Exploring Object-Centric and Scene-Centric CNN Features and their Complementarity for Human Rights Violations Recognition in Images

Identifying potential abuses of human rights through imagery is a novel and challenging task in the field of computer vision, that will enable to expose human rights violations over large-scale data that may otherwise be impossible. While standard databases for object and scene categorisation contain hundreds of different classes, the largest available dataset of human rights violations contains only 4 classes. Here, we introduce the ‘Human Rights Archive Database’ (HRA), a verified-by-experts repository of 3050 human rights violations photographs, labelled with human rights semantic categories, comprising a list of the types of human rights abuses encountered at present. With the HRA dataset and a two-phase transfer learning scheme, we fine-tuned the state-of-the-art deep convolutional neural networks (CNNs) to provide human rights violations classification CNNs (HRA-CNNs). We also present extensive experiments refined to evaluate how well object-centric and scene-centric CNN features can be combined for the task of recognising human rights abuses. With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context. We expect this dataset can help to open up new horizons on creating systems able of recognising rich information about human rights violations

GET-AID: Visual Recognition of Human Rights Abuses via Global Emotional Traits

In the era of social media and big data, the use of visual evidence to document conflict and human rights abuse has become an important element for human rights organizations and advocates. In this paper, we address the task of detecting two types of human rights abuses in challenging, everyday photos: (1) child labour, and (2) displaced populations. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the emotional state of a person -- how positive or pleasant an emotion is, and the control level of the situation by the person -- are powerful cues for perceiving potential human rights violations. To exploit these cues, our model learns to predict global emotional traits over a given image based on the joint analysis of every detected person and the whole scene. By integrating these predictions with a data-driven convolutional neural network (CNN) classifier, our system efficiently infers potential human rights abuses in a clean, end-to-end system we call GET-AID (from Global Emotional Traits for Abuse IDentification). Extensive experiments are performed to verify our method on the recently introduced subset of Human Rights Archive (HRA) dataset (2 violation categories with the same number of positive and negative samples), where we show quantitatively compelling results. Compared with previous works and the sole use of a CNN classifier, this paper improves the coverage up to 23.73% for child labour and 57.21% for displaced populations. Our dataset, codes and trained models are available online at https://github.com/GKalliatakis/GET-AID

Visual Recognition of Human Rights Violations

This thesis is concerned with the automation of human rights violation recognition in images. Solving this problem is extremely beneficial to human rights organisations and investigators, who are often interested in identifying and documenting potential violations of human rights within images. It will allow them to avoid the overwhelming task of analysing large volumes of images manually. However, visual recognition of human rights violations is challenging and previously unattempted. Through the use of computer vision, the notion of visual recognition of human rights violations is forged in this thesis, whilst this area is addressed by strongly considering the constraints related to the usability and flexibility of a real practice. Firstly, image datasets of human rights violations which are suitable for training and testing modern visual representations, such as convolutional neural networks (CNNs) are introduced for the first time ever. Secondly, we develop and apply transfer learning models specific to the human rights violation recognition problem. Various fusion methods are proposed for performing an equivalence and complementarity analysis of object-centric and scene-centric deep image representations for the task of human rights violation recognition. Additionally, a web demo for predicting human rights violations that may be used directly by human rights advocates and analysts is developed. Next, the problem of recognising displaced people from still images is considered. To solve this, a novel mechanism centred around the level of control each person feels of the situation is developed. By leveraging this mechanism, typical image classification turns into a uniform framework that infers potential displaced people from images. Finally, a human-centric approach for recognising rich information about two emotional states is proposed. The derived global emotional traits are harnessed alongside a data-driven CNN classifier to efficiently infer two of the most widespread modern abuses against human rights, child labour and displaced populations

