People and animals use various kinds of motion in a multitude of ways to communicate their ideas and affective state, such as their moods or emotions. Further, people attribute affect and personalities to movements of even non-life like entities based solely on the style of their motions, e.g., the locomotion style of a geometric shape (how it moves about) can be interpreted as being shy, aggressive, etc. We investigate how robots can leverage this locomotion-style communication channel for communication with people. Specifically, our work deals with designing stylistic flying-robot locomotion paths for communicating affective state. To author and unpack the parameters of affect-oriented flying-robot locomotion styles we employ the Laban Effort System, a standard method for interpreting human motion commonly used in the performing arts. This paper describes our adaption of the Laban Effort System to author motions for flying robots, and the results of a formal experiment that investigated how various Laban Effort System parameters influence people's perception of the resulting robotic motions. We summarize with a set of guidelines for aiding designers in using the Laban Effort System to author flying robot motions to elicit desired affective responses.

The objective of this exploratory randomized controlled trial (RCT) was to provide evidence for the feasibility and therapeutic value of a novel game-based dual-task balance exercise program in children with cerebral palsy (CP). Twenty children with CP were recruited and randomized into two groups: (a) the conventional balance training group (CG) and (b) the experimental group (XG), which received a game-based dual-task (DT) balance exercise program. Both groups received their respective therapy programs for 12 weeks at a frequency of three sessions per week. Semi-structured interviews with the parents and children and qualitative analysis were conducted to evaluate the children’s experiences with the game-based exercise program. The quantitative analysis included (a) the Pediatric Balance Scale (PBS), (b) Gross Motor Function Measure-88 (GMFM-88), and (c) computerized measures of standing balance performance during various dual-task conditions. Compliance was 100% for all 20 participants. Four themes captured the range of each participant’s experiences and opinions: (a) reasons for participation, (b) likes and dislikes with the technologies, (c) positive effects of the program, and (d) future expectations. Children in the XG demonstrated greater improvements in PBS, GMFM, and DT balance measures as compared to children in the CG. The findings demonstrate feasible trial procedures and acceptable DT-oriented training with a high compliance rate and positive outcomes. These findings support further research and development and progression to the next phase of a full-scale RCT to evaluate the clinical effectiveness of the game-based DT balance exercise program for children with CP.

Data grids provide geographically distributed storage for large-scale data-intensive applications. Ensuring efficient access to such large and widely distributed datasets is hindered by high latencies. To speed up data access, data grid systems replicate data in multiple locations so a user can access the data from a nearby site. In addition to reducing data access time, replication also aims to use network and storage resources efficiently. While replication is a well-known technique, the problem of replica placement has not been widely studied for data grid environments. To obtain the best possible gains from replication, strategic placement of the replicas is critical. In a grid environment resource availability, network latency, and userspsila requests can vary. To address these issues a placement strategy is needed that adapts to dynamic behavior. This paper proposes a new dynamic replica placement algorithm for hierarchical data grids based on file ldquopopularityrdquo. Our goal is to place replicas close to the clients to reduce access time while using the network and storage efficiently thereby effectively balancing storage cost and access latency. We evaluate our algorithm using OptorSim which shows that our approach outperforms other techniques in terms of access time and bandwidth used.

Data Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed access in Data Grids is replication, which provides nearby data access. Much of the work on the replica placement problem has focused on average system performance and ignored quality assurance issues. In a data grid environment, resource availability, network latency, and users' requests may change. Moreover, different sites may have different service quality requirements. In this paper, we introduce a new highly distributed and decentralized replica placement algorithm for hierarchical Data Grids that determines the positions of a minimum number of replicas expected to satisfy certain quality requirements. Our placement algorithm exploits the data access history for popular data files and computes replica locations by minimizing overall replication cost (read and update) while maximizing QoS satisfaction for a given traffic pattern. The problem is formulated using dynamic programming. We assess our algorithm using OptorSim. A comparison between our algorithm and its QoS-unconstrained counterpart shows that our algorithm can shorten job execution time greatly while consuming moderate bandwidth for data transfer.

Data grids support distributed data-intensive applications that need to access massive datasets stored around the world. Ensuring efficient access to such datasets is hindered by the high latencies of wide-area networks. To speed up access, files can be replicated so a user can access a nearby replica. Much of the work on the replica placement problem in data grids has focused on average system performance and ignored quality assurance issues. In the existing work that considers QoS, a simplified replication model is often assumed, therefore, resulting solutions may not be applicable to real systems. In this paper, we introduce a more realistic model for replica placement in hierarchical Data Grids which determines the positions of a minimum number of replicas expected to satisfy certain quality requirements both from user and system perspectives. Our placement algorithm is based on a highly distributed and decentralized technique that exploits the data access history for popular data files and computes replica locations by minimizing overall replication cost (read and update) while maximizing QoS satisfaction for a given traffic pattern. The problem is formulated using dynamic programming. We assess our algorithm using OptorSim. Simulation results demonstrate the effectiveness of our replica placement technique considering various factors such as storage and workload constraints of replica servers, link capacity constraints, user QoS requirements, etc.