An associative information visualizer

IEEE Symposium on Information Visualization, INFOVIS 2004, p. r8

Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions

BioScape is a concurrent language motivated by the biological landscapes found at the interface of biology and biomaterials. It has been motivated by the need to model antibacterial surfaces, biofilm formation, and the effect of DNAse in treating and preventing biofilm infections. As its predecessor, SPiM, BioScape has a sequential semantics based on Gillespie's algorithm, and its implementation does not scale beyond 1000 agents. However, in order to model larger and more realistic systems, a semantics that may take advantage of the new multi-core and GPU architectures is needed. This motivates the introduction of parallel semantics, which is the contribution of this paper: Parallel BioScape, an extension with fully parallel semantics.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347

Investigating the relationship between the speed of automatization and linguistic abilities: data collection during the COVID-19 pandemic

Our research explores the relationship between cognition and language. The focus of this paper is to discuss how we embarked upon remote data collection with children during the COVID-19 pandemic. In this study we investigate cognitive processes of non-verbal intelligence, working memory, implicit statistical learning, and speed of automatization (measured with the multiple-trial Tower of Hanoi puzzle). Here we focus primarily on the speed of automatization, partly because of theoretical interest, and because it is more difficult to adapt to an online format due to the motor component of the task. We established a hybrid method of data collection where the researcher was present online to guide children through a battery of language and cognitive tasks. We used a videoconferencing platform, a digital visualiser, and a physical puzzle which we posted to each child prior to commencing the research sessions. We also designed an online version of the puzzle with support from the Getting Data project. We discuss the methodology of our study and the lessons learned during remote data collection

Visual Representations Is Lexical Learning Environments: Application To The Alexia System

Cognition-based arguments in support of using multimedia aids for the learning of vocabulary have so far offered only an imprecise, general framework. CALL experimentalists have also tried to establish the effectiveness of multimedia for vocabulary learning, but their attempts reveal that the underlying representations have not been clearly defined. After reviewing these points, we propose criteria for evaluating the quality of a visual representation in a lexical environment. These criteria are then used to discuss visual representations in paper and electronic dictionaries and in CALL environments. A kind of confusion has been made between multimedia and nonverbal knowledge. Hence visual representations are scarce and limited to concrete words. One way to extend multimedia in lexical learning is to rely on linguistic knowledge and build lexical networks. We present the ALEXIA system, a lexical learning environment for French as a second/foreign language. We detail its network module which can automatically build graphs of some lexical semantic relations. It is a first step for offering learners representations they can easily interpret. Visual representations which can cover a significant part of the lexicon are computable, extendable and interactive

