Luis Blando

MIT Thesis, with Dan Ariely (advisor)
Perhaps the most exciting aspect of electronic commerce is the potential ability to learn the preferences of individual customers and make tailored recommendations to them. When making such recommendations the recommendation system is facing a fundamental dilemma. Should it provide the best recommendation based on its current state of understanding of the customer or should it try to learn more about the customer in order to provide higher potential… Show more

MIT Thesis, with Dan Ariely (advisor)
Perhaps the most exciting aspect of electronic commerce is the potential ability to learn the preferences of individual customers and make tailored recommendations to them. When making such recommendations the recommendation system is facing a fundamental dilemma. Should it provide the best recommendation based on its current state of understanding of the customer or should it try to learn more about the customer in order to provide higher potential payoffs in the future? This dilemma is at the heart of the current work. The dilemma facing a recommendation system is presented conceptually, and an approach for ideal learning is proposed and tested. In order to test our hypothesis, we modified one commercially available recommendation engine to consider measures of novelty in an initial learning phase. We analyzed results from the normal and modified engine for different datasets and characteristics of customers. Show less

Automatic form validation enables telecommunication carriers to process incoming service order requests more effectively. Validation rules, however, can be nontrivial to test and ultimately depend on the carrier's internal software systems. Traditionally, these validation checks are spread throughout an application's source code, which makes maintaining and evolving the system a very complex task. Our approach to solving this problem involves decoupling the rules, giving them a simple… Show more

Automatic form validation enables telecommunication carriers to process incoming service order requests more effectively. Validation rules, however, can be nontrivial to test and ultimately depend on the carrier's internal software systems. Traditionally, these validation checks are spread throughout an application's source code, which makes maintaining and evolving the system a very complex task. Our approach to solving this problem involves decoupling the rules, giving them a simple, easy-to-understand representation, and creating an engine to apply these rules to incoming forms automatically. This paper presents our approach in detail, explains its parallelism and briefly presents the differences with other common rule-based engines. Show less

Decoupling behavior modeling from a specific inheritance hierarchy has become one of the challenges for object-oriented software engineering. The goal is to encapsulate behavior on its own, and yet be able to freely apply it to a given class structure. We claim that standard object-oriented languages do not directly address this problem and propose the concept of Personalities as a design and programming artifice to model stand alone behavior. Allowing behavior to stand alone enables its reuse… Show more

Decoupling behavior modeling from a specific inheritance hierarchy has become one of the challenges for object-oriented software engineering. The goal is to encapsulate behavior on its own, and yet be able to freely apply it to a given class structure. We claim that standard object-oriented languages do not directly address this problem and propose the concept of Personalities as a design and programming artifice to model stand alone behavior. Allowing behavior to stand alone enables its reuse in different places in an inheritance hierarchy Show less

Thesis, with Karl Lieberherr (advisor). Decoupling behavior modeling from a specific inheritance hierarchy is one of the challenges for object-oriented software engineering. The goal is to encapsulate behavior on its own, and yet be able to freely apply it to a given class structure. We claim that standard object-oriented languages do not directly address this problem and propose the concept of Personalities as a design and programming artifice to model stand alone behavior that embodies what… Show more

Thesis, with Karl Lieberherr (advisor). Decoupling behavior modeling from a specific inheritance hierarchy is one of the challenges for object-oriented software engineering. The goal is to encapsulate behavior on its own, and yet be able to freely apply it to a given class structure. We claim that standard object-oriented languages do not directly address this problem and propose the concept of Personalities as a design and programming artifice to model stand alone behavior that embodies what we have termed micro-framework style of programming. Allowing behavior to stand alone enables its reuse in different places in an inheritance hierarchy. The micro-framework style ensures that the semantics are preserved during reuse. Furthermore, we show how Personalities can help solve the problem of object migration and how they can easily integrate with frameworks. We present two different Personalities implementations by extending the Java Programming Language. Show less

