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- All Subjects: Computer Science
- Genre: Doctoral Dissertation
- Creators: Huang, Dijiang
- Creators: Baral, Chitta
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.
ContributorsVerdicchio, Michael (Author) / Kim, Seungchan (Thesis advisor) / Baral, Chitta (Committee member) / Stolovitzky, Gustavo (Committee member) / Collofello, James (Committee member) / Arizona State University (Publisher)
Created2013
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this dissertation I develop a deep theory of temporal planning well-suited to analyzing, understanding, and improving the state of the art implementations (as of 2012). At face-value the work is strictly theoretical; nonetheless its impact is entirely real and practical. The easiest portion of that impact to highlight concerns the notable improvements to the format of the temporal fragment of the International Planning Competitions (IPCs). Particularly: the theory I expound upon here is the primary cause of--and justification for--the altered (i) selection of benchmark problems, and (ii) notion of "winning temporal planner". For higher level motivation: robotics, web service composition, industrial manufacturing, business process management, cybersecurity, space exploration, deep ocean exploration, and logistics all benefit from applying domain-independent automated planning technique. Naturally, actually carrying out such case studies has much to offer. For example, we may extract the lesson that reasoning carefully about deadlines is rather crucial to planning in practice. More generally, effectively automating specifically temporal planning is well-motivated from applications. Entirely abstractly, the aim is to improve the theory of automated temporal planning by distilling from its practice. My thesis is that the key feature of computational interest is concurrency. To support, I demonstrate by way of compilation methods, worst-case counting arguments, and analysis of algorithmic properties such as completeness that the more immediately pressing computational obstacles (facing would-be temporal generalizations of classical planning systems) can be dealt with in theoretically efficient manner. So more accurately the technical contribution here is to demonstrate: The computationally significant obstacle to automated temporal planning that remains is just concurrency.
ContributorsCushing, William Albemarle (Author) / Kambhampati, Subbarao (Thesis advisor) / Weld, Daniel S. (Committee member) / Smith, David E. (Committee member) / Baral, Chitta (Committee member) / Davalcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. High-throughput technologies, new algorithms and speed improvements over the last decade have resulted in deeper knowledge about biological systems, leading to more refined pathways. Such pathways tend to be large and complex, making it difficult for an individual to remember all aspects. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against the real biological system. Many existing models focus on structural and/or factoid questions, relying on surface-level information. These are generally not the kind of questions that a biologist may ask someone to test their understanding of biological processes. Examples of questions requiring understanding of biological processes are available in introductory college level biology text books. Such questions serve as a model for the question answering system developed in this thesis. Thus, the main goal of this thesis is to develop a system that allows the encoding of knowledge about biological pathways to answer questions demonstrating understanding of the pathways. To that end, a language is developed to specify a pathway and pose questions against it. Some existing tools are modified and used to accomplish this goal. The utility of the framework developed in this thesis is illustrated with applications in the biological domain. Finally, the question answering system is used in real world applications by extracting pathway knowledge from text and answering questions related to drug development.
ContributorsAnwar, Saadat (Author) / Baral, Chitta (Thesis advisor) / Inoue, Katsumi (Committee member) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2014
Description
Current work in planning assumes that user preferences and/or domain dynamics are completely specified in advance, and aims to search for a single solution plan to satisfy these. In many real world scenarios, however, providing a complete specification of user preferences and domain dynamics becomes a time-consuming and error-prone task. More often than not, a user may provide no knowledge or at best partial knowledge of her preferences with respect to a desired plan. Similarly, a domain writer may only be able to determine certain parts, not all, of the model of some actions in a domain. Such modeling issues requires new concepts on what a solution should be, and novel techniques in solving the problem. When user preferences are incomplete, rather than presenting a single plan, the planner must instead provide a set of plans containing one or more plans that are similar to the one that the user prefers. This research first proposes the usage of different measures to capture the quality of such plan sets. These are domain-independent distance measures based on plan elements if no knowledge of the user preferences is given, or the Integrated Preference Function measure in case incomplete knowledge of such preferences is provided. It then investigates various heuristic approaches to generate plan sets in accordance with these measures, and presents empirical results demonstrating the promise of the methods. The second part of this research addresses planning problems with incomplete domain models, specifically those annotated with possible preconditions and effects of actions. It formalizes the notion of plan robustness capturing the probability of success for plans during execution. A method of assessing plan robustness based on the weighted model counting approach is proposed. Two approaches for synthesizing robust plans are introduced. The first one compiles the robust plan synthesis problems to the conformant probabilistic planning problems. The second approximates the robustness measure with lower and upper bounds, incorporating them into a stochastic local search for estimating distance heuristic to a goal state. The resulting planner outperforms a state-of-the-art planner that can handle incomplete domain models in both plan quality and planning time.
ContributorsNguyễn, Tuấn Anh (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Do, Minh (Committee member) / Lee, Joohyung (Committee member) / Smith, David E. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Cloud computing is regarded as one of the most revolutionary technologies in the past decades. It provides scalable, flexible and secure resource provisioning services, which is also the reason why users prefer to migrate their locally processing workloads onto remote clouds. Besides commercial cloud system (i.e., Amazon EC2), ProtoGENI and PlanetLab have further improved the current Internet-based resource provisioning system by allowing end users to construct a virtual networking environment. By archiving the similar goal but with more flexible and efficient performance, I present the design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform, and G-PLaNE that focuses on how to construct the virtual networking environment upon the self-designed resource provisioning system consisting of multiple geo-distributed clusters. Furthermore, I conduct a comprehensive study to layout existing Mobile Cloud Computing (MCC) service models and corresponding representative related work. A new user-centric mobile cloud computing service model is proposed to advance the existing mobile cloud computing research.
After building the MobiCloud, G-PLaNE and studying the MCC model, I have been using Software Defined Networking (SDN) approaches to enhance the system security in the cloud virtual networking environment. I present an OpenFlow based IPS solution called SDNIPS that includes a new IPS architecture based on Open vSwitch (OVS) in the cloud software-based networking environment. It is enabled with elasticity service provisioning and Network Reconfiguration (NR) features based on POX controller. Finally, SDNIPS demonstrates the feasibility and shows more efficiency than traditional approaches through a thorough evaluation.
At last, I propose an OpenFlow-based defensive module composition framework called CloudArmour that is able to perform query, aggregation, analysis, and control function over distributed OpenFlow-enabled devices. I propose several modules and use the DDoS attack as an example to illustrate how to composite the comprehensive defensive solution based on CloudArmour framework. I introduce total 20 Python-based CloudArmour APIs. Finally, evaluation results prove the feasibility and efficiency of CloudArmour framework.
After building the MobiCloud, G-PLaNE and studying the MCC model, I have been using Software Defined Networking (SDN) approaches to enhance the system security in the cloud virtual networking environment. I present an OpenFlow based IPS solution called SDNIPS that includes a new IPS architecture based on Open vSwitch (OVS) in the cloud software-based networking environment. It is enabled with elasticity service provisioning and Network Reconfiguration (NR) features based on POX controller. Finally, SDNIPS demonstrates the feasibility and shows more efficiency than traditional approaches through a thorough evaluation.
At last, I propose an OpenFlow-based defensive module composition framework called CloudArmour that is able to perform query, aggregation, analysis, and control function over distributed OpenFlow-enabled devices. I propose several modules and use the DDoS attack as an example to illustrate how to composite the comprehensive defensive solution based on CloudArmour framework. I introduce total 20 Python-based CloudArmour APIs. Finally, evaluation results prove the feasibility and efficiency of CloudArmour framework.
ContributorsXing, Tianyi (Author) / Huang, Dijiang (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Medhi, Deepankar (Committee member) / Arizona State University (Publisher)
Created2014
Description
Most existing security decisions for both defending and attacking are made based on some deterministic approaches that only give binary answers. Even though these approaches can achieve low false positive rate for decision making, they have high false negative rates due to the lack of accommodations to new attack methods and defense techniques. In this dissertation, I study how to discover and use patterns with uncertainty and randomness to counter security challenges. By extracting and modeling patterns in security events, I am able to handle previously unknown security events with quantified confidence, rather than simply making binary decisions. In particular, I cope with the following four real-world security challenges by modeling and analyzing with pattern-based approaches: 1) How to detect and attribute previously unknown shellcode? I propose instruction sequence abstraction that extracts coarse-grained patterns from an instruction sequence and use Markov chain-based model and support vector machines to detect and attribute shellcode; 2) How to safely mitigate routing attacks in mobile ad hoc networks? I identify routing table change patterns caused by attacks, propose an extended Dempster-Shafer theory to measure the risk of such changes, and use a risk-aware response mechanism to mitigate routing attacks; 3) How to model, understand, and guess human-chosen picture passwords? I analyze collected human-chosen picture passwords, propose selection function that models patterns in password selection, and design two algorithms to optimize password guessing paths; and 4) How to identify influential figures and events in underground social networks? I analyze collected underground social network data, identify user interaction patterns, and propose a suite of measures for systematically discovering and mining adversarial evidence. By solving these four problems, I demonstrate that discovering and using patterns could help deal with challenges in computer security, network security, human-computer interaction security, and social network security.
