Digital Peacekeepers, Drone Surveillance and Information Fusion: A Philosophical Analysis of New Peacekeeping

In June 2014 an Expert Panel on Technology and Innovation in UN Peacekeeping was commissioned to examine how technology and innovation could strengthen peacekeeping missions. The panel\u27s report argues for wider deployment of advanced technologies, including greater use of ground and airborne sensors and other technical sources of data, advanced data analytics and information fusion to assist in data integration. This article explores the emerging intelligence-led, informationist conception of UN peacekeeping against the backdrop of increasingly complex peacekeeping mandates and precarious security conditions. New peacekeeping with its heightened commitment to information as a political resource and the endorsement of offensive military action within robust mandates reflects the multiple and conflicting trajectories generated by asymmetric conflicts, the responsibility to protect and a technology-driven information revolution. We argue that the idea of peacekeeping is being revised (and has been revised) by realities beyond peacekeeping itself that require rethinking the morality of peacekeeping in light of the emergence of \u27digital peacekeeping\u27 and the knowledge revolution engendered by new technologies

Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery

Report from a 2017 National Science Foundation workshop on promising research directions for applications of operations research and data analytics toward the disruption of illicit supply networks like human trafficking. The workshop was funded by the NSF’s Operations Engineering (ENG) and the Law & Social Sciences Program (SBE) under grant # CMMI-1726895. The report addresses the opportunity to apply advances from the fields of operations research, management science, analytics, machine learning, and data science toward the development of disruptive interventions against illicit networks. Such an extension of the current research agenda for trafficking would move understanding of such dynamic systems from descriptive characterization and predictive estimation toward improved dynamic operational control.Bureau of Business Researc

Detection violent behaviors: A survey

Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n ∘ 039334; Funding Reference: POCI-01-0247-FEDER-039334]. This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Spatial Practices in Borderlands: Bottom-Up Experiences and Their Influence on Border Communities

Differences and conflicts are most evident at borderlands, which act as balancing tools to organize and filter economic and migratory flows. The increased militarization of these areas, which often requires creating empty spaces next to the fences, fosters deterritorialization processes that not only have profound effects on the territory, but also on the people living in these areas. As space shapes people, this paper analyses the effects of marginalization and violence, as well as hope for a better future for people and migrants living in these places. After evidencing place disattachment and life disruption originated by strong transformations to their environments, a review based on literature of several bottom-up experiences acting in these areas is presented. Based on subversion, contamination, hybridization and transgression, these examples show the interesting ambivalence of borderlands, which provide a provocative and inspiring arena for new local planning and architectural design for recovering place attachment, stronger community identities and the development of new models of coexistence

