Efficient filtering of adult content using textual information

Nowadays adult content represents a non negligible proportion of the Web content. It is of the utmost importance to protect children from this content. Search engines, as an entry point for Web navigation are ideally placed to deal with this issue. In this paper, we propose a method that builds a safe index i.e. adult-content free for search engines. This method is based on a filter that uses only textual information from the web page and the associated URL

Estimating the Size and Structure of the Underground Commercial Sex Economy in Eight Major U.S. Cities

The underground commercial sex economy (UCSE) generates millions of dollars annually, yet investigation and data collection remain under resourced. Our study aimed to unveil the scale of the UCSE in eight major US cities. Across cities, the UCSE's worth was estimated between $39.9 and$290 million in 2007, but decreased since 2003 in all but two cities. Interviews with pimps, traffickers, sex workers, child pornographers, and law enforcement revealed the dynamics central to the underground commercial sex trade -- and shaped the policy suggestions to combat it

Deep learning-based graffiti detection: A study using Images from the streets of Lisbon

This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set.info:eu-repo/semantics/publishedVersio

Study of Fundamental Rights Limitations for Online Enforcement through Self-Regulation

The use of self-regulatory or privatized enforcement measures in the online environment can give rise to various legal issues that affect the fundamental rights of internet users. First, privatized enforcement by internet services, without state involvement, can interfere with the effective exercise of fundamental rights by internet users. Such interference may, on occasion, be disproportionate, but there are legal complexities involved in determining the precise circumstances in which this is the case. This is because, for instance, the private entities can themselves claim protection under the fundamental rights framework (e.g. the protection of property and the freedom to conduct business). Second, the role of public authorities in the development of self-regulation in view of certain public policy objectives can become problematic, but has to be carefully assessed. The fundamental rights framework puts limitations on government regulation that interferes with fundamental rights. Essentially, such limitations involve the (negative) obligation for States not to interfere with fundamental rights. Interferences have to be prescribed by law, pursue a legitimate aim and be necessary in a democratic society. At the same time, however, States are also under the (positive) obligation to take active measures in order to ensure the effective exercise of fundamental rights. In other words, States must do more than simply refrain from interference. These positive obligations are of specific interest in the context of private ordering impact on fundamental rights, but tend to be abstract and hard to operationalize in specific legal constellations. This study’s central research question is: What legal limitations follow from the fundamental rights framework for self-regulation and privatized enforcement online? It examines the circumstances in which State responsibility can be engaged as a result of selfregulation or privatized enforcement online. Part I of the study provides an overview and analysis of the relevant elements in the European and international fundamental rights framework that place limitations on privatized enforcement. Part II gives an assessment of specific instances of self-regulation or other instances of privatized enforcement in light of these elements

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews