The artistic design of user interaction experience for mobile systems based on context-awareness and machine learning

2021 ◽  
Author(s):  
Lina Liu
Keyword(s):  
Machine Learning ◽  
Context Awareness ◽  
User Interaction ◽  
Mobile Systems

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2012 ◽  
pp. 969-985
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2011 ◽  
pp. 121-138
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

scholarly journals Machine Learning Improves the Precision and Robustness of High-Content Screens

2011 ◽  
Vol 16 (9) ◽  
pp. 1059-1067 ◽  
Author(s):  
Peter Horvath ◽  
Thomas Wild ◽  
Ulrike Kutay ◽  
Gabor Csucs
Keyword(s):  
Machine Learning ◽  
User Interaction ◽  
Texture Features ◽  
Large Data ◽  
Complete Analysis ◽  
Data Sets ◽  
Reporter Protein ◽  
Fluorescent Intensity ◽  
Analysis Workflow

Imaging-based high-content screens often rely on single cell-based evaluation of phenotypes in large data sets of microscopic images. Traditionally, these screens are analyzed by extracting a few image-related parameters and use their ratios (linear single or multiparametric separation) to classify the cells into various phenotypic classes. In this study, the authors show how machine learning–based classification of individual cells outperforms those classical ratio-based techniques. Using fluorescent intensity and morphological and texture features, they evaluated how the performance of data analysis increases with increasing feature numbers. Their findings are based on a case study involving an siRNA screen monitoring nucleoplasmic and nucleolar accumulation of a fluorescently tagged reporter protein. For the analysis, they developed a complete analysis workflow incorporating image segmentation, feature extraction, cell classification, hit detection, and visualization of the results. For the classification task, the authors have established a new graphical framework, the Advanced Cell Classifier, which provides a very accurate high-content screen analysis with minimal user interaction, offering access to a variety of advanced machine learning methods.

scholarly journals Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256696
Author(s):  
Anna Keuchenius ◽  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Language Processing ◽  
User Interaction ◽  
User Interactions ◽  
Online Interactions ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

scholarly journals Context-awareness for control consoles in integrated operating rooms

2018 ◽  
Vol 4 (1) ◽  
pp. 291-295
Author(s):  
Stefan Franke ◽  
Max Rockstroh ◽  
Thomas Neumuth
Keyword(s):  
Context Awareness ◽  
User Interaction ◽  
Operating Rooms ◽  
Context Aware ◽  
Fully Automatic

AbstractIn surgery, the possibilities of fully automatic assistance are limited. A valuable automated assistance can be implemented by realizing context-aware behaviour of control consoles in integrated operating rooms (ORs). We developed an intraoperative pipeline for surgical context interpretation and an integrated console. Based on that, we propose a method for the context-aware ranking of configuration profiles, which facilitate the handling of devices during endoscopic interventions. The method is evaluated with twenty-four simulated endoscopic ear-nosethroat (ENT) workflows. The results indicate that a continuous automatic ranking based on the surgical situation, can significantly reduce manual user interaction while the surgeon’s control is preserved.

Application Mobility

2015 ◽  
pp. 169-186
Author(s):  
Dan Johansson ◽  
Mikael Wiberg
Keyword(s):  
Empirical Study ◽  
Home Care ◽  
Context Awareness ◽  
Care Service ◽  
Relevant Information ◽  
Mobile Systems ◽  
Design Work ◽  
Systems Research ◽  
User Centric

Mobility has become an omnipresent part of our modern IT society. Alongside the general taxonomy of mobile users, terminals, sessions, and services, there are also more specialized forms of mobility. Context-Awareness Supported Application Mobility (CASAM) or “Application Mobility” is one such form that is explored in this chapter. CASAM builds on the idea of using context to move an application between different devices during its execution in order to provide relevant information and/or services. The authors use a concept-driven approach to advance mobile systems research, integrating it with a more traditional user-centric method and a case study, further exploring the concept of CASAM. To empirically situate our design work they conducted an empirical study of a home care service group serving the Swedish municipality of Skellefteå, followed by an exercise in matching the properties of the CASAM concept in relation to problems within current workflow.

scholarly journals Improving Automatic Control of an Ankle-Foot Prosthesis Using Machine Learning Algorithms

2017 ◽  
Author(s):  
Stuart R. Fairhurst ◽  
Sara R. Koehler-McNicholas ◽  
Billie C. S. Slater ◽  
Eric A. Nickel ◽  
Karl A. Koester ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Learning Algorithms ◽  
User Interaction ◽  
Standing Position ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Smartphone App ◽  
Automatic Mode ◽  
Va Health Care System

Most commercially available lower-limb prostheses are designed for walking, not for standing. The Minneapolis VA Health Care System has developed a bimodal prosthetic ankle-foot system with distinct modes for walking and standing [1]. With this device, a prosthesis user can select standing or walking mode in order to maximize standing stability or walking functionality, depending on the activity and context. Additionally, the prosthesis was designed to allow for an “automatic mode” to switch between standing and walking modes based on readings from an onboard Inertial Measurement Unit (IMU) without requiring user interaction to manually switch modes. A smartphone app was also developed to facilitate changing between walking, standing and automatic modes. The prosthesis described in [1] was used in a pilot study with 18 Veterans with lower-limb amputations to test static, dynamic, and functional postural stability. As part of the study, 17 Veterans were asked for qualitative feedback on the bimodal ankle-foot system (Table 1). The majority of participants (82%) expressed an interest in having an automatic mode. The participants also indicated that the automatic mode would need to reach walking mode on their first step and to lock the ankle quickly once the standing position was achieved. When asked about how they wanted to control the modes of the prosthesis, 82% wanted to use a physical switch and only 12% wanted to use a smartphone app. The results indicated that the following major design changes would be needed: 1) A fast and accurate automatic mode 2) A physical switch for mode changes This paper describes the use of machine learning algorithms to create an improved automatic mode and the use of stakeholder feedback to design a physical switch for the bimodal ankle-foot system.

scholarly journals An SDN/NFV Proof-of-Concept Test-Bed for Machine Learning-based Network Management

2021 ◽  
Author(s):  
Wei Jiang ◽  
Mathias Strufe ◽  
Michael Gundall ◽  
Hans Dieter Schotten
Keyword(s):  
Machine Learning ◽  
Network Management ◽  
Open Source Software ◽  
Research Direction ◽  
Mobile Systems ◽  
Test Bed ◽  
Self Organized ◽  
Fifth Generation ◽  
Art Research ◽  
Theoretical Analyses

Complexity and heterogeneity of the fifth generation (5G) and beyond mobile systems impose a great challenge on current network managing approaches, which are vulnerable, time-consuming and costly. The state-of-the-art research direction in this field is to apply machine learning (ML) techniques to realize intelligent and highly self-organized networking. Unlike the physical layer, theoretical analyses and numerical simulations on the management layer are generally infeasible or not scientifically rigorous enough. Therefore, in this paper, we present a software-defined and virtualized wireless test-bed that is established to evaluate ML-based network management. Based on open-source software and off-the-shelf hardware, this test-bed is easily reproducible, which in turn is hopeful to foster innovative works in this field.

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