Spreadsheets models are frequently used by scientists to analyze research data. These models are typically described in a paper or a report, which serves as single source of information on the underlying research project. As the calculation workflow in these models is not made explicit, readers are not able to fully understand how the research results are calculated, and trace them back to the underlying spreadsheets. This paper proposes a methodology for semi-automatically deriving the calculation workflow underlying a set of spreadsheets. The starting point of our methodology is the cell dependency graph, representing all spreadsheet cells and connections. We automatically aggregate all cells in the graph that represent instances and duplicates of the same quantities, based on analysis of the formula syntax. Subsequently, we use a set of heuristics, incorporating knowledge on spreadsheet design, computational procedures and domain knowledge, to select those quantities, that are relevant for understanding the calculation workflow. We explain and illustrate our methodology by actually applying it on three sets of spreadsheets from existing research projects in the domains of environmental and life science. Results from these case studies show that our constructed calculation models approximate the ground truth calculation workflows, both in terms of content and size, but are not a perfect match.

Within cultural heritage collections, objects are often grounded in a particular historical setting. This setting can currently not be made explicit, as structured descriptions of events are either missing or not marked up explicitly. This paper reports a study on automatic extraction of an historical event thesaurus from unstructured texts. We show how this preliminary thesaurus accommodates event- and object-driven search and browsing of two cultural heritage collections.

Gold standard mappings created by experts are at the core of alignment evaluation. At the same time, the process of manual evaluation is rarely discussed. While the practice of having multiple raters evaluate results is accepted, their level of agreement is often not measured. In this paper we describe three experiments in manual evaluation and study the way different raters evaluate mappings. We used alignments generated using different techniques and between vocabularies of different type. In each experiment, five raters evaluated alignments and talked through their decisions using the think aloud method. In all three experiments we found that inter-rater agreement was low and analyzed our data to find the reasons for it. Our analysis shows which variables can be controlled to affect the level of agreement including the mapping relations, the evaluation guidelines and the background of the raters. On the other hand, differences in the perception of raters, and the complexity of the relations between often ill-defined natural language concepts remain inherent sources of disagreement. Our results indicate that the manual evaluation of ontology alignments is by no means an easy task and that the ontology alignment community should be careful in the construction and use of reference alignments.

Mobile services can provide users with information relevant to their current circumstances. Distant services in turn can acquire local information from people in an area of interest. Socially expressive agent behaviour has been suggested as a way to build reciprocal relationships and to increase user response to such requests. This between-subject, Wizard-of-Oz experiment aimed to investigate the potential of such behaviours. 44 participants performed a search task in an urgent context while being interrupted by a mobile agent that both provided and requested information. The socially expressive behaviour shown in this study did not increase compliance to requests; it instead reduced trust in provided information and compliance to warnings. It also negatively impacted the affective experience of users scoring lower on empathy as a personality trait. Inappropriate social expressiveness can have serious consequences; we here elaborate on the reasons for our negative results.

Empathy has great potential in human-robot interaction. However, the challenging nature of assessing the user's emotional state points to the importance of also understanding the effects of empathic behaviours incongruent with users' affective experience. A 3x2 between-subject video-based survey experiment (N=133) was conducted with empathic robot behaviour (empathically accurate, neutral, inaccurate) and valence of the situation (positive, negative) as dimensions. Trust decreased when empathic responses were incongruent with the affective state of the user. However, in the negative valence condition, reported perceived empathic abilities were greater when the robot responded as if the situation were positive.

Abstract In this work we present an automatic method for the extraction of time periods related to ontological concepts from the Web. The method consists of two parts: an Information Extraction phase and a Semantic Representation phase. In the Information Extraction phase, temporal information about events that are associated with the target instance are extracted from Web documents. The resulting distribution is normalized and a model is fit to it. This distribution is then converted into a Semantic Representation in the second phase. We present the method and describe experiments where time periods for four different types of concepts are extracted and converted to a time representation vocabulary, based on the TIMEX2 annotation standard.

Even though adaptive (trainable) spam filters are a common example of systems that make (semi-)autonomous decisions on behalf of the user, trust in these filters has been underexplored. This paper reports a study of usage of spam filters in the daily workplace and user behaviour in training these filters (N=43). User observation, interview and survey techniques were applied to investigate attitudes towards two types of filters: a user-adaptive (trainable) and a rule-based filter. While many of our participants invested extensive effort in training their filters, training did not influence filter trust. Instead, the findings indicate that users' filter awareness and understanding seriously impacts attitudes and behaviour. Specific examples of difficulties related to awareness of filter activity and adaptivity are described showing concerns relevant to all adaptive and (semi-)autonomous systems that rely on explicit user feedback.

The human robot interaction community is multidisciplinary by nature and has members from social science to engineering backgrounds. In this paper we aim to provide human robot developers with a straightforward toolkit to evaluate users' acceptance of assistive social robots they are designing or developing for elderly care environments. We will explain how we developed the measures for this analysis, provide do's and don'ts in designing the experiments, demonstrate the application of the measures we have developed for this purpose and the analysis and interpretation of the data. As such we hope to engage human robot interaction developers in evaluating the acceptability of their own robot to inform the development process and improve the final robot's design.

