No abstract

As social robots begin to enter our lives as providers of information, assistance, companionship, and motivation, it becomes increasingly important that these robots are capable of interacting effectively with human users across different cultural settings worldwide. A key capability in establishing acceptance and usability is the way in which robots structure their speech to build credibility and express information in a meaningful and persuasive way. Previous work has established that robots can use speech to improve credibility in two ways: expressing practical knowledge and using rhetorical linguistic cues. In this paper, we present two studies that build on prior work to explore the effects of language and cultural context on the credibility of robot speech. In the first study (n = 96), we compared the relative effectiveness of knowledge and rhetoric on the credibility of robot speech between Arabic-speaking robots in Lebanon and Englishspeaking robots in the United States, finding the rhetorical linguistic cues to be more important in Arabic than in English. In the second study (n = 32), we compared the effectiveness of credible robot speech between robots speaking either Modern Standard Arabic or the local Arabic dialect, finding the expression of both practical knowledge and rhetorical ability to be most important when using the local dialect. These results reveal nuanced cultural differences in perceptions of robots as credible agents and have important implications for the design of human-robot interactions across Arabic and Western cultures. English-speaking robotsEnglish-speaking participantArabic-speaking robots Arabic-speaking participant Arabic site Figure 1: The first study was conducted in both the United States (top) and Lebanon (bottom). Participants interacted with two robots acting as competing tour guides, each speaking with a different degree of practical knowledge and/or rhetorical ability.

We present a compiler algorithm called BitValue, which can discover both unused and constant bits in dusty-deck C programs. Bit-Value uses forward and backward dataflow analyses, generalizing constantfolding and dead-code detection at the bit-level. This algorithm enables compiler optimizations which target special processor architectures for computing on non-standard bitwidths. Using this algorithm we show that up to 31% of the computed bytes are thrown away (for programs from SpecINT95 and Mediabench). A compiler for reconfigurable hardware uses this algorithm to achieve substantial reductions (up to 20-fold) in the size of the synthesized circuits.

MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines.Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-ofgravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.

Believability of characters has been an objective in literature, theater, film, and animation. We argue that believable robot characters are important in human-robot interaction, as well. In particular, we contend that believable characters evoke users’ social responses that, for some tasks, lead to more natural interactions and are associated with improved task performance. In a dialogue-capable robot, a key to such believability is the integration of a consistent storyline, verbal and nonverbal behaviors, and sociocultural context. We describe our work in this area and present empirical results from three robot receptionist testbeds that operate "in the wild."