Tabulation of multiple responses

Although multiple response questions are quite common in survey research, Stata's official release does not provide much possibility for an effective analysis of multiple response variables. For example, in a study on drug addiction an interview question might be, "Which substances did you consume during the last four weeks?" The respondents just list all the drugs they took if any, e.g., an answer could be "cannabis, cocaine, heroin" or "ecstasy, cannabis" or "none", etc. Usually, the responses to such questions are held as a set of variables and, therefore, cannot be easily tabulated. I will address this issue and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results.

Does the use of multiple FTAs force firms to raise local input share? : evidence of the spaghetti bowl phenomenon

This paper empirically investigates the firm-level relationship between the local input share and the number of used FTAs by employing the data on FTA utilization in Japanese affiliates in ASEAN. As a result, we do not find a robust linear relationship. However, affiliates using a large number of FTAs (seven or eight) have an extremely higher share of local inputs. This result might be interpreted as the first evidence of the “spaghetti bowl phenomenon”.

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process

Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

Methodology for automated generation of multiple choice questions in self assessment

Current trends in the European Higher Education Area (EHEA) are moving towards the continuous evaluation of the students in substitution of the traditional evaluation based on a single test or exam. This fact and the increase in the number of students during last years in Engineering Schools, requires to modify evaluation procedures making them compatible with the educational and research activities. This work presents a methodology for the automatic generation of questions. These questions can be used as self assessment questions by the student and/or as queries by the teacher. The proposed approach is based on the utilization of parametric questions, formulated as multiple choice questions and generated and supported by the utilization of common programs of data sheets and word processors. Through this approach, every teacher can apply the proposed methodology without the use of programs or tools different from those normally used in his/her daily activity

Implementation of multiple specialization in logic programs

We study the múltiple specialization of logic programs based on abstract interpretation. This involves in general generating several versions of a program predícate for different uses of such predícate, making use of information obtained from global analysis performed by an abstract interpreter, and finally producing a new, "multiply specialized" program. While the topic of múltiple specialization of logic programs has received considerable theoretical attention, it has never been actually incorporated in a compiler and its effects quantified. We perform such a study in the context of a parallelizing compiler and show that it is indeed a relevant technique in practice. Also, we propose an implementation technique which has the same power as the strongest of the previously proposed techniques but requires little or no modification of an existing abstract interpreter.