ContributorsZhao, Ziming (Author) / Ahn, Gail-Joon (Thesis advisor) / Yau, Stephen S. (Committee member) / Huang, Dijiang (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research describes software based remote attestation schemes for obtaining the integrity of an executing user application and the Operating System (OS) text section of an untrusted client platform. A trusted external entity issues a challenge to the client platform. The challenge is executable code which the client must execute, and the code generates results which are sent to the external entity. These results provide the external entity an assurance as to whether the client application and the OS are in pristine condition. This work also presents a technique where it can be verified that the application which was attested, did not get replaced by a different application after completion of the attestation. The implementation of these three techniques was achieved entirely in software and is backward compatible with legacy machines on the Intel x86 architecture. This research also presents two approaches to incorporating software based "root of trust" using Virtual Machine Monitors (VMMs). The first approach determines the integrity of an executing Guest OS from the Host OS using Linux Kernel-based Virtual Machine (KVM) and qemu emulation software. The second approach implements a small VMM called MIvmm that can be utilized as a trusted codebase to build security applications such as those implemented in this research. MIvmm was conceptualized and implemented without using any existing codebase; its minimal size allows it to be trustworthy. Both the VMM approaches leverage processor support for virtualization in the Intel x86 architecture.
ContributorsSrinivasan, Raghunathan (Author) / Dasgupta, Partha (Thesis advisor) / Colbourn, Charles (Committee member) / Shrivastava, Aviral (Committee member) / Huang, Dijiang (Committee member) / Dewan, Prashant (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation is focused on building scalable Attribute Based Security Systems (ABSS), including efficient and privacy-preserving attribute based encryption schemes and applications to group communications and cloud computing. First of all, a Constant Ciphertext Policy Attribute Based Encryption (CCP-ABE) is proposed. Existing Attribute Based Encryption (ABE) schemes usually incur large, linearly increasing ciphertext. The proposed CCP-ABE dramatically reduces the ciphertext to small, constant size. This is the first existing ABE scheme that achieves constant ciphertext size. Also, the proposed CCP-ABE scheme is fully collusion-resistant such that users can not combine their attributes to elevate their decryption capacity. Next step, efficient ABE schemes are applied to construct optimal group communication schemes and broadcast encryption schemes. An attribute based Optimal Group Key (OGK) management scheme that attains communication-storage optimality without collusion vulnerability is presented. Then, a novel broadcast encryption model: Attribute Based Broadcast Encryption (ABBE) is introduced, which exploits the many-to-many nature of attributes to dramatically reduce the storage complexity from linear to logarithm and enable expressive attribute based access policies. The privacy issues are also considered and addressed in ABSS. Firstly, a hidden policy based ABE schemes is proposed to protect receivers' privacy by hiding the access policy. Secondly,a new concept: Gradual Identity Exposure (GIE) is introduced to address the restrictions of hidden policy based ABE schemes. GIE's approach is to reveal the receivers' information gradually by allowing ciphertext recipients to decrypt the message using their possessed attributes one-by-one. If the receiver does not possess one attribute in this procedure, the rest of attributes are still hidden. Compared to hidden-policy based solutions, GIE provides significant performance improvement in terms of reducing both computation and communication overhead. Last but not least, ABSS are incorporated into the mobile cloud computing scenarios. In the proposed secure mobile cloud data management framework, the light weight mobile devices can securely outsource expensive ABE operations and data storage to untrusted cloud service providers. The reported scheme includes two components: (1) a Cloud-Assisted Attribute-Based Encryption/Decryption (CA-ABE) scheme and (2) An Attribute-Based Data Storage (ABDS) scheme that achieves information theoretical optimality.
ContributorsZhou, Zhibin (Author) / Huang, Dijiang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ahn, Gail-Joon (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2011