Dissertations / Theses on the topic 'Consumers Classification Computer programs'

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 133-136).
Scratch is a graphical programming platform that empowers children to create computer programs and realize their ideas. Although the Scratch online community is filled with a variety of diverse projects, many of these projects also share similarities. For example, they tend to fall into certain categories, including games, animations, stories, and more. Throughout this thesis, I describe the application of Natural Language Processing (NLP) techniques to vectorize and classify Scratch projects by type. This effort included constructing a labeled dataset of 873 Scratch projects and their corresponding types, to be used for training a supervised classifier model. This dataset was constructed through a collective process of consensus-based annotation by experts. To realize the goal of classifying Scratch projects by type, I first train an unsupervised model of meaningful vector representations for Scratch blocks based on the composition of 500,000 projects. Using the unsupervised model as a basis for representing Scratch blocks, I then train a supervised classifier model that categorizes Scratch projects by type into one of: "animation", "game", and "other". After an extensive hyperparameter tuning process, I am able to train a classifier model with an F1 Score of 0.737. I include in this paper an in-depth analysis of the unsupervised and supervised models, and explore the different elements that were learned during training. Overall, I demonstrate that NLP techniques can be used in the classification of computer programs to a reasonable level of accuracy.
by Lena Abdalla.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

Malicious software authors have shifted their focus from illegal and clearly malicious software to potentially unwanted programs (PUPs) to earn revenue. PUPs blur the border between legitimate and illegitimate programs and thus fall into a grey zone. Existing anti-virus and anti-spyware software are in many instances unable to detect previously unseen or zero-day attacks and separate PUPs from legitimate software. Many tools also require frequent updates to be effective. By predicting the class of particular piece of software, users can get support before taking the decision to install the software. This Licentiate thesis introduces approaches to distinguish PUP from legitimate software based on the supervised learning of file features represented as n-grams. The overall research method applied in this thesis is experiments. For these experiments, malicious software applications were obtained from anti-malware industrial partners. The legitimate software applications were collected from various online repositories. The general steps of supervised learning, from data preparation (n-gram generation) to evaluation were, followed. Different data representations, such as byte codes and operation codes, with different configurations, such as fixed-size, variable-length, and overlap, were investigated to generate different n-gram sizes. The experimental variables were controlled to measure the correlation between n-gram size, the number of features required for optimal training, and classifier performance. The thesis results suggest that, despite the subtle difference between legitimate software and PUP, this type of software can be classified accurately with a low false positive and false negative rate. The thesis results further suggest an optimal size of operation code-based n-grams for data representation. Finally, the results indicate that classification accuracy can be increased by using a customized ensemble learner that makes use of multiple representations of the data set. The investigated approaches can be implemented as a software tool with a less frequently required update in comparison to existing commercial tools.

The purpose of the proposed thesis is to:1. Define expert systems and discuss various implementation techniques for the components of expert systems. This includes discussion on knowledge representation, inference methods, methods for dealing with uncertainty, and methods of explanation. Specifically, the focus will be on the implementation of rule-based expert systems;2. Apply selected expert system techniques to a case study. The case study will be a rule-based expert system in Prolog to recognize and identify musical chords from tonal harmony. The system will have a general knowledge base containing fundamental rules about chord construction. It will also contain some knowledge that will allow it to deduce non-trivial chords. Furthermore, it will contain procedures to deal with uncertainty and explanation;3. Explain general concepts about music theory and tonal chord classification to put the case study in context; and4. Discuss the limitations of expert systems based on the results of the case study and the current literature.
Department of Computer Science

This paper deals with the visualization of numerical results as a very convenient method to understand and evaluate a solution which has been calculated as a set of millions of numerical values. One of the central research fields of the Chemnitz SFB 393 is the analysis of parallel numerical algorithms for large systems of linear equations arising from differential equations (e.g. in solid and fluid mechanics). Solving large problems on massively parallel computers makes it more and more impossible to store numerical data from the distributed memory of the parallel computer to the disk for later postprocessing. However, the developer of algorithms is interested in an on-line response of his algorithms. Both visual and numerical response of the running program may be evaluated by the user for a decision how to switch or adjust interactively certain parameters that may influence the solution process. The paper gives a survey of current programmer and user interfaces that are used in our various 2D and 3D parallel finite element programs for the visualization of the solution.

