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In this paper, we present techniques for recognizing complex, multitasked activities from video. Visual information like image features and motion appearances, combined with domain-specific information, like object context is used initially to label events. Each action event is represented with a unique symbol, allowing for a sequence of interactions to be described as an ordered symbolic string. Then, a model of stochastic context-free grammar (SCFG), which is developed using underlying rules of an activity, is used to provide the structure for recognizing semantically meaningful behavior over extended periods. Symbolic strings are parsed using the Earley-Stolcke algorithm to determine the most likely semantic derivation for recognition. Parsing substrings allows us to recognize patterns that describe high-level, complex events taking place over segments of the video sequence.

In this paper, we present techniques for characterizing complex, multi-tasked activities that require both exemplars and models. Exemplars are used to represent object context, image features, and motion appearances to label domainspecific events. Then, by representing each event with a unique symbol, a sequence of interactions can be described as an ordered symbolic string. A model of stochastic contextfree grammar, which is developed using underlying rules of an activity, provides the structure for recognizing semantically meaningful behavior over extended periods. Symbolic strings are parsed using the Earley-Stolcke algorithm to determine the most likely semantic derivation for recognition. Parsing substrings allows us to recognize patterns that describe high-level, complex events taking place over segments of the video sequence. We introduce new parsing strategies to enable error detection and recovery in stochastic context-free grammar and methods of quantifying group and individual behavior in activities with separable roles. We show through experiments with a popular card game how high-level narratives of multi-player games as well as identification of player strategies and behavior can be extracted in real-time using vision.

We review Models of Computation (MoC) and organize them with respect to the time abstraction they use. We distinguish between continuous time, discrete time, synchronous and untimed MoCs. System level models serve a variety of objectives with partially contradicting requirements. Consequently we argue that different MoCs are necessary for the various tasks and phases in the design of an embedded system. Moreover, different MoCs have to be integrated to provide a coherent system modeling and analysis environment. We discuss the relation between some popular languages and the reviewed MoCs to find that a given MoC is offered by many languages and a single language can support multiple MoCs. We contend that it is of importance for the quality of tools and overall design productivity, which abstraction levels and which primitive operators are provided in a language. However, we also observe that there are various flexible ways to do this, e.g. by way of heterogeneous frameworks, coordination languages and embedding of different MoCs in the same language.

The introduction of educational software such as JFLAP into the course Formal Languages and Automata (FLA) has created a learning environment with automatic feedback on theoretical topics. In this paper we show how we further increase the interaction in the FLA course with the expansion of additional theoretical topics in JFLAP, and how we have added grading support into JFLAP for instructors.

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The algorithm selection problem aims at selecting the best algorithm for a given computational problem instance according to some characteristics of the instance. In this dissertation, we first introduce some results from theoretical investigation of the algorithm selection problem. We show, by Rice's theorem, the nonexistence of an automatic algorithm selection program based only on the description of the input instance and the competing algorithms. We also describe an abstract theoretical framework of instance hardness and algorithm performance based on Kolmogorov complexity to show that algorithm selection for search is also incomputable. Driven by the theoretical results, we propose a machine learning-based inductive approach using experimental algorithmic methods and machine learning techniques to solve the algorithm selection problem. Experimentally, we have

In this dissertation, we address the problem of recognizing human interactions with objects from video. Methods for recognizing these activities using human motion and information about objects are developed for practical, real-time systems. We introduce a framework, called ObjectSpaces, that sorts, stores, and manages data acquired using low-level vision techniques into intuitive classes. Our framework decomposes the recognition process into layers, i.e., a low-level layer for routine hand and object tracking and a high-level layer for domain-specific representation of activities. Segmenting recognition tasks and information in this way encourages model reuse and provides the flexibility to use a single framework in a variety of domains. We present several ways of...

First, we trace the history of models of computation (MoC) from the early days of computing to the current situation. In the beginning models of computation have been used to study what can be computed in principle. Then, they have been used to address the question how much time and resources are required in principle to solve a given problem. For parallel computers various computational models have been developed to capture the essential features of communication and memory architecture of parallel machines. Then we observe, that the situation becomes really complicated for embedded systems due to real-time requirements, heterogeneous architectures and applications, and real-world interaction. We argue that computational models should be devised for aiding the analysis and design of concrete embedded systems by striking a difficult balance between general and abstract MoCs on one hand and accurate and efficient MoCs on the other hand.

We describe the results from a two-year study with fourteen universities on presenting formal languages in a more visual, interactive and applied manner using JFLAP. In our results the majority of students felt that having access to JFLAP made learning course concepts easier, made them feel more engaged in the course and made the course more enjoyable. We also describe changes and additions to JFLAP we have made based on feedback from users. These changes include new algorithms such as a CYK parser and a user-controlled parser, and new resources that include a JFLAP online tutorial, a wiki and a listserv.