Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning

Providing a safe environment for juveniles and children in online social networks is considered as a major factor in improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of online predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Sûreté du Québec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Sûreté du Québec

A human-centered systematic literature review of the computational approaches for online sexual risk detection

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.Accepted manuscrip

A Human-Centered Approach to Improving Adolescent Online Sexual Risk Detection Algorithms

Computational risk detection has the potential to protect especially vulnerable populations from online victimization. Conducting a comprehensive literature review on computational approaches for online sexual risk detection led to the identification that the majority of this work has focused on identifying sexual predators after-the-fact. Also, many studies rely on public datasets and third-party annotators to establish ground truth and train their algorithms, which do not accurately represent young social media users and their perspectives to prevent victimization. To address these gaps, this dissertation integrated human-centered approaches to both creating representative datasets and developing sexual risk detection machine learning models to ensure the broader societal impacts of this important work. In order to understand what and how adolescents talk about their online sexual interactions to inform study designs, a thematic content analysis of posts by adolescents on an online peer support mental health was conducted. Then, a user study and web-based platform, Instagram Data Donation (IGDD), was designed to create an ecologically valid dataset. Youth could donate and annotate their Instagram data for online risks. After participating in the study, an interview study was conducted to understand how youth felt annotating data for online risks. Based on private conversations annotated by participants, sexual risk detection classifiers were created. The results indicated Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level. Our experiments showed that classifiers trained on entire conversations performed better than message-level classifiers. We also trained classifiers to detect the severity risk level of a given message with CNN outperforming other models. We found that contextual (e.g., age, gender, and relationship type) and psycho-linguistic features contributed the most to accurately detecting sexual conversations. Our analysis provides insights into the important factors that enhance automated detection of sexual risks within youths\u27 private conversations

Writing the railway: biosemiotic strategies for enforming meaning and dispersing authorship in site-specific text-based artworks

This practice-led PhD is concerned with the subject matter of contemporary art. It proposes methods by which a writer-maker’s authorship can be dispersed throughout reticulated networks of interpretation, and tests the limits of detail articulable in an artwork. To counter the literary and discursive turns that have dominated art theory and practice since the 1970s, the thesis demands a reassessment of the privileging of the viewer and of the adoption of indeterminacy as a generic style. It proposes instead a turn to biosemiotics as a means to situate the artwork materially, bodily, historically. That ambiguity and pluralism can consequently be deployed strategically, affectively and to critical effect is tested and evaluated in the accompanying practice. The thesis gives an account of the theorising and devising of text-based artworks which take the UK railway as site, and considers site-specificity a particular sort of engagement with subject matter. The railway is approached as a complex technical object consisting in multiple entangled intentions and interpretations – social, emotional and political valences, diffracted by a spectrum of practices, knowledges and semiotic ontologies – all of which are available to the writermaker as immanent materials of the artwork. Part One of the thesis presents a transdisciplinary argument that draws on biosemiotics, linguistic anthropology, philosophy of time and socio-psychology as well as art history and critical theory. Part Two performs an analysis of paradigmatic descriptions of the railway, speculates on the social dynamics of a train carriage interior and empirically tests the bureaucratic structures of London Underground. Part Three is an exegesis of three pieces submitted as documentation in the practice portfolio: an audio work, a guided tour and a live performance on a train carriage tabletop

2013 Oklahoma Research Day Full Program

This document contains all abstracts from the 2013 Oklahoma Research Day held at the University of Central Oklahoma

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

The Value of Technics: An Ontogenetic Approach to Money, Markets, and Networks

This thesis investigates the impact of the digitalization of monetary and financial flows on the political-economic sphere in order to provide a novel perspective on the relations between economic and technological forces at the present global juncture. In the aftermath of the Global Financial Crisis and with the rise of the cryptoeconomy, an increasing number of scholars have highlighted the immanence of market logic to cultural and social life. At the same time, speculative practices have emerged that attempt to challenge the political economy through financial experiments. This dissertation complements these approaches by stressing the need to pair the critical study of finance with scholarship in the philosophy of technology that emphasizes the value immanent to technics and technology – i.e. the normative and genetic role of ubiquitous algorithmic networks in the organization of markets and socius. In order to explore these events, I propose an interdisciplinary theoretical framework informed largely by Gilbert Simondon’s philosophy of individuation and technics and the contemporary literature on the ontology of computation, supported by insights drawn from the history of finance and economic theory. This novel framework will provide the means to investigate the ontogenetic processes at work in the techno-cultural ecosystem following the digitalization of monetary and financial flows. Through an exploration of the fleeting materiality and multifaceted character of digital fiat money, the social power of algorithmic financial logic, and the new possibilities offered by the invention of the Bitcoin protocol, this research aims to challenge some of the bedrocks of the economic orthodoxy – economic and monetary value, liquidity, market rationality – in order to move beyond the overarching narrative of capitalism as a monolithic system. The thesis instead foregrounds the techno-historical contingencies that have led to the contemporary power formation. Furthermore, it argues that the ontogenetic character of algorithmic technology ushers in novel possibilities for the speculative engineering of alternative networks of value creation and distribution that have the potential to reverse the current balance of power

Mathematics and the USSR: organising a discipline

This thesis aspires to establish a new research direction in STS. In the first chapter a literature review is conducted and the research questions are being formulated. The second chapter is devoted to presenting research findings from the archaeological, biological and brain sciences in a unified form. The various stone tool technologies are analysed, and a brief introduction follows into human evolution and the effects that artefacts had on it; then recent neurobiological research on the deeper relationships between consciousness, artefacts and the brain is presented. In the third chapter, after an introduction in the deeper neurological relationships between language and gestures, a gestural analysis of mathematical speech follows, based on visual data generated from an interview with a working mathematician; the last section examines recent research on gesture and mathematics as special cases of Roman Ingarden’s aesthetic theory. In the fourth chapter, four approaches to the social history of mathematics in the USSR are presented, based on data generated from interviews with former professional Soviet mathematicians. Following a Maussian approach, the Soviet mathematical community is presented as a gift economy of scientific articles. Then, in line with a Marxian approach, the Soviet university mathematical school is presented as a factory with its own mode of self-production. In the following section, based on a Parsonian systemic approach, the Soviet mathematical community is presented as a banking system, with the scientific journals as the banking institutions. In the next section of the fourth chapter, following a Weberian approach, the mathematical community in the USSR is presented as a social estate, as separate and distinct from other Soviet social estates. The final section integrates the previous approaches and presents the Soviet mathematics research community as a modern version of an ancient city-state. In the fifth chapter Hilbert spaces are briefly presented, as an example of the fictional universe of modern mathematics, along with some conjectured differences between Soviet and Western mathematics research. In the final chapter, the conclusions of this research project are summarised, and this thesis is presented as an instance of a proposed revised version of David Bloor’s Strong Programme