A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

[EN] This article shows a novel geo-visualization method of dynamic spatiotemporal data that allows mobility and concentration of criminal activity to be study. The method was developed using, only and significantly, real data of Santiago de Cali (Colombia), collected by the Colombian National Police (PONAL). This method constitutes a tool that allows criminal influx to be analyzed by concentration, zone, time slot and date. In addition to the field experience of police commanders, it allows patterns of criminal activity to be detected, thereby enabling a better distribution and management of police resources allocated to crime deterrence, prevention and control. Additionally, it may be applied to the concepts of safe city and smart city of the PONAL within the architecture of Command and Control System (C2S) of Command and Control Centers for Public Safety. Furthermore, it contributes to a better situational awareness and improves the future projection, agility, efficiency and decision-making processes of police officers, which are all essential for fulfillment of police missions against crime. Finally, this was developed using an open source software, it can be adapted to any other city, be used with real-time data and be implemented, if necessary, with the geographic software of any other C2S.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-thrtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The authors would like to thank Colombian National Police and its Office of Telematics for their support on development of this project.Salcedo-González, ML.; Suarez-Paez, JE.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE. (2020). A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety. ISPRS International Journal of Geo-Information. 9(3):1-17. https://doi.org/10.3390/ijgi9030160S11793Lacinák, M., & Ristvej, J. (2017). Smart City, Safety and Security. Procedia Engineering, 192, 522-527. doi:10.1016/j.proeng.2017.06.090Neumann, M., & Elsenbroich, C. (2016). Introduction: the societal dimensions of organized crime. Trends in Organized Crime, 20(1-2), 1-15. doi:10.1007/s12117-016-9294-zPhillips, P., & Lee, I. (2012). Mining co-distribution patterns for large crime datasets. Expert Systems with Applications, 39(14), 11556-11563. doi:10.1016/j.eswa.2012.03.071Linning, S. J. (2015). Crime seasonality and the micro-spatial patterns of property crime in Vancouver, BC and Ottawa, ON. Journal of Criminal Justice, 43(6), 544-555. doi:10.1016/j.jcrimjus.2015.05.007Spicer, V., & Song, J. (2017). The impact of transit growth on the perception of crime. Journal of Environmental Psychology, 54, 151-159. doi:10.1016/j.jenvp.2017.09.002Beland, L.-P., & Brent, D. A. (2018). Traffic and crime. Journal of Public Economics, 160, 96-116. doi:10.1016/j.jpubeco.2018.03.002Newspaper of National Circulation in Colombia, E.T. Robos en Trancones en El Tintal—Bogotá—.ELTIEMPO.COM https://www.eltiempo.com/bogota/robos-en-trancones-en-el-tintal-168226Nueva Modalidad de Atraco a Conductores en Los Trancones de Bogotá|ELESPECTADOR.COM http://www.elespectador.com/noticias/bogota/nueva-modalidad-de-atraco-conductores-en-los-trancones-de-bogota-articulo-697716Carrillo, P. E., Lopez-Luzuriaga, A., & Malik, A. S. (2018). Pollution or crime: The effect of driving restrictions on criminal activity. Journal of Public Economics, 164, 50-69. doi:10.1016/j.jpubeco.2018.05.007Twinam, T. (2017). Danger zone: Land use and the geography of neighborhood crime. Journal of Urban Economics, 100, 104-119. doi:10.1016/j.jue.2017.05.006Sadler, R. C., Pizarro, J., Turchan, B., Gasteyer, S. P., & McGarrell, E. F. (2017). Exploring the spatial-temporal relationships between a community greening program and neighborhood rates of crime. Applied Geography, 83, 13-26. doi:10.1016/j.apgeog.2017.03.017Roth, R. E., Ross, K. S., Finch, B. G., Luo, W., & MacEachren, A. M. (2013). Spatiotemporal crime analysis in U.S. law enforcement agencies: Current practices and unmet needs. Government Information Quarterly, 30(3), 226-240. doi:10.1016/j.giq.2013.02.001Sustainable Development Goals|UNDP https://www.undp.org/content/undp/en/home/sustainable-development-goals.htmlGiménez-Santana, A., Caplan, J. M., & Drawve, G. (2018). Risk Terrain Modeling and Socio-Economic Stratification: Identifying Risky Places for Violent Crime Victimization in Bogotá, Colombia. European Journal on Criminal Policy and Research, 24(4), 417-431. doi:10.1007/s10610-018-9374-5Kim, S., Jeong, S., Woo, I., Jang, Y., Maciejewski, R., & Ebert, D. S. (2018). Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information. IEEE Transactions on Visualization and Computer Graphics, 24(3), 1287-1300. doi:10.1109/tvcg.2017.2666146Kounadi, O., & Leitner, M. (2014). Spatial Information Divergence: Using Global and Local Indices to Compare Geographical Masks Applied to Crime Data. Transactions in GIS, 19(5), 737-757. doi:10.1111/tgis.12125Khalid, S., Shoaib, F., Qian, T., Rui, Y., Bari, A. I., Sajjad, M., … Wang, J. (2017). Network Constrained Spatio-Temporal Hotspot Mapping of Crimes in Faisalabad. Applied Spatial Analysis and Policy, 11(3), 599-622. doi:10.1007/s12061-017-9230-xLopez-Cuevas, A., Medina-Perez, M. A., Monroy, R., Ramirez-Marquez, J. E., & Trejo, L. A. (2018). FiToViz: A Visualisation Approach for Real-Time Risk Situation Awareness. IEEE Transactions on Affective Computing, 9(3), 372-382. doi:10.1109/taffc.2017.2741478Xue, Y., & Brown, D. E. (2006). Spatial analysis with preference specification of latent decision makers for criminal event prediction. Decision Support Systems, 41(3), 560-573. doi:10.1016/j.dss.2004.06.007Nakaya, T., & Yano, K. (2010). Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics. Transactions in GIS, 14(3), 223-239. doi:10.1111/j.1467-9671.2010.01194.xAnuar, N. B., & Yap, B. W. (2018). Data Visualization of Violent Crime Hotspots in Malaysia. Soft Computing in Data Science, 350-363. doi:10.1007/978-981-13-3441-2_27Malik, A., Maciejewski, R., Towers, S., McCullough, S., & Ebert, D. S. (2014). Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1863-1872. doi:10.1109/tvcg.2014.2346926Arietta, S. M., Efros, A. A., Ramamoorthi, R., & Agrawala, M. (2014). City Forensics: Using Visual Elements to Predict Non-Visual City Attributes. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2624-2633. doi:10.1109/tvcg.2014.2346446Hu, Y., Wang, F., Guin, C., & Zhu, H. (2018). A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied Geography, 99, 89-97. doi:10.1016/j.apgeog.2018.08.001Yang, D., Heaney, T., Tonon, A., Wang, L., & Cudré-Mauroux, P. (2017). CrimeTelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web, 21(5), 1323-1347. doi:10.1007/s11280-017-0515-4ToppiReddy, H. K. R., Saini, B., & Mahajan, G. (2018). Crime Prediction & Monitoring Framework Based on Spatial Analysis. Procedia Computer Science, 132, 696-705. doi:10.1016/j.procs.2018.05.075Devia, N., & Weber, R. (2013). Generating crime data using agent-based simulation. Computers, Environment and Urban Systems, 42, 26-41. doi:10.1016/j.compenvurbsys.2013.09.001Kuo, P.-F., Lord, D., & Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138-148. doi:10.1016/j.jtrangeo.2013.04.006Camacho-Collados, M., & Liberatore, F. (2015). A Decision Support System for predictive police patrolling. Decision Support Systems, 75, 25-37. doi:10.1016/j.dss.2015.04.012Kagawa, T., Saiki, S., & Nakamura, M. (2019). Analyzing street crimes in Kobe city using PRISM. International Journal of Web Information Systems, 15(2), 183-200. doi:10.1108/ijwis-04-2018-0032Jentner, W., Sacha, D., Stoffel, F., Ellis, G., Zhang, L., & Keim, D. A. (2018). Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool. The Visual Computer, 34(9), 1225-1241. doi:10.1007/s00371-018-1483-0Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186Suarez-Paez, J., Salcedo-Gonzalez, M., Climente, A., Esteve, M., Gómez, J. A., Palau, C. E., & Pérez-Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information, 10(12), 365. doi:10.3390/info10120365Esteve, M., Perez-Llopis, I., & Palau, C. E. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine, 28(1), 14-21. doi:10.1109/maes.2013.6470440Esteve, M., Perez-Llopis, I., Hernandez-Blanco, L. E., Palau, C. E., & Carvajal, F. (2007). SIMACOP: Small Units Management C4ISR System. Multimedia and Expo, 2007 IEEE International Conference on. doi:10.1109/icme.2007.4284862OpenStreetMap http://www.openstreetmap.or

