Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research The paper presents a model integrating theories from collaboration research (i.e., social presence theory, channel expansion theory, and the task closure model) with a recent theory from technology adoption research (i.e., unified theory of acceptance and use of technology, abbreviated to UTAUT) to explain the adoption and use of collaboration technology. We theorize that collaboration technology characteristics, individual and group characteristics, task characteristics, and situational characteristics are predictors of performance expectancy, effort expectancy, social influence, and facilitating conditions in UTAUT. We further theorize that the UTAUT constructs, in concert with gender, age, and experience, predict intention to use a collaboration technology, which in turn predicts use. We conducted two field studies in Finland among (1) 349 short message service (SMS) users and (2) 447 employees who were potential users of a new collaboration technology in an organization. Our model was supported in both studies. The current work contributes to research by developing and testing a technology-specific model of adoption in the collaboration context. Figure 2 presents our research model. In addition to contextualizing utaut to collaboration technologies, we present determinants of the four key utaut predictorsperformance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy and effort expectancy are influenced by technology characteristics (social presence, immediacy, and concurrency) and individual and group characteristics (technology experience, computer self-efficacy, and familiarity with communication partners). task characteristics are expected to moderate the relationship between technology characteristics and performance expectancy. the situational variables attributed to co-workers (influence of peers and superiors) are expected to influence social influence while the situational variables attributed to the environment are expected to influence facilitating conditions. Method we conDucteD two StuDieS. the objective of the first study was to test our model in the context of a general collaboration tool used to support day-to-day communicationshort message service (SMS). the first study did not incorporate a specific task and the data were cross-sectional. the second study was conducted to complement the first study, with the objective of testing the model in the context of an organizational implementation of a collaboration technology that allowed for an examination of task differences. Further, we collected use data six months after we collected data about perceptions and intentions.. Measures the survey instrument used previously validated measures where available. the items used in Study 2, the organizational study, are shown in the appendix, with similar adapted items being used in Study 1. the constructs of intention to use, performance expectancy, effort expectancy, social influence, and facilitating conditions were measured using scales adapted from Davis et al. [17] and Venkatesh et al. [76] . Our measure of use was adapted and extended from prior research on technology adoption [17, 76] that used four items examining intensity, frequency, duration, and choice. these items were reflective indicators of the latent variable of use. the choice item is new and measured the percentage of time the individual chose to use SMS relative to the overall need. we felt that different employees may need to collaborate to a different extent, thus potentially constraining how much they would choose to use a collaboration tool. By considering choice as part of use, we account for an important aspect of an individual's collaboration in the context of work that is not typically considered in measures of use, be it self-reported or actual [76] . By paying attention to all aspects of the behavior-that is, the act of using and the choice to use-we are enhancing the content validity of the measure relative to previous measures of use [38] .. the scales for technology characteristics, individual and group characteristics, and situational characteristics were adapted from prior research where possible. the measures for social presence and familiarity with others (communication partners) were adapted from Short et al. [64] and Carlson and Zmud [11] , respectively. resource-facilitating conditions, technology-facilitating conditions, peer influence, and superior influence were adapted from prior work [67, 76] . Computer self-efficacy was measured using a scale adapted from Compeau and higgins [15] . the effect of task was assessed only in the second study by asking participants to answer the survey questions one time considering an idea-generation task and one time considering a decision-making task. the order of the tasks was randomized and no significant order effect was found. Gender and age were measured using single items. No scales were available to measure experience, immediacy, and concurrency. we created the scales for these three constructs using standard procedures of scale development [28] . we created several candidate items, which we carefully examined for content validity. these items were circulated for peer feedback and card sorts in order to arrive at the final set of items, which possessed face validity and content validity.. Pretests and Pilot Study two pretests of the instrument were conducted to ensure that the measures were applicable in the current context. First, ten individuals (in two groups of five) affiliated with the university were recruited to participate in this pretest. Each individual was asked to complete the questionnaire and then provided the opportunity to comment on any aspect of the questionnaire. the primary feedback from the first group was with regard to the use of some "complex" English words/terms in the questions. Based on this feedback, a few questions were slightly reworded. the updated questionnaire was then validated with the second group of pretest participants and feedback solicited. No significant suggestions were made and, thus, no further changes were made.. the revised survey was administered among 111 undergraduate students. the focus of the pilot study was to examine the reliability and validity of the scales in the context of a collaboration technology-here, SMS. we were particularly interested in establishing the reliability and validity of the new scales-immediacy and concurrency. the new scales were found to be reliable, with Cronbach's alpha exceeding 0.80. the other scales were also highly reliable, with similar Cronbach's alpha scores. Next, a principal components analysis with varimax rotation was conducted among the multi-item constructs from collaboration technology research-that is, social presence, media richness, immediacy, concurrency, and familiarity with others. a clean factor structure was obtained, with loadings greater than 0.70 and cross-loadings less than 0.35, thus supporting internal consistency and discriminant validity. a similar analysis was conducted among the utaut predictors and a clean factor structure was obtained there as well.. Given the total number of items from all multi-item constructs in the model, the sample size in this pilot study was not sufficient to test internal consistency and discriminant validity of all constructs in a single test using exploratory factor analysis. however, this concern is somewhat alleviated for three reasons: (1) the new scales were developed in the context of collaboration technology research and the likelihood of overlap was more with the constructs in that domain, (2) the technology adoption constructs and collaboration technology constructs come from very different bodies of research where there has been minimal conceptual overlap thus far, and (3) the entire model and scales will be validated in the actual data set using confirmatory factor analysis in partial least squares (PlS). we DeVelopeD anD ValiDateD a moDel of the use of collaborative technologies. the model was developed by integrating utaut constructs with constructs drawn from collaboration technology theories-specifically, social presence theory, media richness theory and its descendants, and the task closure model. the constructs were selected to be applicable to the general class of collaboration technologies. the model was validated in two different settings, using two different collaboration tools. Specifically, we found that utaut mediated the effects of various constructs from collaboration research on intention to use a collaboration technology. the results from our study have important implications for research on collaboration technologies and provide practical guidance regarding collaboration technology use in general. this work integrates major streams of work into a single nomological network, and the proposed integrated model provides guidance for the design of collaboration technologies to foster adoption. 1 . the terms "acceptance" and "adoption" are frequently used interchangeably in the literature. In this paper, we primarily use the term "adoption," but we stay true to the original sources when it comes to model names, such as the unified theory of acceptance and use of technology (utaut).. 2. we explicate the utaut hypotheses for the context of this study and highlight the underlying logic. Given the vast amount of technology adoption research and our focus on the integration of collaboration technology research and utaut, we refer the reader to Venkatesh et al. [76] for a detailed discussion of the general logic underlying the utaut hypotheses.. 3. SMS can also be used for providing automated message services, such as welcome messages, targeted advertising, and voice mail notifications, and can even support "commerce" applications such as ticket purchases [29] . Our focus in this paper is on the use of SMS as a collaboration technology, so we excluded non-collaboration-oriented uses of SMS.. 4. as pointed out by an anonymous reviewer, this is a fairly high response rate for a survey with no incentives. we attribute this to the respondents' loyalty to their university as well as follow-up calls made by the university to encourage responses.. 5. as in Study 1, this is a high response rate. In this case, strong top management support for the collaboration technology and for our study are the reasons for the response rate.. 6. In Study 2, we collected user reactions to the collaboration technology for two separate, different task contexts, so we had two records per respondent in our data set. One concern with including multiple responses from a single participant is that it is possible that there may be correlated errors and consequent spurious relationships, although such correlation does not impair the use of PlS [12] . Nonetheless, to ensure there was nothing unusual in our specific data set that would cause problems, we conducted an additional empirical analysis to simulate a between-subjects test of the model. we built 100 data sets that randomly included one of the responses from each respondent and conducted a separate PlS analysis on each of these 100 data sets. For 98 of these data sets, the results were identical to what we have reported from our full model test. thus, the use of both responses from each respondent does not seem to cause inflated significance for our data set.. 7. Note that effort expectancy is coded such that a higher value means lower effort expectancy or easier to use.. 8. remember that effort expectancy is reverse coded, so higher effort expectancy means less effort is expected. Intention to use (seven-point likert agreement scale). I intend to use the <collaboration tool> in the next 6 months. I predict I would use the system in the next 6 months. I plan to use the system in the next 6 months.. Performance Expectancy (seven-point likert agreement scale). I believe <collaboration tool> will be useful for communication. using <collaboration tool> will enable me to accomplish work tasks more quickly. using the collaboration tool will increase my productivity.. Effort Expectancy (seven-point likert agreement scale) using <collaboration tool> will not require a lot of mental effort. I believe <collaboration tool> will be easy to use. using <collaboration tool> will be easy for me. Social Presence (seven-point likert agreement scale). using <collaboration tool> to interact with others creates a warm environment for communication. using <collaboration tool> to interact with others creates a sociable environment for communication. using <collaboration tool> to interact with others creates a personal environment for communication.. Immediacy (seven-point likert agreement scale) <Collaboration tool> enables me to quickly reach communication partners. when I communicate with someone using <collaboration tool>, they usually respond quickly. when someone communicates with me using <collaboration tool>, I try to respond immediately. the key oBjectiVe of thiS paper was to develop and test a model to understand collaboration technology use. the model integrated utaut [76] with theories from collaboration technology research [11, 16, 25, 64, 66] . the model was supported in two studies examining two different collaboration technologies that used different subject pools. the results from the two studies were similar. we found that utaut is the conduit through which collaboration technology research constructs of technology, individual/group, task, and situational characteristics influence behavioral intention and use of that collaboration technology. Collaboration technology collaBoration technology iS a package of hardware and software that can provide one or more of the following: (1) support for communication among participants, such as electronic communication to augment or replace verbal communication; (2) information-processing support, such as mathematical modeling or voting tools; and (3) support to help participants adopt and use the technology, such as agenda tools or real-time training (e.g., [24, 26, 81] ). a variety of terms have been used to refer to collaboration technology over the years-such as group decision support systems, group support systems, electronic meeting systems, groupware, computer-supported cooperative work, and negotiation support systems-but these, as well as specific systems, such as e-mail, voice mail, and videoconferencing, are generally encompassed under the larger umbrella term of collaboration technology.. Collaboration technology has been the subject of formal research at least since the 1970s, although its emergence as a key domain of research did not occur until the 1980s [18] . Many reviews of collaboration technology research have been published over the years outlining the development of research and highlighting trends in the empirical results [21, 24, 31, 32] . while early collaboration technology research initiatives were centered on decision room environments [18] , attention has more recently turned to collaboration technologies that support virtual teams and distributed work (e.g., e-mail, instant messaging, asynchronous discussion tools).. as collaboration technologies have evolved, our understanding of what contributes to their use has not kept pace [62] . Past research has found that the use of collaboration technology can produce strikingly different outcomes [21, 24, 31, 32] . Communication tools, such as e-mail, produce outcomes different from what is produced by more complete systems that include information-processing support tools [21] . likewise, the nature of the task-such as decision making versus idea generation [21] -can influence the value of a particular technology. thus, the fit of the technology to the task is important [24, 81] . Past research [24] suggests several key observations. First, if the technology fits the needs of the task, then the use of collaboration technology can improve decision quality and increase the number of ideas generated compared to not using it [24] . But, if the technology is a poor fit, little is gained, at least initially (see also [35] ). Further, the aspects of the technology that the group chooses to use and how they use them affect outcomes. Second, if groups new to a collaboration technology receive no support in choosing what aspects of the technology to use and guidance on how to use them, they take longer to complete tasks than groups working without technology [24] . If these same groups receive support (or if they have prior experience with the technology and task), they take less time and are more satisfied [24] . thus, use is a key factor affecting group performance.. unified theory of acceptance and use of technology Venkatesh et al. [76] proposed a unified model-namely, utaut-that incorporates four key predictors of intention to use technology: performance expectancy, effort expectancy, social influence, and facilitating conditions. Intention, in turn, predicts technology use. Performance expectancy is the extent to which an individual perceives that using a system will enhance his or her productivity, and thus lead to performance gains-performance expectancy is conceptually and empirically identical to perceived usefulness from taM [76] . Effort expectancy is the extent to which using a system is free from effort-effort expectancy is conceptually and empirically identical to perceived ease of use from taM [76] ; note that high effort expectancy suggests high ease of use and not high effort. Social influence is the extent to which an individual perceives that important others think that he or she should use the target system [67, 73] . Facilitating conditions is the perception regarding the availability of organizational and technical resources to support use of the target system [76] . Further, utaut argues that the various relationships are moderated by a combination of gender, age, experience, and voluntariness [76] . Several studies have reported use of systems in organizations as being either voluntary [69, 73, 76] or mandatory [9, 73, 76] . Given that our work focuses on voluntary contexts, we exclude voluntariness from our model. as noted earlier, the key question of interest in this paper is: Why do people choose to use collaboration technology? In general, people adopt a technology because they believe it will be useful in improving the effectiveness and efficiency of performing some task [20, 76] . these effectiveness and efficiency motives correspond directly to core underpinnings of performance expectancy and effort expectancy, respectively, thus making utaut particularly suitable as the basis for the model development. utaut also accounts for social influences and environmental factors not considered in the original conceptualization of taM. Further, utaut has explained over 70 percent variance in intention to use several different technologies [76] , thus making it a robust and comprehensive model.

