Efficiency of Spearcon-Enhanced Navigation of One Dimensional Electronic Menus

This study simulated and compared cell phone contact book menu navigation using combinations of both auditory (text-to-speech and spearcons) and visual cues. A total of 127 undergraduates participated in a study that required using one of five conditions of alphabetically listed menu cues to find a target name. Participants using visual cues (either alone or combined with auditory cues) outperformed those using only auditory cues. Performance was not found to be significantly different among the three auditory only conditions. When combined with visual cues, spearcons improved navigational efficiency more than both text-to-speech cues and menus using no sound, and provided evidence for the ability of sound to enhance visual menus. Research results provide evidence applicable to efficient auditory menu creation.Gregory Corso - Committee Member/Second Reader ; Bruce Walker - Faculty Mento

No interaction between tDCS current strength and baseline performance: a conceptual replication

Several recent studies have reported non-linear effects of transcranial direct current stimulation (tDCS), which has been attributed to an interaction between the stimulation parameters (e.g., current strength, duration) and the neural state of the cortex being stimulated (e.g., indexed by baseline performance ability, age) (see Fertonani and Miniussi, 2016). We have recently described one such non-linear interaction between current strength and baseline performance on a visuospatial attention (landmark) task (Benwell et al., 2015). In this previous study, we induced a small overall rightward shift of spatial attention across 38 participants using bi-hemispheric tDCS applied for 20 min (concurrent left posterior parietal (P5) anode and right posterior parietal (P6) cathode) relative to a sham protocol. Importantly, this shift in bias was driven by a state-dependent interaction between current intensity and the discrimination sensitivity of the participant at baseline (pre-stimulation) for the landmark task. Individuals with high discrimination sensitivity (HDS) shifted rightward in response to low- (1 mA) but not high-intensity (2 mA) tDCS, whereas individuals with low discrimination sensitivity (LDS) shifted rightward with high- but not low-intensity stimulation. However, in Benwell et al. (2015) current strength was applied as a between-groups factor, where half of the participants received 1 mA and half received 2 mA tDCS, thus we were unable to compare high and low-intensity tDCS directly within each individual. Here we aimed to replicate these findings using a within-group design. Thirty young adults received 15 min of 1 and 2 mA tDCS, and a sham protocol, each on different days, to test the concept of an interaction between baseline performance and current strength. We found no overall rightward shift of spatial attention with either current strength, and no interaction between performance and current strength. These results provide further evidence of low replicability of non-invasive brain stimulation protocols, and the need for further attempts to replicate the key experimental findings within this field

Modeling and Evaluation of Multisource Streaming Strategies in P2P VoD Systems

In recent years, multimedia content distribution has largely been moved to the Internet, inducing broadcasters, operators and service providers to upgrade with large expenses their infrastructures. In this context, streaming solutions that rely on user devices such as set-top boxes (STBs) to offload dedicated streaming servers are particularly appropriate. In these systems, contents are usually replicated and scattered over the network established by STBs placed at users' home, and the video-on-demand (VoD) service is provisioned through streaming sessions established among neighboring STBs following a Peer-to-Peer fashion. Up to now the majority of research works have focused on the design and optimization of content replicas mechanisms to minimize server costs. The optimization of replicas mechanisms has been typically performed either considering very crude system performance indicators or analyzing asymptotic behavior. In this work, instead, we propose an analytical model that complements previous works providing fairly accurate predictions of system performance (i.e., blocking probability). Our model turns out to be a highly scalable, flexible, and extensible tool that may be helpful both for designers and developers to efficiently predict the effect of system design choices in large scale STB-VoD system

Only Aggressive Elephants are Fast Elephants

Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider's orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.Comment: VLDB201