Making history: intentional capture of future memories

Lifelogging' technology makes it possible to amass digital data about every aspect of our everyday lives. Instead of focusing on such technical possibilities, here we investigate the way people compose long-term mnemonic representations of their lives. We asked 10 families to create a time capsule, a collection of objects used to trigger remembering in the distant future. Our results show that contrary to the lifelogging view, people are less interested in exhaustively digitally recording their past than in reconstructing it from carefully selected cues that are often physical objects. Time capsules were highly expressive and personal, many objects were made explicitly for inclusion, however with little object annotation. We use these findings to propose principles for designing technology that supports the active reconstruction of our future past

A user study on visualizing directed edges in graphs

Graphs are often visualized using node-link representations: vertices are depicted as dots, edges are depicted as (poly)lines connecting two vertices. A directed edge running from vertex A to B is generally visualized using an arrow representation: a (poly)line with a triangular arrowhead at vertex B. Although this representation is intuitive, it is not guaranteed that a user is able to determine edge direction as quickly and unambiguously as possible; alternative representations that exhibit less occlusion and visual clutter might be better suited. To investigate this, we developed five additional directed-edge representations using combinations of shape and color. We performed a user study in which subjects performed different tasks on a collection of graphs using these representations and combinations thereof to investigate which representation is best in terms of speed and accuracy. We present our initial hypotheses, the outcome of the user studies, and recommendations regarding directed-edge visualization

Awareness, training and trust in interaction with adaptive spam filters

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

Cooperative Physical Human-Human and Human-Robot Interaction

This chapter examines the physical interaction between two humans and between a human and a robot simulating a human in the absence of all other modes of interaction, such as visual and verbal. Generally, when asked, people prefer to work alone on tasks requiring accuracy. However, as demonstrated by the research in this chapter, when individuals are placed in teams requiring physical cooperation, their performance is frequently better than their individual performance despite perceptions that the other person was an impediment. Although dyads are able to perform certain actions significantly faster than individuals, dyads also exert large opposition forces. These opposition forces do not contribute to completing the task, but are the sole means of haptic communication between the dyads. Solely using this haptic communication channel, dyads were able to temporally divide the task based on task phase. This chapter provides further details on how two people haptically cooperate on physical tasks