22,407 research outputs found
QoE Modelling, Measurement and Prediction: A Review
In mobile computing systems, users can access network services anywhere and
anytime using mobile devices such as tablets and smart phones. These devices
connect to the Internet via network or telecommunications operators. Users
usually have some expectations about the services provided to them by different
operators. Users' expectations along with additional factors such as cognitive
and behavioural states, cost, and network quality of service (QoS) may
determine their quality of experience (QoE). If users are not satisfied with
their QoE, they may switch to different providers or may stop using a
particular application or service. Thus, QoE measurement and prediction
techniques may benefit users in availing personalized services from service
providers. On the other hand, it can help service providers to achieve lower
user-operator switchover. This paper presents a review of the state-the-art
research in the area of QoE modelling, measurement and prediction. In
particular, we investigate and discuss the strengths and shortcomings of
existing techniques. Finally, we present future research directions for
developing novel QoE measurement and prediction technique
Web based methodologies and techniques to monitor electronic resources use in university libraries
The aim of this paper is to measure user satisfaction and the quality of the electronic resources consultation services offered by the Milano Bicocca University Library
A comprehensive study of the usability of multiple graphical passwords
Recognition-based graphical authentication systems (RBGSs) using
images as passwords have been proposed as one potential solution to the need
for more usable authentication. The rapid increase in the technologies requiring
user authentication has increased the number of passwords that users have to
remember. But nearly all prior work with RBGSs has studied the usability of a
single password. In this paper, we present the first published comparison of the
usability of multiple graphical passwords with four different image types:
Mikon, doodle, art and everyday objects (food, buildings, sports etc.). A longi-tudinal experiment was performed with 100 participants over a period of 8
weeks, to examine the usability performance of each of the image types. The re-sults of the study demonstrate that object images are most usable in the sense of
being more memorable and less time-consuming to employ, Mikon images are
close behind but doodle and art images are significantly inferior. The results of
our study complement cognitive literature on the picture superiority effect, vis-ual search process and nameability of visually complex images
Scalable Privacy-Compliant Virality Prediction on Twitter
The digital town hall of Twitter becomes a preferred medium of communication
for individuals and organizations across the globe. Some of them reach
audiences of millions, while others struggle to get noticed. Given the impact
of social media, the question remains more relevant than ever: how to model the
dynamics of attention in Twitter. Researchers around the world turn to machine
learning to predict the most influential tweets and authors, navigating the
volume, velocity, and variety of social big data, with many compromises. In
this paper, we revisit content popularity prediction on Twitter. We argue that
strict alignment of data acquisition, storage and analysis algorithms is
necessary to avoid the common trade-offs between scalability, accuracy and
privacy compliance. We propose a new framework for the rapid acquisition of
large-scale datasets, high accuracy supervisory signal and multilanguage
sentiment prediction while respecting every privacy request applicable. We then
apply a novel gradient boosting framework to achieve state-of-the-art results
in virality ranking, already before including tweet's visual or propagation
features. Our Gradient Boosted Regression Tree is the first to offer
explainable, strong ranking performance on benchmark datasets. Since the
analysis focused on features available early, the model is immediately
applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective
Content Analysi
The impact of innovation and organizational factors on APS adoption: Evidence from the Dutch discrete parts industry
Advanced Planning and Scheduling (APS) systems have gained renewed interest from academics and practitioners. However, literature on APS adoption is scant. This study explores the impact of organizational and innovation related factors on the adoption of APS systems from a factors approach. The results from our field survey of 136 Dutch discrete manufacturing firms, show that management support, cost of purchase, number of end-products, and the value that firms attach to other usersââŹâ˘ opinions are key-factors that directly influence the adoption of APS systems. In addition, professionalism, external communications, and innovation experience indirectly influence APS adoption.innovation;impact;advanced planning and scheduling (APS) systems;causal model;factors research;organizational context
- âŚ