Understanding Human Behaviors Based on Eye-Head-Hand Coordination
Action recognition has traditionally focused on processing fixed camera observations while ignoring non-visual information. In this paper, we explore the dynamic properties of the movements of different body parts in natural tasks: eye, head and hand movements are quite tightly coupled with the ongoing task. In light of this, our method takes an agent-centered view and incorporates an extensive description of eye-head-hand coordination. With the ability to track the course of gaze and head movements, our approach uses gaze and head cues to detect agent-centered attention switches that can then be utilized to segment an action sequence into action units. Based on recognizing those action primitives, parallel hidden Markov models are applied to model and integrate the probabilistic sequences of the action units of different body parts. An experimental system is built for recognizing human behaviors in three natural tasks: "unscrewing a jar", "stapling a letter" and "pouring water", which demonstrates the effectiveness of the approach.

title: The emergence of links between lexical acquisition and object categorization: a computational study
abstract: Language is about symbols, and those symbols must be grounded in the physical world. Children learn to associate language with sensorimotor experiences during their development. In light of this, we first provide a computational account of how words are mapped to their perceptually grounded meanings. Moreover, the main part of this work proposes and implements a computational model of how word learning influences the formation of object categories to which those words refer. This model simulates the bi-directional relationship between word and object category learning: (1) object categorization provides mental representations of meanings that are mapped to words to form lexical items; (2) linguistic labels help object categorization by providing additional teaching signals; and (3) these two learning processes interplay with each other and form a developmental feedback loop. Compared with the method that performs these two tasks separately, our model shows promising improvements in both word-to-world mapping and perceptual categorization, suggesting a unified view of lexical and category learning in an integrative framework. Most importantly, this work provides a cognitively plausible explanation of the mechanistic nature of early word learning and object learning from co-occurring multisensory data.

title: On the Integration of Grounding Language and Learning Objects
abstract: This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision.

title: Attentional object spotting by integrating multimodal input
abstract: An intelligent human-computer interface is expected to allow computers to work with users in a cooperative manner. To achieve this goal, computers need to be aware of user attention and provide assistance without explicit user requests. Cognitive studies of eye movements suggest that in accomplishing well-learned tasks, the performer's focus of attention is locked onto ongoing work and more than 90% of eye movements are closely related to the objects being manipulated in the tasks. In light of this, we have developed an attentional object spotting system that integrates multimodal data consisting of eye position, head position and video from the "first-person" perspective. To detect the user's focus of attention, we modeled eye gaze and head movements using a hidden Markov model (HMM) representation. For each attentional point in time, the object of user interest is automatically extracted and recognized. We report the results of experiments on finding attentional objects in the natural task of "making a peanut-butter sandwich".

title: Detecting user engagement in everyday conversations
abstract: This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users’ emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspects of conversational engagement. We therefore propose the use of a multilevel structure based on coupled hidden Markov models (HMM) to estimate engagement levels in continuous natural speech. The first level is comprised of SVM-based classifiers that recognize emotional states, which could be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM then uses these emotional states as input, estimating users’ engagement in conversation by decoding the internal states of the HMM. We report experimental results obtained by applying our algorithms to the LDC EMOTIONAL PROSODY and CALLFRIEND speech corpora.

title: A multimodal learning interface for grounding spoken language in sensory perceptions
abstract: We present a multimodal interface that learns words from natural interactions with users. In light of studies of human language development, the learning system is trained in an unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. The system collects acoustic signals in concert with user-centric multisensory information from nonspeech modalities, such as user's perspective video, gaze positions, head directions, and hand movements. A multimodal learning algorithm uses this data to first spot words from continuous speech and then associate action verbs and object names with their perceptually grounded meanings. The central ideas are to make use of nonspeech contextual information to facilitate word spotting, and utilize body movements as deictic references to associate temporally cooccurring data from different modalities and build hypothesized lexical items. From those items, an EM-based method is developed to select correct word--meaning pairs. Successful learning is demonstrated in the experiments of three natural tasks: "unscrewing a jar," "stapling a letter," and "pouring water."

title: WISE-Integrator: An Automatic Integrator of Web Search Interfaces for E-Commerce
abstract: More and more databases are becoming Web accessible through form-based search interfaces, and many of these sources are E-commerce sites. Providing a unified access to multiple E-commerce search engines selling similar products is of great importance in allowing users to search and compare products from multiple sites with ease. One key task for providing such a capability is to integrate the Web interfaces of these E-commerce search engines so that user queries can be submitted against the integrated interface. Currently, integrating such search interfaces is carried out either manually or semi-automatically, which is inefficient and difficult to maintain. In this paper, we present WISE-Integrator - a tool that performs automatic integration of Web Interfaces of Search Engines. WISE-Integrator employs sophisticated techniques to identify matching attributes from different search interfaces for integration. It also resolves domain differences of matching attributes. Our experimental results based on 20 and 50 interfaces in two different domains indicate that WISE-Integrator can achieve high attribute matching accuracy and can produce high-quality integrated search interfaces without human interactions.

title: Schema summarization
abstract: Real database systems can often be very complex. A person wishing to access data from an unfamiliar database has the daunting task of understanding its schema before being able to pose a correct query against it. A schema summary can be of great help, providing a succinct overview of the entire schema, and making it possible to explore in depth only the relevant schema components.In this paper we formally define a schema summary and two desirable properties (in addition to minimizing size) of a summary: presenting important schema elements and achieving broad information coverage. We develop algorithms that allow us to automatically generate schema summaries based on these two goals. We further develop an objective metric for assessing the quality of a schema summary using query information. Experimental evaluation using this metric demonstrates that the summaries produced by our algorithms can significantly reduce the amount of user effort required to formulate a query through schema exploration.

title: Effective Integration of Protein Data through Better Data Modeling
abstract: Protein data, from sequence and structure to interaction, is being generated through many diverse methodologies; it is stored and reported in numerous forms and multiple places. The magnitude of the data limits researchers abilities to utilize all information generated. Effective integration of protein data can be accomplished through better data modeling. We demonstrate this through the MIPD project.

title: Distributed Top-N Query Processing with Possibly Uncooperative Local Systems
abstract: We consider the problem of processing top-N queries in a distributed environment with possibly uncooperative local database systems. For a given top-N query, the problem is to find the N tuples that satisfy the query the best but not necessarily completely in an efficient manner. Top-N queries are gaining popularity in relational databases and are expected to be very useful for e-commerce applications. Many companies provide the same type of goods and services to the public on the Web, and relational databases may be employed to manage the data. It is not feasible for a user to query a large number of databases. It is therefore desirable to provide a facility where a user query is accepted at some site, suitable tuples from appropriate sites are retrieved and the results are merged and then presented to the user. In this paper, we present a method for constructing the desired facility. Our method consists of two steps. The first step determines which databases are likely to contain the desired tuples for a given query so that the databases can be ranked based on their desirability with respect to the query. Four different techniques are introduced for this step with one requiring no cooperation from local systems. The second step determines how the ranked databases should be searched and what tuples from the searched databases should be returned. A new algorithm is proposed for this purpose. Experimental results are presented to compare different methods and very promising results are obtained using the method that requires no cooperation from local databases.

title: Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together
abstract: Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: .