In many domains, characterizations of a given attribute are imprecise, uncertain and incomplete in the available learning examples. The definitions of classes may be vague. Learning systems are frequently forced to deal with such uncertainty. Traditional learning systems are designed to work in the domains where imprecision and uncertainty in the data are absent. Those learning systems are limited because of their impossibility to cope with uncertainty--a typical feature of real-world data. In this thesis, we developed a fuzzy learning system which combines inductive learning with a fuzzy approach to solve problems arising in learning tasks in the domains affected by uncertainty and vagueness. Based on Fuzzy Logic, rather than pure First Order Logic used in FOIL, this system extends FOIL with learning fuzzy logic relation from both imprecise examples and background knowledge represented by Fuzzy Prolog. The classification into the positive and negative examples is allowed to be a degree (of positiveness or negativeness) between 0 and 1. The values of a given attribute in examples need not to be the same type. Symbolic and continuous data can exist in the same attribute, allowing for fuzzy unification (inexact matching). An inductive learning problem is formulated as to find a fuzzy logic relation with a degree of truth, in which a fuzzy gain calculation method is used to guide heuristic search. The Fuzzy FOIL's ability of learning the required fuzzy logic relations and dealing with vague data enhances FOIL's usefulness.

This thesis addresses the problem of learning concept descriptions that are interpretable, or explainable. Explainability is understood as the ability to justify the learned concept in terms of the existing background knowledge. The starting point for the work was an existing system that would induce only fully explainable rules. The system performed well when the model used during induction was complete and correct. In practice, however, models are likely to be imperfect, i.e. incomplete and incorrect. We report here a new approach that achieves explainability with imperfect models. The basis of the system is the standard inductive search driven by an accuracy-oriented heuristic, biased towards rule explainability. The bias is abandoned when there is heuristic evidence that a significant loss of accuracy results from constraining the search to explainable rules only. The users can express their relative preference for accuracy vs. explainability. Experiments with the system indicate that, even with a partially incomplete and/or incorrect model, insisting on explainability results in only a small loss of accuracy. We also show how the new approach described can repair a faulty model using evidence derived from data during induction.

As more sophisticated systems are being developed, powerful approaches for modeling their behavior and test their reliability are necessary. The research work in this thesis takes on the problem of building a Graphical Programming Environment that permits to create models of DESs in a timed temporal logic framework and simulate the DES models in real-time using an object oriented environment through the interconnection of visual symbols. A temporal logic framework is developed to write the formal models of the temporal references of DESs. This approach is enhanced by the inclusion of a global clock variable to add real-time properties to the formal specifications of real-time DESs. The interactive visual environment allows the programmer to activate graphical symbols by means of menu selections. The graphical symbols are grouped into classes which are eventually properly interconnected, parsed and mapped into source code written in the timed temporal logic language. A knowledge-based system is composed of knowledge databases (database of facts and database of rules), These databases, representing the system behavior, can be created using this tool, for which a reasoning mechanism is required. An inference engine is designed to interpret these knowledge databases. An OO programming language is used, Objective-C. It is used throughout the design, however, when using the tool, the user does not notice the underlying programming language, in other words, the programming language is transparent to the user. The Graphical Programming Environment designed in this thesis can be used to build the specifications of real-time DESs. Different knowledge databases have been created using this interactive tool for three examples to verify their behaviors, such examples are: The ABP communication protocol, the packet-switched communication protocol, and the telephone system.

Over the past few years, the subject of Intelligent Network (IN) has captured the interest of the telecommunications community. The objective of IN is to allow the introduction of new capabilities in the telecommunications network and to facilitate and accelerate in a cost-effective manner, service implementation and provisioning, in a multivendor environment. However, this objective confronts a major obstacle known as the feature interaction problem. The feature interaction problem occurs when a feature is prevented from performing its functionalities in the presence of other features. In the first part of the thesis, we describe a LOTOS model for structuring the Functional Entities (FEs) that are defined in the Distributed Functional Plane (DFP) of the CS1 IN Conceptual Model (INCM), and that are involved in the establishment of a call/connection and invocation and processing of services. The specification of IN services is achieved using Service Independent building Blocks (SIBs). It is designed in a way that independent specification and rapid introduction of services is provided. In the second part of the thesis, a method for detecting feature interactions between services is developed. The method is limited to the detection of interactions caused by violation of features properties. It is based on formalization of feature's properties, derivation of goals satisfying the negation of these properties and use of Goal Oriented Execution to detect traces satisfying these goals. A trace satisfying a goal shows that an interaction exists between the specified features by describing a scenario violating one of the properties of the introduced features. It is concluded that LOTOS is useful as a Formal Description Technique (FDT) in the Service Creation Environment (SCE). The developed specification can be used for adding specifications of new services, and for detecting interactions caused by violation of properties, if there are any.

