence of real business cycles. To implement these results empirically, they employ a test for fractionally integrated time series based on the Hurst-Mandelbrot rescaled range. This test is robust to short-range dependence and is applied to quarterly and annual real GDP to determine the sources and nature of long-range dependence in the business cycle.Macroeconomics ; Econometric models

The sources and nature of long-term memory in the business cycle

This paper examines the stochastic properties of aggregate macroeconomic time series from the standpoint of fractionally integrated models, focusing on the persistence of economic shocks. We develop a simple macroeconomic model that exhibits long-range dependence, a consequence of aggregation in the presence of real business cycles. We then derive the relation between properties of fractionally integrated macroeconomic time series and those of microeconomic data and discuss how fiscal policy may alter the stochastic behavior of the former. To implement these results empirically, we employ a test for fractionally integrated time series based on the Hurst-Mandelbrot rescaled range. This test, which is robust to short-term dependence, is applied to quarterly and annual real GNP to determine the sources and nature of long-term dependence in the business cycle..Business cycles ; Time-series analysis