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

“Speak About Destruction”: Representing 9/11 in The Sopranos

Broadly definable as an interdisciplinary study of televisual texts, literature, and history, this thesis analyses David Chase’s The Sopranos (1999-2007) and its engagement of the September 11, 2001 terror attacks. Tracing the show’s narrative and aesthetic roots to its pilot episode, I explore how 9/11 elicited both an alteration and an exaggeration of the show’s structural and symbolic elements. Furthermore, I illustrate the impact of televisual mediation on the act of viewership, demonstrating the manner in which The Sopranos and 9/11 newscasts employed authoritative narrative perspectives as a means of disseminating vital information to viewers. Methodologically, I employ a narratological approach to show through close textualanalysis how elements of location and sequential ordering inform the creation of unique story worlds, and how these story worlds operate symbiotically with viewers in creating meanings beyond the texts

Remix Dialectics and the Material Conditions of Immaterial Art

Remix Dialectics and the Material Conditions of Immaterial Art proposes the art of remixing as both a dialectical approach and creative tool for understanding immaterial art, and by extension, the immaterial economy. Artworks defined as ‘immaterial’ are not limited to digital domains, but instead describe objects that reduce their concrete presence to incorporate more communicative means of artistic expression. A work’s inherent concepts and narrative anecdotes, the status of its author, its provenance from known collections as much as its process of fabrication and links to a particular history or geographic location, all contribute to its value as immaterial art. Having said that, such objects are not altogether ethereal and often generate artefacts that are reviewed as material culture, promote socio-political structures that one may analyse under historical materialism, and reflect the financial interests of immaterial economies which thrive on monetizing service, knowledge, and cultural industries. As a remix artist, I transform and combine such immaterial features, and utilize these processes as the subject matter of my artistic production. To organize my discussions around the theoretical concepts, studio creations, and case studies to come, I devised a framework first inspired by the dialectical methods attributed to Hegel, to position ‘subjects’ and ‘objects’ as opposite categories of beings, then ‘index’ humanity’s experience of reality in the gaps between such opposites. From this layout, my chapters focus on issues of authorship, objecthood, and indexicality to explore the praxis of remixing in current contexts of globally networked societies. I then problematize the resistance of certain mass- oriented cultural industries to fully convert to network-oriented processes, which results in generating a crisis of representation. My studio works address this crisis via creative strategies of negation, withdrawal, and destruction. With No More Heroes, I remix Hollywood films by deleting every frame in which the main character is seen or heard. Video Pistoletto is inspired by the gestures of Michelangelo Pistoletto, where I damage LCD video monitors. In Fontana Mashup, I simulate the slashing of priceless paintings to contrast the inflated value of the original masterpieces against their deflated value when copied

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Un-stating order: The Autonomous Administration of North and East Syria

This thesis asks how we can make sense of the popular characterisation of the Autonomous Administration of North and East Syria (AANES) as a state-making project despite explicit rejections that this is the case from the Administration, who claim instead to be crafting an alternative, nonstatist vision of governance based on ‘democratic autonomy’. Through discourse analysis of key political theoretical texts and political practices within the AANES, I show how adherents of democratic autonomy reject three key statist discourses: the centralisation of authority and the necessity of monopolised legitimate force for stability; liberalism (secularism, Western feminism); and capitalism. Using my translations of Abdullah Öcalan’s works of political theory, and of articulations by female fighters and activists in the region, the thesis argues that AANES discourses on non-state armed groups and foreign fighters, on sectarian and gendered violence, and on political economy and the environment, all propose compelling challenges to the widely presumed requirements for order and democracy. Nevertheless, the thesis argues that the power of external discourses that recognise AANES as ‘state-like’ help to constitute it as such, both by making possible the activities that have rendered AANES more state-like over time, and by narrowing the space from which alternatives to statehood may be articulated. The thesis therefore provides an account of why ‘nonstate’ revolutions, such as that in AANES, may be short-lived, by demonstrating the disciplinary role played by a statist international order, which re-enacts itself by re-shaping non-state polities in states’ image