Human Trafficking and Machine Learning: A Data Pipeline from Law Agencies to Research Groups

Human trafficking is a form of modern-day slavery that, while highly illegal, is more dangerous with the advancements of modern technology (such as the Internet), which allows such a practice to spread more easily and quickly all over the world. While the number of victims of human trafficking is large (according to non-profit organization Safe House, there are estimated to be about 20.5 million human trafficking victims, worldwide (“Human Trafficking Statistics & Facts.” Safe Horizon)- co-erced or manipulated by traffickers into either forced labor, or sexual exploitation and encounters), the number of heard cases is proportionally low- several thousand successful case prosecutions (Feehs K., p10-14). This disparaging fraction of unsettled human trafficking cases and trapped victims mandates that the system of fighting against human trafficking must be advanced. This thesis presents an advancement of this field using a data pipeline that flows directly from law agencies and similar data-collecting groups to a web-based user-friendly interface that can be used for both research and analytical purposes and aims to allow legal-based efforts to proactively identify victims and traffickers as opposed to reacting to crimes after they happen. It displays data such as human trafficking case metadata (from title, to location, to verdict) and victim demographics (race, age, and sentence or conviction length, for example). This cleaned data is then stored and displayed through a Southern Methodist University-hosted infrastructure. vi Currently, only one source of data is curated, used, and stored, but this groundwork pipeline is built for expansion for a wide variety of sources- one projected source being PACER, (Public Access to Court Electronic Records). This expansive and flexible quality adds to the pipeline’s utility and projected future uses within the sphere of human trafficking discourse

Identifying human trafcking indicators in the UK online sex market

This study identifes the presence of human trafcking indicators in a UK-based sample of sex workers who advertise their services online. To this end, we developed a crawling and scraping software that enabled the collection of information from 17, 362 advertisements for female sex workers posted on the largest dedicated platform for sex work services in the UK. We then established a set of 10 indicators of human trafcking and a transparent and replicable methodology through which to detect their presence in our sample. Most of the advertisements (58.3%) contained only one indicator, while 3,694 of the advertisements (21.3%) presented 2 indicators of human trafcking. Only 1.7% of the advertisements reported three or more indicators, while there were no advertisements that featured more than four. 3, 255 advertisements (19.0%) did not contain any indicators of human trafcking. Based on this analysis, we propose that this approach constitutes an efective screening process for quickly identifying suspicious cases, which can then be examined by more comprehensive and accurate tools to identify if human trafcking is occurring. We conclude by calling for more empirical research into human trafcking indicators

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Improving Computer Network Operations Through Automated Interpretation of State

Networked systems today are hyper-scaled entities that provide core functionality for distributed services and applications spanning personal, business, and government use. It is critical to maintain correct operation of these networks to avoid adverse business outcomes. The advent of programmable networks has provided much needed fine-grained network control, enabling providers and operators alike to build some innovative networking architectures and solutions. At the same time, they have given rise to new challenges in network management. These architectures, coupled with a multitude of devices, protocols, virtual overlays on top of physical data-plane etc. make network management a highly challenging task. Existing network management methodologies have not evolved at the same pace as the technologies and architectures. Current network management practices do not provide adequate solutions for highly dynamic, programmable environments. We have a long way to go in developing management methodologies that can meaningfully contribute to networks becoming self-healing entities. The goal of my research is to contribute to the design and development of networks towards transforming them into self-healing entities. Network management includes a multitude of tasks, not limited to diagnosis and troubleshooting, but also performance engineering and tuning, security analysis etc. This research explores novel methods of utilizing network state to enhance networking capabilities. It is constructed around hypotheses based on careful analysis of practical deficiencies in the field. I try to generate real-world impact with my research by tackling problems that are prevalent in deployed networks, and that bear practical relevance to the current state of networking. The overarching goal of this body of work is to examine various approaches that could help enhance network management paradigms, providing administrators with a better understanding of the underlying state of the network, thus leading to more informed decision-making. The research looks into two distinct areas of network management, troubleshooting and routing, presenting novel approaches to accomplishing certain goals in each of these areas, demonstrating that they can indeed enhance the network management experience

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Mass spectral imaging of clinical samples using deep learning

A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics. Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues. Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required. Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces