ICT (I) Session 1
Time and Date: 14:15 - 15:45 on 19th Sep 2016
Room: F - Rode kamer
Chair: Andrea Nanetti
|543|| A swarm of drones is more than the sum of the drones that make it up
Abstract: Real world applications that use Unmanned Aerial Vehicles (UAVs or drones) are now a reality. They are used in situations known as Dull, Dangerous and Dirty (DDD). The next step is the adoption of swarms, i.e. of a number of UAVs that collaborate to achieve a mission. We focus on autonomous collaborative drones, i.e. drones that take decisions without any control from the outside. Autonomy is mandatory because in large swarms one cannot technically afford neither to have each UAV connected to the ground nor to have one ground pilot per UAV. Swarms offer a number of advantages, among which are continuous flight, resilience support, complementarity of sensing capacities. Combining UAVs gives rise to features that would otherwise be unfeasible. For example, it is possible to analyse the quality of air over a city by flying the UAVs of a swarm at different altitudes; this cannot be achieved with a single UAV. In terms of advantages, to paraphrase Aristotle, a swarm of drones is more than the sum of the drones that make it up (“The whole is more than the sum of its parts” Aristotle, Metaphysics). Of course, if a swarm brings more in terms of benefits it also brings more in terms of issues. Among these are communication, authentication, compact flight, safety. For instance, if considering the fault tree analysis of a swarm, the difficulty is the combinatorial explosion of the tree due to events that do not exist when one single UAV is considered, such as the possibility that one of the UAVs crashes into another in case of emergency landing. In terms of issues, as was the case regarding advantages, a swarm of drones is more than the sum of the drones that make it up. Swarms of autonomous drones are complex systems by nature.
|474|| ICT Contribution to Development: Insights Using Agent Based Modeling Approach
Abstract: The literature has extensive claims about a causal relationship between growth in ICT investment and economic growth and the value it brings to businesses, education, and health. Previous studies had argued that with ICT diffusion, there is economic growth, and others argued that growth is conditional and partial. There is a lack of literature that explicitly states how ICT investments contributes to economic development or other impact. This is probably because measuring the impact of ICT is a challenge and a complex problem because there are a number of different ICTs, with different impacts in different contexts and countries. In addition, there is a web of relationships between impact areas and with the broader economy, society and environment. The aim of this complexity study is to gain insights on the value of ICT contributions to development by examining the interaction between different dimensions such as socio-economic growth, education, health, and the environment using agent based modeling (ABM). This paper argues for building models to understand emergence created by this complex environment in order to see if we are building better world through ICT investment and to direct investments in resources and efforts in the “right” place. ABM is a useful tool because it can effectively provide us with an experimentation environment that can answer complex questions. ABM is to be used to study individual and collective behavioral changes in using ICT in its different forms while interacting with other agents, such as aspects of the economy, health, education and the environment. The outcome of these models will provide in depth understanding of the emergence between multiple agents interacting with ICT at the micro-macro levels. The purpose of this paper is to mainly establish a foundation and interest for further research in using ABM to better understand ICT contribution to Development.
|361|| Temporal and Spatial Analysis of Ebola Outbreak using Online Search Pattern and Microblogging data
Abstract: User generated contents (UGCs) have gained immense popularity for exploring different socio-economic issues. However, considering all UGCs uniformly might be problematic. For example, UGCs can be intentional (such as microblogging sites) as well as unintentional (such as searching pattern). The intriguing question is - how these two types of UGCs are interrelated to each other? In this paper, we explored the similarities and differences between intentional and unintentional UGCs in the context of 2014 Ebola outbreak. Prior studies on the epidemic, mostly analyzed the entire UGC corpus or the time series data as a whole. This can be misleading in our context since there might exist a time-lag between unintentional and intentional UGCs. So, based on the anomalies in our time series data, we considered various subsamples (for a shorter period of time) for our analysis. Data were retrieved from Google (for online search pattern), Twitter (a real-time broadcasting channel for the epidemic) and Wikipedia (largest UGC for first-hand information) for our study. Wiki Trends data were collected for detecting anomalies (important events) which confirm WHO notifications. Google data were extracted around these events, to explore the topics that cropped up through searching. We also crawled the tweet feeds to probe the discussion on the Twitter platform during the same time period. We applied Latent Dirichlet Allocation(LDA) to probe underlying topics in the microblogging discussion. In addition to this temporal analysis, we also performed a spatial analysis by comparing geotagged tweets and locational information of Google Trends. Broadly our study indicates a similar pattern between intentional and unintentional UGCs. So, it is possible to identify and trace areas of concern, both in terms of spatial and temporal dimensions, during an epidemic by exploring UGCs. This approach can hence be useful for health organizations to tackle an epidemic.
|Aparup Khatua, Kuntal Ghosh and Nabendu Chaki|
|522|| What tag is this!? Studying hashtag propagation in Twitter
Abstract: The microblogging platform Twitter has received much attention from researchers in the recent years. Many researchers have sought for an explanation as to why certain topics become trending and other do not. Usually, these studies focus on predictive aspects of the topic itself, rather than on the growth of the topic. In previous work, we employed a random graph model to mimic the growth of a topic and get a better understanding of how a topic can become trending, for which we found that the size of the Largest Connected Component (LCC) is a good indicator. Using a dataset containing a year of Dutch tweets scraped from Twitter using its streaming API, we analyze the retweet graphs corresponding to all hashtags used by more than hundred users in that year. We find that the corresponding retweet graphs tend to either have one LCC or are scattered in many small components. We then compare these outcomes with the estimates of the random graph model parameters.
|Marijn ten Thij|
|505|| Early and Real-Time Detection of Seasonal Influenza Onset
Abstract: Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We will present a machine-learning based method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases.
|Joana Gonçalves-Sá and Miguel Won|