Relevant bibliographies by topics / Computational Criminology

Academic literature on the topic 'Computational Criminology'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Computational Criminology"

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PRYKOLOTINA, Y. "CHALLENGES AND OPPORTUNITIES FOR CRIMINOLOGICAL RESEARCH IN A TRANSFORMING REALITY." Vestnik of Polotsk State University Part D Economic and legal sciences 62, no. 12 (November 14, 2022): 152–57. http://dx.doi.org/10.52928/2070-1632-2022-62-12-152-157.

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In the context of intense and accelerating transformations of social reality, science acts as one of the adaptive mechanisms to understand the nature of what is happening and the prospects for future change. However, in the face of rapid and substantial social change, the social empirical sciences, including criminology, also need to be transformed to take account of the digitalisation of relations, their exponentially increasing complexity and the growth of universal connectivity. These properties of a transforming reality require not only a move away from traditional sources of information on criminologically relevant phenomena, not only the application (expansion) of an interdisciplinary approach, but also the establishment of a 'quantitative criminology paradigm', a 'computational criminology' that enables the tracking of both criminal and potential criminogenic phenomena as well as background crime phenomena in real time through monitoring and using both preset and self-generated algorithms.
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Topalli, Volkan, Timothy Dickinson, and Scott Jacques. "Learning from Criminals: Active Offender Research for Criminology." Annual Review of Criminology 3, no. 1 (January 13, 2020): 189–215. http://dx.doi.org/10.1146/annurev-criminol-032317-092005.

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Active offender research relies on the collection of data from noninstitutionalized criminals and has made significant contributions to our understanding of the etiology of serious crime. This review covers its history as well as its methodological, scientific, and ethical pitfalls and advantages. Because study subjects are currently and freely engaging in crime at the time of data collection, their memories, attitudes, and feelings about their criminality and specific criminal events are rich, detailed, and accurate. Contemporary approaches to active offender research employ systematized formats for data collection and analysis that improve the validity of findings and help illuminate the foreground of crime. Although active offender research has traditionally relied on qualitative techniques, we outline the potential for it to make contributions via mixed methods, experiments, and emerging computational and technological approaches, such as virtual reality simulation studies and agent-based modeling.
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Berk, Richard. "How you can tell if the simulations in computational criminology are any good." Journal of Experimental Criminology 4, no. 3 (August 15, 2008): 289–308. http://dx.doi.org/10.1007/s11292-008-9053-5.

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Dyakov, V. G. "SOME LEGAL ASPECTS OF REGULATING OF RELATIONS ARISING IN THE USE OF POST-GENOMIC TECHNOLOGIES." Courier of Kutafin Moscow State Law University (MSAL)), no. 4 (June 22, 2020): 108–13. http://dx.doi.org/10.17803/2311-5998.2020.68.4.108-113.

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The current level of scientifi c knowledge and understanding of the genome, as well as the level of human computational capabilities, form new, so-called «post-genomic technologies» for genome research. The results of such studies can be used in diff erent areas of human life and society, for example in criminology, including solving crimes. And there may be both positive and negative legal consequences. Genetic research is an extremely useful tool for crime investigation. But it is still unclear to what extent law enforcement agencies should be able to obtain genetic data stored in public and private databases, and how this may aff ect human rights in the future.
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Williams, Matthew L., and Pete Burnap. "Cyberhate on Social Media in the aftermath of Woolwich: A Case Study in Computational Criminology and Big Data." British Journal of Criminology 56, no. 2 (June 25, 2015): 211–38. http://dx.doi.org/10.1093/bjc/azv059.

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Xiong, Yun, Yangyong Zhu, Philip Yu, and Jian Pei. "Towards Cohesive Anomaly Mining." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 984–90. http://dx.doi.org/10.1609/aaai.v27i1.8553.

