Community detection applied on big linked data

The Linked Open Data (LOD) Cloud has more than tripled its sources in just six years (from 295 sources in 2011 to 1163 datasets in 2017). The actual Web of Data contains more then 150 Billions of triples. We are assisting at a staggering growth in the production and consumption of LOD and the generation of increasingly large datasets. In this scenario, providing researchers, domain experts, but also businessmen and citizens with visual representations and intuitive interactions can significantly aid the exploration and understanding of the domains and knowledge represented by Linked Data. Various tools and web applications have been developed to enable the navigation, and browsing of the Web of Data. However, these tools lack in producing high level representations for large datasets, and in supporting users in the exploration and querying of these big sources. Following this trend, we devised a new method and a tool called H-BOLD (High level visualizations on Big Open Linked Data). H-BOLD enables the exploratory search and multilevel analysis of Linked Open Data. It offers different levels of abstraction on Big Linked Data. Through the user interaction and the dynamic adaptation of the graph representing the dataset, it will be possible to perform an effective exploration of the dataset, starting from a set of few classes and adding new ones. Performance and portability of H-BOLD have been evaluated on the SPARQL endpoint listed on SPARQL ENDPOINT STATUS. The effectiveness of H-BOLD as a visualization tool is described through a user study

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next

Optimization of Computer Aided Detection systems: an evolutionary approach

Computer Aided Diagnosis (CAD) systems are designed to aid the radiologist in interpreting medical images. They are usually based on lesion detection and segmentation algorithms whose performance depends on a large number of parameters. While time consuming and sub-optimal, manual adjustment is still widely used to adjust parameter values. Genetic or evolutionary algorithms (GA) are effective optimization methods that mimic biological evolution. Genetic algorithms have been shown to efficiently manage complex search spaces, and can be applied to all kinds of objective functions, including discontinuous, nondifferentiable, or highly nonlinear ones. In this study, we have adopted an evolutionary approach to the problem of parameter optimization. We show that the genetic algorithm is able to effectively converge to a better solution than manual optimization on a case study for digital breast tomosynthesis CAD. Parameter optimization was framed as a constrained optimization problem, where the function to be maximized was defined as weighted sum of sensitivity, false positive rate and segmentation accuracy. A modified Dice coefficient was defined to assess the segmentation quality of individual lesions. Finally, all viable solutions evaluated by the GA were studied by means of exploratory data analysis techniques, such as association rules, to gain useful insight on the strength of the influence of each parameter on overall algorithm performance. We showed that this combination was able to identify multiple ranges of viable solutions with good segmentation accuracy