<p> As the complexity of mankind&rsquo;s day-to-day challenges increase, so does a need for the optimization of know solutions to accommodate for this increase in complexity. Today&rsquo;s computer systems use the Input, Processing, and Output (IPO) model as a way to deliver efficiency and optimization in human activities. Since the relative quality of an output utility derived from an IPO based computer system is closely coupled to the quality of its input media, the measure of the Optimal Quotient (OQ) is the ratio of the input to output which is 1:1. This relationship ensures that all IPO based computers are not just linearly predictable, but also characterized by the Garbage In Garbage Out (GIGO) design concept. While current IPO based computer systems have been relatively successful at delivering some measure of optimization, there is a need to examine (Li &amp; Malik, 2016) alternative methods of achieving optimization. The purpose of this quantitative research study, through an experimental research design, is to determine the effects of the application of a Swarm Intelligence algorithm on the efficiency of the Cosmos Open Source Managed Operating System. </p><p> By incorporating swarm intelligence into an improved IPO design, this research addresses the need for optimization in computer systems through the creation of an improved operating system Scheduler. The design of a Swarm Intelligence Operating System (SIOS) is an attempt to solve some inherent vulnerabilities and problems of complexity and optimization otherwise unresolved in the design of conventional operating systems. This research will use the Cosmos open source operating system as a test harness to ensure improved internal validity while the subsequent measurement between the conventional and improved IPO designs will demonstrate external validity to real world applications. </p><p>

<p> Speaker age and gender classification is one of the most challenging problems in speech processing. Recently with developing technologies, identifying a speaker age and gender has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human-machine interaction, and adapting music for awaiting people queue. Although many studies have been carried out focusing on feature extraction and classifier design for improvement, classification accuracies are still not satisfactory. The key issue in identifying speaker&rsquo;s age and gender is to generate robust features and to design an in-depth classifier. Age and gender information is concealed in speaker&rsquo;s speech, which is liable for many factors such as, background noise, speech contents, and phonetic divergences.</p><p> In this work, different methods are proposed to enhance the speaker age and gender classification based on the deep neural networks (DNNs) as a feature extractor and classifier. First, a model for generating new features from a DNN is proposed. The proposed method uses the Hidden Markov Model toolkit (HTK) tool to find tied-state triphones for all utterances, which are used as labels for the output layer in the DNN. The DNN with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers, then it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). Second, the shared class labels method is introduced among misclassified classes to regularize the weights in DNN. Third, DNN-based speakers models using the SDC feature set is proposed. The speakers-aware model can capture the characteristics of the speaker age and gender more effectively than a model that represents a group of speakers. In addition, AGender-Tune system is proposed to classify the speaker age and gender by jointly fine-tuning two DNN models; the first model is pre-trained to classify the speaker age, and second model is pre-trained to classify the speaker gender. Moreover, the new T-MFCCs feature set is used as the input of a fusion model of two systems. The first system is the DNN-based class model and the second system is the DNN-based speaker model. Utilizing the T-MFCCs as input and fusing the final score with the score of a DNN-based class model enhanced the classification accuracies. Finally, the DNN-based speaker models are embedded into an AGender-Tune system to exploit the advantages of each method for a better speaker age and gender classification.</p><p> The experimental results on a public challenging database showed the effectiveness of the proposed methods for enhancing the speaker age and gender classification and achieved the state of the art on this database.</p><p>

<p> When reviewing the performance of Intelligent Virtual Assistants (IVAs), it is desirable to prioritize conversations involving misunderstood human inputs. These conversations uncover error in natural language understanding and help prioritize and expedite improvements to the IVA. As human reviewer time is valuable and manual analysis is time consuming, prioritizing the conversations where misunderstanding has likely occurred reduces costs and speeds improvement. A system for measuring the posthoc <i>risk of missed intent </i> associated with a single human input is presented. Numerous indicators of risk are explored and implemented. These indicators are combined using various means and evaluated on real world data. In addition, the ability for the system to adapt to different domains of language is explored. Finally, the system performance in identifying errors in IVA understanding is compared to that of human reviewers and multiple aspects of system deployment for commercial use are discussed.</p><p>

abstract: Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios. / Dissertation/Thesis / Masters Thesis Computer Science 2019

Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Intuition about clustering reflects the ideal case -- exact data sets endowed with flawless dissimilarity between individual instances. In practice however, these cases are in the minority, and clustering applications are typically characterized by noisy data sets with approximate pairwise dissimilarities. As such, the efficacy of clustering methods necessitates robustness to perturbations. In this paper, we address foundational questions on perturbation robustness, studying to what extent can clustering techniques exhibit this desirable characteristic. Our results also demonstrate the type of cluster structures required for robustness of popular clustering paradigms. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2017. / May 4, 2017. / Includes bibliographical references. / Margareta Ackerman, Professor Co-Directing Thesis; Gary Tyson, Professor Co-Directing Thesis; Sonia Haiduc, Committee Member; Peixiang Zhao, Committee Member.

Typescript (photocopy). / Digitized by Kansas Correctional Industries