Dissertations / Theses on the topic 'Computer software Classification'
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1
Manley, Gary W. "The classification and evaluation of Computer-Aided Software Engineering tools." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/34910.
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Approved for public release; distribution unlimited.The use of Computer-Aided Software Engineering (CASE) tools has been viewed as a remedy for the software development crisis by achieving improved productivity and system quality via the automation of all or part of the software engineering process. The proliferation and tremendous variety of tools available have stretched the understanding of experienced practitioners and has had a profound impact on the software engineering process itself. To understand what a tool does and compare it to similar tools is a formidable task given the existing diversity of functionality. This thesis investigates what tools are available, proposes a general classification scheme to assist those investigating tools to decide where a tool falls within the software engineering process and identifies a tool's capabilities and limitations. This thesis also provides guidance for the evaluation of a tool and evaluates three commercially available tools.
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Williams, Byron Joseph. "A FRAMEWORK FOR ASSESSING THE IMPACT OF SOFTWARE CHANGES TO SOFTWARE ARCHITECTURE USING CHANGE CLASSIFICATION." MSSTATE, 2006. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04172006-150444/.
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Software developers must produce software that can be changed without the risk of degrading the software architecture. One way to address software changes is to classify their causes and effects. A software change classification mechanism allows engineers to develop a common approach for handling changes. This information can be used to show the potential impact of the change. The goal of this research is to develop a change classification scheme that can be used to address causes of architectural degradation. This scheme can be used to model the effects of changes to software architecture. This research also presents a study of the initial architecture change classification scheme. The results of the study indicated that the classification scheme was easy to use and provided some benefit to developers. In addition, the results provided some evidence that changes of different types (in this classification scheme) required different amounts of effort to implement.
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Mahmood, Qazafi. "LC - an effective classification based association rule mining algorithm." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24274/.
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Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider.
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Lee, Kee Khoon. "Interpretable classification model for automotive material fatigue." Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/361578/.
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Nguyen, Victor Allen. "A Simplified Faceted Approach To Information Retrieval for Reusable Software Classification." NSUWorks, 1998. http://nsuworks.nova.edu/gscis_etd/749.
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Software Reuse is widely recognized as the most promising technique presently available in reducing the cost of software production. It is the adaptation or incorporation of previously developed software components, designs or other software-related artifacts (i.e. test plans) into new software or software development regimes. Researchers and vendors are doubling their efforts and devoting their time primarily to the topic of software reuse. Most have focused on mechanisms to construct reusable software but few have focused on the problem of discovering components or designs to meet specific needs. In order for software reuse to be successful, it must be perceived to be less costly to discover a software component or related artifact to satisfy a given need than to discover one anew. As results, this study will describe a method to classify software components that meet a specified need.
Specifically, the purpose of the present research study is to provide a flexible system, comprised of a classification scheme and searcher system, entitled Guides-Search, in which processes can be retrieved by carrying out a structured dialogue with the user. The classification scheme provides both the structure of questions to be posed to the user, and the set of possible answers to each question. The model is not an attempt to replace current structures; but rather, seeks to provide a conceptual and structural method to support the improvement of software reuse methodology.
The investigation focuses on the following goals and objectives for the classification scheme and searcher system: the classification will be flexible and extensible, but usable by the Searcher; the user will not be presented with a large number of questions; the user will never be required to answer a question not known to be germane to the query;
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Graham, Martin. "Visualising multiple overlapping classification hierarchies." Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/2430.
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The revision or reorganisation of hierarchical data sets can result in many possible hierarchical classifications composed of the same or overlapping data sets existing in parallel with each other. These data sets are difficult for people to handle and conceptualise, as they try to reconcile the different perspectives and structures that such data represents. One area where this situation occurs is the study of botanical taxonomy, essentially the classification and naming of plants. Revisions, new discoveries and new dimensions for classifying plants lead to a proliferation of classifications over the same set of plant data. Taxonomists would like a method of exploring these multiple overlapping hierarchies for interesting information, correlations, or anomalies. The application and extension of Information Visualisation (IV) techniques, the graphical display of abstract information, is put forward as a solution to this problem. Displaying the multiple classification hierarchies in a visually appealing manner along with powerful interaction mechanisms for examination and exploration of the data allows taxonomists to unearth previously hidden information. This visualisation gives detail that previous visualisations and statistical overviews cannot offer. This thesis work has extended previous IV work in several respects to achieve this goal. Compact, yet full and unambiguous, hierarchy visualisations have been developed. Linking and brushing techniques have been extended to work on a higher class of structure, namely overlapping trees and hierarchies. Focus and context techniques have been pushed to achieve new effects across the visually distinct representations of these multiple hierarchies. Other data types, such as multidimensional data and large cluster hierarchies have also been displayed using the final version of the visualisation.
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Henningsson, Kennet. "A Fault Classification Approach to Software Process Improvement." Licentiate thesis, Karlskrona : Blekinge Institute of Technology [Blekinge tekniska högskola], 2005. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/2b9d5998e26ed1b2c12571230047386b?OpenDocument.
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Masood, Khalid. "Histological image analysis and gland modelling for biopsy classification." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3918/.
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The area of computer-aided diagnosis (CAD) has undergone tremendous growth in recent years. In CAD, the computer output is used as a second opinion for cancer diagnosis. Development of cancer is a multiphase process and mutation of genes is involved over the years. Cancer grows out of normal cells in the body and it usually occurs when growth of the cells in the body is out of control. This phenomenon changes the shape and structure of the tissue glands. In this thesis, we have developed three algorithms for classification of colon and prostate biopsy samples. First, we computed morphological and shape based parameters from hyperspectral images of colon samples and used linear and non-linear classifiers for the identification of cancerous regions. To investigate the importance of hyperspectral imagery in histopathology, we selected a single spectral band from its hyperspectral cube and performed an analysis based on texture of the images. Texture refers to an arrangement of the basic constituents of the material and it is represented by the interrelationships between the spatial arrangements of the image pixels. A novel feature selection method based on the quality of clustering is developed to discard redundant information. In the third algorithm, we used Bayesian inference for segmentation of glands in colon and prostate biopsy samples. In this approach, glands in a tissue are represented by polygonal models with variuos number of vertices depending on the size of glands. An appropriate set of proposals for Metropolis- Hastings-Green algorithm is formulated and a series of Markov chain Monte Carlo (MCMC) simulations are run to extract polygonal models for the glands. We demonstrate the performance of 3D spectral and spatial and 2D spatial analyses with over 90% classification accuracies and less than 10% average segmentation error for the polygonal models.
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Walia, Gursimran Singh. "Empirical Validation of Requirement Error Abstraction and Classification: A Multidisciplinary Approach." MSSTATE, 2006. http://sun.library.msstate.edu/ETD-db/theses/available/etd-05152006-151903/.
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Software quality and reliability is a primary concern for successful development organizations. Over the years, researchers have focused on monitoring and controlling quality throughout the software process by helping developers to detect as many faults as possible using different fault based techniques. This thesis analyzed the software quality problem from a different perspective by taking a step back from faults to abstract the fundamental causes of faults. The first step in this direction is developing a process of abstracting errors from faults throughout the software process. I have described the error abstraction process (EAP) and used it to develop error taxonomy for the requirement stage. This thesis presents the results of a study, which uses techniques based on an error abstraction process and investigates its application to requirement documents. The initial results show promise and provide some useful insights. These results are important for our further investigation.
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Albarrak, Abdulrahman. "Three-dimensional image classification using hierarchical spatial decomposition : a study using retinal data." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2006419/.
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This thesis describes research conducted in the field of image mining especially volumetric image mining. The study investigates volumetric representation techniques based on hierarchical spatial decomposition to classify three-dimensional (3D) images. The aim of this study was to investigate the effectiveness of using hierarchical spatial decomposition coupled with regional homogeneity in the context of volumetric data representation. The proposed methods involve the following: (i) decomposition, (ii) representation, (iii) single feature vector generation and (iv) classifier generation. In the decomposition step, a given image (volume) is recursively decomposed until either homogeneous regions or a predefined maximum level are reached. For measuring the regional homogeneity, different critical functions are proposed. These critical functions are based on histograms of a given region. Once the image is decomposed, two representation methods are proposed: (i) to represent the decomposition using regions identified in the decomposition (region-based) or (ii) to represent the entire decomposition (whole image-based). The first method is based on individual regions, whereby each decomposed sub-volume (region) is represented in terms of different statistical and histogram-based techniques. Feature vector generation techniques are used to convert the set of feature vectors for each sub-volume into a single feature vector. In the whole image-based representation method, a tree is used to represent each image. Each node in the tree represents a region (sub-volume) using a single value and each edge describes the difference between the node and its parent node. A frequent sub-tree mining technique was adapted to identified a set of frequent sub-graphs. Selected sub-graphs are then used to build a feature vector for each image. In both cases, a standard classifier generator is applied, to the generated feature vectors, to model and predict the class of each image. Evaluation was conducted with respect to retinal optical coherence tomography images in terms of identifying Age-related Macular Degeneration (AMD). Two types of evaluation were used: (i) classification performance evaluation and (ii) statistical significance testing using ANalysis Of VAriance (ANOVA). The evaluation revealed that the proposed methods were effective for classifying 3D retinal images. It is consequently argued that the approaches are generic.