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In some applications, such as bioinformatics, social network analysis, and computational criminology, it is desirable to find compact clusters formed by a (very) small portion of objects in a large data set. Since such clusters are comprised of a small number of objects, they are extraordinary and anomalous with respect to the entire data set. This specific type of clustering task cannot be solved well by the conventional clustering methods since generally those methods try to assign most of the data objects into clusters. In this paper, we model this novel and application-inspired task as the problem of mining cohesive anomalies. We propose a general framework and a principled approach to tackle the problem. The experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our approach.
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Sie Chiew, L., A. Shahabuddin, and M. Y. Zainab. "A Review of Simulation and Application of Agent-Based Model Approaches." Journal of Physics: Conference Series 2129, no. 1 (December 1, 2021): 012053. http://dx.doi.org/10.1088/1742-6596/2129/1/012053.

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Abstract In the past, various traditional methods used experiments and statistical data to examine and solve the occurred problem and social-environmental issue. However, the traditional method is not suitable for expressing or solving the complex dynamics of human environmental crisis (such as the spread of diseases, natural disaster management, social problems, etc.). Therefore, the implementation of computational modelling methods such as Agent-Based Models (ABM) has become an effective technology for solving complex problems arising from the interpretation of human behaviour such as human society, environment, and biological systems. Overall, this article will outline the ABM model properties and its applications in the criminology, flood management, and the COVID-19 pandemic fields. In addition, this article will review the limitations that occurred to be overcome in the further development of the ABM model.
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KURSUN, OLCAY, ANNA KOUFAKOU, ABHIJIT WAKCHAURE, MICHAEL GEORGIOPOULOS, KENNETH REYNOLDS, and RONALD EAGLIN. "ANSWER: APPROXIMATE NAME SEARCH WITH ERRORS IN LARGE DATABASES BY A NOVEL APPROACH BASED ON PREFIX-DICTIONARY." International Journal on Artificial Intelligence Tools 15, no. 05 (October 2006): 839–48. http://dx.doi.org/10.1142/s0218213006002977.

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The obvious need for using modern computer networking capabilities to enable the effective sharing of information has resulted in data-sharing systems, which store, and manage large amounts of data. These data need to be effectively searched and analyzed. More specifically, in the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information, as required in homeland security, criminology, and medical applications, amongst others. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. These techniques have demonstrated varying levels of success, but there is a pressing need for name matching approaches that provide high levels of accuracy in matching names, while at the same time maintaining low computational complexity. In this paper, such a technique, called ANSWER, is proposed and its characteristics are discussed. Our results demonstrate that ANSWER possesses high accuracy, as well as high speed and is superior to other techniques of retrieving fuzzy name matches in large databases.
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Rodríguez Oconitrillo, Luis Raúl Rodríguez, Juan José Vargas, Arturo Camacho, Álvaro Burgos, and Juan Manuel Corchado. "RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning." Electronics 10, no. 12 (June 21, 2021): 1500. http://dx.doi.org/10.3390/electronics10121500.

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The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based on Subject-Matter Experts (SME) to investigate new techniques for acquiring higher-order thinking, the perception, the use of new computational explanatory techniques, support decision-making, and the judge’s cognition and behavior. Thus, a new spectrum is covered and promises to be a new area of study called Interpretation-Assessment/Assessment-Interpretation (IA-AI), consisting of explaining machine inferences and the interpretation and assessment from a human. It allows expressing a semantic, ontological, and hermeneutical meaning related to the psyche of a human (judge). The system has an interpretative and explanatory nature, and in the future, could be used in other domains of discourse. More than 33 experts in Law and Artificial Intelligence validated the functional design. More than 26 judges, most of them specializing in psychology and criminology from Colombia, Ecuador, Panama, Spain, Argentina, and Costa Rica, participated in the experiments. The results of the experimentation have been very positive. As a challenge, this research represents a paradigm shift in legal data processing.
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Sukhodolov, Alexander, Sergey Ivantsov, Tatiana Molchanova, and Boris Spasennikov. "Big Data as a Modern Criminological Method of Studying and Measuring Organized Crime." Russian Journal of Criminology 13, no. 5 (October 31, 2019): 718–26. http://dx.doi.org/10.17150/2500-4255.2019.13(5).718-726.

