Investigating effort prediction of web-based applications using CBR on the ISBSG dataset

As web-based applications become more popular and more sophisticated, so does the requirement for early accurate estimates of the effort required to build such systems. Case-based reasoning (CBR) has been shown to be a reasonably effective estimation strategy, although it has not been widely explored in the context of web applications. This paper reports on a study carried out on a subset of the ISBSG dataset to examine the optimal number of analogies that should be used in making a prediction. The results show that it is not possible to select such a value with confidence, and that, in common with other findings in different domains, the effectiveness of CBR is hampered by other factors including the characteristics of the underlying dataset (such as the spread of data and presence of outliers) and the calculation employed to evaluate the distance function (in particular, the treatment of numeric and categorical data)

Measurement error and imputation of consumption in survey data

We study how estimators used to impute consumption in survey data are inconsistent due to measurement error in consumption. Previous research suggests instrumenting consumption to overcome this problem. We show that, if additional regressors are present, then instrumenting consumption may still produce inconsistent estimators. This inconsistency arises from the correlation between additional regressors and measurement error. We propose an additional condition to be satisfied by the instrument that reduces measurement error bias. This condition is directly observable in the data. We apply our findings by revisiting recent research that imputes consumption data from the CEX to the PSID.Campos and Reggio gratefully acknowledge the financial support by the Spanish Ministerio de Ciencia y Tecnología (Grants ECO2009-13169 and ECO2009-11165) and Ministerio de Economía y Competitividad (grants ECO2012-38134 and ECO2012-31358)

Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters

We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both the spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hours before strong solar flare events

Relativistic model of hidden bottom tetraquarks

The relativistic model of the ground state and excited heavy tetraquarks with hidden bottom is formulated within the diquark-antidiquark picture. The diquark structure is taken into account by calculating the diquark-gluon vertex in terms of the diquark wave functions. Predictions for the masses of bottom counterparts to the charm tetraquark candidates are given.Comment: 6 page

Forensics’ Fight: A Need for Aggressive Strategies Against Confirmation Bias

In 2009, the National Academy of Sciences produced a lengthy report illuminating significant weaknesses present within the forensic community. One complex fault found in forensics was conformation bias. Since it is within human nature to make decisions based on contextual information, assumptions, and pre-held opinions, confirmation bias is an issue that will continue to persist. Therefore, stronger efforts must be made to recognize and abate the problem of bias within the field of forensics in order to preserve the notion that forensic science exists to serve principles of both truth and justice. Accordingly, this paper argues for the fight against bias to return to the forefront of forensic concern while providing a list of viable suggestions to help battle these unwarranted biases

3-D inelastic analysis methods for hot section components

The objective is to produce a series of new computer codes that permit more accurate and efficient three dimensional inelastic structural analysis of combustor liners, turbine blades, and turbine vanes. Each code embodies a progression of mathematical models for increasingly comprehensive representaion of the geometrical features, loading conditions, and forms of nonlinear material response that distinguish these three groups of hot section components

Audio Encryption Framework Using the Laplace Transformation

Digital information, especially multimedia and its applications, has grown exponentially in recent years. It is important to strengthen sophisticated encryption algorithms due to the security needs of these innovative systems. The security of real-time audio applications is ensured in the present study through a framework for encryption. The design framework protects the confidentiality and integrity of voice communications by encrypting audio applications. A modern method of securing communication and protecting data is cryptography. Using cryptography is one of the most important techniques for protecting data and ensuring the security of messaging. The main purpose of this paper is to present a novel encryption scheme that can be used in real-time audio applications. We encrypt the sound using a combination of an infinite series of hyperbolic functions and the Laplace transform, and then decrypt it using the inverse Laplace transform. The modular arithmetic rules are used to generate the key for the coefficients acquired from the transformation. There is no loss of data or noise in the decryption sound. We also put several sound examples to the tes