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Worthy, Paul James. "Investigation of artificial neural networks for forecasting and classification." Thesis, City University London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264247.
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Winter, Mark J. "Knowledge refinement in constraint satisfaction and case classification problems." Thesis, University of Aberdeen, 1999. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU106810.
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Knowledge-Base Refinement (KBR) systems are an attempt to help with the difficulties of detecting and correcting errors in a knowledge-base. This thesis investigates Knowledge-Base Refinement within the two problem solving paradigms of Case-Based Reasoning and Constraint Based Reasoning. Case-Based Reasoners make use of cases which represent previous problem solving incidents. Constraint Satisfaction Problems are represented by a set of variables, the possible values these variables can take and a set of constraints further restricting their possible values. This thesis argues that if the problem-solving paradigms of Case-Based Reasoning and Constraint-Based Reasoning are to become truly viable, then research has to be directed at providing support for knowledge-base refinement, but aimed at the knowledge representation formalisms used by the two paradigms rather than more traditional rule-based representations. The CRIMSON system has been developed within the context of an industrial inventory management problem and uses constraint satisfaction techniques. The system makes use of design knowledge to form a constraint satisfaction problem (CSP) which is solved to determine which items from an inventory are suitable for a given problem. Additionally, the system is equipped with a KBR facility allowing the designer to criticise the results of the CSP, leading to knowledge being refined. The REFINER systems are knowledge-base refinement systems that detect and help remove inconsistencies in case-bases. The systems detect and report inconsistencies to domain expert together with a set of refinements which, if implemented would remove the appropriate inconsistency. REFINER+ attempts to overcome the problems associated with REFINER, mainly its inefficiency with large case-bases. The systems can make use of background knowledge to aid in the refinement process, although they can function without any. However, care must be taken to ensure that any background knowledge that is used is correct. If this is not the case, then the refinement process may be adversely affected. Countering this problem is the main aim of BROCKER, which further extends the ideas of REFINER+ to include a facility allowing incorrect background knowledge used to be refined in response to expert criticism of the system's performance. The systems were mainly developed making use of a medical dataset.
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Kaky, Ahmed Jasim Mohammed (Aljaaf). "Intelligent systems approach for classification and management of patients with headache." Thesis, Liverpool John Moores University, 2017. http://researchonline.ljmu.ac.uk/7418/.
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Primary headache disorders are the most common complaints worldwide. The socioeconomic and personal impact of headache disorders is enormous, as it is the leading cause of workplace absence. Headache patients’ consultations are increasing as the population has increased in size, live longer and many people have multiple conditions, however, access to specialist services across the UK is currently inequitable because the numbers of trained consultant neurologists in the UK are 10 times lower than other European countries. Additionally, more than two third of headache cases presented to primary care were labelled with unspecified headache. Therefore, an alternative pathway to diagnose and manage patients with primary headache could be crucial to reducing the need for specialist assessment and increase capacity within the current service model. Several recent studies have targeted this issue through the development of clinical decision support systems, which can help non-specialist doctors and general practitioners to diagnose patients with primary headache disorders in primary clinics. However, the majority of these studies were following a rule-based system style, in which the rules were summarised and expressed by a computer engineer. This style carries many downsides, and we will discuss them later on in this dissertation. In this study, we are adopting a completely different approach. The use of machine learning is recruited for the classification of primary headache disorders, for which a dataset of 832 records of patients with primary headaches was considered, originating from three medical centres located in Turkey. Three main types of primary headaches were derived from the data set including Tension Type Headache in both episodic and chronic forms, Migraine with and without Aura, followed by Trigeminal Autonomic Cephalalgia that further subdivided into Cluster headache, paroxysmal hemicrania and short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing. Six popular machine-learning based classifiers, including linear and non-linear ensemble learning, in addition to one regression based procedure, have been evaluated for the classification of primary headaches within a supervised learning setting, achieving highest aggregate performance outcomes of AUC 0.923, sensitivity 0.897, and overall classification accuracy of 0.843. This study also introduces the proposed HydroApp system, which is an M-health based personalised application for the follow-up of patients with long-term conditions such as chronic headache and hydrocephalus. We managed to develop this system with the supervision of headache specialists at Ashford hospital, London, and neurology experts at Walton Centre and Alder Hey hospital Liverpool. We have successfully investigated the acceptance of using such an M-health based system via an online questionnaire, where 86% of paediatric patients and 60% of adult patients were interested in using HydroApp system to manage their conditions. Features and functions offered by HydroApp system such as recording headache score, recording of general health and well-being as well as alerting the treating team, have been perceived as very or extremely important aspects from patients’ point of view. The study concludes that the advances in intelligent systems and M-health applications represent a promising atmosphere through which to identify alternative solutions, which in turn increases the capacity in the current service model and improves diagnostic capability in the primary headache domain and beyond.
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Ammari, Faisal Tawfiq. "Securing financial XML transactions using intelligent fuzzy classification techniques." Thesis, University of Huddersfield, 2013. http://eprints.hud.ac.uk/id/eprint/19506/.
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The eXtensible Markup Language (XML) has been widely adopted in many financial institutions in their daily transactions; this adoption was due to the flexible nature of XML providing a common syntax for systems messaging in general and in financial messaging in specific. Excessive use of XML in financial transactions messaging created an aligned interest in security protocols integrated into XML solutions in order to protect exchanged XML messages in an efficient yet powerful mechanism. However, financial institutions (i.e. banks) perform large volume of transactions on daily basis which require securing XML messages on large scale. Securing large volume of messages will result performance and resource issues. Therefore, an approach is needed to secure specified portions of an XML document, syntax and processing rules for representing secured parts. In this research we have developed a smart approach for securing financial XML transactions using effective and intelligent fuzzy classification techniques. Our approach defines the process of classifying XML content using a set of fuzzy variables. Upon fuzzy classification phase, a unique value is assigned to a defined attribute named "Importance Level". Assigned value indicates the data sensitivity for each XML tag. This thesis also defines the process of securing classified financial XML message content by performing element-wise XML encryption on selected parts defined in fuzzy classification phase. Element-wise encryption is performed using symmetric encryption using AES algorithm with different key sizes. Key size of 128-bit is being used on tags classified with "Medium" importance level; a key size of 256-bit is being used on tags classified with "High" importance level. An implementation has been performed on a real life environment using online banking system in Jordan Ahli Bank one of the leading banks in Jordan to demonstrate its flexibility, feasibility, and efficiency. Our experimental results of the system verified tangible enhancements in encryption efficiency, processing time reduction, and resulting XML message sizes. Finally, our proposed system was designed, developed, and evaluated using a live data extracted from an internet banking service in one of the leading banks in Jordan. The results obtained from our experiments are promising, showing that our model can provide an effective yet resilient support for financial systems to secure exchanged financial XML messages.
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Khan, Masood Mehmood. "Cluster-analytic classification of facial expressions using infrared measurements of facial thermal features." Thesis, University of Huddersfield, 2008. http://eprints.hud.ac.uk/id/eprint/732/.
