Showing papers by "Magdalini Eirinaki published in 2016"

PRO-Fit: A personalized fitness assistant framework.

Abstract: The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous feedback from the end users and fail to engage those who have busy schedules, or are not as committed and self-motivated. In this work, we propose a framework that employs machine learning and recommendation algorithms in order to smartly track and identify user’s activity by collecting accelerometer data, synchronizes with the user’s calendar, and recommends personalized workout sessions based on the user’s and similar users’ past activities, their preferences, as well as their physical state and availability. KEYWORDS—wearable technology, activity tracking, classification, recommendation, personalized assistant

PRO-fit: exercise with friends

Abstract: The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous commitment from the end users, who need to proactively interact with the application in order to connect with friends and attain their goals. These applications fail to engage and motivate users who have busy schedules, or are not as committed and self-motivated. In this work, we present PRO-Fit, a personalized fitness assistant application that employs machine learning and recommendation algorithms in order to smartly track and identify user's activity, synchronizes with the user's calendar, recommends personalized workout sessions based on the user's preferences, fitness goals, and availability. Moreover, PRO-Fit integrates with the user's social network and recommends "fitness buddies" with similar preferences and availability.

A cloud-based framework for smart permit system for buildings

Abstract: In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on location, time, and type of request. The novelty of the proposed framework lies in the integration of the pre-permit processing front-end with permit processing and data analytics & mining modules, along with utilization of techniques for extracting knowledge from the data generated through the use of the system.

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Abstract: The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections This helps individuals to make decisions or adopt behaviors, opinions or products In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading occurs in several rounds and has fixed number of influentials In our approach we consider only one round of influence, which finds applications in settings where timely influence is vital We tackle the problem by proposing a two-phase framework that aims at identifying influentials in the first phase and form influential neighborhoods to generate recommendations to users with no prior knowledge in the second phase The difference of the proposed framework with most social recommender systems is that we need to generate recommendations including more than one item and in the absence of explicit ratings, solely relying on the social network's graph

Time-Based Ensembles for Prediction of Rare Events in News Stream

Abstract: Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when related social feedback is unavailable. The second strategy, a posteriori, is focused on predicting the popularity of news using related social feedback. However, both strategies present shortcomings related to data availability and time of prediction. To overcome such shortcomings, we propose a hybrid strategy of time-based ensembles using models from both strategies. Using news data from Google News and popularity data from Twitter, we show that the proposed ensembles significantly improve the early and accurate prediction of rare cases of highly popular news.