1,721,047 research outputs found
A low-cost infrared-optical head tracking solution for virtual 3D audio environment using the Nintendo Wii-remote
No full textA virtual audio system needs to track both the translation and rotation of an observer to simulate a realistic sound environment. Current existing virtual audio systems either do not fully account for rotation or require the user to carry a controller at all times. This paper presents a three-dimensional (3D) virtual audio system with a head tracking unit that fully accounts for both translation and rotation of a user without the need of a controller. The system consists of four infrared light-emitting diodes on the user's headset together with a Wii-remote to track their movement through a graphical user interface. The system was tested with a simulation that used a pinhole camera model to map the 3D-coordinates of each diode onto the two-dimensional (2D) camera plane. This simulation of 3D head movement yields 2D coordinate data that were put into the tracking algorithm and to reproduced the 3D motion. The results from a prototype system, assembled to track the 3D movements of a rigid object were also consistent with the simulation results. The tracking system has been integrated into an Ericsson 3D-audio system and its effectiveness has been verified in a headtracked virtual 3D-audio system with real-time animating graphical outputs
ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT
No full textA Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
No full text
The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems
Full text linkPoint-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. Previous studies have shown that incorporating contextual information such as geographical, temporal, social, and categorical substantially improves the quality of POI recommendations. However, fewer works have studied in-depth the multi-aspect benefits of context fusion on POI recommendation, in particular on beyond-accuracy, fairness, and interpretability of recommendations. In this work, we propose a linear regression-based fusion of POI contexts that effectively finds the best combination of contexts for each (i) user, or (ii) group of users from their historical interactions. The results of large-scale experiments on two popular datasets Gowalla and Yelp reveal several interesting findings. First, the proposed approach does not present significant loss in accuracy and unfairness of popularity bias as with classical collaborative baselines, and yet improves the beyond-accuracy of recommendation compared with existing context-aware (CA) approaches using heuristic context fusions; for instance, the proposed approach improves the accuracy and beyond-accuracy compare to best baseline model by 25% and 30%, respectively. Second, our proposed approach is interpretable, allowing to explain to the user why she has been recommended specific POIs, based on the learned context weights from user past check-ins; for example, if you are in Rome and our method recommends you a historical place like 'Colosseum', it can also provide an explanation why this item is recommended to you based on your personal preference on context (e.g., you were recommended to visit 'Colosseum' because in the past your visited historical places). Third, by analyzing the fairness of recommendation with respect to users (based on their activity levels) and items (based on the popularity of items), we found that a model which is recommend fairly on one dataset can recommend unfair on another dataset.Overall, our study suggests that appropriate context fusion is an essential element of an accurate, fair, and transparent POI recommendation system. We highlight that while we have tested the efficacy of our context fusion methods on two popular CA recommendation models in the POI domain, namely GeoSoCa and LORE, our system can be flexibly utilized to extend the capability of other CA algorithms
A Novel Fuzzy-Based Smoke Detection System Using Dynamic and Static Smoke Features
No full textAutomatic fire surveillance is an important task for providing emergency response in the event of unexpected fire hazards. Early detection of fire can substantially mitigate the ecological or economical costs associated with a fire disaster. In this regard, as smoke usually always precedes fire, an intelligent smoke detection system is proposed that exploits a Fuzzy Inference System (FIS) in order to aggregate the features of smoke. In addition, robust smoke feature detection algorithms are implemented that take into account both dynamic and static characteristics of smoke. The smoke features include motion, motion orientation (estimated by using the accumulation of motion) for the former and texture for the latter. Experimental results on different video frames show that the proposed smoke detection system has robust performance on detecting the existence of smoke which shows the effectiveness of the proposed smoke detection system
Letting Users Assist What to Watch: An Interactive Query-by-Example Movie Recommendation System
No full textApplication of connected dominating sets in wildfire detection based on wireless sensor networks
No full textWireless sensor networks enable us to collect data such as temperature and humidity which are measured from different points of environment continuously and transmitted to the firefighter centre rapidly. On the other hand, sensor networks are facing serious barriers such as limited energy resources and high vulnerability in extreme environmental conditions which should be seriously taken into account. Factors such as limited energy resources and low processing capabilities cause data transmission to be one of the most important concerns in these networks. Using wireless sensor networks is a method proposed in recent years which enables us to detect and prevent wildfires in the least possible time. In this paper, a new scheme for data transmission is introduced which is used for routing based on virtual clustering. The concept of dominating set is used for clustering and choosing dominating nodes for routing. The aim of this work is to decrease energy consumption, increase network lifetime and the amount of received data. Comparing results of this simulation with other methods shows that the proposed method in this paper is very efficient and fulfils all mentioned goals
MMTF-14K: A Multifaceted Movie Trailer Dataset for Recommendation and Retrieval
No full text<p>The MMTF-14K dataset provides a stable and extensive source for devising and evaluating movie recommender systems. MMTF-14K contains <strong><a href="https://mmprj.github.io/mtrm_dataset/datasets">audio and visual descriptors</a></strong> in addition to ratings and metadata for 13,623 Hollywood-type movie trailers. The dataset therefore facilitates research on content-based recommender systems, where content refers not only to metadata, but specifically to visual and auditory characteristics of movies. The data comes also with several baselines <a href="https://mmprj.github.io/mtrm_dataset/benchmark">benchmarking results</a> for uni-modal and multi-modal recommendation systems. The dataset therefore facilitates research on movie recommendation. In addition, the rich data supports the exploration of other multimedia tasks such as popularity prediction, genre classification, or auto-tagging (aka tag prediction).</p>
<p>The MMTF-14K dataset has been created as a joint research work by <a href="http://www.ir.disco.unimib.it/yashar-deldjoo/">Yashar Deldjoo </a>(Politecnico di Milano, Italy), <a href="http://www.campus.pub.ro/lab7/gconstantin/">Mihai Gabriel Constantin </a>and <a href="http://campus.pub.ro/lab7/bionescu/">Bogdan Ionescu </a>(University Politehnica of Bucharest, Romania), <a href="http://www.cp.jku.at/people/schedl/">Markus Schedl </a>(Johannes Kepler University Linz, Austria), and <a href="https://scholar.google.it/citations?hl=en&user=dTSOPCMAAAAJ&view_op=list_works&sortby=pubdate">Paolo Cremonesi </a>(Politecnico di Milano, Italy).</p>
<p>We would like to acknowledge MovieLens here for providing a stable benchmark dataset of movies containing individual user ratings and metadata which is an enabler for doing research on movie recommendation. Please consider the <a href="http://files.grouplens.org/datasets/movielens/ml-20m-README.html">MovieLens-20M web page</a> for more details on the ratings and tags datasets.</p>
<p>For acknowledgments please use our paper:</p>
<p>@inproceedings{deldjooMMTF14K, <br>
title={MMTF-14K: A Multifaceted Movie Trailer Feature Dataset for Recommendation and Retrieval}, <br>
author={Deldjoo, Yashar and Constantin, Mihai Gabriel and Schedl, Markus and Ionescu, Bogdan and Cremonesi, Paolo}, <br>
booktitle={Proceedings of the 9th ACM Multimedia Systems Conference}, <br>
year={2018}, <br>
organization={ACM}}</p>
<p>For further inquiries you are free to contact Yashar Deldjoo through his email: <a href="mailto:[email protected]">[email protected] </a>.</p>The link to the dataset can be also found in: https://mmprj.github.io/mtrm_dataset/inde
- …
