Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...     

Modeling culture in intelligent virtual agents

This work addresses the challenge of creating virtual agents that are able to portray culturally appropriate behavior when interacting with other agents or humans. Because culture influences how people perceive their social reality it is important to have agent models that explicitly consider social elements, such as existing relational factors. We addressed this necessity by integrating culture into a novel model for simulating human social behavior. With this model, we operationalized a particular dimension of culture—individualism versus collectivism—within the context of an interactive narrative scenario that is part of an agent-based tool for intercultural training. Using this scenario we conducted a cross-cultural study in which participants from a collectivistic country (Portugal) were compared with participants from an individualistic country (the Netherlands) in the way they perceived and interacted with agents whose behavior was either individualistic or collectivistic, according to the configuration of the proposed model. In the obtained results, Portuguese subjects rated the collectivistic agents more positively than the Dutch but both countries had a similarly positive opinion about the individualistic agents. This experiment sheds new light on how people from different countries differ when assessing the social appropriateness of virtual agents, while also raising new research questions on this matter.     

Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

Assessment of the Impact of Stormwater Characteristics on Clogging in Stormwater Filters

Hydraulic conductivity of granular filter media and its evolution over time is a key design parameter for stormwater filtration and infiltration systems that are now widely used in management of polluted urban runoff. In fact, clogging of filter media is recognised as the main limiting factor of these stormwater treatment systems. This paper focuses on the effect of stormwater characteristics on the clogging of stormwater filters. Effect of five different operational regimes has been tested in this study of sediment concentration; pollutant concentrations; stormwater sediment size; loading rate and stormwater loading/dosing regime and compared with the Base case. For each operational condition, five column replicates were tested. Results suggest that sediment concentration in stormwater is a significant parameter affecting hydraulic and treatment performance, eventually affecting longevity of these stormwater treatment systems. Further, the size of sediments (and their relation to the size of filter media grains) in stormwater was found to be an important parameter to be considered in design of coarse filters with high infiltration rates that are used for stormwater treatment. As expected, the addition of metals and nutrients had limited or no contribution to changes in hydraulic or sediment removal performance of the studied stormwater filters. Whilst loading rate was found to be an important parameter affecting the hydraulic and treatment performance of these systems, any variation in the stormwater loading regime had a limited effect on their performance. This study therefore develops an understanding of the effect of catchment characteristics on design of filters and hence their longevity and maintenance needs.     

Open Challenges in Relationship-Based Privacy Mechanisms for Social Network Services

Social networking services (SNSs) such as Facebook and Twitter have experienced explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, and meet others who share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information about their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This article presents a list of privacy threats that can affect SNS users and what requirements privacy mechanisms should fulfill to prevent this threats. Then the article reviews current approaches and analyzes to what extent they cover the requirements.     

Preventing Youth Pregnancy: Dialogue and Deliberation in a Science Museum Exhibit

ABSTRACT

In this article, we explore how visitors engage with a science museum exhibit that displays controversial topics. Through a case study methodology, we focus on the Brazilian exhibit Preventing Youth Pregnancy that delves into teen pregnancy, sexual practices, and sexually transmitted diseases. Using the lens of science communication (an emergent field of research) we explore (a) communication patterns established between the exhibit and the public and (b) the dimensions of engagement that visitors experienced. Data were collected through observations and interviews with visitors, field notes, exit comment cards, and interviews with staffers. Our findings revealed the potential of articulating different models of science communication and exposed a visitor experience in which informed decision making, sharing of personal stories, co-production of knowledge, and learning how to care (and act) were at play while negotiating the complexities of the issues raised by the displays.     

Forecasting binary longitudinal data by a functional PC-ARIMA model

In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.     

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.