Exploring cybercrime in the era of coronavirus

The world has changed due to the COVID-19 pandemic, and so does cybercrime. Cybercriminals are exploiting the coronavirus scenario and as such, questions arise around how they are doing it and its impact. Coronavirus and cybercrime are relatively new topics in the scene and there are only a handful of studies that have been released. Therefore, this research tries to be an introduction to the cybercrime and phenomenon from a classic criminological perspective, to later on go on details on how COVID-19 is providing a shift in opportunities for cybercriminals and how it influences regular, old cybercrime techniques. Finally, the empirical work will try to discover if cybercrime victimization rates have increased in 2020 in lack of official records that prove so.El mundo ha cambiado debido a la pandemia causada por el COVID-19, y también lo ha hecho la ciberdelincuencia. Los ciberdelincuentes están explotando el escenario creado por el coronavirus, lo que genera preguntas sobre su modus operandi y su impacto. Coronavirus y ciberdelincuencia son temas relativamente nuevos en la escena y es por ello que hay muy pocos estudios publicados. Por lo tanto, este trabajo trata de ser una introducción al fenómeno de la ciberdelincuencia desde la perspectiva de la criminología clásica, para luego dar detalles sobre como el COVID-19 está provocando un cambio en las oportunidades para delinquir y como éste influencia a las técnicas de ciberdelincuencia que ya estaban presentes. Finalmente, el trabajo empírico tratará de descubrir si las ratios de victimización de ciberdelincuencia han subido durante 2020 a falta de datos oficiales que lo confirme

Use of data mining to identify trends between variables to improve implementation of an immersive environment

Globally, the implementation of immersive environments for leaming activities have been in constant growth whch indcates that their development must improve daily. For this reason, this study identifies trends (co-occurrences) and relatiomhps between variables associated with an immersive environment to improve its implementation. Results were found which show that a good design of information guides, organization of menus and useful instructiom generates that the users enjoy using the immersive environment for leaming and foments recommendations of use to other users

Adaptation, Learning, and Optimization over Networks

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. The results derived in this work are useful in comparing network topologies against each other, and in comparing networked solutions against centralized or batch implementations. There are many good reasons for the peaked interest in distributed implementations, especially in this day and age when the word “network” has become commonplace whether one is referring to social networks, power networks, transportation networks, biological networks, or other types of networks. Some of these reasons have to do with the benefits of cooperation in terms of improved performance and improved resilience to failure. Other reasons deal with privacy and secrecy considerations where agents may not be comfortable sharing their data with remote fusion centers. In other situations, the data may already be available in dispersed locations, as happens with cloud computing. One may also be interested in learning through data mining from big data sets. Motivated by these considerations, this work examines the limits of performance of distributed solutions and discusses procedures that help bring forth their potential more fully. The presentation adopts a useful statistical framework and derives performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks. At the same time, the work illustrates how distributed processing over graphs gives rise to some revealing phenomena due to the coupling effect among the agents. These phenomena are discussed in the context of adaptive networks, along with examples from a variety of areas including distributed sensing, intrusion detection, distributed estimation, online adaptation, network system theory, and machine learning

Two decades of studies on learning management system in higher education: A bibliometric analysis with Scopus database 2000-2020

Over the past twenty years, using learning management systems in higher education has attracted increasing interest from researchers around the globe. In this context, the current study aimed to explore the volume, growth trajectory, and geographic distribution of learning management systems in higher education literature, along with identifying impactful authors, sources, and publications, and highlight emerging research issues. The authors conducted bibliometric analysis on 1334 documents, related to the use of learning management systems in the context of higher education, extracted from Scopus database. The findings show a rapidly growing knowledge base on learning management systems in higher education, especially intensely in the years 2015-2020 and primarily from research in developed societies. This flourishing is consistent with the development trend of international education and the strong development of technology. In addition, the core literature was identified based on the volume of publications and citations. The results also reveal the emerging intellectual structure of the field and provide points of reference for scholars studying the discipline. This paper offers a knowledge map for future research assessments of learning management systems in higher education