Abstract Embodied social agents are programmed to display human‐like social behaviour to increase intuitiveness of interacting with these agents. It is yet unclear what the differences in peoples' responses are to different types of agents' social behaviours. One example is touch. Despite robots' physical embodiment and increasing autonomy, the effect of communicative touch has been a mostly overlooked aspect of human‐robot interaction. This video‐based, 2 × 2 between‐subject survey experiment (N = 119) found that the combination of touch and proactivity influenced whether people saw the robot as machine‐like and dependable. Furthermore, participants' attitude toward robots in general was found to influence perceived closeness between a human and a robot. Results show that communicative touch could be considered a more appropriate behaviour for proactive agents rather than reactive agents. Also, people that are generally more positive towards robots find robots that interact by touch less machine‐like. These effects illustrate that careful consideration is necessary when incorporating social behaviours in agents' physical interaction design. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper we explore the concept of enjoyment as a possible factor influencing acceptance of robotic technology by elderly people. We describe an experiment with a conversational robot and elderly users (n=30) that incorporates both a test session and a long term user observation. The experiment did confirm the hypothesis that perceived enjoyment has an effect on the intention to use a robotic system. Furthermore, findings show that the general assumption in technology acceptance models that intention to use predicts actual use is also applicable to this specific technology used by elderly people.

Summary form only given. The possibilities of using robots in eldercare have inspired a growing number of research projects. Not only can robotic technologies be used for rehabilitation, also they could facilitate the work of caregivers and provide social company for elders. In our project we are interested in the acceptance of robotic technology by elders and particularly in the influence of a robots social abilities on acceptance. Several studies on interaction with robots stress the importance of social intelligence and even more so in a health- and eldercare environment. It shows a more social intelligent robot will be more effective in its communication and it can therefore be expected to be easier and more pleasant to interact with and therefore would be indeed accepted easier.

When using a robot or a screen agent, elderly users might feel more enjoyment if they experience a stronger social presence. In two experiments with a robotic agent and a screen agent (both n=30) this relationship between these two concepts could be established. Besides, both studies showed that social presence correlates with the intention to use the system, although there were some differences between the agents. This implicates that factors that influence social presence are relevant when designing assistive agents for elderly people.

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment ( N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

Analysis of existing methods for automatic optimization of link structures shows that these methods rely heavily on assumptions about the preferences and navigation behavior of users. Authors often do not state these assumptions explicitly and do not evaluate whether the assumptions are consistent with the actual behavior of the users of the site. This is a serious deficiency as experiments with simulated users show that incorrect assumptions can easily lead to inefficient link structures. In this work we present a framework that gives a systematic overview of alternative assumptions. On the basis of the framework we can select a set of assumptions that best matches the navigation behavior of the users in the site’s log files. We also present a method for optimizing hierarchical navigation menus on the basis of the selected assumptions. This method can be used interactively under full control of a web master. The system proposes modifications of the structure and explains why these modifications lead to more efficient menus. Evaluation by means of a case study shows that the modifications that are proposed effectively reduce the expected navigation time while preserving the coherence of the menu structure.

This study examines the influence of perceived social abilities of a robot on user's attitude towards and acceptance of the robot. An interface robot with simulated conversational capabilities was used in a Wizard of Oz experiment with two conditions: a more socially communicative (the robot made use of a larger set of social abilities in interaction) and a less socially communicative interface. Participants (n=40) were observed in 5 minute interaction sessions and were asked to answer questions on perceived social abilities and technology acceptance. Results show that participants who were confronted with the more socially communicative version of the robot felt more comfortable and were more expressive in communicating with it. This suggests that the more socially communicative condition would be more likely to be accepted as a conversational partner. However, the findings did not show a significant correlation between perceived social abilities and technology acceptance

Abstract In this report, we investigate how retrieving information can be improved through task‐related indexing of documents based on ontologies. Different index types, varying from content‐based keywords to structured task‐based indexing ontologies, are compared in an experiment that simulates the task of creating instructional material from a database of source material. To be able to judge the added value of task‐ and ontology‐related indexes, traditional information retrieval performance measures are extended with new measures reflecting the quality of the material produced with the retrieved information. The results of the experiment show that a structured task‐based indexing ontology improves the quality of the product created from retrieved material only to some extent, but that it certainly improves the efficiency and effectiveness of search and retrieval and precision of use.

Goal driven authoring of trainingmaterial from existing technical manuals requires automatic indexing of the content of the manual. In this contribution we consider the different representation levels and document knowledge required to do the task. On that basis we have developed tools for automatic indexing in diverse domains.

Thesauri such as the Art and Architecture Thesaurus (AAT) provide structured vocabularies for describing art objects. However, if we want to create a knowledge-rich description of an (image of an) art object, such as required by the "semantic web", thesauri turn out to provide only part of the knowledge needed. In this paper we look at problems related to capturing background knowledge for art resources. We describe a case study in which we attempt to construct an ontology for a subset of art-object descriptions, namely antique furniture, using AAT as well as metadata standards as input. We discuss the representation requirements for such an ontology as well as representational problems for our sample ontology with respect to the emerging web standards for knowledge representation (RDF, RDFS, OIL).

Automated handling of tutoring and training functions in educational systems requires the availability of articulate domain models. In this article we further develop the application of qualitative models for this purpose. A framework is presented that defines a key role for qualitative models as interactive simulations of the subject matter. Within this framework our research focuses on automating the diagnosis of learner behaviour. We show how a qualitative simulation model of the subject matter can be reformulated to fit the requirements of general diagnostic engines such as GDE. It turns out that, due to the specific characteristics of such models, additional structuring is required to produce useful diagnostic results. A set of procedures is presented that automatically maps detailed simulation models into a hierarchy of aggregated models by hiding non-essential details and chunking chains of causal dependencies. The result is a highly structured subject matter model that enables the diagnosis of learner behaviour by means of an adapted version of the GDE algorithm. An experiment has been conducted that shows the viability of the approach taken, i.e., given the output of a qualitative simulator the procedures we have developed automatically generate a structured subject matter model and subsequently use this model to successfully diagnoses learner behaviour.