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In previous research, scientists were able to use transient facial thermal features extracted from Thermal Infra-Red Images (TIRIs) for making binary distinction between the affective states. For example, thermal asymmetries localised in facial TIRIs have been used to distinguish anxiety and deceit. Since affective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states, computers’ able to make such binary distinctions would not suffice a robust human-computer interaction. This work, for the first time, uses affective-state-specific transient facial thermal features extracted from TIRIs to recognise a much wider range of facial expressions under a much wider range of conditions. Using infrared thermal imaging within the 8-14 μm, a database of 324 discrete, time-sequential, visible-spectrum and thermal facial images was acquired, representing different facial expressions from 23 participants in different situations. A facial thermal feature extraction and pattern classification approach was developed, refined and tested on various Gaussian mixture models constructed using the image database. Attempts were made to classify: neutral and pretended happy and sad faces; multiple positive and negative facial expressions; six (pretended) basic facial expressions; partially covered or occluded faces; and faces with evoked happiness, sadness, disgust and anger. The cluster-analytic classification in this work began by segmentation and detection of thermal faces in the acquired TIRIs. The affective-state-specific temperature distributions on the facial skin surface were realised through the pixel grey-level analysis. Examining the affectivestate- specific temperature variations within the selected regions of interest in the TIRIs led to the discovery of some significant Facial Thermal Feature Points (FTFPs) along the major facial muscles. Following a multivariate analysis of the Thermal Intensity values (TIVs) measured at the FTFPs, the TIRIs were represented along the Principal Components (PCs) of a covariance matrix. The resulting PCs were ranked in the order of their effectiveness in the between-cluster separation. Only the most effective PCs were retained to construct an optimised eigenspace. A supervised learning algorithm was invoked for linear subdivision of the optimised eigenspace. The statistical significance levels of the classification results were estimated for validating the discriminant functions. The main contribution of this research has been to show that: the infrared imaging of facial thermal features within the 8-14 μm bandwidth may be used to observe affective-state-specific thermal variations on the face; the pixel-grey level analysis of TIRIs can help localise FTFPs along the major facial muscles of the face; cluster-analytic classification of transient thermal features may help distinguish between the facial expressions of affective states in an optimized eigenspace of input thermal feature vectors. The Gaussian mixture model with one cluster per affect worked better for some facial expressions than others. This made the influence of the Gaussian mixture model structure on the accuracy of the classification results obvious. However, the linear discrimination and confusion patterns observed in this work were consistent with the ones reported in several earlier studies. This investigation also unveiled some important dimensions of the future research on use of facial thermal features in affective human-computer interaction.
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Otero, Fernando E. B. "New ant colony optimisation algorithms for hierarchial classification of protein functions." Thesis, University of Kent, 2010. http://www.cs.kent.ac.uk/pubs/2010/3057.
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Ant colony optimisation (ACO) is a metaheuristic to solve optimisation problems inspired by the foraging behaviour of ant colonies. It has been successfully applied to several types of optimisation problems, such as scheduling and routing, and more recently for the discovery of classification rules. The classification task in data mining aims at predicting the value of a given goal attribute for an example, based on the values of a set of predictor attributes for that example. Since real-world classification problems are generally described by nominal (categorical or discrete) and continuous (real-valued) attributes, classification algorithms are required to be able to cope with both nominal and continuous attributes. Current ACO classification algorithms have been designed with the limitation of discovering rules using nominal attributes describing the data. Furthermore, they also have the limitation of not coping with more complex types of classification problems e.g., hierarchical multi-label classification problems. This thesis investigates the extension of ACO classification algorithms to cope with the aforementioned limitations. Firstly, a method is proposed to extend the rule construction process of ACO classification algorithms to cope with continuous attributes directly. Four new ACO classification algorithms are presented, as well as a comparison between them and well-known classification algorithms from the literature. Secondly, an ACO classification algorithm for the hierarchical problem of protein function prediction which is a major type of bioinformatics problem addressed in this thesis is presented. Finally, three different approaches to extend ACO classification algorithms to the more complex case of hierarchical multi-label classification are described, elaborating on the ideas of the proposed hierarchical classification ACO algorithm. These algorithms are compare against state-of-the-art decision tree induction algorithms for hierarchical multi-label classification in the context of protein function prediction. The computational results of experiments with a wide range of data sets including challenging protein function prediction data sets with very large number.
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Allanqawi, Khaled Kh S. Kh. "A framework for the classification and detection of design defects and software quality assurance." Thesis, Kingston University, 2015. http://eprints.kingston.ac.uk/34534/.
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In current software development lifecyeles of heterogeneous environments, the pitfalls businesses have to face are that software defect tracking, measurements and quality assurance do not start early enough in the development process. In fact the cost of fixing a defect in a production environment is much higher than in the initial phases of the Software Development Life Cycle (SDLC) which is particularly true for Service Oriented Architecture (SOA). Thus the aim of this study is to develop a new framework for defect tracking and detection and quality estimation for early stages particularly for the design stage of the SDLC. Part of the objectives of this work is to conceptualize, borrow and customize from known frameworks, such as object-oriented programming to build a solid framework using automated rule based intelligent mechanisms to detect and classify defects in software design of SOA. The framework on design defects and software quality assurance (DESQA) will blend various design defect metrics and quality measurement approaches and will provide measurements for both defect and quality factors. Unlike existing frameworks, mechanisms are incorporated for the conversion of defect metrics into software quality measurements. The framework is evaluated using a research tool supported by sample used to complete the Design Defects Measuring Matrix, and data collection process. In addition, the evaluation using a case study aims to demonstrate the use of the framework on a number of designs and produces an overall picture regarding defects and quality. The implementation part demonstrated how the framework can predict the quality level of the designed software. The results showed a good level of quality estimation can be achieved based on the number of design attributes, the number of quality attributes and the number of SOA Design Defects. Assessment shows that metrics provide guidelines to indicate the progress that a software system has made and the quality of design. Using these guidelines, we can develop more usable and maintainable software systems to fulfil the demand of efficient systems for software applications. Another valuable result coming from this study is that developers are trying to keep backwards compatibility when they introduce new functionality. Sometimes, in the same newly-introduced elements developers perform necessary breaking changes in future versions. In that way they give time to their clients to adapt their systems. This is a very valuable practice for the developers because they have more time to assess the quality of their software before releasing it. Other improvements in this research include investigation of other design attributes and SOA Design Defects which can be computed in extending the tests we performed.
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Akpinar, Kutalmis. "Human Activity Classification Using Spatio-temporal." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614587/index.pdf.
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This thesis compares the state of the art methods and proposes solutions for human activity classification from video data. Human activity classification is finding the meaning of human activities, which are captured by the video. Classification of human activity is needed in order to improve surveillance video analysis and summarization, video data mining and robot intelligence. This thesis focuses on the classification of low level human activities which are used as an important information source to determine high level activities.
In this study, the feature relation histogram based activity description proposed by Ryoo et al. (2009) is implemented and extended. The feature histogram is widely used in feature based approacheshowever, the feature relation histogram has the ability to represent the locational information of the features. Our extension defines a new set of relations between the features, which makes the method more effective for action description. Classifications are performed and results are compared using feature histogram, Ryoo&rsquo
s feature relation histogram and our feature relation histogram using the same datasets and the feature type. Our experiments show that feature relation histogram performs slightly better than the feature histogram, our feature relation histogram is even better than both of the two. Although the difference is not clearly observable in the datasets containing periodic actions, a 12% improvement is observed for the non-periodic action datasets. Our work shows that the spatio-temporal relation represented by our new set of relations is a better way to represent the activity for classification.
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Zhang, Jing Bai. "Automatic hidden-web database classification." Thesis, University of Macau, 2007. http://umaclib3.umac.mo/record=b1677225.
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Wang, Yi. "Hierarchhical classification of web pages." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1943013.
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Kidwell, Billy R. "MiSFIT: Mining Software Fault Information and Types." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/33.
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As software becomes more important to society, the number, age, and complexity of systems grow. Software organizations require continuous process improvement to maintain the reliability, security, and quality of these software systems. Software organizations can utilize data from manual fault classification to meet their process improvement needs, but organizations lack the expertise or resources to implement them correctly.
This dissertation addresses the need for the automation of software fault classification. Validation results show that automated fault classification, as implemented in the MiSFIT tool, can group faults of similar nature. The resulting classifications result in good agreement for common software faults with no manual effort.
To evaluate the method and tool, I develop and apply an extended change taxonomy to classify the source code changes that repaired software faults from an open source project. MiSFIT clusters the faults based on the changes. I manually inspect a random sample of faults from each cluster to validate the results. The automatically classified faults are used to analyze the evolution of a software application over seven major releases. The contributions of this dissertation are an extended change taxonomy for software fault analysis, a method to cluster faults by the syntax of the repair, empirical evidence that fault distribution varies according to the purpose of the module, and the identification of project-specific trends from the analysis of the changes.
