This paper highlights some of our recent research efforts in designing and evaluating life-like characters that are capable of entertaining affective and social communication with human users. The key novelty of our approach is the use of human physiological information: first, as a method to evaluate the effect of life-like character behavior on a moment-to-moment basis, and second, as an input modality for a new generation of interface agents that we call 'physiologically perceptive' life-like characters. By exploiting the stream of primarily involuntary human responses, such as autonomic nervous system activity or eye movements, those characters are expected to respond to users' affective and social needs in a truly sensitive, and hence effective, friendly, and beneficial way.

The error-rate performance of digital FM with discriminator-detection under the fast Rayleigh fading environment is theoretically analyzed in the presence of Gaussian noise and co-channel interference. While the effect of intersymbol interference caused by band width lmitations and time-delay spread is neglected, the fading spectrum effect is taken into consideration. The error-rate including not only the Rayleigh envelope fading effect but also the random FM noise effect is given in a simple closed form.

The most creative tasks in synthesizing pipelined data paths executing software descriptions are determinations of latency and stage of pipeline, operation scheduling and hardware allocation. They are interrelated closely and depend on each other; thus finding its optimal solution has been a hard problem so far. By using simulated annealing methodology, these three tasks can be formulated as a three dimensional placement problem of operations in stage, time step and functional units space. This paper presents an efficient method based on simulated annealing to provide excellent solutions to the problem of not only the determinations of latency and stage of pipeline, operation scheduling and hardware allocation simultaneously, but also the pipelined data path synthesis under the constraints of performance or hardware cost. It is able to find a near optimal latency and stage of pipeline, an operation schedule and a hardware allocation in a reasonable time, while effectively exploring the existing tradeoffs in the design space.

Measuring the relational similarity between word pairs is important in numerous natural language processing tasks such as solving word analogy questions, classifying noun-modifier relations and disambiguating word senses. We propose a supervised classification method to measure the similarity between semantic relations that exist between words in two word pairs. First, each pair of words is represented by a vector of automatically extracted lexical patterns. Then a binary Support Vector Machine is trained to recognize word pairs with similar semantic relations to a given word pair. To train and evaluate the proposed method, we use a benchmark dataset that contains 374 SAT multiple-choice word-analogy questions. To represent the relations that exist between two word pairs, we experiment with 11 different feature functions, including both symmetric and asymmetric feature functions. Our experimental results show that the proposed method outperforms several previously proposed relational similarity measures on this benchmark dataset, achieving an SAT score of 46.9.

Recent years have witnessed a growing interest in employing animated agents for tasks that are typically performed by humans. They serve as communicative partners in a variety of applications, such as tutoring systems, sales, or entertainment. This paper first discusses design principles for animated agents to enhance their effectiveness as tutors, sales persons, or actors, among other roles. It is argued that agents should support their perception as social actors by displaying human-like social cues such as affect and gestures. An architecture for emotion-based agents will be described and a simplified version of the model will be illustrated by two interaction scenarios that feature cartoon-style characters and can be run in a web browser. The second focus of this paper is an empirical evaluation of the effect of an affective agent on users' emotional state which is derived from physiological signals of the user. Our findings suggest that an agent with affective behavior may significantly decrease user frustration.

Although there has been a great deal of research on automatic summarization, most methods rely on statistical methods, disregarding relationships between extracted textual segments. We propose a novel method to extract a set of comprehensible sentences which centers on several key points to ensure sentence connectivity. It features a similarity network from documents with a lexical dictionary, and spreading activation to rank sentences. We show evaluation results of a multi-document summarization system based on the method participating in a competition of summarization, TSC (Text Summarization Challenge) task, organized by the third NTCIR project.

This paper describes a vision system with dual viewing angles, i. e., wide and narrow viewing angles, and a scheme of user-friendly speech dialogue environment based on the vision system. The wide viewing angle provides a wide viewing field for wide range motion tracking, and the narrow viewing angle is capable of following a target in wide viewing field to take the image of the target with sufficient resolution. For a fast and robust motion tracking, modified motion energy (MME) and existence energy (EE) are defined to detect the motion of the target and extract the motion region at the same time. Instead of using a physical device such as a foot switch commonly used in speech dialogue systems, the begin/end of an utterance is detected from the movement of user's mouth in our system. Without recognizing the movement of lips directly, the shape variation of the region between lips is tracked for more stable recognition of the span of a dialogue. The tracking speed is about 10 frames/sec when no recognition is performed and about 5 frames/sec when both tracking and recognition are performed without using any special hardware.

Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.