Opportunistic Data Dissemination Using Real-World User Mobility Traces

2008 ◽  
Author(s):  
Andreas Heinemann ◽  
Jussi Kangasharju ◽  
Max Muhlhauser
Keyword(s):  
Real World ◽  
Data Dissemination ◽  
User Mobility

scholarly journals Player Profile Management on NFC Smart Card for Multiplayer Ubiquitous Games

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Romain Pellerin ◽  
Chen Yan ◽  
Julien Cordry ◽  
Eric Gressier-Soudan
Keyword(s):  
Smart Card ◽  
Real World ◽  
Mixed Reality ◽  
Smart Cards ◽  
User Mobility ◽  
Personalized Services ◽  
Player Profiles ◽  
Profile Management ◽  
User Centric ◽  
Personal Content

One of the goals of mixed reality and ubiquitous computing technologies is to provide an adaptable and personal content at any moment, anywhere, and in any context. In Multiplayer Ubiquitous Games (MUGs), players have to interact in the real world at both physical and virtual levels. Player profiles in MUGs offer an opportunity to provide personalized services to gamers. This paper presents a way to manage MUG player profiles on an NFC Smart Card, and proposes a Java API to integrate Smart Cards in the development of MUGs. This user centric approach brings new forms of gameplay, allowing the player to interact with the game or with other players any time and anywhere. Smart Cards should also help improve the security, ubiquity, and the user mobility in traditional MUGs.

scholarly journals An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 83-90
Author(s):  
Qing Guo ◽  
Zhu Sun ◽  
Jie Zhang ◽  
Yin-Leng Theng
Keyword(s):  
Neural Network ◽  
Random Walk ◽  
Recurrent Neural Network ◽  
Real World ◽  
State Of The Art ◽  
Knowledge Graph ◽  
User Mobility ◽  
Location Recommendation ◽  
Meta Path ◽  
Real World Datasets

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

scholarly journals Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference

2019 ◽  
Author(s):  
Yongji Wu ◽  
Defu Lian ◽  
Shuowei Jin ◽  
Enhong Chen
Keyword(s):  
Social Relations ◽  
Real World ◽  
Complex Dynamics ◽  
Structural Information ◽  
User Mobility ◽  
Trajectory Data ◽  
Convolutional Networks ◽  
Semantic Data ◽  
The Rich ◽  
Real World Datasets

Inferring social relations from user trajectory data is of great value in real-world applications such as friend recommendation and ride-sharing. Most existing methods predict relationship based on a pairwise approach using some hand-crafted features or rely on a simple skip-gram based model to learn embeddings on graphs. Using hand-crafted features often fails to capture the complex dynamics in human social relations, while the graph embedding based methods only use random walks to propagate information and cannot incorporate external semantic data provided. We propose a novel model that utilizes Graph Convolutional Networks (GCNs) to learn user embeddings on the User Mobility Heterogeneous Graph in an unsupervised manner. This model is capable of propagating relation layer-wisely as well as combining both the rich structural information in the heterogeneous graph and predictive node features provided. Our method can also be extended to a semi-supervised setting if a part of the social network is available. The evaluation on three real-world datasets demonstrates that our method outperforms the state-of-the-art approaches.

scholarly journals Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed

2018 ◽  
Vol 2672 (22) ◽  
pp. 10-23 ◽  
Author(s):  
Mashrur Chowdhury ◽  
Mizanur Rahman ◽  
Anjan Rayamajhi ◽  
Sakib Mahmud Khan ◽  
Mhafuzul Islam ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Field Experiments ◽  
Data Dissemination ◽  
Heterogeneous Wireless Networks ◽  
Lessons Learned ◽  
Connected Vehicle ◽  
Time Data ◽  
Data Archiving ◽  
The Real

The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.

Replications can cause distorted belief in scientific progress

2018 ◽  
Vol 41 ◽  
Author(s):  
Michał Białek
Keyword(s):  
Real World ◽  
Scientific Progress ◽  
The Public ◽  
Psychological Science

AbstractIf we want psychological science to have a meaningful real-world impact, it has to be trusted by the public. Scientific progress is noisy; accordingly, replications sometimes fail even for true findings. We need to communicate the acceptability of uncertainty to the public and our peers, to prevent psychology from being perceived as having nothing to say about reality.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

Qualitative Case Study Research Design

2015 ◽  
Vol 25 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Jennifer Tetnowski
Keyword(s):  
Research Design ◽  
Real World ◽  
Qualitative Case Study ◽  
Case Study Research ◽  
Study Research

Qualitative case study research can be a valuable tool for answering complex, real-world questions. This method is often misunderstood or neglected due to a lack of understanding by researchers and reviewers. This tutorial defines the characteristics of qualitative case study research and its application to a broader understanding of stuttering that cannot be defined through other methodologies. This article will describe ways that data can be collected and analyzed.

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