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Towards a neurocomputational model of speech production and perception The limitation in performance of current speech synthesis and speech recognition systems may result from the fact that these systems are not designed with respect to the human neural processes of speech production and perception. A neurocomputational model of speech production and perception is introduced which is organized with respect to human neural processes of speech production and perception. The production-perception model comprises an artificial computer-implemented vocal tract as a front-end module, which is capable of generating articulatory speech movements and acoustic speech signals. The structure of the production-perception model comprises motor and sensory processing pathways. Speech knowledge is collected during training stages which imitate early stages of speech acquisition.This knowledge is stored in artificial self-organizing maps. The current neurocomputational model is capable of producing and perceiving vowels, VC-, and CV-syllables (V = vowels and C = voiced plosives). Basic features of natural speech production and perception are predicted from this model in a straight forward way: Production of speech items is feedforward and feedback controlled and phoneme realizations vary within perceptually defined regions. Perception is less categorical in the case of vowels in comparison to consonants. Due to its human-like production-perception processing the model should be discussed as a basic module for more technical relevant approaches for high quality speech synthesis and for high performance speech recognition. The experimental results presented in this paper indicate that a model of speech production and perception which is shaped with respect to basic neurophysiological facts is capable of embedding important features of speech production and speech perception in a straight peer-00550283, version 1 -26 Dec 2010

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The impact of learning styles on student grouping for collaborative learning: a case study Abstract Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules. The effect of taking into account information about learning styles for grouping students for collaborative learning activities in the context of a course on Theory of Computation was analyzed in this paper.. With the goal of studying the impact of learning styles and group homogeneity/ heterogeneity on the results obtained by students in collaborative tasks, a case study with 166 students of computer science was carried out, and its results have been presented in the paper. From this case study, it can be concluded that some dimensions of the learning style model, namely active-reflective and sensing-intuitive, seem to affect the quality of the resulting work.. These results suggest the possibility of improving collaborative learning by grouping students in specific ways. This can be of interest for collaborative learning systems such as TANGOW, which has been used as an application example. TANGOW delivers adaptive courses including collaboration activities. It supports automatic grouping of users. Therefore, it is suitable of using this knowledge to improve the way students are grouped for solving collaborative activities related to the subject presented in the case study. With this aim, new grouping rules have been incorporated in TANGOW to be used in the corresponding courses.. Currently we are working on a mechanism for establishing relationships between learning styles plus group formation and the student performance, in order to infer rules that can be applied for subsequent groupings, as it has been explained in Sect. 4.2. The next step is to carry out similar experiments on different subjects, in order to get information about this type of correlations, to infer conclusions for different disciplines starting from the observation of student behaviors.. It must be mentioned that there will probably be no "absolute best rules" for a course. Variations may occur from one course edition to another and the students may be pretty different each time. This may make difficult to compare their results with that obtained when using different grouping rules. Yet the aim here is to evaluate whether it is really possible to find "a good set of rules" for each course and to use it for grouping students.. The set of good rules for grouping students according to their learning style and previous actions could be different for distinct disciplines (even for different subjects related to the same discipline). Therefore, the need of analyzing new data and inferring the rules for grouping is clearly there, and the possibility of doing it automatically would be an important step forward.. It is expected that similar experiments with different subjects will allow us to contrast the results and to extract more general conclusions. Other variables such as the student performance in other courses would be considered during the analysis of the data obtained from these experiments. We expect that future studies, as a follow-up to this one, can throw light on the nuances discovered in this paper.. We find this field promising and our challenge is to bring more insight about the role of learning styles in collaborative learning, which can have an impact on research fields such as user modeling, collaborative learning, adaptive hypermedia and artificial intelligence in education, among others. Some conclusions that can be extracted from the previous results are:. -Learning styles seem to affect the performance of the students when working together, since there are some differences in the scores for the different groups, although not being statistically significant. -The tendency seems to be that mixed pairs (pos-neg, pos-med and med-neg) in the active/reflective and the sensing/intuitive dimensions work better. -It also seems that heterogeneous groups, when considering Euclidean distance, get better results. -There is not any clear correlation between the dimensions. -The students seem to group themselves randomly, according to no pattern with respect to their learning styles. -There are far more visual students than verbal ones in Computer Science. There are also more intuitive than sensing students. The other two dimensions seem to have a more symmetrical distribution. The capacity of the Internet for delivering information has given rise to different approaches to support e-learning, ranging from web-based individual learning environments (Brusilovsky et al. 1996a; Carro et al. 1999b ) to collaboration workspaces in which students can learn together (Barros and Verdejo 1998; Dillenbourg 1999) . In the educational area, adaptive hypermedia plays a significant role, since it supports the adaptation of the educational resources to each student (Brusilovsky 2001) . Students can be individually guided and their specific needs can be fulfilled during the learning process. In order to do that, it is necessary for adaptive systems to store information about each user that is considered relevant for the adaptation process (i.e., the student personal features, preferences or actions) (Kobsa 2001) . This information constitutes the user model, which is stored by the system and used for adaptation purposes during the whole learning process.. Among the user features considered in user models, learning styles constitute a valuable tool for improving individual learning, as stated by Brailsford (2004), De Bra et al. (2003) , Grigoriadou et al. (2001) , Paredes and Rodríguez (2002a) , Stash et al. (2004) , Stern and Woolf (2000) , Triantafillou et al. (2002) , and Wolf (2002) .. Yet not only can the students learn from their individual interactions with educational resources, but also they can acquire knowledge during the accomplishment of activities in collaboration with others. Collaborative activities have been used with educational purposes in traditional classrooms since the 70s (Vygotsky 1978) , and it has been widely postulated that the realization of this type of activity has a great impact on learning (Dillenbourg 1999) . It also helps the students to develop social, cognitive and reasoning skills such as thinking, making ideas explicit, communicating ideas, being responsible and cooperating with others (Barros and Verdejo 1998; Schlichter 1997) .. One of the recent works of our research group concerns the development of mechanisms to support and enhance learning through the Web by providing both adaptation to individual students (considering, among other student features, their learning styles) and a framework for collaborative learning. Students can profit from the collaborative experience without losing the benefits that a personalized experience can provide. Moreover, they can benefit not just from adaptation and collaboration independently, but also from their combination, since adaptive hypermedia techniques are also used for the adaptation of collaboration-related aspects (Carro et al. 2003a) .. In collaborative learning, the way in which students are grouped may affect the results of the learning experience. A wrong selection of colleagues can turn a positive learning experience into a negative one. According to (Johnson and Johnson 1975) , when students group themselves they have a tendency to gather with other students with similar features and skills. Nevertheless, it is not clear whether homogeneous groups, with respect to learning styles, perform better than heterogeneous ones.. In this direction, grouping students according to both their individual features and the synergy that the combination of these features can achieve may constitute a good opportunity to improve the results of the learning process. This motivates us to find out, first, the influence of learning styles on the outcome of collaborative work developed by self-selected groups, if any; and second, the way this knowledge can be used for grouping students automatically in adaptive e-learning systems. These are the aims of the case study, we have developed with real students, which is explained in detail in this paper.. The results of this study are expected to bring more insight into the role learning styles play in collaborative learning. The importance of users' learning styles for the success of collaborative work may have important implications for user modeling and collaboration support. Therefore, the results obtained can be of interest for the research communities involved in user modeling, collaborative learning, adaptive hypermedia and artificial intelligence in education, among others.. The rest of the paper is organized as follows: Sect. 2, describes the state of the art, concerning the relevant aspects for our work, of adaptive hypermedia, user modeling, learning styles, and collaborative learning. In Sect. 3 the case study is described, including its goals, assumptions, set up and results. Sect. 4, deals with the application of the results observed. It also includes a description of TANGOW, a system in which the results can be applied. Finally, in Sect. 5 conclusions are drawn and future work is outlined.