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Musa, Mohamed Elhafiz Mustafa. "Towards Finding Optimal Mixture Of Subspaces For Data Classification." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1104512/index.pdf.
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In pattern recognition, when data has different structures in different parts of the
input space, fitting one global model can be slow and inaccurate. Learning methods
can quickly learn the structure of the data in local regions, consequently, offering faster
and more accurate model fitting. Breaking training data set into smaller subsets may
lead to curse of dimensionality problem, as a training sample subset may not be enough
for estimating the required set of parameters for the submodels. Increasing the size of
training data may not be at hand in many situations. Interestingly, the data in local
regions becomes more correlated. Therefore, by decorrelation methods we can reduce
data dimensions and hence the number of parameters. In other words, we can find
uncorrelated low dimensional subspaces that capture most of the data variability. The
current subspace modelling methods have proved better performance than the global
modelling methods for the given type of training data structure. Nevertheless these
methods still need more research work as they are suffering from two limitations
2 There is no standard method to specify the optimal number of subspaces.
²There is no standard method to specify the optimal dimensionality for each subspace. In the current models these two parameters are determined beforehand. In this dissertation we propose and test algorithms that try to find a suboptimal number of principal subspaces and a suboptimal dimensionality for each principal subspaces automatically.
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Hsu, Samantha. "CLEAVER: Classification of Everyday Activities Via Ensemble Recognizers." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1960.
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Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real life data. Therefore, more work is required to validate these methods in free-living settings.
For our work, 25 participants were directly observed by trained observers for two two-hour activity sessions over a seven day timespan. During the sessions, the participants wore accelerometers on the wrist, thigh, and chest. In this thesis, we tested a battery of machine learning techniques, including a hierarchical classification schema and a confusion matrix boosting method to predict activity type, activity intensity, and sedentary time in one-second intervals. To do this, we created a dataset containing almost 100 hours worth of observations from three sets of accelerometer data from an ActiGraph wrist monitor, a BioStampRC thigh monitor, and a BioStampRC chest monitor. Random forest and k-nearest neighbors are shown to consistently perform the best out of our traditional machine learning techniques. In addition, we reduce the severity of error from our traditional random forest classifiers on some monitors using a hierarchical classification approach, and combat the imbalanced nature of our dataset using a multi-class (confusion matrix) boosting method. Out of the three monitors, our models most accurately predict activity using either or both of the BioStamp accelerometers (with the exception of the chest BioStamp predicting sedentary time). Our results show that we outperform previous methods while still predicting behavior at a more granular level.
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Alay, Gokcen. "A Classification System For The Problem Of Protein Subcellular Localization." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608914/index.pdf.
Full textAbstract:
The focus of this study is on predicting the subcellular localization of a protein. Subcellular localization
information is important for protein function annotation which is a fundamental problem in computational
biology. For this problem, a classification system is built that has two main parts: a predictor that is
based on a feature mapping technique to extract biologically meaningful information from protein sequences
and a client/server architecture for searching and predicting subcellular localizations. In the first part of the
thesis, we describe a feature mapping technique based on frequent patterns. In the feature mapping technique we describe,
frequent patterns in a protein sequence dataset were identified using a search technique based on a priori
property and the distribution of these patterns over a new sample is used as a feature vector for classification.
The effect of a number of feature selection methods on the classification performance is investigated and the best
one is applied. The method is assessed on the subcellular localization
prediction problem with 4 compartments (Endoplasmic reticulum (ER) targeted, cytosolic, mitochondrial, and nuclear)
and the dataset is the same used in P2SL. Our method improved the overall accuracy to 91.71% which was
originally 81.96% by P2SL. In the second part of the thesis, a client/server architecture is designed and implemented
based on Simple Object Access Protocol (SOAP) technology which provides a user-friendly interface for accessing the
protein subcellular localization predictions. Client part is in fact a Cytoscape plug-in that is used for functional
enrichment of biological networks. Instead of the individual use of subcellular localization information,
this plug-in lets biologists to analyze a set of genes/proteins under system view.
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Black, James Francis Patrick 1959. "Applications of artificial neural networks in epidemiology : prediction and classification." Monash University, Dept. of Epidemiology and Preventive Medicine, 2002. http://arrow.monash.edu.au/hdl/1959.1/8103.
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Goeschel, Kathleen. "Feature Set Selection for Improved Classification of Static Analysis Alerts." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1091.
Full textAbstract:
With the extreme growth in third party cloud applications, increased exposure of applications to the internet, and the impact of successful breaches, improving the security of software being produced is imperative. Static analysis tools can alert to quality and security vulnerabilities of an application; however, they present developers and analysts with a high rate of false positives and unactionable alerts. This problem may lead to the loss of confidence in the scanning tools, possibly resulting in the tools not being used. The discontinued use of these tools may increase the likelihood of insecure software being released into production. Insecure software can be successfully attacked resulting in the compromise of one or several information security principles such as confidentiality, availability, and integrity.
Feature selection methods have the potential to improve the classification of static analysis alerts and thereby reduce the false positive rates. Thus, the goal of this research effort was to improve the classification of static analysis alerts by proposing and testing a novel method leveraging feature selection. The proposed model was developed and subsequently tested on three open source PHP applications spanning several years. The results were compared to a classification model utilizing all features to gauge the classification improvement of the feature selection model. The model presented did result in the improved classification accuracy and reduction of the false positive rate on a reduced feature set.
This work contributes a real-world static analysis dataset based upon three open source PHP applications. It also enhanced an existing data set generation framework to include additional predictive software features. However, the main contribution is a feature selection methodology that may be used to discover optimal feature sets that increase the classification accuracy of static analysis alerts.
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Tekkaya, Gokhan. "Improving Interactive Classification Of Satellite Image Content." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608326/index.pdf.
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Interactive classifi
cation is an attractive alternative and complementary for automatic classi&
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cation of satellite image content, since the subject is visual and there are not yet powerful computational features corresponding to the sought visual features. In this study, we improve our previous attempt by building a more stable software system with better capabilities for interactive classi&
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cation of the content of satellite images. The system allows user to indicate a few number of image regions that contain a speci&
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c geographical object, for example, a bridge, and to retrieve similar objects on the same satellite images. Retrieval process is iterative in the sense that user guides the classi&
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cation procedure by interaction and visual observation of the results. The classi&
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cation.
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Stalmans, Etienne Raymond. "DNS traffic based classifiers for the automatic classification of botnet domains." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1007739.
Full textAbstract:
Networks of maliciously compromised computers, known as botnets, consisting of thousands of hosts have emerged as a serious threat to Internet security in recent years. These compromised systems, under the control of an operator are used to steal data, distribute malware and spam, launch phishing attacks and in Distributed Denial-of-Service (DDoS) attacks. The operators of these botnets use Command and Control (C2) servers to communicate with the members of the botnet and send commands. The communications channels between the C2 nodes and endpoints have employed numerous detection avoidance mechanisms to prevent the shutdown of the C2 servers. Two prevalent detection avoidance techniques used by current botnets are algorithmically generated domain names and DNS Fast-Flux. The use of these mechanisms can however be observed and used to create distinct signatures that in turn can be used to detect DNS domains being used for C2 operation. This report details research conducted into the implementation of three classes of classification techniques that exploit these signatures in order to accurately detect botnet traffic. The techniques described make use of the traffic from DNS query responses created when members of a botnet try to contact the C2 servers. Traffic observation and categorisation is passive from the perspective of the communicating nodes. The first set of classifiers explored employ frequency analysis to detect the algorithmically generated domain names used by botnets. These were found to have a high degree of accuracy with a low false positive rate. The characteristics of Fast-Flux domains are used in the second set of classifiers. It is shown that using these characteristics Fast-Flux domains can be accurately identified and differentiated from legitimate domains (such as Content Distribution Networks exhibit similar behaviour). The final set of classifiers use spatial autocorrelation to detect Fast-Flux domains based on the geographic distribution of the botnet C2 servers to which the detected domains resolve. It is shown that botnet C2 servers can be detected solely based on their geographic location. This technique is shown to clearly distinguish between malicious and legitimate domains. The implemented classifiers are lightweight and use existing network traffic to detect botnets and thus do not require major architectural changes to the network. The performance impact of implementing classification of DNS traffic is examined and it is shown that the performance impact is at an acceptable level.
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Juziuk, Joanna. "Towards a Classification of Design Patterns for Web Programming." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-12834.
