Quality of experience in prosocial activity and intent to continue: An experience sampling study.

2021 ◽  
Author(s):  
Jeanne Nakamura ◽  
Dwight C. K. Tse ◽  
Ajit Singh Mann
Keyword(s):  
Quality Of Experience ◽  
Experience Sampling ◽  
Intent To Continue

Quality of experience during psychosocial rehabilitation: A real-time investigation with experience sampling method.

2012 ◽  
Vol 35 (6) ◽  
pp. 447-453 ◽  
Author(s):  
Marta Bassi ◽  
Nicoletta Ferrario ◽  
Gabriella Ba ◽  
Antonella Delle Fave ◽  
Caterina Viganò
Keyword(s):  
Real Time ◽  
Quality Of Experience ◽  
Sampling Method ◽  
Psychosocial Rehabilitation ◽  
Experience Sampling ◽  
Experience Sampling Method

scholarly journals Autonomy Matters: Experiential and Individual Differences in Chosen and Unchosen Solitary Activities From Three Experience Sampling Studies

2021 ◽  
pp. 194855062110480
Author(s):  
Dwight C. K. Tse ◽  
Jennifer C. Lay ◽  
Jeanne Nakamura
Keyword(s):  
Meaning In Life ◽  
Quality Of Experience ◽  
Satisfaction With Life ◽  
Experience Sampling ◽  
Well Being ◽  
Two Dimensions ◽  
Total N ◽  
Sampling Studies ◽  
Lower Productivity

Solitude––the absence of social interaction––can bring both positive and negative experiences. Drawing on self-determination theory, we conducted three experience sampling studies to investigate quality of experience and dispositions associated with activities varying on two dimensions––chosenness (chosen/unchosen) and social context (solitary/interactive). Participants (total N = 283) completed surveys 6–7 times each day over a 7-day period (total: 8,769 surveys). Multilevel modeling confirmed that participants reported the lowest quality momentary experiences when engaged in unchosen (vs. chosen) solitary activities. Further, individuals who spent more time on unchosen solitary activities reported lower meaning in life and satisfaction with life. Extraversion was positively associated with time spent on chosen interactive activities but negatively with chosen solitary activities. Post hoc analyses revealed that people low (vs. high) in extraversion reported lower productivity only during unchosen interactive activities. Chosen (vs. unchosen) solitary activities seem to have a relatively benign impact on quality of experience and well-being.

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 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.

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