O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The unpredictable deviousness of models Computation models are central to algorithmic research. A model, designed to capture the essential features of a technology dispensing with irrelevant and burdensome details, is a judicious compromise between simplicity and fidelity (or reflectivity). This approach has unleashed an enormous amount of valuable algorithmic research over the years. However, the pursuit of simplicity may filter out details, once deemed irrelevant, which may later reassert their significance under either technological pressure or more careful scrutiny, in which case the inadequacy of the model cripples the validity of the derived results. Examples of this situation, drawn from computational geometry, numerical parallel computation, VLSI theory, and computational biology, will be reviewed and examined in detail. Over the past four decades the design and analysis of algorithms has been a vibrant area of computer science research. While sustained technological innovation improved the performance of digital computers, and the introduction of highlevel programming languages eased the task of programming and enabled program portability, it was widely perceived in the early days of the computer era that the development of efficient algorithms for the solution of given problems had to be a main research focus, since the adoption of a superior algorithm could achieve accelerations unattainable by conceivable technological improvements (and would consistently outperform its competitors on future platforms).. Such widely held perception, however, needed to be placed on a solid quantitative basis. It was obvious that better algorithmic performance had to translate into a more economical use of resources, thereby revealing a natural engineering component in the emerging discipline of computer science. The few available computers had different organizations, instruction repertories, and memory structures, all differences which complicated a meaningful quantitative evaluation of performance. However, the specific differences appeared to be of secondary importance with respect to the large body of the shared features. Indeed, the extant machines all shared the following features: a store for instructions and data, an accumulator register, and an arithmetic-logic unit; the flow of information between these components was governed by a control unit, which executed instructions fetched from the store. The identification of these common characteristics is tantamount to the conception of an abstract machine of which the existing machines are instantiations. The abstract machine -basically, the ''stored-program computer'' conceived in the mid forties by Von Neumann, Eckert, and Mauchley 1 -became a ''model'' of a vast class of computing platforms.. Why is a model useful? Abstractly, one may say that any description we offer of an extant ''system'' is inherently a model of that system, (where a ''system'' here is supposed to evolve in time). But this use of the word ''model'' conceals the idea of ''simplification'', and a model is more accurate (or ''faithful'') to the extent in which more details of the system are represented in it. Obviously, the more details that are reflected in the model, the more onerous it becomes to predict its time-evolution. The ''art'' of modeling may therefore be characterized as the dialectics between simplicity and reflectivity. Simplicity is essential to guarantee formal tractability of the model, i.e., the ability to make predictions about its evolution; reflectivity strives to ensure that essential features of the system are retained in the model and only secondary items are filtered out. Ultimately, the validity of a model is measured by how faithfully the formally derived predictions are applicable to real-world concrete systems. The elegance and effectiveness of a model is therefore expressed by its simplicity and reflectivity.. In connection with algorithm design and analysis, this philosophy took shape in the early days of the computer eras and was eloquently expressed in the books by Knuth [1] and by Aho, Hopcroft and Ullman [2] . The latter, in particular, spelled out the outlook which informed algorithmic research thereafter: A model of computation is a trade-off between simplicity and reflectivity, and provides the cost structure which forms the basis of performance evaluation. Indeed, A model of computation specifies the primitive operations that may be executed and their respective costs.. Within this general philosophy, the model of the RAM (Random Access Machine) was formalized, and was inspired by the prevalent features of most real computers. Such machines were synchronous, had similar instruction sets and comparable word sizes, and each address of a sufficiently large store (the ''main memory'') was accessible in constant time. Therefore, the RAM model has a random-access memory of arbitrary size, a single accumulator, and word-size adequate to hold the data of interest.. One central feature of the computation model viewpoint is that the use of a resource (such as time or space) is not to be estimated in absolute terms. An absolute estimate would be unnecessarily clumsy and complicated when comparing the performance of competing algorithms (it is intuitive that a ''better'' algorithm will remain better as technology evolves). Rather, with reference to time, for example, one would identify a key operation repeatedly executed by an algorithm (such as comparisons in sorting), and since the actual running time is closely ''proportional'' to the number of such executed operations, one would determine how this number grows with the size of the problem. The rate-of-growth approach implies that each performance measure is asserted modulo a multiplicative constant and expressed as a function of parameters specifying the problem size.. The viewpoint of the RAM model, eminently acceptable due to its apparent reflectivity of reality, by its inherent simplicity unleashed vigorous algorithmic research that, over the decades, has produced an impressive body of knowledge. However, any simplification implies the selection of the features not to be represented in the model; but details that in good faith may appear secondary or irrelevant when the model is first formulated may be likely to reassert their significance when, under the presence of technological innovations, the model reaches beyond its realistic confines.. In fact, not surprisingly, once it is formalized and accepted by a research community, a model takes a life of its own. It becomes itself the only reality and defines the ''rules of the game''.. The most significant illustration of this potential danger is the occasional misuse of the ''asymptotic-analysis viewpoint'' (which -it must be stressed -is a unique, powerful feature of algorithmic analysis, adopted today in all areas of computer science). As noted above, the necessity to abstract from specific machines leads one to neglect multiplicative constants and to focus exclusively on the rate of growth of functions expressing the use of resources. Neglect of constants was wisely intended as a liberation from burdensome inessential details, not as a license to neglect constants no matter what their size. Indeed, the definition of the asymptotic notation, when referring to problem size n, explicitly contains a clause such as: ''there exists an integer n 0 such that for all n ≥ n 0 '', with the intent that any appraisal of performance must take into consideration how the critical parameter n 0 compares with values of n of practical significance. Unfortunately, explicit reference to this clause is normally avoided, leading sometimes to dubious, or outlandish, statements of declaring superior, or even ''optimal'', algorithms that under no circumstances will ever be implemented, contrary to the conventional interpretation of the word ''optimal''. An example of this excess was the declared ''optimality'' of the Ajtai-Komlos-Szemeredi sorting network [3] , which -while representing a true masterpiece of combinatorial acumen -in no conceivable practical instance will ever outperform the suboptimal, but eminently elegant, sorting networks of Kenneth Batcher [4] .. Not all unwarranted claims of this type are so obviously egregious; others are much more subtle, so that a critical avoidance of this type of abuse should become an essential part of algorithm design.. While the abuse of the asymptotic notation is the most obvious uncritical use of a computation model, it is certainly not alone, as has been noted by many researchers. Although, in general, model development in algorithmics has been very successful and productive, one should not forget that a model is inherently an approximation, albeit extremely productive, of reality. Being remiss in critically scrutinizing the applicability of the model to specific situations may be the source of very serious disappointments when trying to translate algorithms into executable programs.. In the next sections I shall review some cases, part of my personal experience, which substantiate the shortcomings of a blind acceptance of computation models. Parts of the material are succinct paraphrases of results presented in papers I have authored or co-authored. I also honestly recognize that I am myself responsible for some of the exuberance which I expose in this paper; as they say, the passage of time normally makes people wiser.

