505 research outputs found
A machine learning approach for mapping and accelerating multiple sclerosis research
The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research.info:eu-repo/semantics/publishedVersio
Search Results: Predicting Ranking Algorithms With User Ratings and User-Driven Data
The purpose of this correlational quantitative study was to examine the possible relationship between user-driven parameters, user ratings, and ranking algorithms. The studyâs population consisted of students and faculty in the information technology (IT) field at a university in Huntington, WV. Arrowâs impossibility theorem was used as the theoretical framework for this study. Complete survey data were collected from 47 students and faculty members in the IT field, and a multiple regression analysis was used to measure the correlations between the variables. The model was able to explain 85% of the total variability in the ranking algorithm. The overall model was able to significantly predict the algorithm ranking discounted cumulative gain, R2 = .852, F(3,115) = 220.13, p \u3c .01. The Respondent DCG and Search term variables were the most significant predictor with p = .0001. The overall findings can potentially be useful to content providers who focus their content on a specific niche. The content created by these providers would most likely be focused entirely on that subgroup of interested users. While it is necessary to focus content to the interested users, it may be beneficial to expand the content to more generic terms to help reach potential new users outside of the subgroups of interest. Userâs searching for more generic terms could potentially be exposed to more content that would generally require more specific search terms. This exposure with more generic terms could help users expand their knowledge of new content more quickly
Politische Maschinen: Maschinelles Lernen fĂŒr das VerstĂ€ndnis von sozialen Maschinen
This thesis investigates human-algorithm interactions in sociotechnological ecosystems. Specifically, it applies machine learning and statistical methods to uncover political dimensions of algorithmic influence in social media platforms and automated decision making systems. Based on the results, the study discusses the legal, political and ethical consequences of algorithmic implementations.Diese Arbeit untersucht Mensch-Algorithmen-Interaktionen in sozio-technologischen Ăkosystemen. Sie wendet maschinelles Lernen und statistische Methoden an, um politische Dimensionen des algorithmischen Einflusses auf Socialen Medien und automatisierten Entscheidungssystemen aufzudecken. Aufgrund der Ergebnisse diskutiert die Studie die rechtlichen, politischen und ethischen Konsequenzen von algorithmischen Anwendungen
Towards Proactive Context-aware Computing and Systems
A primary goal of context-aware systems is delivering the right information at the
right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal:
determining what information is relevant, personalizing it based on the usersâ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as âProactive Context-aware Computingâ.
Most of the existing context-aware systems fulfill only a subset of these requirements.
Many of these systems focus only on personalization of the requested information
based on usersâ current context. Moreover, they are often designed for specific domains.
In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate usersâ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains.
To support this dissertation, we explore several directions. Clearly the most significant
sources of information about users today are smartphones. A large amount of usersâ context can be acquired through them and they can be used as an effective means
to deliver information to users. In addition, social media such as Facebook, Flickr and
Foursquare provide a rich and powerful platform to mine usersâ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years.
Since location is one of the most important context for users, we have developed
âLocusâ, an indoor localization, tracking and navigation system for multi-story buildings.
Other important dimensions of usersâ context include the activities that they are engaged
in. To this end, we have developed âSenseMeâ, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the âSenseMeâ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications.
To determine what information would be relevant to usersâ situations, we have developed âTellMeâ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of usersâ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization.
For timely delivery of personalized and relevant information, it is essential to anticipate
and predict usersâ behavior. To this end, we have developed a unified infrastructure,
within the Rover framework, and implemented several novel approaches and algorithms
that employ various contextual features and state of the art machine learning techniques
for building diverse behavioral models of users. Examples of generated models include
classifying usersâ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to
enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online
platforms, but recommendation remains a challenging task, particularly in
specific scenarios and domains. Multimodality can help tap into richer
information sources and construct more refined user/item profiles for
recommendations. However, existing literature lacks a shared and universal
schema for modeling and solving the recommendation problem through the lens of
multimodality. This work aims to formalize a general multimodal schema for
multimedia recommendation. It provides a comprehensive literature review of
multimodal approaches for multimedia recommendation from the last eight years,
outlines the theoretical foundations of a multimodal pipeline, and demonstrates
its rationale by applying it to selected state-of-the-art approaches. The work
also conducts a benchmarking analysis of recent algorithms for multimedia
recommendation within Elliot, a rigorous framework for evaluating recommender
systems. The main aim is to provide guidelines for designing and implementing
the next generation of multimodal approaches in multimedia recommendation
Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review
A health recommender system (HRS) provides a user with personalized medical information based on the userâs health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management
Enriching companion robots with enhanced reminiscence abilities
In this document I will go on discussing a project conceived by Professor Andrea Giovanni Nuzzolese and Alessandro Russo, both researchers and developers of some of the main aspects of project Mario at CNR Rome.
MARIO is a robot, part of a robotics company called KOMPAĂ Robotics that deals with the production and management of Robots who take care of elderly people who suffer from dementia or who still need an aid; more generally speaking, there is talk of weak and lonely people within an organization and / or institutions (nursing homes ...) or in their own homes.
There are numerous characteristics of MARIO, which ultimately contribute to all those which are the manufacturing objectives of KOMPAĂ Robotics.
My project, or rather my contribution to MARIO, is to look for a specific method which let the robot show a specific set of photos to the user according to the expressions, feelings and emotions, the user will reveal.
Example: the robot randomly chooses a marriage photo and the user suddenly start to laugh and to express positive feelings with positive words; the robot will try to understand if itâs a good photo for the user or not, and in the first case will continue to show the same kind of pictures while in the second case, will change completely set of photos to be shown.
The pleasure of the subject expressed in relation to a photo must be subject to an index of interest between predefined and specified values that may be to show a certain interest in a picture or the subjects within the image or the situation that surrounds them
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