Predicting and improving the recognition of emotions

The technological world is moving towards more effective and friendly human computer interaction. A key factor of these emerging requirements is the ability of future systems to recognise human emotions, since emotional information is an important part of human-human communication and is therefore expected to be essential in natural and intelligent human-computer interaction. Extensive research has been done on emotion recognition using facial expressions, but all of these methods rely mainly on the results of some classifier based on the apparent expressions. However, the results of classifier may be badly affected by the noise including occlusions, inappropriate lighting conditions, sudden movement of head and body, talking, and other possible problems. In this paper, we propose a system using exponential moving averages and Markov chain to improve the classifier results and somewhat predict the future emotions by taking into account the current as well as previous emotions

Cost-effectiveness of traffic enforcement: case study from Uganda

BACKGROUND: In October 2004, the Ugandan Police department deployed enhanced traffic safety patrols on the four major roads to the capital Kampala. OBJECTIVE: To assess the costs and potential effectiveness of increasing traffic enforcement in Uganda. METHODS: Record review and key informant interviews were conducted at 10 police stations along the highways that were patrolled. Monthly data on traffic citations and casualties were reviewed for January 2001 to December 2005; time series (ARIMA) regression was used to assess for a statistically significant change in traffic deaths. Costs were computed from the perspective of the police department in $US 2005. Cost offsets from savings to the health sector were not included. RESULTS: The annual cost of deploying the four squads of traffic patrols (20 officers, four vehicles, equipment, administration) is estimated at$72,000. Since deployment, the number of citations has increased substantially with a value of $327 311 annually. Monthly crash data pre- and post-intervention show a statistically significant 17$603 per death averted or $27 per life year saved discounted at 3$300 GDP per capita). CONCLUSION: The costs of traffic safety enforcement are low in comparison to the potential number of lives saved and revenue generated. Increasing enforcement of existing traffic safety norms can prove to be an extremely cost-effective public health intervention in low-income countries, even from a government perspective

A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator

Panel data characterized by groupwise heteroscedasticity, cross-sectional correlation, and AR(1) serial correlation pose problems for econometric analyses. It is well known that the asymptotically efficient, FGLS estimator (Parks) sometimes performs poorly in finite samples. In a widely cited paper, Beck and Katz (1995) claim that their estimator (PCSE) is able to produce more accurate coefficient standard errors without any loss in efficiency in ¡°practical research situations.¡± This study disputes that claim. We find that the PCSE estimator is usually less efficient than Parks -- and substantially so -- except when the number of time periods is close to the number of cross-sections.Panel data estimation; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample

Dairying in the Waikato Region of New Zealand: An overview of historical statistics

The dairy industry is an important contributor to the economy of the Waikato region of New Zealand. An understanding of the history and development of the dairy sector in the different districts of the Waikato region is important in terms of informing future policy. Unfortunately there are currently no consistent long-run spatially disaggregated data sets available for the districts of the Waikato region that extend any further back than 1990. In this paper, we present the current state of dairy farming data available for the territorial local authorities within the Waikato region, and briefly discuss a set of methods that will be employed to develop consistent long-run spatially disaggregated data series for (i) milk production; (ii) total number of productive dairy cattle; (iii) total number of dairy farms; and (iv) total effective hectares devoted to dairy production

Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think

We show that the common practice of converting 1-day volatility estimates to h-day estimates by scaling by the sqaure root of h is inappropriate and produces overestimates of the variability of long-horizon volatility. We conclude that volatility models are best tailored to tasks: if interest centers on long-horizon volatility, then a long-horizon volatility model should be used. Economic considerations, however, confound even that prescription and point to important directions for future research.

A Limited Information Bayesian Forecasting Model of the Cattle SubSector

The first step towards forecasting the price and output of the cattle industry is understanding the dynamics of the livestock production process. This study follows up on the Weimar and Stillman (1990) paper by using data from 1970 to 2005 to estimate the parameters that characterizes the cattle output supply. The model is then used to estimate forecast values for the periods 2006 and 2007. Bayesian limited information likelihood method is used to estimate the parameters when endogeneity exists between these variables. The forecasting ability of the model for a two-step ahead forecast for majority of the variables are relatively good and test statistic of the forecast are reported.Cattle, Bayesian, forecasting, Inventory, Slaughter, Agribusiness, Agricultural Finance, Financial Economics, Livestock Production/Industries, Marketing, Production Economics, Research Methods/ Statistical Methods,

Stock returns and expected business conditions : half a century of direct evidence

We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R2. Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk aversion. Klassifikation: G1