The quiet eye without a target: The primacy of visual information processing

Motor-performance-enhancing effects of long final fixations before movement initiation – a phenomenon called Quiet Eye (QE) – have repeatedly been demonstrated. Drawing on the information-processing framework, it is assumed that the QE supports information processing revealed by the close link between QE duration and task demands concerning, in particular, response selection and movement parameterisation. However, the question remains whether the suggested mechanism also holds for processes referring to stimulus identification. Thus, in a series of two experiments, performance in a targeting task was tested as a function of experimentally manipulated visual processing demands as well as experimentally manipulated QE durations. The results support the suggested link because a performance-enhancing QE effect was found under increased visual processing demands only: Whereas QE duration did not affect performance as long as positional information was preserved (Experiment 1), in the full vs. no target visibility comparison, QE efficiency turned out to depend on information processing time as soon as the interval falls below a certain threshold (Experiment 2). Thus, the results rather contradict alternative, e.g., posture-based explanations of QE effects and support the assumption that the crucial mechanism behind the QE phenomenon is rooted in the cognitive domain.

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). {SLAs} define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.

Key points for patient safety in dialysis access.

For more than 15 years, patient safety has been an issue in different domains of medicine. There is evidence for this subject and also a great need for information. First, we should be familiar with the basic terminology such as the relationship between adverse events and errors, and understand the variations of error. In patient management, besides skills and knowledge (evidence-based medicine), the ability (competence) of healthcare professionals to act and react in unexpected situations is key to prevent and treat adverse events. Not only healthcare professionals should be involved in the process but also healthy people in a way that they understand and patients in a way that they are actively involved. This paper will show how a more general view of patient safety can and should be implemented in the daily work of caregivers dealing with dialysis access in different aspects. A key factor to advance in this subject is to be open-minded and sensualized for this topic. The reader should get an idea of how an institution can create a culture of safety.

Predicting dependencies using domain-based coupling

Software dependencies play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non-technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependencies by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependencies without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65 of the source code dependencies and 77% of the database dependencies are predicted solely based on domain information.

BitWorker, a Decentralized Distributed Computing System based on BitTorrent

In this paper we present BitWorker, a platform for community distributed computing based on BitTorrent. Any splittable task can be easily specified by a user in a meta-information task file, such that it can be downloaded and performed by other volunteers. Peers find each other using Distributed Hash Tables, download existing results, and compute missing ones. Unlike existing distributed computing schemes relying on centralized coordination point(s), our scheme is totally distributed, therefore, highly robust. We evaluate the performance of BitWorker using mathematical models and real tests, showing processing and robustness gains. BitWorker is available for download and use by the community.

An Exploration Based Cognitive Bias Test for Mice: Effects of Handling Method and Stereotypic Behaviour

Behavioural tests to assess affective states are widely used in human research and have recently been extended to animals. These tests assume that affective state influences cognitive processing, and that animals in a negative affective state interpret ambiguous information as expecting a negative outcome (displaying a negative cognitive bias). Most of these tests however, require long discrimination training. The aim of the study was to validate an exploration based cognitive bias test, using two different handling methods, as previous studies have shown that standard tail handling of mice increases physiological and behavioural measures of anxiety compared to cupped handling. Therefore, we hypothesised that tail handled mice would display a negative cognitive bias. We handled 28 female CD-1 mice for 16 weeks using either tail handling or cupped handling. The mice were then trained in an eight arm radial maze, where two adjacent arms predicted a positive outcome (darkness and food), while the two opposite arms predicted a negative outcome (no food, white noise and light). After six days of training, the mice were also given access to the four previously unavailable intermediate ambiguous arms of the radial maze and tested for cognitive bias. We were unable to validate this test, as mice from both handling groups displayed a similar pattern of exploration. Furthermore, we examined whether maze exploration is affected by the expression of stereotypic behaviour in the home cage. Mice with higher levels of stereotypic behaviour spent more time in positive arms and avoided ambiguous arms, displaying a negative cognitive bias. While this test needs further validation, our results indicate that it may allow the assessment of affective state in mice with minimal training— a major confound in current cognitive bias paradigms.