Khaled Bachour

User perceptions of anthropomorphic robots as monitoring devices

The principle behind anthropomorphic robots is that the appearance and behaviours enable the pre-defined social skills that people use with each other each day to be used as a means of interaction. One of the problems with this approach is that there are many attributes of such a robot which can influence a user’s behaviour, potentially causing undesirable effects. This paper aims to identify and discuss a series of the most salient behaviour influencing factors in the literature, related to a number of robot attributes. A particularly novel aspect of this work is the study of robots as monitoring or data collection devices, one of several behaviour influencing factors which has received insufficient attention. As a first step towards this, the PSA matrix is produced, which visualises the relationships between system attributes and user perceptions, grounded in empirical evidence in the literature. This matrix highlights gaps in the literature, brings together a series of salient behaviour influencing factors for the first time and gives a clear view of the state of the art.

Provenance for the People: An HCI Perspective on the W3C PROV Standard through an Online Game

In the information age, tools for examining the validity of data are invaluable. Provenance is one such tool, and the PROV model proposed by the World Wide Web Consortium in 2013 offers a means of expressing provenance in a machine readable format. In this paper, we examine from a users standpoint notions of provenance, the accessibility of the PROV model, and the general attitudes towards history and the verifiability of information in modern data society. We do this through the medium of an online-game designed to explore these issues and present the findings of the study along with a discussion of some of its implications.

Potential of imprecision: exploring vague language in agent instructors

As we find greater potential for agent instructors, we must be aware of how their language use can affect the user and interaction as a whole. This study investigates the use of intentionally imprecise or vague language as a communicative strategy to mitigate the impact of instructions. We look at the effects it has on improving the perception of agents and user performance. A series of assembly tasks were ran in which users constructed Lego models with the spoken instructions of vague and non-vague agents. Results show that though the non-vague agent was seen as more direct and authoritative, responses to other attributes and performance were much more varied. Findings suggest there is potential for vague language human-agent interaction, though there are several obstacles in agent design to overcome first.

A Consumer-Centred Sensory Vocabulary for Open-Food Innovation

Through crowdsourcing and open innovation, product manufacturers are exploiting digital technologies to communicate with their consumers, drawing on the crowd to propose new products and designs. The food industry has struggled with adopting this model due to the lack of an effective language around the taste and texture of food. Existing sensory vocabularies are complex and target food professionals instead of consumers. To address this, we created a new consumer-centred sensory vocabulary aimed at underpinning future crowdsourcing platforms for open innovation in food manufacturing, with a focus on cake.

“Give me what I want” - enabling complex queries on rich multi-attribute data

Consumer and more generally, human preferences are highly complex, depending on a multitude of factors, most of which are not crisp, but uncertain/fuzzy in nature. Thus, user selection amongst a set of items is dependent on the complex comparison of items based on a large number of imprecise item-attributes such as price, size, colour, etc. This paper proposes the mechanisms to underpin the digital replication of such complex preference-based item selection with the view to enabling improved digital item search and recommendation systems. For example, a user may query “I would like a product of similar size but at a cheaper price.” The proposed method involves splitting query-attributes into two categories; those to remain similar (e.g., size) and those to be changed in a specific direction (e.g., price - to be lower). A combination of similarity and distance measures is then used to compare and rank recommendations. Initial results are presented indicating that the proposed method is effective at ranking items according to intuition and expected user preferences.