Visual object imagery and autobiographical memory: object imagers are better at remembering their personal past

In the present study we examined whether higher levels of object imagery, a stable characteristic that reflects the ability and preference in generating pictorial mental images of objects, facilitate involuntary and voluntary retrieval of autobiographical memories (ABMs). Individuals with high (High-OI) and low (Low-OI) levels of object imagery were asked to perform an involuntary and a voluntary ABM task in the laboratory. Results showed that High-OI participants generated more involuntary and voluntary ABMs than Low-OI, with faster retrieval times. High-OI also reported more detailed memories compared to Low-OI and retrieved memories as visual images. Theoretical implications of these findings for research on voluntary and involuntary ABMs are discussed

Doctor of Philosophy

dissertationComputer programs have complex interactions with their underlying hardware, exhibiting complex behaviors as a result. It is critical to understand these programs, as they serve an importantrole: researchers use them to express new ideas in computer science, while many others derive production value from them. In both cases, program understanding leads to mastery over these functions, adding value to human endeavors. Memory behavior is one of the hallmarks of general program behavior: it represents the critical function of retrieving data for the program to work on; it often reflects the overall actions taken by the program, providing a signature of program behavior; and it is often an important performance bottleneck, as the the memory subsystem is typically much slower than the processor. These reasons justify an investigation into the memory behavior of programs. A memory reference trace is a list of memory transactions performed by a program at runtime, a rich data source capturing the whole of a program's interaction with the memory subsystem, and a clear starting point for investigating program memory behavior. However, such a trace is extremely difficult to interpret by mere inspection, as it consists solely of many, many addresses and operation codes, without any more structure or context. This dissertation proposes to use visualization to construct images and animations of the data within a reference trace, thereby visually transmitting structures and events as encoded in the trace. These visualization approaches are designed with different focuses, meant to expose various aspects of the trace. For instance, the time dimension of the reference traces can be handled either with animation, showing events as they occur, or by laying time out in a spatial dimension, giving a view of the entire history of the trace at once. The approaches also vary in their level of abstraction from the hardware: some are concretely connected to representations of the memory itself, while others are more free-form, using more abstract metaphors to highlight general behaviors and patterns, which in turn characterize the program behavior. Each approach delivers its own set of insights, as demonstrated in this dissertation