Expectancy effects in tennis: The impact of body language and playing record on impressions of a tennis player and ratings of performance

This study examined the impact that pre-event body language and knowledge of a performer’s playing record had on ratings of tennis performance. Participants (N = 123) were allocated to one of four experimental groups (good body language/bad body language vs. positive playing record/negative playing record) and viewed a live player warming up and completing a series of tennis shots. Information outlining the player’s recent win/loss record was coupled with body language condition during a period of warm-up footage. Likert-type scales were employed to record impressions of the player and judgements as to the quality of the play. ANCOVA revealed that the player was viewed more favourably having displayed positive as opposed to negative body language (p<.001). Participants presented with a positive playing record (p = .001) formed a more favourable impression and rated the players performance more positively (p = 0.001). The study corroborates and extends the findings of recent work incorporating live models in expectancy effects investigations.

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.