Full textAbstract:
The evolution of WWW leads to continuous growth of demands that are placed on web applications that results in creating sophisticated web architectures. To minimize the complexity behind their design, software frameworks were introduced. There are hundreds of web frameworks, hence the choice of the right framework can be seen as searching for the holy grail. This thesis investigates the possibility of creating and validates usefulness of a classification scheme which organizes well-known object-oriented design patterns found in popular web frameworks: Apache Struts, Ruby on Rails, CakePHP and Zend Framework. The proposed classification scheme is based on two criteria: purpose and scope. The classification of such patterns that capture design rationale behind the decisions and best practices, is potentially important for building or restructuring a generic web framework, for capturing expertise knowledge and for orientation purposes in the problem domain - web engineering. The methodology used in this thesis is based on case studies and the identification of design patterns in web frameworks uses manual approaches. The results revealed popular design patterns in web frameworks and that the proposed classification scheme in a form of a 2D matrix must be refined, because relationships among design patterns in web frameworks are important and have a tendency to be formed as complex hierarchies. It is proposed to use a classification scheme in a form of a map or a tree when refining the scheme.
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Atas, Musa. "Hyperspectral Imaging And Machine Learning Of Texture Foods For Classification." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613883/index.pdf.
Full textAbstract:
In this thesis the main objective is to design a machine vision system that classifies aflatoxin contaminated chili peppers from uncontaminated ones in a rapid and non-destructive manner via hyperspectral imaging and machine learning techniques. Hyperspectral image series of chili pepper samples collected from different regions of Turkey have been acquired under halogen and UV illuminations. A novel feature set based on quantized absolute difference of consecutive spectral band features is proposed. Spectral band energies along with absolute difference energies of the consecutive spectral bands are utilized as features and compared with other feature extraction methods such as Teager energy operator and 2D wavelet Linear Discriminant Bases (2D-LDB). For feature selection, Fisher discrimination power, information theoretic Minimum Redundancy Maximum Relevance (mRMR) method and proposed Multi Layer Perceptron (MLP) based feature selection schemes are utilized.Finally, Linear Discriminant Classifier (LDC), Support Vector Machines (SVM) and MLP are used as classifiers. It is observed that MLP outperforms other learning models in terms of predictor performance. We verified the performance and robustness of our proposed methods on different real world datasets. It is suggested that to achieve high classification accuracy and predictor robustness, a machine vision system with halogen excitation and quantized absolute difference of consecutive spectral band features should be utilized.
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shafiq, Hafiz Adnan, and Zaki Arshad. "Automated Debugging and Bug Fixing Solutions : A Systematic Literature Review and Classification." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3105.
Full textAbstract:
Context: Bug fixing is the process of ensuring correct source code and is done by developer. Automated debugging and bug fixing solutions minimize human intervention and hence minimize the chance of producing new bugs in the corrected program. Scope and Objectives: In this study we performed a detailed systematic literature review. The scope of work is to identify all those solutions that correct software automatically or semi-automatically. Solutions for automatic correction of software do not need human intervention while semi-automatic solutions facilitate a developer in fixing a bug. We aim to gather all such solutions to fix bugs in design, i.e., code, UML design, algorithms and software architecture. Automated detection, isolation and localization of bug are not in our scope. Moreover, we are only concerned with software bugs and excluding hardware and networking domains. Methods: A detailed systematic literature review (SLR) has been performed. A number of bibliographic sources are searched, including Inspec, IEEE Xplore, ACM digital library, Scopus, Springer Link and Google Scholar. Inclusion/exclusion, study quality assessment, data extraction and synthesis have been performed in depth according to guidelines provided for performing SLR. Grounded theory is used to analyze literature data. To check agreement level between two researchers, Kappa analysis is used. Results: Through SLR we identified 46 techniques. These techniques are classified in automated/semi-automated debugging and bug fixing. Strengths and weaknesses of each of them are identified, along with which types of bugs each can fix and in which language they can be implement. In the end, classification is performed which generate a list of approaches, techniques, tools, frameworks, methods and systems. Along, this classification and categorization we separated bug fixing and debugging on the bases of search algorithms. Conclusion: In conclusion achieved results are all automated/semi-automated debugging and bug fixing solutions that are available in literature. The strengths/benefits and weaknesses/limitations of these solutions are identified. We also recognize type of bugs that can be fixed using these solutions. And those programming languages in which these solutions can be implemented are discovered as well. In the end a detail classification is performed.alla automatiska / halvautomatiska felsökning och felrättning lösningar som är tillgängliga i litteraturen. De styrkor / fördelar och svagheter / begränsningar av dessa lösningar identifieras. Vi erkänner också typ av fel som kan fastställas med hjälp av dessa lösningar. Och de programmeringsspråk där dessa lösningar kan genomföras upptäcks också. Till slut en detalj klassificering utförs
+46 763 23 93 87, +46 70 966 09 51
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Chintalapati, Lakshmi Venkata Bharadwaj. "Integration of Mission Control System, On-board Computer Core and spacecraft Simulator for a Satellite Test Bench." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-212663.
Full textAbstract:
The satellite avionics platform has been developed in cooperation with Airbus and is called „Future Low-cost Platform“ (FLP). It is based on an Onboard Computer (OBC) with redundant processor boards based on SPARC V8 microchips of type Cobham Aeroflex UT699. At the University of Stuttgart a test bench with a real hardware OBC and a fully simulated satellite is available for testing real flight scenarios with the Onboard Software (OBSW) running on representative hardware. The test bench as later the real flying satellite "Flying Laptop" – is commanded from a real Ground Control Centre (GCC). The main challenges in the FLP project were
- Onboard computer design,
- Software design and
- Interfaces between platform and payloads
In the course of industrialization of this FLP platform technology for later use in satellite constellations, Airbus has started to set up an in-house test bench where all the technologies shall be developed. The initial plan is to get first core elements of the FLP OBSW ported to the new dual-core processor and the new Space Wire(SpW) routing network. The plan also has an inclusion of new Mission Control Software with which one can command the OBC. The new OBC has a dual core processor Cobham Gaisler GR712 and hence, all the payload and related functionality are to be implemented only in a second core which involves a lot of low-level task distribution. The consequent SpW router network application and dual-core platform/payload OBSW sharing are entirely new in the field of satellite engineering.
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Yaprakkaya, Gokhan. "Face Identification, Gender And Age Groups Classifications For Semantic Annotation Of Videos." Thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612848/index.pdf.
Full textAbstract:
This thesis presents a robust face recognition method and a combination of methods for gender identification and age group classification for semantic annotation of videos. Local binary pattern histogram which has 256 bins and pixel intensity differences are used as extracted facial features for gender classification. DCT Mod2 features and edge detection results around facial landmarks are used as extracted facial features for age group classification. In gender classification module, a Random Trees classifier is trained with LBP features and an adaboost classifier is trained with pixel intensity differences. DCT Mod2 features are used for training of a Random Trees classifier and LBP features around facial landmark points are used for training another Random Trees classifier in age group classification module. DCT Mod2 features of the detected faces morped by two dimensional face morphing method based on Active Appearance Model and Barycentric Coordinates are used as the inputs of the nearest neighbor classifier with weights obtained from the trained Random Forest classifier in face identification module. Different feature extraction methods are tried and compared and the best achievements in the face recognition module to be used in the method chosen. We compared our classification results with some successful earlier works results in our experiments performed with same datasets and got satisfactory results.
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Florean, Alexander, and Laoa Jalal. "Mapping Java Source Code To Architectural Concerns Through Machine Learning." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84250.
Full textAbstract:
The explosive growth of software systems with both size and complexity results in the recognised need of techniques to combat architectural degradation. Reflexion Modelling is a method commonly used for Software Architectural Consistency Checking (SACC). However, the steps needed to utilise the method involve manual mapping, which could become tedious depending on the system's size. Recently, machine learning has been showing promising results outperforming other approaches. However, neither a comparison of different classifiers nor a comprehensive investigation of how to best pre-process source code has yet been performed. This thesis compares different classifier and their performance to the manual effort needed to train them and how different pre-processing settings affect their accuracy. The study can be divided into two areas: pre-processing and how large the manual mapping should be to achieve satisfactory performance. Across the three software systems used in this study, the overall best performing model, MaxEnt, achieved the following average results, accuracy 0.88, weighted precision 0.89 and weighted recall 0.88. SVM performed almost identically to MaxEnt. Furthermore, the results show that Naive-Bayes, the algorithm in recent related work approaches, performs worse than SVM and MaxEnt. The results yielded that the pre-processing that extracts packages and libraries, together with the feature representation method Bag-of-Words had the best performance. Furthermore, it was found that manual mapping of a minimum of ten files per concern is needed for satisfactory performance. The research results represent a further step towards automating code-to-architecture mappings, as required in reflexion modelling and similar techniques.
