Ph.D. Theses
Permanent URI for this collection
Browse
Browsing Ph.D. Theses by Subject "Artificial intelligence."
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Improving text classification performance with the analysis of lexical dependencies and class-based feature selection(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2010., 2010.) Özgür, Levent.; Güngör, Tunga.In this thesis, we present a comprehensive analysis of the feature extraction and feature selection techniques for the text classification problem in order to achieve more successful results using much smaller feature vector sizes. For feature extraction, 36 different lexical dependencies are included and analyzed independently in the feature vector as an extension to the standard bag-of-words approach. Feature selection analysis is twofold. In the first stage, pruning implementation is analyzed and optimal pruning levels are extracted with respect to dataset properties and feature variations (words, dependencies, combination of the leading dependencies). In the second stage, we compare the performance of corpus-based and class-based approaches for feature selection coverage and then, extend pruning implementation by the optimized class-based feature selection. For the final and most advanced test, we serialize the optimal use of the leading dependencies for each experimented dataset with the two stage (corpus and class-based) feature selection approach. For performance evaluation, we use the state-of-the-art measures for text classification problems: two different success score metrics and three different significance tests. With respect to these measures, the results reveal that for each extension in the methods, a corresponding significant improvement is obtained. The most advanced method combining the leading dependencies with optimal pruning levels and optimal number of class-based features mostly outperform the other methods in terms of success rates with reasonable feature sizes. To the best of our knowledge, this is the first study that makes such a detailed analysis on extracting individual dependencies and employing feature selection with two stage selection approach in text classification and more generally in text domain.Item Optimization and orchestration in multi-tier edge computing(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021., 2021.) Baktır, Ahmet Cihat.; Ersoy, Cem.; Özgövde, Bahri Atay.In addition to the efforts in the next-generation cellular networks and traditional network services, the demand for a novel set of services leveraged through smart devices and artificial intelligence (AI) techniques increases tremendously. Pervasive healthcare, online gaming, augmented reality, smart city and many other service types with various performance and functional requirements are supplied with data generated by end-user devices. In this highly dynamic environment, the legacy network infrastructure and operations remain incapable of satisfying the expectations of the users and require ments of the services, especially those demanding real-time interaction with ultra-low latency. Therefore, this thesis focuses on the task offloading operations in a multi-tier edge environment and network slicing optimization problems to enable service-oriented behavior and address the demands of both operators and end-users. In this direction, an extensive literature review is carried out, the requirements are determined, and we provide a formal optimization model for each problem definition. In order to address the scalability issues and finding good quality solutions in a short time, heuristic so lutions are proposed. Besides efforts in optimization purposes, two different solution proposals using programmable network paradigms are provided as short-term and long term for implementing the service-centric behavior. The short-term solution based on Software-defined Networking (SDN) is further evaluated by implementing a fall-risk assessment service with real sensory data. The proposed solutions are novel and pro vide comprehensive guidance for operators and service providers on implementing a service-centric behavior and optimizing the operations in multi-tier edge systems.Item Qualitative system identification(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 1992., 1992.) Say, Ahmet Celal Cem.; Kuru, Selahattin,The main contribution of this research in the qualitative reasoning area of Artificial Intelligence is the development of the qualitative system identification algorithm QSI. QSI's input is a description of the qualitative behaviors of the system to be identified. Its output is a constraint model (possibly containing "deep" parameters absent in the input) of that system, in the format of Kuipers' qualitative simulation algorithm QSIM. The QSI approach to qualitative modeling makes no assumptions and requires no knowledge about the "meanings" of the system parameters. QSI is discussed in detail. Other contributions are a new method of eliminating a class of spurious QSIM predictions, and an algorithm for postdiction. Unlike other approaches to spurious behavior reduction, the method