Efficient MaxCount and threshold operators of moving objects

Calculating operators of continuously moving objects presents some unique challenges, especially when the operators involve aggregation or the concept of congestion, which happens when the number of moving objects in a changing or dynamic query space exceeds some threshold value. This paper presents the following six d-dimensional moving object operators: (1) MaxCount (or MinCount), which finds the Maximum (or Minimum) number of moving objects simultaneously present in the dynamic query space at any time during the query time interval. (2) CountRange, which finds a count of point objects whose trajectories intersect the dynamic query space during the query time interval. (3) ThresholdRange, which finds the set of time intervals during which the dynamic query space is congested. (4) ThresholdSum, which finds the total length of all the time intervals during which the dynamic query space is congested. (5) ThresholdCount, which finds the number of disjoint time intervals during which the dynamic query space is congested. And (6) ThresholdAverage, which finds the average length of time of all the time intervals when the dynamic query space is congested. For these operators separate algorithms are given to find only estimate or only precise values. Experimental results from more than 7,500 queries indicate that the estimation algorithms produce fast, efficient results with error under 5%

Software tools for conducting bibliometric analysis in science: An up-to-date review

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims

Combining Residual Networks with LSTMs for Lipreading

We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size target-words consisting of 1.28sec video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0, yielding 6.8 absolute improvement over the current state-of-the-art, without using information about word boundaries during training or testing.Comment: Submitted to Interspeech 201

Historical collaborative geocoding

The latest developments in digital have provided large data sets that can increasingly easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with the temporal aspect and are based on a strict hierarchy (..., city, street, house number) that is hard or impossible to use with historical data. Indeed historical data are full of uncertainties (temporal aspect, semantic aspect, spatial precision, confidence in historical source, ...) that can not be resolved, as there is no way to go back in time to check. We propose an open source, open data, extensible solution for geocoding that is based on the building of gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address. The matching criteriae are customisable and include several dimensions (fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that they can be checked and collaboratively edited. The system is tested on Paris city for the 19-20th centuries, shows high returns rate and is fast enough to be used interactively.Comment: WORKING PAPE

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Study on behavioral impedance for route planning techniques from the pedestrian's perspective: some findings and considerations

The multi-disciplinary characteristics of transportation force a new design of geographic information systems, within which these characteristics are considered. In this context, geographic information systems for transportation are the result of the integration of transportation information systems and conventional geographic information systems. An interesting research area in geographic information systems for transportation is constraint management in route planning algorithms from the pedestrian s perspective. Constraint management becomes more complex when route planning takes into account an integrated public transportation network (i.e. a multimodal network). A study on the theoretical contextualization and taxonomy of a pedestrian s behavioral impedance has been developed in order to improve the constraint management from the pedestrian s perspective. This study entails strategies of travel reduction by private transport (e.g. travel by car) through switching to or substitution by alternative public transport (e.g. travel by walk, bus or rail). The grounded theory method has been used to develop the proposed taxonomy. Using the partial results of a questionnaire applied to a reduced group of people from Barcelona as a starting point, important data are being collected to define the mathematical model of the behavioral impedance domain. The goal of this paper is to provide some considerations about theoretical contextualization on identification and management of constraints regarding the behavioral impedance domain from the pedestrian s perspective within the urban public transportation context. The research project where this work is included is composed of six major phases. The first phase represents a continuous bibliographic review. The second phase was a study on sidewalks in the university zone of Barcelona. In this phase, an experimental application has been proposed and the management, map and route modules have been implemented on the ArcInfo GIS package and C++. This paper reports the partial work of the third phase, which is composed of two parts. The first part was a theoretical study on behavioral impedance for route planning techniques, in which taxonomy was proposed. The results of the second part are partially presented in this paper. The fourth (i.e. design and implementation), fifth (i.e. calibration and validation) and sixth (i.e. generalization of the results) phases are characterized by the application of the prototype regarding the multimodal network model for urban public transportation from the pedestrian s perspective. The main contribution of this article is the behavioral impedance taxonomy review from the pedestrian s perspective, which will allow designing a mathematical model and be used to implement a constraint management algorithm. Within this context, the proposed taxonomy could be used to model cost functions more precisely.Postprint (published version