Towards adaptive quality-aware Complex Event Processing in the Internet of Things

This paper investigates how to complement Complex Event Processing (CEP) with dynamic quality monitoring mechanisms and support the dynamic integration of suitable sensory data sources. In the proposed approach, queries to detect complex events are annotated with consumer-definable quality policies that are evaluated and used to autonomously assign (or even configure) suitable data sources of the sensing infrastructure. We present and study different forms of expressing quality policies and explore how they affect the process of quality monitoring including different modes of assessing and applying quality-related adaptations. A performance study in an IoT scenario shows that the proposed mechanisms in supporting quality policy monitoring and adaptively selecting suitable data sources succeed in enhancing the acquired quality of results while fulfilling consumers’ quality requirements. We show that the quality-based selection of sensor sources also extends the network’s lifetime by optimizing the data sources’ energy consumption

Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing

IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm\u27s default allocation

Optimization towards Efficiency and Stateful of dispel4py

Scientific workflows bridge scientific challenges with computational resources. While dispel4py, a stream-based workflow system, offers mappings to parallel enactment engines like MPI or Multiprocessing, its optimization primarily focuses on dynamic process-to-task allocation for improved performance. An efficiency gap persists, particularly with the growing emphasis on conserving computing resources. Moreover, the existing dynamic optimization lacks support for stateful applications and grouping operations. To address these issues, our work introduces a novel hybrid approach for handling stateful operations and groupings within workflows, leveraging a new Redis mapping. We also propose an auto-scaling mechanism integrated into dispel4py's dynamic optimization. Our experiments showcase the effectiveness of auto-scaling optimization, achieving efficiency while upholding performance. In the best case, auto-scaling reduces dispel4py's runtime to 87% compared to the baseline, using only 76% of process resources. Importantly, our optimized stateful dispel4py demonstrates a remarkable speedup, utilizing just 32% of the runtime compared to the contender.Comment: 13 pages, 13 figure

Predictability effects in auditory scene analysis: a review

Many sound sources emit signals in a predictable manner. The idea that predictability can be exploited to support the segregation of one source's signal emissions from the overlapping signals of other sources has been expressed for a long time. Yet experimental evidence for a strong role of predictability within auditory scene analysis (ASA) has been scarce. Recently, there has been an upsurge in experimental and theoretical work on this topic resulting from fundamental changes in our perspective on how the brain extracts predictability from series of sensory events. Based on effortless predictive processing in the auditory system, it becomes more plausible that predictability would be available as a cue for sound source decomposition. In the present contribution, empirical evidence for such a role of predictability in ASA will be reviewed. It will be shown that predictability affects ASA both when it is present in the sound source of interest (perceptual foreground) and when it is present in other sound sources that the listener wishes to ignore (perceptual background). First evidence pointing toward age-related impairments in the latter capacity will be addressed. Moreover, it will be illustrated how effects of predictability can be shown by means of objective listening tests as well as by subjective report procedures, with the latter approach typically exploiting the multi-stable nature of auditory perception. Critical aspects of study design will be delineated to ensure that predictability effects can be unambiguously interpreted. Possible mechanisms for a functional role of predictability within ASA will be discussed, and an analogy with the old-plus-new heuristic for grouping simultaneous acoustic signals will be suggested

Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the invariant

Neural Mechanisms of Transsaccadic Integration of Visual Features

This thesis explores the neural mechanisms of transsaccadic integration of visual features. In the study, I investigated the cortical correlates of transsaccadic integration of object orientation in multiple reference frames. In a functional MRI adaptation (fMRIa) paradigm, participants viewed sets of two orientation stimuli in each trial and were asked to indicate if the orientations were the same (Repeat condition) or different (Novel condition). Stimuli were presented in one of three spatial conditions: 1) space-fixed, 2) retina-fixed and 3) frame-independent. Results indicate that, in addition to common activation in frontal motor cortical regions in all three spatial conditions, parietal and occipitotemporal regions are active in the space-fixed condition, parietofrontal regions are active in the retina-fixed condition, and parietofrontal and occipitotemporal regions are active in the frame-independent condition. In conclusion, these results indicate that transsaccadic integration involves differential activation of cortical areas, depending on the frame of reference