The conflict escalation resolution (CONFER) database

Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audiovisual recordings of televised political debates where conflicts naturally arise, and b)reporting baseline experiments on audiovisual conflict intensity estimation. The database contains approximately 142min of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject- and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict classification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/. The Conflict Escalation Resolution (CONFER) Database is presented.CONFER contains 142min (120 episodes) of recordings in Greek language.Episodes are extracted from TV political debates where conflicts naturally arise.Experiments are the first approach to continuous estimation of conflict intensity.Performance of various audio and visual features and classifiers is evaluated

The conflict escalation resolution (CONFER) database

Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audiovisual recordings of televised political debates where conflicts naturally arise, and b)reporting baseline experiments on audiovisual conflict intensity estimation. The database contains approximately 142min of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject- and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict classification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/. The Conflict Escalation Resolution (CONFER) Database is presented.CONFER contains 142min (120 episodes) of recordings in Greek language.Episodes are extracted from TV political debates where conflicts naturally arise.Experiments are the first approach to continuous estimation of conflict intensity.Performance of various audio and visual features and classifiers is evaluated

Robust subspace learning for static and dynamic affect and behaviour modelling

Machine analysis of human affect and behavior in naturalistic contexts has witnessed a growing attention in the last decade from various disciplines ranging from social and cognitive sciences to machine learning and computer vision. Endowing machines with the ability to seamlessly detect, analyze, model, predict as well as simulate and synthesize manifestations of internal emotional and behavioral states in real-world data is deemed essential for the deployment of next-generation, emotionally- and socially-competent human-centered interfaces. In this thesis, we are primarily motivated by the problem of modeling, recognizing and predicting spontaneous expressions of non-verbal human affect and behavior manifested through either low-level facial attributes in static images or high-level semantic events in image sequences. Both visual data and annotations of naturalistic affect and behavior naturally contain noisy measurements of unbounded magnitude at random locations, commonly referred to as ‘outliers’. We present here machine learning methods that are robust to such gross, sparse noise. First, we deal with static analysis of face images, viewing the latter as a superposition of mutually-incoherent, low-complexity components corresponding to facial attributes, such as facial identity, expressions and activation of atomic facial muscle actions. We develop a robust, discriminant dictionary learning framework to extract these components from grossly corrupted training data and combine it with sparse representation to recognize the associated attributes. We demonstrate that our framework can jointly address interrelated classification tasks such as face and facial expression recognition. Inspired by the well-documented importance of the temporal aspect in perceiving affect and behavior, we direct the bulk of our research efforts into continuous-time modeling of dimensional affect and social behavior. Having identified a gap in the literature which is the lack of data containing annotations of social attitudes in continuous time and scale, we first curate a new audio-visual database of multi-party conversations from political debates annotated frame-by-frame in terms of real-valued conflict intensity and use it to conduct the first study on continuous-time conflict intensity estimation. Our experimental findings corroborate previous evidence indicating the inability of existing classifiers in capturing the hidden temporal structures of affective and behavioral displays. We present here a novel dynamic behavior analysis framework which models temporal dynamics in an explicit way, based on the natural assumption that continuous- time annotations of smoothly-varying affect or behavior can be viewed as outputs of a low-complexity linear dynamical system when behavioral cues (features) act as system inputs. A novel robust structured rank minimization framework is proposed to estimate the system parameters in the presence of gross corruptions and partially missing data. Experiments on prediction of dimensional conflict and affect as well as multi-object tracking from detection validate the effectiveness of our predictive framework and demonstrate that for the first time that complex human behavior and affect can be learned and predicted based on small training sets of person(s)-specific observations.Open Acces

The President and Nuclear Weapons: Authorities, Limits, and Process

There is no more consequential decision for a president than ordering a nuclear strike. In the Cold War, the threat of sudden nuclear annihilation necessitated procedures emphasizing speed and efficiency and placing sole decision-making authority in the president’s hands. In today’s changed threat environment, the legal authorities and process a U.S. president would confront when making this grave decision merit reexamination. This paper serves as a resource in the national discussion about a president’s legal authority and the procedures for ordering a nuclear strike, and whether to update them

Online attention for interpretable conflict estimation in political debates

Conflict arises naturally in dyadic interactions when involved individuals act on incompatible goals, interests, or actions. In this paper, the problem of conflict intensity estimation from audiovisual recordings is addressed. To this end, we propose an online attention-based neural network in order to learn a mapping from a sequence of audiovisual features to time-series describing conflict intensity. The proposed method is evaluated by conducting experiments in conflict intensity estimation by employing the CONFER dataset. Experimental results indicate the superiority of the proposed model compared to the state of the art. Furthermore, we demonstrate that by incorporating sparsity in the model, the origin of conflict can be traced back to specific key frames facilitating the interpretation of conflict escalation

