scholarly journals Control over sampling boosts numerical evidence processing in human decisions from experience

2021 ◽  
Author(s):  
Stefan Appelhoff ◽  
Ralph Hertwig ◽  
Bernhard Spitzer
Keyword(s):  
Information Acquisition ◽  
Quality Of Experience ◽  
Decision Makers ◽  
Choice Accuracy ◽  
Behavioral Choice ◽  
Neural Recordings ◽  
Sample Information ◽  
Electroencephalogram Eeg ◽  
Levels Of Control

When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over sampling affect the quality of experience-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram (EEG) patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Meaningful, but effective? A critical evaluation of Ireland’s citizens’ dialogues on the future of Europe

Politics ◽  
2021 ◽  
pp. 026339572199148
Author(s):  
Anthony Costello
Keyword(s):  
European Union ◽  
Public Relations ◽  
Public Discourse ◽  
Critical Evaluation ◽  
Participatory Approach ◽  
Decision Makers ◽  
Deliberative Process ◽  
Political Will ◽  
The Future

On the 25 March 2017, leaders of the EU27 and European Union (EU) institutions ratified the Rome Declaration. They committed to invite citizens to discuss Europe’s future and to provide recommendations that would facilitate their decision-makers in shaping their national positions on Europe. In response, citizens’ dialogues on the future of Europe were instituted across the Union to facilitate public participation in shaping Europe. This paper explores Ireland’s set of dialogues which took place during 2018. Although event organisers in Ireland applied a relatively atypical and more systematic and participatory approach to their dialogues, evidence suggests that Irelands’ dialogues were reminiscent of a public relations exercise which showcased the country’s commitment to incorporating citizens into the debate on Europe while avoiding a deliberative design which could have strengthened the quality of public discourse and the quality of public recommendations. Due to an absence of elite political will for a deliberative process, as well as structural weaknesses in design, participants’ recommendations lacked any clear and prescriptive direction which could shape Ireland’s national position on the future of Europe in any constructive or meaningful way.

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

Concealment Support and Error Resilience for HEVC to Improve Consumer Quality of Experience

2021 ◽  
pp. 1-1
Author(s):  
Gosala Kulupana ◽  
Dumidu S. Talagala ◽  
Hemantha Kodikara Arachchi ◽  
Mobolaji Akinola ◽  
Anil Fernando
Keyword(s):  
Quality Of Experience ◽  
Error Resilience
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