Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features

A multi-agent simulation framework for automated negotiation in order promising

The purpose of this paper is to propose and test a multi-agent-based system for automated multi-attribute negotiation in order promising. In a make-to-order production model, it is not always possible to satisfy the Available-to-Promise (ATP) and Capable-to-Promise (CTP) conditions. Therefore, it is important to quickly explore alternate solutions that would satisfy both the customer and the supplier. We adopt the concepts of evolutionary system design that advocates for continuous exploration of new solutions based on extensive search and multi-attribute simulations that help identify for better negotiated solutions based on real-life ordering situations – changes of delivery date, price adjustments, addition/modifications of valueadded services as part of the order. Results of our simulations showed that negotiation procedures did reduce the number of rejected orders and increase the overall revenue when negotiation concepts are introduced

Scoring epidemiological forecasts on transformed scales

Forecast evaluation is essential for the development of predictive epidemic models and can inform their use for public health decision-making. Common scores to evaluate epidemiological forecasts are the Continuous Ranked Probability Score (CRPS) and the Weighted Interval Score (WIS), which can be seen as measures of the absolute distance between the forecast distribution and the observation. However, applying these scores directly to predicted and observed incidence counts may not be the most appropriate due to the exponential nature of epidemic processes and the varying magnitudes of observed values across space and time. In this paper, we argue that transforming counts before applying scores such as the CRPS or WIS can effectively mitigate these difficulties and yield epidemiologically meaningful and easily interpretable results. Using the CRPS on log-transformed values as an example, we list three attractive properties: Firstly, it can be interpreted as a probabilistic version of a relative error. Secondly, it reflects how well models predicted the time-varying epidemic growth rate. And lastly, using arguments on variance-stabilizing transformations, it can be shown that under the assumption of a quadratic mean-variance relationship, the logarithmic transformation leads to expected CRPS values which are independent of the order of magnitude of the predicted quantity. Applying a transformation of log(x + 1) to data and forecasts from the European COVID-19 Forecast Hub, we find that it changes model rankings regardless of stratification by forecast date, location or target types. Situations in which models missed the beginning of upward swings are more strongly emphasised while failing to predict a downturn following a peak is less severely penalised when scoring transformed forecasts as opposed to untransformed ones. We conclude that appropriate transformations, of which the natural logarithm is only one particularly attractive option, should be considered when assessing the performance of different models in the context of infectious disease incidence

EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

A common framework for the evaluation of psychophysiological visual quality assessment

The assessment of perceived quality based on psychophysiological methods recently gained attraction as it potentiallyovercomes certain flaws of psychophysical approaches. Although studies report promising results, it is not possible toarrive at decisive and comparable conclusions that recommend the use of one or another method for a specific applicationor research question. The video quality expert group started a project on psychophysiological quality assessment to studythese novel approaches and to develop a test plan that enables more systematic research. This test plan comprises of a specificallydesigned set of quality annotated video sequences, suggestions for psychophysiological methods to be studied inquality assessment, and recommendations for the documentation and publications of test results. The test plan is presentedin this article.Celtc-Next 5G Perfecta (2018-00735

Medien der Kooperation

Die digital-vernetzten Medien erfordern neue Analysen, Theorien und Geschichten. Sie verändern unseren Blick auf die Geschichte von Infrastrukturen, Öffentlichkeiten und Medienpraktiken. Was wären Ansätze für eine Medientheorie, die praktischen „skills“ des Mediengebrauchs,seiner soziotechnischen Materialität und den bürokratischen wie epistemischen Qualitäten der Medien gerecht wird? Die vorliegende Ausgabe 1/2015 der Navigationen widmet sich Medien als kooperativ bewerkstelligten Kooperationsbedingungen. Sie erbringen, so die These, konstitutive Vermittlungsleistungen zwischen der Organisation von Arbeit, Praktiken des Infrastrukturierens und der Genese von Öffentlichkeiten in wechselseitiger Interaktion

Animation and interactivity facilitate acquisition of pediatric life support skills: a randomized controlled trial using virtual patients versus video instruction

Background: Several promising studies suggest a positive impact of interactive and media-enriched e-learning resources such as virtual patients (VP) on skill acquisition in pediatric basic life support (PBLS). This study investigates which immanent VP components account for this effect. Methods: N = 103 medical students in their 5th year were assigned to one of three groups: a video group prepared with self-instructional videos on PBLS (N = 37); an animation-enriched VP group with VP containing interactive questions (N = 35), static and animated media, and a static VP group with VP containing interactive questions and only static media (N = 31). Subsequent PBLS demonstrations were video-documented and scored for adherence to guideline-based algorithm, temporal demands (such as correct pace of rescue breaths and chest compressions), and quality of procedural steps (e.g., correct head positioning), as well as overall competency by two group-blinded, independent pediatricians. Results: Groups did not differ with regard to adherence to correct algorithm (88.7 ± 10.3, 93.3 ± 6.7 and 90.3 ± 10.5, respectively). Self-instruction with animated media – through videos or animation-enriched VP – resulted in a better adherence to temporal demands, as compared with training with static VP (64.5 ± 26.3 and 50.7 ± 25.7, respectively, vs. 23.8 ± 21.0). Procedural quality by the video group was slightly inferior compared with the animation-enriched VP group (79.5 ± 12.3 vs. 82.0 ± 11.9), and distinct inferior in overall ‘competent’ ratings (43.2% vs. 65.7%). The static VP group performed considerably most poorly of all three groups (temporal adherence 73.2 ± 11.9 and 19.4% ‘competent’ ratings). Conclusions: VP can feasibly enhance PBLS skill acquisition. Thoughtful design of animations and interactivity of the VP further improves such skill acquisition, both in quality of performance and in adherence to temporal demands

Comparing human and model-based forecasts of COVID-19 in Germany and Poland

Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts.

BACKGROUND: Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. METHODS: We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. FINDINGS: Simulated outbreaks starting with five initial cases, an R0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R0 of 2·5 more than 70% of contacts had to be traced, and for an R0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R0 was 1·5. For R0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. INTERPRETATION: In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. FUNDING: Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK