Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators’ Reactions.

Detection of aesthetic highlights is a challenge for understanding the affective processes taking place during movie watching. In this paper we study spectators’ responses to movie aesthetic stimuli in a social context. Moreover, we look for uncovering the emotional component of aesthetic highlights in movies. Our assumption is that synchronized spectators’ physiological and behavioral reactions occur during these highlights because: (i) aesthetic choices of filmmakers are made to elicit specific emotional reactions (e.g. special effects, empathy and compassion toward a character, etc.) and (ii) watching a movie together causes spectators’ affective reactions to be synchronized through emotional contagion. We compare different approaches to estimation of synchronization among multiple spectators’ signals, such as pairwise, group and overall synchronization measures to detect aesthetic highlights in movies. The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators’ synchronization and detect aesthetic highlights based on both spectators’ electrodermal and acceleration signals. We discover that pairwise synchronization measures perform the most accurately independently of the category of the highlights and movie genres. Moreover, we observe that electrodermal signals have more discriminative power than acceleration signals for highlight detection

Automated Mortality Prediction in Critically-ill Patients with Thrombosis using Machine Learning

Venous thromboembolism (VTE) is the third most common cardiovascular condition. Some high risk patients diagnosed with VTE need immediate treatment and monitoring in intensive care units (ICU) as the mortality rate is high. Most of the published predictive models for ICU mortality give information on in-hospital mortality using data recorded in the first day of ICU admission. The purpose of the current study is to predict in-hospital and after-discharge mortality in patients with VTE admitted to ICU using a machine learning (ML) framework. We studied 2,468 patients from the Medical Information Mart for Intensive Care (MIMIC-III) database, admitted to ICU with a diagnosis of VTE. We formed ML classification tasks for early and late mortality prediction. In total, 1,471 features were extracted for each patient, grouped in seven categories each representing a different type of medical assessment. We used an automated ML platform, JADBIO, as well as a class balancing combined with a Random Forest classifier, in order to evaluate the importance of class imbalance. Both methods showed significant ability in prediction of early mortality (AUC=0.92). Nevertheless, the task of predicting late mortality was less efficient (AUC=0.82). To the best of our knowledge, this is the first study in which ML is used to predict short-term and long-term mortality for ICU patients with VTE based on a multitude of clinical features collected over time

Recognizing Induced Emotions of Movie Audiences: Are Induced and Perceived Emotions the Same?

Predicting the emotional response of movie audi- ences to affective movie content is a challenging task in affective computing. Previous work has focused on using audiovisual movie content to predict movie induced emotions. However, the relationship between the audience’s perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audience (induced emotions) remains unexplored. In this work, we address the relationship between perceived and in- duced emotions in movies, and identify features and modelling approaches effective for predicting movie induced emotions. First, we extend the LIRIS-ACCEDE database by annotating perceived emotions in a crowd-sourced manner, and find that perceived and induced emotions are not always consistent. Second, we show that dialogue events and aesthetic highlights are effective predictors of movie induced emotions. In addition to movie based features, we also study physiological and be- havioural measurements of audiences. Our experiments show that induced emotion recognition can benefit from including temporal context and from including multimodal information. Our study bridges the gap between affective content analysis and induced emotion prediction

Emotions and Gambling: Towards a Computational Model of Gambling Experience

Gambling has been on the rise over the past years and understanding different patterns of the human behavior while gambling involves the identification of the emotions experienced while gambling, as well as how these change during a gambling activity. This work attempts to address these components towards the creation of a computational model of gambling experience. Specifically, we created a gambling game (roulette) and evaluated the interaction of participants with the game by assessing their emotional responses using the video modality. This work provides the basis for developing a multimodal interface that can help capturing the gambling experience. Within our research we attempt to answer the following research questions: (a) which are the emotions experienced by someone gambling and (b) how do the emotions detected change before and after an event

A conceptual architecture for empowering responsible online gambling with predictive, real- time, persuasive and interactive intervention

Online gambling, unlike other mediums of addiction and problematic behaviour, such as tobacco and alcohol, offers unprecedented opportunities for monitoring and understanding an addict's behaviour in real-time and adapting persuasive messages and interactions that would fit their usage and personal context. Online gambling sites usually provide Application Programming Interfaces (APIs) mainly to enable third party applications to enhance the gambling experience. In this work, we propose that gamblers' online data, such as navigation path and available offers, can be used to enable a more intelligent and proactive responsible gambling care in a real-time persuasive style. To this end, we propose a conceptual architecture of persuasive responsible online gambling technology. The novelty in our approach is indeed reliant on the real time and interactivity aspects as the intervention and the persuasion can happen in the same time as the gamblers’ behaviour is taking place