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Sarac, Omer Sinan. "Subsequence Feature Maps For Protein Function Annotation." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609767/index.pdf.
Full textAbstract:
With the advances in sequencing technologies, the number of protein sequences with
unknown function increases rapidly. Hence, computational methods for functional annotation
of these protein sequences become of the upmost importance. In this thesis,
we first defined a feature space mapping of protein primary sequences to fixed dimensional
numerical vectors. This mapping, which is called the Subsequence Profile Map
(SPMap), takes into account the models of the subsequences of protein sequences. The
resulting vectors were used as an input to support vector machines (SVM) for functional
classification of proteins. Second, we defined the protein functional annotation problem
as a classification problem and construct a classification framework defined on Gene Ontology
(GO) terms. Dierent classification methods as well as their combinations are
assessed on this framework which is based on 300 GO molecular function terms. The reiv
sults showed that combination enhances the classification accuracy. The resultant system
is made publicly available as an online function annotation tool.
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YOUSSIF, ROSHDY S. "HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219.
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Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.
Full textAbstract:
In this work, I view incremental experiential learning in intelligent software agents as progressive agent self-adaptation. When an agent produces an incorrect behavior, then it may reflect on, and thus diagnose and repair, the reasoning and knowledge that produced the incorrect behavior. In particular, I focus on the self-diagnosis and self-repair of an agent's domain knowledge. The implementation of systems with the capability to self-diagnose and self-repair involves building both reasoning processes capable of such learning and knowledge representations capable of supporting those reasoning processes. The core issue my dissertation addresses is: what kind of metaknowledge (knowledge about knowledge) may enable the agent to diagnose faults in its domain knowledge? In providing a solution to this issue, the central contribution of this research is a theory of the kind of metaknowledge that enables a system to reason about and adapt its conceptual knowledge. For this purpose, I propose a representation that explicitly encodes metaknowledge in the form of procedures called Empirical Verification Procedures (EVPs). In the proposed knowledge representation, an EVP is associated with each concept within the agent's domain knowledge. Each EVP explicitly semantically grounds the associated concept in the agent's perception, and can thus be used as a test to determine the validity of knowledge of that concept during diagnosis.
I present the formal and empirical evaluation of a system, Augur, that makes use of EVP metaknowledge to adapt its own domain knowledge in the context of a particular subclass of classification problem that I call compositional classification, in which the overall classification task can be broken into a hierarchically organized set of subtasks. I hypothesize that EVP metaknowledge will enable a system to automatically adapt its knowledge in two ways: first, by adjusting the ways that inputs are categorized by a concept, in accordance with semantics fixed by an associated EVP; and second, by adjusting the semantics of concepts themselves when they fail to contribute appropriately to system goals. The latter adaptation is realized by altering the EVP associated with the concept in question. I further hypothesize that the semantic grounding of domain concepts in perception through the use of EVPs will increase the generalization power of a learner that operates over those concepts, and thus make learning more efficient. Beyond the support of these hypotheses, I also present results pertinent to the understanding of learning in compositional classification settings using structured knowledge representations.
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Hyberg, Martin. "Software Issue Time Estimation With Natural Language Processing and Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295202.
Full textAbstract:
Time estimation for software issues is crucial to planning projects. Developers and experts have for many decades tried to estimate time requirements for issues as accurately as possible. The methods that are used today are often time-consuming and complex. This thesis investigates if the time estimation process can be done with natural language processing and machine learning. Three different word embeddings were used to represent the free text description, bag-of-words with tf-idf weighing, word2Vec and fastText. The different word embeddings were then fed into two types of machine learning approaches, classification and regression. The classification was binary and can be formulated as will the issue take more than three hours?. The goal of the regression problem was to predict an actual value for the time that the issue would take to complete. The classification models performance were measured with an F1-score, and the regression model was measured with an R2-score. The best F1- score for classification was 0.748 and was achieved with the word2Vec word embedding and an SVM classifier. The best score for the regression analysis was achieved with the bag-of-words word embedding, which achieved an R2- score of 0.380. Further evaluation of the results and a comparison to actual estimates made by the company show that humans only performs slightly better than the models given the binary classification defined above. The F1-score of the employees was 0.792, a difference of just 0.044 from the best F1-score made by the models. This thesis concludes that the models are not good enough to use in a professional setting. An F1-score of 0.748 could be used in other settings, but the classification question in this problem is too broad to be used for a real project. The results for the regression is also too low to be of any valuable use.Tidsuppskattning för programvaruärenden är en avgörande del för planering av projekt. Utvecklare och experter har i många årtionden försökt uppskatta tiden ett ärende kommer ta så exakt som möjligt. Metoderna som används idag är ofta tidskrävande och komplexa. Denna avhandling undersöker om tidsuppskattningsprocessen kan göras med hjälp av språkteknologi och maskininlärning. De flesta programvaruärenden har en fritextbeskrivning av vad som är fel eller behöver läggas till. Tre olika ordinbäddningar användes för att representera fritextbeskrivningen, bag-of-word med tf-idf-viktning, word2Vec och fastText. De olika ordinbäddningarna matades sedan in i två typer av maskininlärningsmetoder, klassificering och regression. Klassificeringen var binär och frågan kan formuleras som tar ärendet mer än tre timmar?. Målet med regressionsproblemet var att förutsäga ett faktiskt värde för den tid som frågan skulle ta att slutföra. Klassificeringsmodellens prestanda mättes med en F1-poäng och regressionsmodellen mättes med en R2-poäng. Den bästa F1-poängen för klassificering var 0.748 och uppnåddes med en word2Vec-ordinbäddning och en SVM-klassificeringsmodell. Den bästa poängen för regressionsanalysen uppnåddes med en bag-of-words-inbäddning, som uppnådde en R2-poäng på 0.380. Vidare undersökning av resultaten och en jämförelse av faktiskta tidsestimat som gjorts av företaget visar att människor bara är lite bättre än modellerna givet klassificeringsfrågan beskriven ovan. F1-poängen för de anställda var 0.792, bara 0.044 bättre än det bästa F1-poängen för modellerna. Slutsatsen för denna avhandling är att modellerna inte är tillräckligt bra för att användas i en professionell miljö. En F1-poäng på 0.748 kan användas i andra situationer, men klassificeringsfrågan i detta problem är för bred för att användas för ett riktigt projekt. Resultatet för regressionen är också för lågt för att vara till någon värdefull användning.
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Vakkalanka, Sairam, and SR Phanindra Kumar Narayanasetty. "Investigating Research on Teaching Modeling in Software Engineering -A Systematic Mapping Study." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2468.