Environmental response to acidic deposition occurs through the vehicle of water movement in the ecosystem. As a part of the environmental studies for acidic deposition in the ecosystem, output-based hydrologic classification was done from basin hydrologies based on the distribution of the baseflow, snowmelt, and the direct runoff sources. Because of the differences in the flow paths and exposure duration, those components were assumed to represent distinct geochemical responses. As a first step, user-friendly software has been developed to calculate the baseflow based on the separation of annual hydrographs. It also generates the hydrograph for visual analysis using trial separation slope. After the software was completed, about 1200 stream flow gauging stations in Northeastern U.S. were accessed for flow separation and other hydrologic characteristics. At the final stage, based on the output from the streamflow analysis, cluster analysis was performed to classify the streamflow behaviors in terms of acidic inflow. The output from the cluster analysis shows more efficient regional boundaries of the subregions than the current regional boundaries used by U.S. Environmental Protection Agency (U.S.E.P.A.) for the environmental management in terms of acidic deposition based on the regional baseflow properties.

The report presents an implemention of a classification algorithm for the Institutional Analysis Project. The algorithm used in this project is the decision tree classification algorithm which uses a gain ratio attribute selectionmethod. The algorithm discovers the hidden rules from the student records, which are used to predict whether or not other students are at risk of dropping out. It is shown that special rules exist in different data sets, each with their natural hidden knowledge. In other words, the rules that are obtained depend on the data that is used for classification. In our preliminary experiments, we show that between 55-78 percent of data with unknown class lables can be correctly classified, using the rules obtained from data whose class labels are known. We feel this is acceptable, given the large number of records, attributes, and attribute values that are used in the experiments. The project results are useful for large data set analysis.
viii, 38 leaves ; 29 cm. --

"SELECT CD is a site-specific, decision-support tool for selecting ecologically viable tree species, reproduction cuttings, and regeneration methods in the coastal forest (CDF, CWH, and MH zones). SELECT CD integrates information from several existing guides with new information from literature and recent research into a single, user-friendly resource. SELECT CD also includes a rich library of visuals and an illustrated glossary of technical terms."

Les différentes populations de granis des agrégats polycristallins monominéraux déformés et cristallises sont classés suivant leur forme d'après le diagramme de blaschke. On étudie les polyèdres trivalents à 13 et 14 faces grâces a un logiciel de calcul des coordonnées des sommets. On peut aussi déduire les caractéristiques d'un polyèdre a partir de ses coordonnées ainsi que des simulations numériques d'intercepts linéaires et planauss du polyèdre isole et de l'agrégat qu'il engendre s'il est empilable

This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with specified error rates, large dataset classification and Ordinal Regression (OR). Using moments of training data, the CCPs are posed as Second Order Cone Programs (SOCPs). Novel iterative algorithms for solving the resulting SOCPs are also derived. Borrowing ideas from robust optimization theory, the proposed formulations are made robust to moment estimation errors. A maximum margin classifier with specified false positive and false negative rates is derived. The key idea is to employ chance-constraints for each class which imply that the actual misclassification rates do not exceed the specified. The formulation is applied to the case of biased classification. The problems of large dataset classification and ordinal regression are addressed by deriving formulations which employ chance-constraints for clusters in training data rather than constraints for each data point. Since the number of clusters can be substantially smaller than the number of data points, the resulting formulation size and number of inequalities are very small. Hence the formulations scale well to large datasets. The scalable classification and OR formulations are extended to feature spaces and the kernelized duals turn out to be instances of SOCPs with a single cone constraint. Exploiting this speciality, fast iterative solvers which outperform generic SOCP solvers, are proposed. Compared to state-of-the-art learners, the proposed algorithms achieve a speed up as high as 10000 times, when the specialized SOCP solvers are employed. The proposed formulations involve second order moments of data and hence are susceptible to moment estimation errors. A generic way of making the formulations robust to such estimation errors is illustrated. Two novel confidence sets for moments are derived and it is shown that when either of the confidence sets are employed, the robust formulations also yield SOCPs.

Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained object classification. The contributions of the thesis are three-fold. First, we introduce two databases of high-resolution images of arthropod specimens we collected to promote the development of highly accurate fine-grained recognition methods. Second, we give a literature review on the development of Bag-of-words (BOW) approaches to image classification and present the stacked evidence tree approach we developed for the fine-grained classification task. We draw connections and analyze differences between those two genres of approaches, which leads to a better understanding about the design of image classification approaches. Third, benchmark results on our two datasets are pre- sented. We further analyze the influence of two important variables on the performance of fine-grained classification. The experiments corroborate our hypotheses that a) high resolution images and b) more aggressive information extraction, such as finer descriptor encoding with large dictionaries or classifiers based on raw descriptors, is required to achieve good fine-grained categorization accuracy.
Graduation date: 2013

Software migrations are mostly performed by organisations using migration teams. Such migration teams need to be aware of how sensitive information ought to be handled and protected during the implementation of the migration projects. There is a need to ensure that sensitive information is identified, classified and protected during the migration process. This thesis suggests how sensitive information in organisations can be handled and protected during migrations by using the migration from proprietary software to open source software to develop a management framework that can be used to manage such a migration process.A rudimentary management framework on information sensitivity during software migrations and a model on the security challenges during open source migrations are utilised to propose a preliminary management framework using a sequential explanatory mixed methods case study. The preliminary management framework resulting from the quantitative data analysis is enhanced and validated to conceptualise the final management framework on information sensitivity during software migrations at the end of the qualitative data analysis. The final management framework is validated and found to be significant, valid and reliable by using statistical techniques like Exploratory Factor Analysis, reliability analysis and multivariate analysis as well as a qualitative coding process.
Information Science
D. Litt. et Phil. (Information Systems)

Indiana University-Purdue University Indianapolis (IUPUI)
Our connections to the digital world are invoked by brands, but the intersection of branding and interaction design is still an under-investigated area. Particularly, current websites are designed not only to support essential user tasks, but also to communicate an institution's intended brand values and traits. What we do not yet know, however, is which design factors affect which aspect of a brand. To demystify this issue, three sub-projects were conducted. The first project developed a systematic approach for evaluating the branding effectiveness of content-intensive websites (BREW). BREW gauges users' brand perceptions on four well-known branding constructs: brand as product, brand as organization, user image, and brand as person. It also provides rich guidelines for eBranding researchers in regard to planning and executing a user study and making improvement recommendations based on the study results. The second project offered a standardized perceived usability questionnaire entitled DEEP (design-oriented evaluation of perceived web usability). DEEP captures the perceived website usability on five design-oriented dimensions: content, information architecture, navigation, layout consistency, and visual guidance. While existing questionnaires assess more holistic concepts, such as ease-of-use and learnability, DEEP can more transparently reveal where the problem actually lies. Moreover, DEEP suggests that the two most critical and reliable usability dimensions are interface consistency and visual guidance. Capitalizing on the BREW approach and the findings from DEEP, a controlled experiment (N=261) was conducted by manipulating interface consistency and visual guidance of an anonymized university website to see how these variables may affect the university's image. Unexpectedly, consistency did not significantly predict brand image, while the effect of visual guidance on brand perception showed a remarkable gender difference. When visual guidance was significantly worsened, females became much less satisfied with the university in terms of brand as product (e.g., teaching and research quality) and user image (e.g., students' characteristics). In contrast, males' perceptions of the university's brand image stayed the same in most circumstances. The reason for this gender difference was revealed through a further path analysis and a follow-up interview, which inspired new research directions to unpack even more the nexus between branding and interaction design.

Main purpose of this thesis is to pursue the question whether it is possible to find differences (and if so, which) in attitudes towards computer (or rather digital) games resulting from various family socio-cultural backgrounds and gender. In order to explore this, the thesis analyzes selected gaming behaviors along with the socio-cultural environment and gender stereotypes shaping the gamers. The research study focuses mainly on gamers' preference in choosing the game type, reflection of their approach and ways of dealing with this phenomenon exhibited by their parents. This thesis does not aim to state any general theory; it is rather attempting to offer specific insight into this issue. Keywords: digital games, computer games, games' classification, computer games entertainment, parental attitudes towards gaming, family socio-cultural standard, gender stereotypes, computer games usefulness/ harmfulness in its consumers, virtual communication in computer games.