This report describes the results of automated facial expression analysis by the CMU/Pittsburgh group. An interdisciplinary team of consultants, who have combined expertise in computer vision and in facial analysis, will compare the results of this report with those in a separate report submitted by UCSD group. BACKGROUND People communicate not only by speech and written language but also by their tone of voice, the way they stand or move and their patterns of gaze. These modes of nonverbal behavior communicate emotion and often are referred to as paralinguistic because they modify, substitute for, and improve the understanding of spoken communication. Of the various modes of nonverbal communication, the human face is especially important. Facial expressions can indicate emotion and pain, regulate social behavior, and reveal brain function. A large literature in psychology (Ekman & Rosenberg, 1997), comparative biology (Darwin, 1872/1998; Fridlund, 1994; Schmidt & Coh

is the state of the art in manual measurement of facial actions. FACS coding, however, is labor intensive and difficult to standardize. A goal of automated FACS coding is to eliminate the need for manual coding and realize automatic recognition and analysis of facial actions. Success of this effort

Abstract. FACS (Facial Action Coding System) coding is the state of the art in manual measurement of facial actions. FACS coding, however, is labor intensive and difficult to standardize. A goal of automated FACS coding is to eliminate the need for manual coding and realize automatic recognition

Abstract. FACS (Facial Action Coding System) coding is the state of the art in manual measurement of facial actions. FACS coding, however, is labor intensive and difficult to standardize. A goal of automated FACS coding is to eliminate the need for manual coding and realize automatic recognition

Abstract not found

an automated method of facial display analysis. In this report we compare the results with those of manual FACS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from

-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial

SIS is an interactive tool for synthesis and optimization of sequential circuits. Given a state transition table, a signal transition graph, or a logic-level description of a sequential circuit, it produces an optimized net-list in the target technology while preserving the sequential input-output behavior. Many different programs and algorithms have been integrated into SIS, allowing the user to choose among a variety of techniques at each stage of the process. It is built on top of MISII [5] and includes all (combinational) optimization techniques therein as well as many enhancements. SIS serves as both a framework within which various algorithms can be tested and compared, and as a tool for automatic synthesis and optimization of sequential circuits. This paper provides an overview of SIS. The first part contains descriptions of the input specification, STG (state transition graph) manipulation, new logic optimization and verification algorithms, ASTG (asynchronous signal transition graph) manipulation, and synthesis for PGA’s (programmable gate arrays). The second part contains a tutorial example illustrating the design process using SIS.

Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (self-explaining) facilitates that integration process. Previously, self-explanation has been shown to improve the acquisition of problem-solving skills when studying worked-out examples. This study extends that finding, showing that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth-grade students were merely asked to self-explain after reading each line of a possage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self-explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated o large number of self-explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model onolysis of the self-explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self-explaining are considered as reasons for the gains in deeper understanding. Preparation of this article was supported in part by an Office of Educational Research and

The installation of high-speed networks using optical fiber and high bandwidth messsage forwarding gateways is changing the physical capabilities of information systems. These capabilities must be complemented with corresponding software systems advances to obtain a real benefit. Without smart software we will gain access to more data, but not improve access to the type and quality of information needed for decision making. To develop the concepts needed for future information systems we model information processing as an interaction of data and knowledge. This model provides criteria for a high-level functional partitioning. These partitions are mapped into information process-ing modules. The modules are assigned to nodes of the distributed information systems. A central role is assigned to modules that mediate between the users ' workstations and data re-sources. Mediators contain the administrative and technical knowledge to create information needed for decision-making. Software which mediates is common today, but the structure, the interfaces, and implementations vary greatly, so that automation of integration is awkward. By formalizing and implementing mediation we establish a partitioned information sys-