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Context: Modeling is an important activity, which is used, in different phases of software engineering. Without models and modeling, it is nearly impossible to design and develop software systems, which demands the need for modeling to be taught in software engineering. There exist a number of reported models, methods, tools and languages to teach modeling in software engineering, which suggests the need for a classification and an overview of the area. This research investigates the state of published research on teaching modeling in software engineering in order to provide a systematic overview and classification of these different ways of teaching modeling with an insight on their importance and relevance to this research area. Objectives: The overall goal of the research was achieved with fulfilling the following objectives: understanding how systematic mapping is conducted, developing a systematic mapping process that will properly provide data for investigating the published research, applying the process, and finally reflecting on the results of the mappings, analyzing the importance and evaluating relevance of the published research. Methods: Systematic literature review was used as a tool to understand and inspect how systematic mapping was carried out in the area of software engineering. Based on the results of systematic literature review, new guidelines were formulated to conduct systematic mapping. These guidelines were used to investigate the published research on teaching modeling in software engineering. The results obtained through the systematic mapping were evaluated based on Industrial relevance, Rigor and citation count to examine their importance and identify research gaps. Results: 131 articles were classified into five classes such as Languages, Course Design, Curriculum design, Diagrams, others using semi-manual classification scheme and classification facets such as the type of audience, type of contribution, type of research, type of publication, type of publication year, type of research method and type of study setting. After the evaluation of Industrial relevance, rigor & citation ranking on the obtained results of the classification, 8 processes, 4 tools, 3 methods, 2 measurement-metrics and 1 model were extracted to teach modeling in software engineering. Also, this classification when compared with an existing classification, which is based on interviews and discussions, showed that our classification provides a wider overview with a deeper insight of the different ways to teach modeling in software engineering. Conclusions: Results of this systematic mapping study indicate that there is an increase in the research activity on teaching modeling in software engineering, with Unified Modeling Language (UML) being the widely research area. Much research is emphasized on teaching modeling to students from academia which indicates a research gap in developing methods, models, tools and processes to teach modeling to students/practitioners from the industry. Also, considering the citation ranking, industrial relevance and rigor of the articles, areas such as course design and curriculum development are highly neglected, suggesting the need for more research focus.Sammanhang : Modellering är en viktig verksamhet , som används i olika faser av programvaruteknik . Utan modeller och modellering , är det nästan omöjligt att utforma och utveckla mjukvarusystem , vilket kräver behovet av modellering för att undervisas i programvaruteknik . Det finns ett antal rapporterade modeller, metoder , verktyg och språk för att undervisa modellering i programvaruteknik , vilket tyder på att det behövs en klassificering och en överblick över området . Denna forskning undersöker tillståndet av publicerad forskning om undervisning modellering i programvaruteknik för att ge en systematisk överblick och klassificering av dessa olika sätt att undervisa modellering med en insikt om deras betydelse och relevans för detta forskningsområde . Mål : Det övergripande målet med forskningen uppnåddes med att uppfylla följande mål : att förstå hur systematisk kartläggning genomförs , att utveckla en systematisk kartläggning process som riktigt kommer att ge data för att undersöka publicerad forskning , tillämpning av processen , och slutligen reflektera över resultaten av de avbildningar, som analyserar betydelsen och utvärdera relevansen av den publicerade forskningen . Metoder : En systematisk litteraturstudie användes som ett verktyg för att förstå och kontrollera hur systematisk kartläggning genomfördes inom området programvaruteknik . Baserat på resultaten av en systematisk litteraturgenomgång har nya riktlinjer som formulerats för att bedriva systematisk kartläggning . Riktlinjerna användes för att undersöka den publicerade forskning om undervisning modellering i programvaruteknik . De resultat som erhållits genom systematisk kartläggning utvärderades baserat på industriell relevans , Rigor och stämningen räkning för att undersöka deras betydelse och identifiera kunskapsluckor . Resultat: 131 artiklar klassificerades i fem klasser , t.ex. språk , kurs Design , Curriculum design, diagram , andra med hjälp av semi - manuell klassificeringssystem och klassificerings fasetter såsom typ av publiken , typ av bidrag , typ av forskning , typ av publikation , typ av årtal , typ av forskningsmetod och typ av studieinställning. Efter utvärderingen av industriell relevans , noggrannhet och stämningen ranking på de erhållna resultaten av klassificeringen , 8 processer , 4 verktyg , 3 metoder , 2 mät - mått och 1 modell extraherades att lära modellering i programvaruteknik . Även denna klassificering i jämförelse med en befintlig klassificering , som bygger på intervjuer och diskussioner , visade att vår klassificering ger en bredare överblick med en djupare insikt om de olika sätten att lära modellering i programvaruteknik . Slutsatser : Resultaten av denna systematiska kartläggning visar att det finns en ökning av forskningsverksamheten på undervisning modellering i programvaruteknik , med Unified Modeling Language ( UML ) är den brett forskningsområde. Mycket forskning framhävs att lära modellering för studenter från den akademiska världen , som indikerar en lucka forskning för att utveckla metoder, modeller , verktyg och processer för att lära modellering för studenter / utövare från branschen . Dessutom , med tanke på stämningen ranking , industriell relevans och noggrannhet av artiklarna , områden som kursdesign och utveckling av läroplaner är mycket eftersatt , vilket tyder på att det behövs mer forskning fokus.
Flat # 503,Sri Krishna Residency, Mangapuram Colony,Vizag, Andhra Pradesh, India- 530017. +9989733724
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Stiff, Adam. "Mitigation of Data Scarcity Issues for Semantic Classification in a Virtual Patient Dialogue Agent." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1591007163243306.
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Clauß, Matthias, Thomas Müller, Frank Richter, and Wolfgang Riedel. "Mitteilungen des URZ 3/2006." Universitätsbibliothek Chemnitz, 2006. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200601739.
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Fießler, Andreas Christoph Kurt. "Hybrid Hardware/Software Architectures for Network Packet Processing in Security Applications." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20023.
Full textAbstract:
Die Menge an in Computernetzwerken verarbeiteten Daten steigt stetig, was Netzwerkgeräte wie Switches, Bridges, Router und Firewalls vor Herausfordungen stellt.
Die Performance der verbreiteten, CPU/softwarebasierten Ansätze für die Implementierung dieser Aufgaben ist durch den inhärenten Overhead in der sequentiellen Datenverarbeitung limitiert, weshalb solche Funktionalitäten vermehrt auf dedizierten Hardwarebausteinen realisiert werden.
Diese bieten eine schnelle, parallele Verarbeitung mit niedriger Latenz, sind allerdings aufwendiger in der Entwicklung und weniger flexibel.
Nicht jede Anwendung kann zudem für parallele Verarbeitung optimiert werden.
Diese Arbeit befasst sich mit hybriden Ansätzen, um eine bessere Ausnutzung der jeweiligen Stärken von Soft- und Hardwaresystemen zu ermöglichen, mit Schwerpunkt auf der Paketklassifikation.
Es wird eine Firewall realisiert, die sowohl Flexibilität und Analysetiefe einer Software-Firewall als auch Durchsatz und Latenz einer Hardware-Firewall erreicht.
Der Ansatz wird auf einem Standard-Rechnersystem, welches für die Hardware-Klassifikation mit einem rekonfigurierbaren Logikbaustein (FPGA) ergänzt wird, evaluiert.
Eine wesentliche Herausforderung einer hybriden Firewall ist die Identifikation von Abhängigkeiten im Regelsatz.
Es werden Ansätze vorgestellt, welche den redundanten Klassifikationsaufwand auf ein Minimum reduzieren, wie etwa die Wiederverwendung von Teilergebnissen der hybriden Klassifikatoren oder eine exakte Abhängigkeitsanalyse mittels Header Space Analysis.
Für weitere Problemstellungen im Bereich der hardwarebasierten Paketklassifikation, wie dynamisch konfigurierbare Filterungsschaltkreise und schnelle, sichere Hashfunktionen für Lookups, werden Machbarkeit und Optimierungen evaluiert.
Der hybride Ansatz wird im Weiteren auf ein System mit einer SDN-Komponente statt einer FPGA-Erweiterung übertragen. Auch hiermit können signifikante Performancegewinne erreicht werden.Network devices like switches, bridges, routers, and firewalls are subject to a continuous development to keep up with ever-rising requirements. As the overhead of software network processing already became the performance-limiting factor for a variety of applications, also former software functions are shifted towards dedicated network processing hardware. Although such application-specific circuits allow fast, parallel, and low latency processing, they require expensive and time-consuming development with minimal possibilities for adaptions. Security can also be a major concern, as these circuits are virtually a black box for the user. Moreover, the highly parallel processing capabilities of specialized hardware are not necessarily an advantage for all kinds of tasks in network processing, where sometimes a classical CPU is better suited. This work introduces and evaluates concepts for building hybrid hardware-software-systems that exploit the advantages of both hardware and software approaches in order to achieve performant, flexible, and versatile network processing and packet classification systems. The approaches are evaluated on standard software systems, extended by a programmable hardware circuit (FPGA) to provide full control and flexibility. One key achievement of this work is the identification and mitigation of challenges inherent when a hybrid combination of multiple packet classification circuits with different characteristics is used. We introduce approaches to reduce redundant classification effort to a minimum, like re-usage of intermediate classification results and determination of dependencies by header space analysis. In addition, for some further challenges in hardware based packet classification like filtering circuits with dynamic updates and fast hash functions for lookups, we describe feasibility and optimizations. At last, the hybrid approach is evaluated using a standard SDN switch instead of the FPGA accelerator to prove portability.
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Turkerud, Stina Ramdahl. "Software safety issues in the maritime industry, and challenges related to human computer interfaces. Theoretical background and results of a survey among equipment suppliers, yards and classification societies in four European countries." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9520.