O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Structuring and modelling norms for the recyclability assessment of products during their design Environmental issues, such as product recyclability, are becoming a crucial social concern for manufacturers. Often formulated in natural language, the norms and standards that govern these issues can be difficult to link to the product definition. It becomes necessary to provide the designer with appropriate support tools allowing, for instance, for the compliance of products with environmental criteria to be checked. In the current paper, we show how normative knowledge, coming from textual sources (eco-labels), can be expressed through constraints, allowing checking in a semi-automated process the recyclability of a product. Owing to the sustainable development paradigm, environmental issues such as product recyclability, are becoming a crucial social concern for manufacturers. Indeed, they will increasingly have to face regulations that will lead them to respect tight environmental constraints. In particular, these constraints will cause them to consider the recyclability of their products and the associated recycling processes. However, addressing the product recyclability after its design, often leads to some basic recovery and destruction processes such as incineration, that are increasingly subjected to tight rules. It is interesting to take into account the product recyclability during the early phases of the lifecycle, particularly during the design phase, in order to improve its efficiency or make a concurrent advantage out of it.. An initial problem is that environmental norms or standards are usually formulated in natural language in a textual form, and are therefore difficult to interpret by a human or software. It can therefore be difficult to link these requirements to the product definition. It is then necessary to provide means for translating these norms and standards into an interpretable form. In order to help the designer to make use of the knowledge extracted from an environmental norm or standard, a final objective would be to make this knowledge available within the computer aided design (CAD)-computer aided manufacturing (CAM) systems, already used during the design of the product. Therefore the main problem to solve is how to model the recyclability knowledge at hand in a way that could be consistent with the product model defined in the existing design tools. To this end, we suggest adding to the usual design parameters of a product (already present in its bill of materials) those specifically concerned with the recyclability area.. Eco-labels have been chosen here as examples of recyclability requirements, because they are dedicated to types of products and therefore do not only consider generic assumptions on eco-friendly design. A sample of well-known eco-labels, among the large panel accessible, has been considered then analysed. These sources have allowed us to suggest an extended product model including the main data required for the recyclability assessment. NIAM/ORM (Halpin 1998) , an ontology-based modelling language, has been used to facilitate the interpretation of the considered knowledge sources, and has allowed us to translate them into rules linking the parameters of the *Corresponding author. Email: rhoue@enit.fr product. This approach is, for instance, close to the one suggested in Blaise et al. (2003) in the field of safe machines.. The rules built from the considered knowledge sources have then been expressed by constraints and propagated in the product structure in order to analyse the compliance of the design of the product with the criteria contained in a given norm or standard. The CLAIRE object language has been used to implement this knowledge-processing step.. The current paper is structured as follows: the context of the design for recyclability is first presented. The main environmental regulations to which the manufacturers are submitted will be highlighted. A brief state of the art related to the support of the design activity in the context of the sustainable development will then be presented. The proposed extended product model will then be described. The use of an appropriate modelling formalism (NIAM/ ORM), allowing assisting in the interpretation of the textual sources, will be illustrated, followed by the description of the translation of an eco-label into rules, then constraints. Finally, the feasibility of the suggested methodology will be shown through the description of a software prototype, used for the test of the compliance of a product described in the literature on the Blue Angel eco-label.. 2. The context of design for recyclability

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O


The StorySpinner sculptural reader This demo is of a hypertext reading system called StorySpinner. It follows the sculptural hypertext methodology and has been used as a test bed for experimenting with the authoring of narrative flow in automatically generated stories. Readers are able to select and read one of two available stories. Reading a story involves selecting tarot cards which are mapped to chunks of story text based on possible interpretations of the cards and information concerning current story state. The StorySpinner system generates narratives based upon symbols selected by users, allowing them to read stories and explore hypertext in a novel fashion.. The idea comes from the novel 'The Castle of Crossed Destinies' by Italo Calvino [4] . In the novel, the narrator arrives at a castle inside which no-one is able to speak. To communicate their stories the other travellers use tarot cards, with the symbols on the cards representing events and characters in their tale. The narrator interprets the tarot cards forming the particular story of each traveller.. StorySpinner follows the sculptural hypertext model of authoring; here, nodes are initially scoped by their available connections to cards through interpretations. As cards are played, connections are effectively removed from the possible readings [1] . Metadata attached to the nodes, cards and card interpretations allows the author to control the pacing of the narrative.. Readers generate a story by selecting tarot cards from an available set and the StorySpinner engine generates a narrative based on a set of pre-authored nodes and possible interpretations of the particular tarot card. The next section discusses StorySpinner in more detail. Issues surrounding the authoring of narrative flow were investigated and results, observations and conclusions from initial user trials form the remainder of this paper. StorySpinner currently acts as both a reading mechanism and an authoring test bed: it allows exploration of the effects of constraining the tale in terms of descriptive nodes and logic constraints. Although the initial prototype focused on a simple three act structure, future versions may look at manipulation of stories according to characters, locations, chronology and mood.. It is intended that the StorySpinner prototype will act as a test bed to allow authors to play with and relax constraints. The problems of authoring stories using sculptural techniques has been discussed previously [2] . The connection of cards to interpretations is assumed fixed for the tarot deck used in the current implementation and a visual interface to easily allow story nodes to be associated with interpretations would seem to be appropriate. The three act structure allows simple episodic scoping but more work is needed for more complex plot development. Also, the division of required and descriptive may be too simplistic, but it provided a useful starting point for the implemented readings styles.. A fuller investigation of how readers choose and interpret the cards' meanings is also planned. The characters in Calvino's story are assumed to be familiar with Tarot cards and their interpretations. Readers using the StorySpinner system are less likely to. Alternative symbols could be used instead with more intuitive interpretations. This would probably limit the interpretations available to the author but might put some of the story control back with the reader.. The StorySpinner system has investigated control of narrative flow in hyperfiction stories through implementation of different reading styles. The system's ability to generate 'sensible' stories as well as general perceptions of this type of hyperfiction have been examined through user feedback.