Web bot detection evasion using generative adversarial networks

Web bots are programs that can be used to browse the web and perform automated actions. These actions can be benign, such as web indexing and website monitoring, or malicious, such as unauthorised content scraping and scalping. To detect bots, web servers consider bots' fingerprint and behaviour, with research showing that techniques that examine the visitor's mouse movements can be very effective. In this work, we showcase that web bots can leverage the latest advances in machine learning to evade detection based on their mouse movements and touchscreen trajectories (for the case of mobile web bots). More specifically, the proposed web bots utilise Generative Adversarial Networks (GANs) to generate images of trajectories similar to those of humans, which can then be used by bots to evade detection. We show that, even if the web server is aware of the attack method, web bots can generate behaviours that can evade detection

Detection of advanced web bots by combining web logs with mouse behavioural biometrics

Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness: (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors’ mouse movements is more effective and robust toward detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches

4th International Workshop on Multimodal Affect and Aesthetic Experience

"Aesthetic experience"corresponds to the inner state of a person exposed to the form and content of artistic objects. Quantifying and interpreting the aesthetic experience of people in various contexts contribute towards a) creating context, and b) better understanding people's affective reactions to aesthetic stimuli. Focusing on different types of artistic content, such as movie, music, literature, urban art, ancient artwork, and modern interactive technology, the 4th international workshop on Multimodal Affect and Aesthetic Experience (MAAE) aims to enhance interdisciplinary collaboration among researchers from affective computing, aesthetics, human-robot/computer interaction, digital archaeology and art, culture, ethics, and addictive games

Empowering Responsible Online Gambling by Real-time Persuasive Information Systems

Online gambling, unlike other mediums of problem- atic and addictive behaviours, such as tobacco and alcohol, offers unprecedented opportunities for building information systems that are able to monitor and understand a user’s behaviour in real-time and adapt persuasive messages and interactions that would fit their personal profile and usage context. Online gambling industry usually provides Application Programming Interfaces (APIs) meant mainly to enable third-party applications to network with their gambling services and enhance a user’s gambling experience. In this industrial practice and experience paper, we advocate that such API’s can also be used to retrieve gamblers’ online data, such as browsing and betting history, promotions and available offers and use it to build more intel- ligent and proactive responsible gambling information systems. We report on our industrial experience in this field and make the argument that data available for persuasive marketing and usability should, under specific usage conditions, also be made available for responsible gambling information systems. This principle would provide equal opportunities for both directions. We discuss the psychological foundations of our proposed solution and the risks and challenges typically found when building such a software-assisted intervention, persuasion and emotion regulation technology. We also shed light on its potential implications from the perspectives of social corporate responsibility and data protection. We finally propose a conceptual architecture to demonstrate our vision and explain how it can be implemented. In the wider context, the paper is meant to provide insights on building behavioural awareness and regulation information systems in relation to problematic digital media usage

Towards a framework for detecting advanced Web bots

Automated programs (bots) are responsible for a large percentage of website traffic. These bots can either be used for benign purposes, such as Web indexing, Website monitoring (validation of hyperlinks and HTML code), feed fetching Web content and data extraction for commercial use or for malicious ones, including, but not limited to, content scraping, vulnerability scanning, account takeover, distributed denial of service attacks, marketing fraud, carding and spam. To ensure their security, Web servers try to identify bot sessions and apply special rules to them, such as throttling their requests or delivering different content. The methods currently used for the identification of bots are based either purely on rule-based bot detection techniques or a combination of rulebased and machine learning techniques. While current research has developed highly adequate methods for Web bot detection, these methods’ adequacy when faced with Web bots that try to remain undetected hasn’t been studied. For this reason, we created and evaluated a Web bot detection framework on its ability to detect conspicuous bots separately from its ability to detect advanced Web bots. We assessed the proposed framework performance using real HTTP traffic from a public Web server. Our experimental results show that the proposed framework has significant ability to detect Web bots that do not try to hide their bot identity using HTTP Web logs (balanced accuracy in a false-positive intolerant server > 95%). However, detecting advanced Web bots that present a browser fingerprint and may present a humanlike behaviour as well is considerably more difficult