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This thesis concerns the safety in user interfaces. In particular it concerns the user interfaces in systems in which safety is critical. I have studied such systems in the maritime industry, where we for instance may find them on the bridges of ships. Computer systems get more and more important in the daily routines of humans, and it is important that this does not go unnoticed. Designers of computer systems need to take human factors into consideration when designing their systems. These considerations might be especially important in complex systems, as these are often safety critical. The bridges on ships are likely to include complex systems for the operator to handle, as they often involve multiple screens, or other factors that increase the complexity of a system. Such factors might include being able to pay attention to several incidents at once. When dealing with complex systems, it is important that the operator knows how to handle the system, and also how to react when an incident occurs. These are factors that need to be considered by the designer when making the system and theories on how to do this are described in the thesis. I have also described standards which consider this, like the ISO 11064 standard, or the Atomos regulation and the ISO 17894 which considers this for the maritime industry in particular. Parts of the industry have made an effort to develop tools to be used to improve the safety. I have studied some of these efforts and presented them in the thesis. Furthermore, I have developed a survey to study how the individual members and different parts of the industry feel and behave towards safety. The survey gave an insight into reality of how safety is being handled in the industry as a whole. In particular it pointed to the main problem of the maritime industry, that the industry is very heterogeneous, and also that the different parts of the industry are in competition with each other. Most of the respondents had not heard about the Atomos regulation or the ISO 17894 standards, efforts that could have been used as a tool to improve the level of safety. The questionnaire also showed that while most of the respondents are satisfied with the level of safety in their organization, they are not satisfied with the level of safety in the overall industry. The thesis consists of six parts. Part I deals with the introduction and general theory from research methods and psychology. Part II deals with usability and related standards. These include ISO 11064, theory on usability and a description of an accident due to poorly designed user interface. Part III describes relevant background from the maritime industry, which involves the ISO 17894 standard, the Atomos regulation and e-navigation, an example of a newly made effort. Part IV gives a description of the development of my questionnaire, and also provides the results and conclusions made from them. Part V provides the conclusions and suggestions for future work, while part VI contains appendices.
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Rößler, Marko. "Parallel Hardware- and Software Threads in a Dynamically Reconfigurable System on a Programmable Chip." Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-129626.
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Today’s embedded systems depend on the availability of hybrid platforms, that contain heterogeneous computing resources such as programmable processors units (CPU’s or DSP’s) and highly specialized hardware cores. These platforms have been scaled down to integrated embedded system-on-chip. Modern platform FPGAs enhance such systems by the flexibility of runtime configurable silicon. One of the major advantages that arises is the ability to use hardware (HW) and software (SW) resources in a time-shared manner. Though the ability to dynamically assign computing resources based on decisions taken at runtime is given.
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Michael, Inies Raphael Chemmannoor. "FAULT LINKS: IDENTIFYING MODULE AND FAULT TYPES AND THEIR RELATIONSHIP." Lexington, Ky. : [University of Kentucky Libraries], 2004. http://lib.uky.edu/ETD/ukycosc2004t00211/MasterDegreeThesisReportIniesRCM.pdf.
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Thesis (m.s.)--University of Kentucky, 2004.Title from document title page (viewed Jan. 7, 2005). Document formatted into pages; contains vii, p. 103 : ill. Includes abstract and vita. Includes bibliographical references (p. 99-101).
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Mertayak, Cuneyt. "Toward The Frontiers Of Stacked Generalization Architecture For Learning." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608793/index.pdf.
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In pattern recognition, &ldquobias-variance&rdquo
trade-off is a challenging issue that the scientists has been working to get better generalization performances over the last decades. Among many learning methods, two-layered homogeneous stacked generalization has been reported to be successful in the literature, in different problem domains such as object recognition and image annotation. The aim of this work is two-folded. First, the problems of stacked generalization are attacked by a proposed novel architecture. Then, a set of success criteria for stacked generalization is studied. A serious drawback of stacked generalization architecture is the sensitivity to curse of dimensionality problem. In order to solve this problem, a new architecture named &ldquo
unanimous decision&rdquo
is designed. The performance of this architecture is shown to be comparably similar to two layered homogeneous stacked generalization architecture in low number of classes while it performs better than stacked generalization architecture in higher number of classes. Additionally, a new success criterion for two layered homogeneous stacked generalization architecture is proposed based on the individual properties of the used descriptors and it is verified in synthetic datasets.
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Boynukalin, Zeynep. "Emotion Analysis Of Turkish Texts By Using Machine Learning Methods." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614521/index.pdf.
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Automatically analysing the emotion in texts is in increasing interest in today&rsquos research fields. The aim is to develop a machine that can detect type of user&rsquo
s emotion from his/her text. Emotion classification of English texts is studied by several researchers and promising results are achieved. In this thesis, an emotion classification study on Turkish texts is introduced. To the best of our knowledge, this is the first study on emotion analysis of Turkish texts. In English there exists some well-defined datasets for the purpose of emotion classification, but we could not find datasets in Turkish suitable for this study. Therefore, another important contribution is the generating a new data set in Turkish for emotion analysis. The dataset is generated by combining two types of sources. Several classification algorithms are applied on the dataset and results are compared. Due to the nature of Turkish language, new features are added to the existing methods to improve the success of the proposed method.
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Löffler-Wirth, Henry, Edith Willscher, Peter Ahnert, Kerstin Wirkner, Christoph Engel, Markus Löffler, and Hans Binder. "Novel anthropometry based on 3D-bodyscans applied to a large population based cohort." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-207844.
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Three-dimensional (3D) whole body scanners are increasingly used as precise measuring tools for the rapid quantification of anthropometric measures in epidemiological studies. We analyzed 3D whole body scanning data of nearly 10,000 participants of a cohort collected from the adult population of Leipzig, one of the largest cities in Eastern Germany. We present a novel approach for the systematic analysis of this data which aims at identifying distinguishable clusters of body shapes called body types. In the first step, our method aggregates body measures provided by the scanner into meta-measures, each representing one relevant dimension of the body shape. In a next step, we stratified the cohort into body types and assessed their stability and dependence on the size of the underlying cohort. Using self-organizing maps (SOM) we identified thirteen robust meta-measures and fifteen body types comprising between 1 and 18 percent of the total cohort size. Thirteen of them are virtually gender specific (six for women and seven for men) and thus reflect most abundant body shapes of women and men. Two body types include both women and men, and describe androgynous body shapes that lack typical gender specific features. The body types disentangle a large variability of body shapes enabling distinctions which go beyond the traditional indices such as body mass index, the waist-to-height ratio, the waist-to-hip ratio and the mortality-hazard ABSI-index. In a next step, we will link the identified body types with disease predispositions to study how size and shape of the human body impact health and disease.
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Yamamoto, Shuichi, and Naonori Ishii. "A way of computer use in mathematics teaching -The effectiveness that visualization brings-." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-81179.
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We report a class of the mathematics in which an animation technology (calculating and plotting capabilities) of the software Mathematica is utilized. This class is taught for university students in a computer laboratory during a second semester. It is our purpose to make a student realize the usefulness and the importance of mathematics easily through visualization. In addition, we hope that students will acquire a new power of mathematics needed in the 21st century. For several years, we have continued this kind of class, and have continued to investigate the effectiveness that our teaching method (especially visualization) brings in the understanding of the mathematics. In this paper, we present some of this teaching method, which is performed in our class. From the questionnaire survey, it
is found that our teaching method not only convinces students that the mathematics is useful or important but also deepens the mathematic understanding of students more.
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Bezek, Perit. "A Clustering Method For The Problem Of Protein Subcellular Localization." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607981/index.pdf.
Full textAbstract:
In this study, the focus is on predicting the subcellular localization of a protein, since subcellular localization is helpful in understanding a protein&rsquos functions. Function of a protein may be estimated from its sequence. Motifs or conserved subsequences are strong indicators of function. In a given sample set of protein sequences known to perform the same function, a certain subsequence or group of subsequences should be common
that is, occurrence (frequency) of common subsequences should be high. Our idea is to find the common subsequences through clustering and use these common groups (implicit motifs) to classify proteins. To calculate the distance between two subsequences, traditional string edit distance is modified so that only replacement is allowed and the cost of replacement is related to an amino acid substitution matrix. Based on the modified string edit distance, spectral clustering embeds the subsequences into some transformed space for which the clustering problem is expected to become easier to solve. For a given protein sequence, distribution of its subsequences over the clusters is the feature vector which is subsequently fed to a classifier. The most important aspect if this approach is the use of spectral clustering based on modified string edit distance.