O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O


Artificial Spiking Neurons and Analog-to-Digital-to-Analog Conversion This paper studies encoding/decoding function of artificial spiking neurons. First, we investigate basic characteristics of spiketrains of the neurons and fix parameter value that can minimize variation of spike-train length for initial value. Second we consider analog-to-digital encoding based upon spike-interval modulation that is suitable for simple and stable signal detection. Third we present a digital-to-analog decoder in which digital input is applied to switch the base signal of the spiking neuron. The system dynamics can be simplified into simple switched dynamical systems and precise analysis is possible. A simple circuit model is also presented. Artificial spiking neurons (ASNs) are nonlinear dynamical systems having rich dynamics and potential applications [1] - [5] . Repeating integrate-and-fire behavior between base and threshold signals, the ASN can exhibit a variety of periodic/chaotic spike-trains and related bifurcation phenomena. The ASNs can be building blocks of pulse-coupled neural networks (PCNNs) that have a variety of synchronization and bifurcation phenomena [6] - [10] . Potential applications of the ASNs and PCNNs are many, including image processing, associative memories, ultra-wide-band communication, and feature selection [7] - [13] . The ASNs and PCNNs have also been studied as simplified models of biological neurons in order to understand neural information processing function where spike-based coding plays important roles [14] - [16] .. On the other hand, analog-to-digital and digital-toanalog converters (ADC and DAC) are indispensable systems in order for communication between real analog world and digital signal processing. There are various architectures of ADCs and DACs [17] - [20] , e.g., cyclic ADC/DAC are known as basic systems to realize binary encoding/decoding. Improvement of resolution, stability and simplicity are in important issues.. The main purpose of this paper is to provide fundamental considerations on spike-based encoding/decoding functions of the ASN and their applications to ADC and DAC.. Manuscript received January 11, 2008 . † The author is with Osaka University, Toyonaka-shi, 560-8531 Japan.. † † The author is with TOSHIBA, Tokyo, 105-8001 Japan. † † † The author is with Hosei University, Koganei-shi, 184-0002 Japan.. a) E-mail: tsaito@hosei.ac.jp DOI: 10.1093/ietfec/e91-a. 6 .1455. First, we introduce an ASN having periodic sawtooth base signal that can cause a variety of spike-trains [9] . We then analyze relationship between length of spike-trains and the number of spikes by theory and numerical experiments.. Based on the analysis we can fix the parameter value that is suitable for spike-based ADC and DAC. Second, we present the ADC based on the ASN (ASN-ADC) where an analog input corresponds to an initial value of the state variable. We present spike-interval modulation (SIM) that encodes a spike-train into a binary sequence based on each inter-spike interval (ISI). Applying the mapping procedure, we clarify that the SIM can realize binary encoding. Third, we present the DAC based on the ASN (ASN-DAC) whose digital input is an inverse sequence of digital output of the ASN-ADC. The input is applied to an ASN as a binary signal that switches the dc component of a base signal. The spike-train is governed by a kind of iterated function systems (IFSs) [21] , [22] and the operation of the ASN-DAC can be confirmed theoretically. Forth, we present a simple circuit model of the ASN-DAC with basic PSPICE simulation data. The circuit can be changed into a test circuit of ASN-ADC in [23] by a slight change of the circuit configuration. Preliminary results along these lines can be found in our conference paper [24] . As significance and novelty of this paper, please note the following: (i) The study of ASN-ADAC contributes to bridge between encoding mechanisms of spiking neurons [14] - [16] and ADC/DAC [17] - [20] via nonlinear dynamical system theories [21] , [25] . It may be a trigger to develop a new signal processing framework.. (ii) The SIM signal in the ASN-ADAC have practical advantages: the signal does not diverge, whereas capacitor voltage of traditional ADCs may diverge, the signal can be detected without phase-locking and thus it is robust for signal delay, and the signal is low power consumption such as the ultra-wide-band communication signals [11] , [12] .. (iii) The ASN-DAC can be regarded as a novel realization of IFS that can generate fractal patterns and has several applications [21] .. (iv) This paper firstly discusses relationship between length of spike-trains and the number of spikes, and proposes the ASN-DAC based on the SIM. We have studied encoding/decoding properties based on integrate-and-fire dynamics of ASN. We have investigated parameter dependence on spike-train length and have fixed the parameter α = 0 that can realize ASN-ADC with the minimum variation and average of spike-train length. In the ASN-ADC, we have proposed the SIM that has several advantages including robustness for delay. In the ASN-DAC, digital input signal is applied to adjust base level of the ASN and inverse operation of the ASN-ADC is achieved. A simple circuit model is also presented. II (2003 II ( -2005 . He is a member of the JNNS and a senior member of the IEEE.

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Knowledge acquisition from simplified text The problem of entering and integrating new knowledge into a logic-based knowledge base is substantial. Our solution is to provide a natural language interface, which reads simplified English, enabled by a knowledge-based memoryretrieval driven natural language understander. This paper presents a set of tools and interfaces for interacting with such a system, and a discussion of the underlying Reader system, the reading component of the Learning Reader project. The interfaces presented provide direct feedback about what portions of the text are understood, and what interpretations are being produced from it. In addition tools are presented to, among other things, provide example sentences, to facilitate users producing simplified English text suitable for the Reader. Entering and integrating new knowledge into a logic-based knowledge base is a difficult and arduous task for users. Our solution to this problem is to provide a natural language interface, capable of reading simplified English, enabled by a knowledge-based memory-retrieval driven natural language understander. This paper presents a set of tools and interfaces for interacting with such a system, as well as a discussion of the underlying Reader system, the reading component of the Learning Reader project. The interfaces presented provide direct feedback about what portions of the text are understood, and what interpretations are being produced from it. In addition tools are presented to, among other things, provide example sentences, to facilitate users producing simplified English text suitable for the Reader.. The goal of the Learning Reader project is to build a system capable of reading simplified English texts, and integrating the information contained therein into a large existing knowledge base. It is a stated goal of the Learning Reader research to provide leveraged knowledge acquisition by utilizing that which is already known. The Reader component of the Learning Reader is intended to provide a natural form for knowledge entry, not a full-strength language understanding system.. The knowledge acquisition (KA) task is typically arduous and often requires the work of both knowledge engineers and domain experts. Project Halo estimates that encoding a single page of information, scientific content at the AP level, for example AP Chemistry text, costs approximately $10,000.. [13] They further estimate that tools to allow for domain experts to more efficiently encode and question the understanding of knowledge systems would bring this cost down significantly, so that it would be more on par with authoring a textbook on the subject.. Since knowledge acquisition through the cooperation of domain experts and knowledge engineers is impractical, research has been done to build systems usable by domain experts alleviating the necessity for a knowledge engineer. SHAKEN [3] is one such system which provides graphical interfaces to the represented knowledge, similar to concept maps. Clark and his colleagues report that this did simplify the KA task, when users were given a week of training before representing new knowledge with SHAKEN.. Cycorp has addressed this problem through form-based knowledge entry tools [17] . These tools leverage knowledge in the Cyc ontology [10] and facts about the structure of various concepts to prompt the user for more information on a topic or make educated guesses about facts for the user to confirm. For example, if the user is introducing Cyc to a new Major League Baseball team, Cyc, knowing that all teams have pitchers will ask who the pitchers are. If the user enters an unknown name, Cyc might then hypothesis that this new name is a Male-Human knowing that all Major League Baseball players are male, and it would ask the user to confirm this assumption. Although both SHAKEN and the Cycorp system alleviate the need for both a knowledge engineer and a domain expert, these approach