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The paper is devoted to the study, measurement and assessment of organized crime in Russia, and to researching the possibility of using «big data» to analyze crime. As the task of counteracting crime is getting more and more complicated, it becomes necessary to reconsider both the traditional methods of researching crime, and the possibilities of going beyond the boundaries of such methods. The existing forms of state statistical reporting reflect only certain crimes committed by organized criminal groups and, in exceptional cases — by criminal communities. The use of official state statistics to measure the level of organized crime leads to numerous distortions in its evaluation. In some cases, all it can do is indicate the trend. There is no assessment of the actual condition of this criminological problem due to a high complexity of measurements, of choosing the technique of information collection and analysis, as well as the theoretical model of predicting organized crime, forecasting its future trends and regularities. In the last decade, the countries where digital technologies are developing actively have witnessed an intensified interest to the use of computing and intellectual methods of analyzing organized crime. The main achievement of science is that there is a widespread understanding among criminologists involved in research that the existing instruments, methods and programs for the intellectual analysis of structured and unstructured data are sufficient for the transition to computational criminology. «Big data» have a special place in the structure of information technologies. The development of data collection and analysis technologies makes it possible to measure criminality at a conceptually new level. A significant growth in the volume of data, the sources and dissemination tools of which are social objects, leads to the establishment of new technologies of processing the information on crimes. The presented article shows the necessity of using a large volume of structured and unstructured data to study and evaluate qualitative parameters of organized crime. This presents considerable opportunities for using «big data» to predict the trends of future changes in organized crime, as well as for applying new technologies to the theory and practice of preventing organized crime. The prediction and prevention of organized crime are considered to be rather difficult, and sometimes impossible when it concerns finding empirical proof and, moreover, obtaining prediction verification of some or other concept. Consequently, up to now traditional research approaches have not been effective in the sphere of researching organized crime. Meanwhile, law enforcement bodies need knowledge on the structure of crime, on the emergence of new forms of organizing criminal groups, on changes in criminal behavior connected with the technologies of the new technological revolution, etc.
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Dissertations / Theses on the topic "Computational Criminology"

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Wang, Tong Ph D. Massachusetts Institute of Technology. "Finding patterns in features and observations : new machine learning models with applications in computational criminology, marketing, and medicine." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107357.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 173-180).
The revolution of "Big Data" has reached various fields like marketing, healthcare, and criminology, where domain experts wish to find and understand interesting patterns from data. This thesis studies patterns that are defined by subsets of observations or subsets of features. The first part of the thesis studies patterns defined by subsets of observations. We look at a specific type of pattern, crime series (a set of crimes committed by the same individual or group) and develop two pattern detection algorithms. The first method is a sequential pattern building algorithm called Series Finder, which resembles how crime analysts process information instinctively and grows a crime series starting from a couple of seed crimes. The second method is a subspace clustering with cluster-specific feature selection, which is supervised when learning similarity graphs in order to reduce computation. Both methods we propose achieved promising results on a decade's worth of crime pattern data collected by the Crime Analysis Unit of the Cambridge Police Department. The second part of the thesis studies patterns defined by subsets of features. We develop methods and theory for building Rule Set models with the hallmark of interpretability. Interpretability is inherent in using association rules to explain predicted results. We first design two methods for building rule sets for binary classification. The first method Bayesian Rule Set (BRS) uses a Bayesian framework with priors that favor small models. The Bayesian priors also bring significant computational benefits to MAP inferences by reducing the search space and restraining the sampling chain within appropriate regions. We apply BRS models to an in-vehicle recommender system data set we collected via Amazon Mechanical Turk to study the customers and contexts that would encourage acceptance of coupons. We develop another model Optimized Rule Set (ORS) using optimization methods to directly construct rule sets from data, without pre-mining rules or discretizing continuous attributes. As a main application of ORS, we build a diagnostic screening tool for obstructive sleep apnea trained on data provided by the Sleep Lab at Mass General Hospital. Our models achieve high accuracy with a substantial gain in interpretability over other methods. Lastly, we build a Causal Rule Set (CRS) model for causal analysis, to identify subgroups that can benefit from a treatment. CRS combines BRS and Bayesian Logistic Regression. We take advantage of different strategies in inference algorithm to speed up computation. Simulations and experiments show that distributing treatment according to CRS models enhances average treatment effect.
by Tong Wang.
Ph. D.
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Books on the topic "Computational Criminology"

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The criminology of white-collar crime. New York, NY: Springer, 2009.