presented here does not require restricting assumptions about the input model. A particular kind of spurious behavior is shown to be caused by pure QSIM's insistence on assigning only point values to "corresponding value tuples" associated with model constraints. The solution put forward here preserves the overall complexity of the algorithm, while producing fewer incorrect predictions, as shown by the presented reports of the case runs and proofs. Postdiction is the task of finding out the possible pasts of the system under consideration, given the laws of change and the current state. For obtaining the algorithm, a different scheme of interpreting the tree built by simulation is imposed, as well as the handling of the "flow" of time. Issues of thig reasoning task, which is promising for diagnosis applications, are discussed.Item Towards trustworthy personal assistants for privacy(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Aycı, Gönül.; Özgür, Arzucan.; Yolum, Pınar.Many software systems, such as online social networks, enable their users to share information about themselves online. However, users worry about the privacy implications of sharing content. It’s a tedious process to make privacy decisions and it makes managing privacy difficult. Recent approaches to help users manage their privacy involve building personal assistants that can recommend whether a user’s content is private or not. However, privacy’s ambiguous nature and difficulties in explaining assistants’ decision-making are challenges hampering users’ trust in these systems and therefore also widespread user adoption. In this dissertation we design trustworthy privacy assistants that can help tackle both challenges. We first propose a personal assistant called PURE that integrates machine learning to make predictions on whether a user would identify an image as private or not. An important characteristic of PURE is its ability to model uncertainty in its decisions explicitly. When uncertainty is high, no prediction is made and the decision is delegated to the user. By factoring in user’s own understanding of privacy, PURE is able to personalize its recommendations. A second crucial factor in fostering trust in personal assistants is their ability to explain their decision-making processes. Our second assistant PEAK is capable of generating such explanations for its recommendations, using latent topics and predefined explanation categories to do so. A user study shows users find PEAK’s explanations useful and easy to understand. Additionally, privacy assistants can use the explanations to improve their own decision-making, with the incorporation of PEAK into PURE resulting in less uncertain images delegated to the user whilst model performance is not compromised. Overall, our work makes an important contribution towards the development of trustworthy personal assistants capable of preserving users’ privacy. NOTE Keywords : Personal information management, Artificial intelligence, Right of privacy and its protection, Breach of confidentiality, Handling uncertainty, Explainable artificial intelligence.Item Using machine learning to improve automated test generation(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Köroğlu, Yavuz.; Şen, Alper.Underestimating the value of software testing had catastrophic results in recent history. Automated Test Generation (ATG) is an approach that aims to minimize the manual effort required for testing. This thesis aims to improve the effectiveness and performance of ATG approaches via Machine Learning (ML) based guidance, and focuses on Android Graphical User Interface (GUI) testing using Reinforcement Learning (RL), specifically. We propose four solutions, Q-learning Based Exploration (QBE), Test Case Mutation (TCM), Fully Automated Reinforcement LEArning Driven (FARLEAD), and FARLEAD2 test generators. QBE uses RL to crawl a set of applications and learns an action generation policy while exploring. Then, it uses this learned policy to either detect more unique crashes or cover more activities in new applications. TCM takes the tests QBE generates and replaces the well-behaving actions in those tests with bad-behaving ones to detect even more crashes. FARLEAD uses RL to learn how to verify a functional behavior that is given as a high-level test scenario in the form of a monitorable formal specification. FARLEAD learns by trial-and- error like QBE but it learns app-specific patterns instead of QBE’s app-generic patterns. To the best of out knowledge, FARLEAD is the first engine fully automating the functional testing of GUI applications. Finally, FARLEAD2 improves FARLEAD with Generalized Experience Replay (GER) and human-readable Staged Test Scenario (STS) language. Experimental results show that, QBE outperforms state-of-the-art test generators in crash detection and coverage. Furthermore, executing QBE first and then switching to TCM detects even more unique crashes. FARLEAD and FARLEAD2 expand the scope of automated testing to verifying functional behavior. Overall, these test generators elevate automated GUI testing closer to replacing manual GUI testing.