still require placing additional, and significant, burden on the domain experts to either learn new interfaces or vocabularies and ontologies for representing knowledge. It is our goal with the Learning Reader project to present a natural language interface for acquiring such deep knowledge.. The MIT Open Mind Common Sense project takes a completely different approach. [15] Users enter English sentences into Open Mind, either free form or with the assistance of parameterized forms. This text is then mined for information using a "library of handcrafted lexico-semantic patternmatching rules" [9] , arguably the easiest approach of all, as it requires no additional burden on the domain experts to enter knowledge. The pragmatic information mined from Open Mind has been leveraged in several applications [8] . Open Mind is designed to acquire knowledge capable of plausible commonsense inference in context, but not to support a general purpose logical inference engines, such as Cyc [10] or Fire [5] .. One interesting alternative to engineering large knowledge bases is to create a kind of virtual KB by using IR techniques on large bodies of text. The work of Hovy et. al. [6] , and others in the TREC competition treat the web as a virtual knowledge base and attempt to mine the answers to questions from it. The work of Etzioni et. al. with KnowItAll [4] takes the techniques of TREC one step further by giving the system a set of questions to proactively search for answers, and build a knowledge base of facts. These systems are driven top-down by pre-specified knowledge goals to combine information from many texts, while Learning Reader is driven bottom-up by whatever information is present in a given input text. These systems, like Learning Reader, use language patterns to understand text, however, they typically use a small number of patterns and large corpora of text. Learning Reader on the other hand uses a large number of patterns to operate on small portions of text. The language patterns used by these systems are usually syntactic in nature, for example, expecting a noun phrase, where as Learning Reader patterns are semantic, for example, expecting a reference to an agent or an event.. There has been much research on mapping language onto database access [1] , which can produce highly functional systems, such as Yates et. al. interfaces for appliances [16] . However, these approaches requires at least some (if not a lot of) learning and adaptation by users to learn and understand the vocabulary and the grammar necessary to communicate with these systems. Systems of this type typically are designed for a specific context or a restricted domain, although the current primary domain for Learning Reader can be construed as geography, history, and political stories, we make no assumptions which require a restricted domain.. Other techniques which have grown out of the Natural Language Parsing traditions such as Unification Grammars [2] have allowed for the production of systems like CELT [12] . These systems have (a near) one to one mapping between lexical structures and semantic structures produced, which effectively defers the knowledge integration task. By not accepting this constraint and performing knowledge integration at reading time, Learning Reader can leverage its memory to assist in understanding the text. A major focus will be to extend the tools for writers generating simplified English to provide even more support for users. One obvious extension is to couple the Reader and the tool that recommends example phrases and sentences. This would provide two benefits. First the sentence recommender could leverage the context of what the Reader has read so far to produce better recommendations. Second, when the Reader fails to interpret a sentence, it can fail soft, by issuing a query to recommender based on the words in the sentence it was trying to read. The results could be presented to the user to help in the rewriting of the sentence they were trying to convey to the Reader, possibly even doing much of the "typing" for the user.. In addition work will be done to improve the algorithms of DMAP to extending coverage. Also, a new version of Research Cyc is also becoming available with nearly twice as many assertions, providing more episodic memory to lever-age during understanding time. Brief inspection also shows that this version of Cyc has more natural language knowledge as well, which hopefully can be put to good use by DMAP. This paper presents the Reader, a natural language interface for knowledge acquisition. The Reader operates on simplified English text, and we present a suite of tools for assisting users in the production of simplified text for processing by the Reader.. An interface for the Reader was presented which allows the user to see what text was and was not understood, and how it is interpreting what is being read. Additional tools were also presented which help users to identify natural language examples for expressing information they wish to convey to the Reader.. We have also discussed how through using DMAP the Reader is able to leverage existing knowledge to assist in the acquisition of new knowledge via language. To be able to use knowledge to acquire new knowledge via language, the Reader is designed to retrieve and apply existing knowledge as early in the process as possible. To do this, the Reader is based on DMAP, a model of language understanding driven by memory structures [11, 14] . DMAP was originally implemented on top of hierarchical frame based memory structures, our implementation is built for logical memories, so that we can interact with Research Cyc [10] . Research Cyc contains over 1.2 million assertions providing DMAP with a large episodic memory to leverage for understanding, and for integrating new knowledge to existing knowledge. As Research Cyc is over two orders of magnitude larger than previous memories used with DMAP systems, issues of scale have arisen. We provide a limited discussion of scale in this work, but more complete discussion of our methods to control combinatorics warrants a paper of its own, more appropriate for other venues. Figure 3 shows the architecture of DMAP. Textual input is tokenized as a sequence of words. Those words are fed into two processes, a proper name recognizer which would map the string "United States" to the instance UniedStatesOfAmerica, and a lexical lookup process which identifies Cyc lexical concepts that correspond to the input text, for example mapping "death" to Death-TheWord. Those lexical concepts are then fed to another process which identifies Cyc semantic concepts that correspond to the lexical concepts, for example mapping Death-TheWord to the Cyc concept Deceased. These processes leverage assertions in the Cyc knowledge base that map strings to corresponding lexical concepts or named entities.. The outputs of all three of these processes are fed into the DMAP phrasal pattern matcher. Functionally the pattern matcher in DMAP is similar to a tabular chart parser [7] . The pattern matcher has mappings from sequences of strings, lexical, and semantic concepts, to semantic assertions which they can be translated into. Figure 4 shows two such mappings. The first sentence is about where an event occurred. The pattern starts by expecting a reference to an Event, followed by an expectation for the lexical concept OccurTheWord, etc. The pattern matcher is recursive, and output from one pattern can be used to contribute to a larger pattern. If no patterns are matched DMAP will skip processing that sentence and move on to the next one.. The assertions sets produced by the pattern matcher are fed into an interpretation manager, which is responsible for linking assertions from one sentence to the next, and identifying relevant remindings in the existing memory (KB). Alternate interpretations of ambiguous texts are captured as multiple competing states. DMAP leverages existing memory to help reduce ambiguity by preferring interpretations that reference already existing memory structures.. The critical portion of merging the assertions from one sentence to the next is the alignment of the role variables they contain. This process is combinatorically expensive. DMAP requires that two variables agree with respect to type for them to be merged. Two types agree if one is a generalization of the other, or if DMAP has, in memory, an example of an instance that is a member of both types; again using its memory to determine what parsing alternatives are acceptable and preferred. DMAP will also prefer those alignments that allow it to retrieve more known facts from memory when the bindings from reading are substituted into the assertions. In lieu of that, DMAP will select an alignment that agrees with respect to types, and does not cause the contradiction of existing knowledge when the bindings are substituted. For example, DMAP considers it a contradiction if an assertion is being formed indicating one agent performed an event, when it has knowledge in memory indicating that another actor performed the event. In the example in Figure 4 there are 7 possible alignments of the variables for DMAP to consider, ultimately it will prefer the following alignment. If DMAP can find no remindings in memory for a given interpretation it will produce new Skolem constants as values for any role variables without values retrieved from memory.