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Subrahmanian, V. S. Handbook of Computational Approaches to Counterterrorism. New York, NY: Springer New York, 2013.

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Argamon, Shlomo. Computational methods for counterterrorism. Dordrecht: Springer, 2009.

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Simpson, Sally S., and David Weisburd. The Criminology of White-Collar Crime. Springer, 2010.

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Howard, Newton, and Shlomo Argamon. Computational Methods for Counterterrorism. Springer, 2010.

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Howard, Newton, and Shlomo Argamon. Computational Methods for Counterterrorism. Springer, 2009.

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Howard, Newton, and Shlomo Argamon. Computational Methods for Counterterrorism. Springer, 2014.

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Birks, Daniel. Computer Simulations. Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.36.

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In recent years, the field of social simulation has been dominated by the individual, or agent-based, computational model (ABM). ABMs provide unique means to explore complex social systems by allowing researchers to construct explicit models of the individual actors and interactions that make them up - people, peer groups, companies, nations, trade, reproduction, victimization, and so on, This chapter aims to provide the reader with a primer in the social simulation method and in particular the application of ABM in the field of environmental criminology. It begins by discussing the rationale behind the ABM approach. Subsequently, drawing on two illustrative simulations, it summarizes fundamental processes involved in designing, constructing, verifying, calibrating, validating, and utilizing ABM. It concludes by discussing some of the overarching strengths and limitations of the approach, and by discussing several areas of research that might aid in furthering the use of ABM within the field of environmental criminology.
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Book chapters on the topic "Computational Criminology"

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Kicsi, András, Péter Sánta, Dániel Horváth, Norbert Kőhegyi, Viktor Szvoreny, Veronika Vincze, Eszter Főző, and László Vidács. "Computer-Aided Forensic Authorship Identification in Criminology." In Computational Science and Its Applications – ICCSA 2022 Workshops, 576–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10548-7_42.

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"Computational Criminology." In Encyclopedia of Criminology and Criminal Justice, 505. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-5690-2_100090.

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Nurunnabi, A. A. M., A. B. M. S. Ali, A. H. M. Rahmatullah Imon, and Mohammed Nasser. "Outlier Detection in Logistic Regression." In Multidisciplinary Computational Intelligence Techniques, 257–78. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch016.

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The use of logistic regression, its modelling and decision making from the estimated model and subsequent analysis has been drawn a great deal of attention since its inception. The current use of logistic regression methods includes epidemiology, biomedical research, criminology, ecology, engineering, pattern recognition, machine learning, wildlife biology, linguistics, business and finance, et cetera. Logistic regression diagnostics have attracted both theoreticians and practitioners in recent years. Detection and handling of outliers is considered as an important task in the data modelling domain, because the presence of outliers often misleads the modelling performances. Traditionally logistic regression models were used to fit data obtained under experimental conditions. But in recent years, it is an important issue to measure the outliers scale before putting the data as a logistic model input. It requires a higher mathematical level than most of the other material that steps backward to its study and application in spite of its inevitability. This chapter presents several diagnostic aspects and methods in logistic regression. Like linear regression, estimates of the logistic regression are sensitive to the unusual observations: outliers, high leverage, and influential observations. Numerical examples and analysis are presented to demonstrate the most recent outlier diagnostic methods using data sets from medical domain.
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Conference papers on the topic "Computational Criminology"

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Brantingham, Patricia L. "Computational Criminology." In 2011 European Intelligence and Security Informatics Conference (EISIC). IEEE, 2011. http://dx.doi.org/10.1109/eisic.2011.79.

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Weidong Tao. "Computational criminology and evolution mechanisms of social crime dynamic system." In 2014 IEEE Workshop on Electronics, Computer and Applications (IWECA). IEEE, 2014. http://dx.doi.org/10.1109/iweca.2014.6845662.

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Ping He and Weidong Tao. "Computational criminology and non-equilibrium evolution mechanisms of social crime dynamic system." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053692.

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