Discovery of topological constraints on spatial object classes using a refined topological model

In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in topological relations between objects of a given class and other spatial objects. In order to capture topological relations between more than two (linear) objects, this paper further proposes a refinement of the 9-intersection model for topological relations of line geometries. The discovered topological relations form topological constraints of an object class that can be used for spatial object classification. A case study has been carried out on bridges in the OpenStreetMap dataset for the state of Victoria, Australia. The results show that the proposed approach can successfully learn topological constraints for the class bridge, and that the proposed refined topological model for line geometries outperforms the 9-intersection model in this task

A transfer learning-based feature reduction method to improve classification accuracy

The need for efficient data use grows in machine learning algorithm for dataset with larger feature sets. Feature selection is the process of selecting minimum set of features that fully represent the learning problem. Transfer learning can motivate in scenario where we train model with the common problem and use it to identify important features needed to build model for target problem. In this thesis, we propose transfer learning algorithm combined with or without suggested features from experts, to learn from the source dataset and recognize important feature sets needed to train models in target dataset. Also, we compared this algorithm with classical machine learning algorithm with or without using the suggested features recommended by the experts. In series of experiment, it shows that our method is adequate to find the minimum feature sets which also outperformed then using only the suggested features by the experts. Furthermore, it also shows that the subsequent reduce in number of features in transfer learning method have better or almost same performance then using all the features of the dataset. We performed our experiments using heart disease, readmission dataset and BMI dataset

Re-engineering justice? Robot judges, computerized courts and semi-automated legal decision-making

This paper takes a sceptical look at the possibility of advanced computer technology replacing judges. Looking first at the example of alternative dispute resolution, where considerable progress has been made in developing tools to assist parties to come to agreement, attention then shifts to evaluating a number of other algorithmic instruments in a criminal justice context. The possibility of human judges being fully replaced within the courtroom strictu sensu is examined, and the various elements of the judicial role that need to be reproduced are considered. Drawing upon understandings of the legal process as an essentially socially determined activity, the paper sounds a note of caution about the capacity of algorithmic approaches to ever fully penetrate this socio-legal milieu and reproduce the activity of judging, properly understood. Finally, the possibilities and dangers of semi-automated justice are reviewed. The risks of seeing this approach as avoiding the recognised problems of fully automated decision-making are highlighted, and attention is directed towards the problems that remain when an algorithmic frame of reference is admitted into the human process of judging

The ghost in the legal machine : algorithmic governmentality, economy, and the practice of law

Purpose: This paper aims to investigate algorithmic governmentality – as proposed by Antoinette Rouvroy – specifically in relation to law. It seeks to show how algorithmic profiling can be particularly attractive for those in legal practice, given restraints on time and resources. It deviates from Rouvroy in two ways. First, it argues that algorithmic governmentality does not contrast with neoliberal modes of government in that it allows indirect rule through economic calculations. Second, it argues that critique of such systems is possible, especially if the creative nature of law can be harnessed effectively. Design/methodology/approach: This is a conceptual paper, with a theory-based approach, that is intended to explore relevant issues related to algorithmic governmentality as a basis for future empirical research. It builds on governmentality and socio-legal studies, as well as research on algorithmic practices and some documentary analysis of reports and public-facing marketing of relevant technologies. Findings: This paper provides insights on how algorithmic knowledge is collected, constructed and applied in different situations. It provides examples of how algorithms are currently used and how trends are developing. It demonstrates how such uses can be informed by socio-political and economic rationalities. Research limitations/implications: Further empirical research is required to test the theoretical findings. Originality/value : This paper takes up Rouvroy’s question of whether we are at the end(s) of critique and seeks to identify where such critique can be made possible. It also highlights the importance of acknowledging the role of political rationalities in informing the activity of algorithmic assemblages