Deep Learning for activity recognition in real-time video streams

Dissertação de mestrado integrado em Engenharia InformáticaIn an ever more connected world, smart cities are becoming ever more present in our society. In these smart cities, use cases in which innovations that will benefit its inhabitants are also growing, improving their quality of life. One of these areas is safety, in which Machine Learning (ML) models reveal potential in real-time video-stream analysis in order to determine if violence exists in them. These ML approaches concern the field of Computer Vision, a field responsible for traducing digital images and videos, and be able to extract knowledge and understandable information from them, in order to be used in diverse contexts. Some of the available alternatives to recognise actions in video streams are based on ML approaches, such as Deep Learning (DL), that grew in popularity in the last years, as it was realised that it had massive potential in several applications that could benefit from having a machine recognising diverse human actions. In this project, the creation of a ML model that can determine if violence exists in a video-stream is proposed. This model will leverage technology being used in State of the Art methods, such as video classifiers, but also audio classifiers, and Early/Late Fusion (EF / LF) schemes that allow the merging different modalities, in the case of the present work: audio and video. Conclusions will also be drawn as to the accuracy rates of the different types of classifiers, to determine if any other type of classifiers should have more prominence in the State of the Art. This document begins with an introduction to the work being conducted, in which both the its context, mo tivation and objectives are explained. Afterwards, the methodology used in order to more efficiently conduct the research in this Thesis is clarified. Following that, the State of the Art concerning ML based approaches to Action Recognition and Violence Detection is explored. After being brought to date in what are the State of the Art approaches, one is able to move forward to the following chapter, in which the Training method that will be employed to train the models that were seen as the best candidates to detect violence is detailed. Subsequently, the selected models are scrutinized in an effort to better understand their architecture, and why they are suited to detect violence. Afterwards, the results achieved by these models are explored, in order to better comprehend how well these performed. Lastly, the conclusions that were reached after conducting this research are stated, and possibilities for expanding this work further are also presented. The obtained results prove the success and prevalence of video classifiers, and also show the efficacy of models that make use of some kind of fusion.Num mundo cada vez mais conetado, as cidades inteligentes tornam-se cada vez mais presentes na nossa sociedade. Nestas cidades inteligentes, crescem também os casos de uso nos quais podem ser aplicadas inovações que beneficiarão os seus habitantes, melhorando a sua qualidade de vida. Uma dessas áreas é a da segurança, na qual modelos de Aprendizagem Máquina (AM) apresentam potencial para analisar streams de vídeo em tempo real e determinar se nestas existe violência. Estas abordagens de AM são referentes ao campo de Visão por Computador, um campo responsável pela tradução de imagens e vídeos digitais, e pela extração de conhecimento e informação inteligível dos mesmos, de modo a ser utilizada em diversos contextos. Algumas das alternativas disponíveis para reconhecer ações em streams de vídeo são baseados em abordagens de AM, tais como Aprendizagem Profunda (AP), que cresceu em popularidade nos últimos anos, à medida que se tornou claro o massivo potencial que tinha em diversas aplicações, que poderiam beneficiar de ter uma máquina a reconhecer diversas ações humanas. Neste projeto, é proposta a criação de um modelo de Machine Learning que permita determinar a existência de violência numa stream de vídeo. Este modelo tomará partido de tecnologia utilizada em métodos do Estado da Arte como classificadores de vídeo, mas também de classificadores áudio, e esquemas de Fusão Antecipada / Tardia (FA / FT) que permitem a combinação de várias modalidades de dados, neste caso: áudio e vídeo. Serão tiradas também conclusões sobre as taxas de acerto dos diversos tipos de classificadores, de modo a determinar se algum outro tipo de classificador deveria de ter mais prominência Este documento começa com uma introdução ao trabalho levado a cabo, em que o seu contexto, motivação, e objetivos são explicados. Seguidamente, a metodologia utilizada de modo a mais eficientemente levar a cabo a pesquisa nesta Tese é clarificada. Após isso, o Estado da Arte no que concerne abordagens baseadas em AM para Reconhecimento de Ações e Deteção de Violência é explorado. Depois de ser atualizado em relação a quais são consideradas abordagens de Estado da Arte, é possível avançar para o capítulo seguinte, onde o método utilisado para treinar os modelos que foram considerados como os melhores candidatos para detetar violência é detalhado. Subsequentemente, os modelos selecionados são escrutinizados de modo a melhor entender a sua arquitetura, e porque são adequados para detetar violência. Depois, os resultados conseguidos por estes modelos são explorados, de modo a melhor compreender o desempenho conseguido. Finalmente, as conclusões que foram chegadas a são apresentadas, tais como possibilidades para expandir e melhorar esta pesquisa. Os resultados obtidos comprovam o sucesso e a prevalência dos classificadores de vídeo, e mostram também a eficácia dos modelos que tomam partido de algum tipo de fusão

Transportation, Terrorism and Crime: Deterrence, Disruption and Resilience

Abstract: Terrorists likely have adopted vehicle ramming as a tactic because it can be carried out by an individual (or “lone wolf terrorist”), and because the skills required are minimal (e.g. the ability to drive a car and determine locations for creating maximum carnage). Studies of terrorist activities against transportation assets have been conducted to help law enforcement agencies prepare their communities, create mitigation measures, conduct effective surveillance and respond quickly to attacks. This study reviews current research on terrorist tactics against transportation assets, with an emphasis on vehicle ramming attacks. It evaluates some of the current attack strategies, and the possible mitigation or response tactics that may be effective in deterring attacks or saving lives in the event of an attack. It includes case studies that can be used as educational tools for understanding terrorist methodologies, as well as ordinary emergencies that might become a terrorist’s blueprint