A classifier for predicting the character accuracy achieved by any Optical Character Recognition (OCR) system on a given page is presented. This classifier is based on measuring the amount of white speckle, the amount of character fragments, and overall size information in the page. No output from the OCR system is used. The given page is classified as either "good" quality (i.e. high OCR accuracy expected) or "poor" (i.e. low OCR accuracy expected). Results of processing 639 pages show a… Show more

A classifier for predicting the character accuracy achieved by any Optical Character Recognition (OCR) system on a given page is presented. This classifier is based on measuring the amount of white speckle, the amount of character fragments, and overall size information in the page. No output from the OCR system is used. The given page is classified as either "good" quality (i.e. high OCR accuracy expected) or "poor" (i.e. low OCR accuracy expected). Results of processing 639 pages show a recognition rate of approximately 85%. This performance compares favorably with the ideal-case performance of a prediction method based upon the number of reject-markers in OCR generated text. Show less

The accuracy of all contemporary OCR technologies varies drastically as a function of input image quality. Given high quality images, many devices consistently deliver output text in excess of 99% correct. For low quality images, even images which are easily read by a human, output accuracy is frequently below 90%. This extreme sensitivity to quality is well known in the document analysis field and is the subject of much current research. In this ongoing project, we have been interested in… Show more

The accuracy of all contemporary OCR technologies varies drastically as a function of input image quality. Given high quality images, many devices consistently deliver output text in excess of 99% correct. For low quality images, even images which are easily read by a human, output accuracy is frequently below 90%. This extreme sensitivity to quality is well known in the document analysis field and is the subject of much current research. In this ongoing project, we have been interested in developing measures of image quality. We are specially interested in learning to predict OCR accuracy using some combination of image quality measures independent of OCR devices themselves. Reliable algorithms for measuring print quality and predicting OCR accuracy would be valuable in several ways. First, in large scale document conversion operations, they could be used to automatically filter out pages that are more economically recovered via manual entry. Second, they might be employed iteratively as part of an adaptive image enhancement system. At the same time, studies into the nature of image quality can contribute to our overall understanding of the effect of noise on algorithms for classification. In this paper, we propose a prediction technique based upon measuring features associated with degraded characters. In order to limit the scope of the research, the following assumptions are made: (a) Pages are printed in black and white (no color); (b) page images have been segmented, and text regions have been correctly identified. The image-based prediction system extracts information from text regions only. This prediction system simply classifies the input images as either good (i.e. high accuracy expected) or poor (i.e. low accuracy expected). Show less

Thesis, with Junichi Kanai (advisor). A classifier to determine page quality from an Optical Character Recognition (OCR) perspective is developed. It classifies a given page image as either "good" (i.e., high OCR accuracy is expected) or "bad" (i.e., low OCR accuracy expected). The classifier is based upon measuring the amount of white speckle, the amount of broken pieces, and the overall size information in the page. Two different sets of test data were used to evaluate the classifier: the… Show more

Thesis, with Junichi Kanai (advisor). A classifier to determine page quality from an Optical Character Recognition (OCR) perspective is developed. It classifies a given page image as either "good" (i.e., high OCR accuracy is expected) or "bad" (i.e., low OCR accuracy expected). The classifier is based upon measuring the amount of white speckle, the amount of broken pieces, and the overall size information in the page. Two different sets of test data were used to evaluate the classifier: the Sample 2 dataset containing 439 pages and the Magazines dataset containing 200 pages. The classifier recognized 85%of the pages in the Sample 2 correctly. However, approximately 40%of the low quality pages were misclassified as "good." To solve this problem, the classifier was modified to reject pages containing tables or less than 200 connected components. The modified classifier rejected 41%of the pages, correctly recognized 86%of the remaining pages, and did not misclassify any low quality page as "good". Similarly, it recognized 86.5%of the pages in the Magazine dataset correctly and did not misclassify any low quality page as "good" without any rejections. Show less