iASSIST

2020 ◽  
Vol 11 (4) ◽  
pp. 38-59
Author(s):  
Zhigang Zhu ◽  
Jin Chen ◽  
Lei Zhang ◽  
Yaohua Chang ◽  
Tyler Franklin ◽  
...  
Keyword(s):  
User Interfaces ◽  
Indoor Environment ◽  
Haptic Feedback ◽  
User Preferences ◽  
User Preference ◽  
Floor Plan ◽  
Multimodal Data ◽  
Client Server ◽  
Visual Models ◽  
Server Architecture

The iASSIST is an iPhone-based assistive sensor solution for independent and safe travel for people who are blind or visually impaired, or those who simply face challenges in navigating an unfamiliar indoor environment. The solution integrates information of Bluetooth beacons, data connectivity, visual models, and user preferences. Hybrid models of interiors are created in a modeling stage with these multimodal data, collected, and mapped to the floor plan as the modeler walks through the building. Client-server architecture allows scaling to large areas by lazy-loading models according to beacon signals and/or adjacent region proximity. During the navigation stage, a user with the navigation app is localized within the floor plan, using visual, connectivity, and user preference data, along an optimal route to their destination. User interfaces for both modeling and navigation use multimedia channels, including visual, audio, and haptic feedback for targeted users. The design of human subject test experiments is also described, in addition to some preliminary experimental results.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

Research and Design of Broadband Wireless Multimedia Trunking Communication Dispatch Client Based on B/S Architecture

2014 ◽  
Vol 556-562 ◽  
pp. 5194-5197
Author(s):  
Lian Fen Huang ◽  
Wei Huang ◽  
Xian Long Yang ◽  
Yu Liang Tang
Keyword(s):  
Mobile Communication ◽  
Public Transportation ◽  
Wireless Multimedia ◽  
Broadband Wireless ◽  
Client Server ◽  
Transportation Security ◽  
Client Software ◽  
Trunking Communication ◽  
Research And Design ◽  
Server Architecture

Broadband trunking communication system has been widely used with a variety of industries, such as public transportation security and navigation, etc. Trunking communication is a branch of mobile communication. Because the client of traditional dispatch services based on C/S (client/server) architecture need to install the client software, this paper studies and designs one dispatch client, which is based on B/S (browser/server) architecture.

Human–robot interaction via voice-controllable intelligent user interface

Robotica ◽  
2007 ◽  
Vol 25 (5) ◽  
pp. 521-527 ◽  
Author(s):  
Harsha Medicherla ◽  
Ali Sekmen
Keyword(s):  
User Interface ◽  
User Interfaces ◽  
Spatial Reasoning ◽  
User Preferences ◽  
Human Robot Interaction ◽  
Manual Control ◽  
Reasoning Ability ◽  
Robot Interaction ◽  
Intelligent User Interface ◽  
Voice Control

SUMMARYAn understanding of how humans and robots can successfully interact to accomplish specific tasks is crucial in creating more sophisticated robots that may eventually become an integral part of human societies. A social robot needs to be able to learn the preferences and capabilities of the people with whom it interacts so that it can adapt its behaviors for more efficient and friendly interaction. Advances in human– computer interaction technologies have been widely used in improving human–robot interaction (HRI). It is now possible to interact with robots via natural communication means such as speech. In this paper, an innovative approach for HRI via voice-controllable intelligent user interfaces is described. The design and implementation of such interfaces are described. The traditional approaches for human–robot user interface design are explained and the advantages of the proposed approach are presented. The designed intelligent user interface, which learns user preferences and capabilities in time, can be controlled with voice. The system was successfully implemented and tested on a Pioneer 3-AT mobile robot. 20 participants, who were assessed on spatial reasoning ability, directed the robot in spatial navigation tasks to evaluate the effectiveness of the voice control in HRI. Time to complete the task, number of steps, and errors were collected. Results indicated that spatial reasoning ability and voice-control were reliable predictors of efficiency of robot teleoperation. 75% of the subjects with high spatial reasoning ability preferred using voice-control over manual control. The effect of spatial reasoning ability in teleoperation with voice-control was lower compared to that of manual control.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

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