Building a finite state automaton for physical processes using queries and counterexamples on long short-term memory models

Most neural networks (NN) are commonly used as black-box functions. A network takes an input and produces an output, without the user knowing what rules and system dynamics have produced the specific output. In some situations, such as safety-critical applications, having the capability of understanding and validating models before applying them can be crucial. In this regard, some approaches for representing NN in more understandable ways, attempt to accurately extract symbolic knowledge from the networks using interpretable and simple systems consisting of a finite set of states and transitions known as deterministic finite-state automata (DFA). In this thesis, we have considered a rule extraction approach developed by Weiss et al. that employs the exact learning method L* to extract DFA from recurrent neural networks (RNNs) trained on classifying symbolic data sequences. Our aim has been to study the practicality of applying their rule extraction approach on more complex data based on physical processes consisting of continuous values. Specifically, we experimented with datasets of varying complexities, considering both the inherent complexity of the dataset itself and complexities introduced from different discretization intervals used to represent the continuous data values. Datasets incorporated in this thesis encompass sine wave prediction datasets, sequence value prediction datasets, and a safety-critical well-drilling pressure scenario generated through the use of the well-drilling simulator OpenLab and the sparse identification of nonlinear dynamical systems (SINDy) algorithm. We observe that the rule extraction algorithm is able to extract simple and small DFA representations of LSTM models. On the considered datasets, extracted DFA generally demonstrates worse performance than the LSTM models used for extraction. Overall, for both increasing problem complexity and more discretization intervals, the performance of the extracted DFA decreases. However, DFA extracted from datasets discretized using few intervals yields more impressive results, and the algorithm can in some cases extract DFA that outperforms their respective LSTM models.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

LEARNING ARITHMETIC READ-ONCE FORMULAS*

Abstract. A formula is read-once if each variable appears at most once in it. An arithmetic read-once formula is one in which the operators are addition, subtraction, multiplication, and division. We present polynomial time algorithms for exact learning of arithmetic read-once formulas over a field. We present a membership and equivalence query algorithm that identifies arithmetic read-once formulas over an arbitrary field. We present a randomized membership query algorithm (i.e., a randomized black box interpolation algorithm) that identifies such formulas over finite fields with at least 2n + 5 elements (where n is the number of variables) and over infinite fields. We also show the existence of nonuniform deterministic membership query algorithms for arbitrary read-once formulas over fields of characteristic 0, and division-free read-once formulas over fields that have at least 2n + elements. For our algorithms, we assume we are able to perform efficiently arithmetic operations on field elements and compute square roots in the field. It is shown that the ability to compute square roots is necessary in the sense that the problem of computing n square roots in a field can be reduced to the problem of identifying an arithmetic formula over n variables in that field. Our equivalence queries are of a slightly nonstandard form, in which counterexamples are required not to be inputs on which the formula evaluates to 0/0. This assumption is shown to be necessary for fields of size o(n! log n) in the sense that we prove there exists no polynomial time identification algorithm that uses only membership and standard equivalence queries

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Making Machines Learn. Applications of Cultural Analytics to the Humanities

The digitization of several million books by Google in 2011 meant the popularization of a new kind of humanities research powered by the treatment of cultural objects as data. Culturomics, as it is called, was born, and other initiatives resonated with such a methodological approach, as is the case with the recently formed Digital Humanities or Cultural Analytics. Intrinsically, these new quantitative approaches to culture all borrow from techniques and methods developed under the wing of the exact sciences, such as computer science, machine learning or statistics. There are numerous examples of studies that take advantage of the possibilities that treating objects as data has to offer for the understanding of the human. This new data science that is now applied to the current trends in culture can also be replicated to study more traditional humanities. Led by proper intellectual inquiry, an adequate use of technology may bring answers to questions intractable by other means, or add evidence to long held assumptions based on a canon built from few examples. This dissertation argues in favor of such approach. Three different case studies are considered. First, in the more general sense of the big and smart data, we collected and analyzed more than 120,000 pictures of paintings from all periods of art history, to gain a clear insight on how the beauty of depicted faces, in the framework of neuroscience and evolutionary theory, has changed over time. A second study covers the nuances of modes of emotions employed by the Spanish Golden Age playwright Calderón de la Barca to empathize with his audience. By means of sentiment analysis, a technique strongly supported by machine learning, we shed some light into the different fictional characters, and how they interact and convey messages otherwise invisible to the public. The last case is a study of non-traditional authorship attribution techniques applied to the forefather of the modern novel, the Lazarillo de Tormes. In the end, we conclude that the successful application of cultural analytics and computer science techniques to traditional humanistic endeavours has been enriching and validating

Multimodal Video Analysis and Modeling

From recalling long forgotten experiences based on a familiar scent or on a piece of music, to lip reading aided conversation in noisy environments or travel sickness caused by mismatch of the signals from vision and the vestibular system, the human perception manifests countless examples of subtle and effortless joint adoption of the multiple senses provided to us by evolution. Emulating such multisensory (or multimodal, i.e., comprising multiple types of input modes or modalities) processing computationally offers tools for more effective, efficient, or robust accomplishment of many multimedia tasks using evidence from the multiple input modalities. Information from the modalities can also be analyzed for patterns and connections across them, opening up interesting applications not feasible with a single modality, such as prediction of some aspects of one modality based on another. In this dissertation, multimodal analysis techniques are applied to selected video tasks with accompanying modalities. More specifically, all the tasks involve some type of analysis of videos recorded by non-professional videographers using mobile devices.Fusion of information from multiple modalities is applied to recording environment classification from video and audio as well as to sport type classification from a set of multi-device videos, corresponding audio, and recording device motion sensor data. The environment classification combines support vector machine (SVM) classifiers trained on various global visual low-level features with audio event histogram based environment classification using k nearest neighbors (k-NN). Rule-based fusion schemes with genetic algorithm (GA)-optimized modality weights are compared to training a SVM classifier to perform the multimodal fusion. A comprehensive selection of fusion strategies is compared for the task of classifying the sport type of a set of recordings from a common event. These include fusion prior to, simultaneously with, and after classification; various approaches for using modality quality estimates; and fusing soft confidence scores as well as crisp single-class predictions. Additionally, different strategies are examined for aggregating the decisions of single videos to a collective prediction from the set of videos recorded concurrently with multiple devices. In both tasks multimodal analysis shows clear advantage over separate classification of the modalities.Another part of the work investigates cross-modal pattern analysis and audio-based video editing. This study examines the feasibility of automatically timing shot cuts of multi-camera concert recordings according to music-related cutting patterns learnt from professional concert videos. Cut timing is a crucial part of automated creation of multicamera mashups, where shots from multiple recording devices from a common event are alternated with the aim at mimicing a professionally produced video. In the framework, separate statistical models are formed for typical patterns of beat-quantized cuts in short segments, differences in beats between consecutive cuts, and relative deviation of cuts from exact beat times. Based on music meter and audio change point analysis of a new recording, the models can be used for synthesizing cut times. In a user study the proposed framework clearly outperforms a baseline automatic method with comparably advanced audio analysis and wins 48.2 % of comparisons against hand-edited videos