Dynamic behavior analysis via structured rank minimization

Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach

Dynamic behavior analysis via structured rank minimization

Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach

The ‘Ambivalence of the Sacred’ in Africa : The Impact of Religion on Peace and Conflict in Sub-Saharan Africa

Given the widespread focus on socioeconomic factors, it comes as no surprise that religion is neglected in most theoretical explanations of African civil conflicts. While scholarly interest is increasing in light of the civil wars in Sudan, Nigeria, and northern Uganda, no systematic empirical analysis has been undertaken to date. Hence, this paper aims to provide a preliminary assessment of the role of religions in sub-Saharan civil conflicts. Quantitative and qualitative analysis based on a newly compiled database including 28 violent conflicts show that religion plays a role more frequently than is usually assumed and that the effects of religions are principally ambiguous. Religious actors and institutions have escalating effects in many cases, yet more often they become active for peace. Religious identities and ideas seem to have a particular impact on conflict. Even though religion seems secondary when compared to classical “risk factors,” the findings demonstrate that religious factors have to be taken seriously when analyzing civil conflicts in Africa.Der Fokus auf sozioökonomischen Faktoren lässt in den meisten Erklärungsansätzen afrikanischer Gewaltkonflikte keinen Raum für den Faktor Religion. Wenngleich das Interesse an seiner Wirkung angesichts der Bürgerkriege im Sudan, in Norduganda und Nigeria gestiegen ist, bleiben systematische Analysen bislang weitestgehend aus. Zur Schließung dieser Lücke versucht das vorliegende Working Paper beizutragen, indem es eine erste, vorläufige Einschätzung der Rolle von Religionen in Gewaltkonflikten im subsaharischen Afrika vornimmt. Quantitative und qualitative Analysen unter Nutzung einer neu erstellten Datenbasis, die bislang 28 Gewaltkonflikte in Afrika umfasst, zeigen, dass Religionen und religiöse Unterschiede in weitaus mehr Fällen eine bedeutende Rolle spielen als gemeinhin angenommen. Der Einfluss religiöser Faktoren ist grundsätzlich ambivalent: In vielen Konflikten tragen religiöse Akteure und Organisationen zur Eskalation der Auseinandersetzung bei; noch häufiger jedoch versuchen sie, sich für eine friedliche Lösung einzusetzen. Dabei scheinen sich vor allem religiöse Identitäten und Inhalte auf die Ausprägung verschiedener Konfliktvariablen auszuwirken. Wenngleich die Wirkung dieser religiösen Faktoren im Vergleich zu klassischen Risikofaktoren sekundär scheint, verdeutlichen die Befunde die Notwendigkeit, den Faktor Religion bei der Analyse afrikanischer Bürgerkriege ernst zu nehmen

The Role of International Alert in Advancing Early Warning and Early Action

This paper focuses on the role of International Alert in promoting early warning and early action in areas of international conflict. It begins by introducing the percepts of preventive diplomacy as used by International Alert. A number of the organisation's objectives and activities are highlighted, showing the many ways in which the principles of conflict prevention and early warning are being developed in areas of potential violence and amongst a wider interdisciplinary audience. In conclusion, the main aims of International Alert and our objective to create a global network with different sectors of the international community and to motivate the creation of a non-military early action mechanism to prevent the escalation of violent conflict are presented.Cet article fait état du rôle joué par International Alert en matière d'alerte préventive et d'intervention rapide dans des situations de conflits internes. Dans un premier temps, les principes de la diplomatie préventive tels que mis en oeuvre par International Alert sont présentés. Un certain nombre d'objectifs et d'activités de cette organisation sont ensuite mis en lumière, illustrant les multiples façons dont les principes en matière de prévention des conflits et d'alerte rapide peuvent être exploités dans les situations de violence potentielle et la façon dont ces notions peuvent être discutées au sein d'un public varié. En conclusion, les objectifs fondamentaux d'International Alert et le projet de créer un Réseau global sont présentés. Ce dernier, en réunissant les divers secteurs de la communauté internationale impliqués en matière d'alerte préventive, vise à répondre à la nécessité de créer un mécanisme non-militaire destiné à prévenir l'éclatement et la propagation des conflits violents