B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O


Transform coding with integer-to-integer transforms Abstract-A new interpretation of transform coding is developed that downplays quantization and emphasizes entropy coding, allowing a comparison of entropy coding methods with different memory requirements. With conventional transform coding, based on computing Karhunen-Loève transform coefficients and then quantizing them, vector entropy coding can be replaced by scalar entropy coding without an increase in rate. Thus the transform coding advantage is a reduction in memory requirements for entropy coding. This paper develops a transform coding technique where the source samples are first scalar-quantized and then transformed with an integer-to-integer approximation to a nonorthogonal linear transform. Among the possible advantages is to reduce the memory requirement further than conventional transform coding by using a single common scalar entropy codebook for all components. The analysis shows that for high-rate coding of a Gaussian source, this reduction in memory requirements comes without any degradation of rate-distortion performance. T RANSFORM coding is the most successful and pervasive technique for lossy compression of audio, images, and video. The conventional framework of transform coding was introduced by Huang and Schultheiss [1] 1 : one is given a discrete-time, continuous-valued, vector source with correlated components; instead of quantizing the components separately, one uses a linear transform to compute transform coefficients and scalar-quantizes the transform coefficients. In either case, entropy codes may be used to improve the coding efficiency. Transform coding is contrasted to direct quantization in the top and bottom paths of Fig. 1 .. Transform coding is an inherently suboptimal source coding technique because it uses the Cartesian product of scalar quantizers, called simply a scalar quantizer. Compared to an appropriately designed vector quantizer, a scalar quantizer has a space-filling loss [3] , [4] that cannot be neutralized by linear pre-and post-processing. This is caused by the high second moment of a cube, as compared to a sphere of the same volume. On the other hand, transform coding has much lower complexity than less constrained forms of vector quantization. Thus transform coding is often called a low-complexity alternative Manuscript received March 16, 1999; revised September 24, 1999. The material in this paper was presented in part at the 32nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November [1] [2] [3] [4] 1998 .. The author is with the Mathematics of Communications Research Department, Bell Laboratories, Lucent Technologies, Murray Hill, NJ 07974-0636 USA.. Communicated by P. A. Chou, Associate Editor for Source Coding. Publisher Item Identifier S 0018-9448(00)01363-8. 1 Earlier work by Kramer and Matthews [2] did not include quantization; hence, it is disconnected from the current practice of transform coding. to vector quantization. The transform changes coordinates to improve the performance of scalar quantization.. The discussion above places great emphasis on quantization. This paper shows that this emphasis is undue. At high rates, the performance obtained by Huang and Schultheiss can be matched by a system that quantizes the original source components and then uses a transform to reduce scalar entropy (see the middle path of Fig. 1 ). This suggests that the value of transform coding comes not from changing the coordinate system for quantization, but almost exclusively from decorrelation.. The most important feature of the proposed system is that the transform is now discrete, not continuous as before. It is shown that the transform can be designed so that the memory requirement of the entropy coding block is reduced without sacrificing rate-distortion performance. Also, since the source is immediately discretized, it is not important that it is continuous-valued. Thus the techniques described here can be applied to discrete-valued sources.. The paper is organized as follows: Section II gives an example of the proposed scheme to provide motivation for the general framework, which is described in Section III. Section IV summarizes the results. Compression of a Gaussian source with all quantization limited to scalars does not allow performance approaching the ratedistortion bound because of the inherent limitation of scalar quantization. Nevertheless, most current source coding systems use scalar quantization in conjunction with a transform.. Conventional transform coding, with an orthogonal linear transform preceding quantization, can be cast as a technique for simplifying entropy coding while maintaining the best rate-distortion performance possible without multidimensional quantization. For a source of dimension and quantization with cells per dimension, a single codebook with elements can be replaced by codebooks with elements without a loss in performance. Using high-rate approximations, this paper has shown that a system that swaps the transform and quantization-placing quantization before an integer-to-integer transform-can simplify the entropy coding further without a loss in performance; only a single -element codebook is needed. The results are summarized in Table III . The memory reduction shown in Table III is possible because of greater freedom in the design of an integer-to-integer transform than of an orthogonal linear transform. This greater freedom can be put to advantage in similar ways if mixtures of scalar and vector entropy coding are used. This paper has considered only the compression of Gaussian sources and some of the statements depend intimately on this choice. In particular, uncorrelated jointly Gaussian variables are independent although this is not true of all random variables. The design of transforms for integer-to-integer transform coding is nothing more than the manipulation of discrete probability densities. This was simplified greatly by continuous approximations, but it should be possible to design an appropriate transform for any source.

B I O O O O O O O B I O O O O O O O B I O O O O B I O O O O O O O B I O O O O O O O O O O O B I O O O O O B I O O O O O O O B I O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A theorem prover for Boolean BI While separation logic is acknowledged as an enabling technology for large-scale program verification, most of the existing verification tools use only a fragment of separation logic that excludes separating implication. As the first step towards a verification tool using full separation logic, we develop a nested sequent calculus for Boolean BI (Bunched Implications), the underlying theory of separation logic, as well as a theorem prover based on it. A salient feature of our nested sequent calculus is that its sequent may have not only smaller child sequents but also multiple parent sequents, thus producing a graph structure of sequents instead of a tree structure. Our theorem prover is based on backward search in a refinement of the nested sequent calculus in which weakening and contraction are built into all the inference rules. We explain the details of designing our theorem prover and provide empirical evidence of its practicality. Despite its close connection with separation logic, Boolean BI has not received much attention from the proof search community. Such a lack of research, which is quite unusual considering the status of separation logic in the field of program verification, is perhaps due to the difficulty of finding a proof theory suitable for theorem proving. Our nested sequent calculus SBBI as well as a theorem prover based on it may serve as a test bed for developing proof search strategies for Boolean BI. In particular, its use of nested sequents allowing multiple parent sequents may shed new light on how to deal with separating implication in a theorem prover for separation logic.

O B I O B I O B I O O O O O O O O O B O O O O B O O O O O O B I O O O O O O O O O O B O O O B I O O O B I I O B I O O O O O O B I O O O O B I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O B I I O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O B I