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This work introduces the notion of computational hypertime, or the simulation and visualization of hypothetical environments possessing multidimensional time. An overview of hypertime is provided,including an intuitive visualization paradigm and a discussion of the failure of common simulation techniques when extended to include multidimensional time. A condition for differential equations describing hypertime motion to be amenable to standard time-iterative simulation techniques is provided,but is not satisfied by any known model of physics. An alternate simulation algorithm involving iterative refinement of entire equations of motion is presented,with an example implementation to solve elastic collisions in hypertime. An artificial intelligence algorithm for navigating crowds in any arbitrary nD/mT environment is discussed,and an implementation is provided using collision cones and stochastic global optimization techniques. Possible models of hypertime energy and other open questions are discussed. Both algorithms are described and show favorable results, meeting all design criteria and running at interactive speeds on common desktop computer systems.

Background: The concept of alexithymia is characterized by difficulties identifying and describing one’s emotions. Alexithymic individuals are impaired in the recognition of others’ emotional facial expressions. Alexithymia is quite common in patients suffering from major depressive disorder. The face-in-the-crowd task is a visual search paradigm that assesses processing of multiple facial emotions. In the present eye-tracking study, the relationship between alexithymia and visual processing of facial emotions was examined in clinical depression. Materials and Methods: Gaze behavior and manual response times of 20 alexithymic and 19 non-alexithymic depressed patients were compared in a face-in-the-crowd task. Alexithymia was empirically measured via the 20-item Toronto Alexithymia-Scale. Angry, happy, and neutral facial expressions of different individuals were shown as target and distractor stimuli. Our analyses of gaze behavior focused on latency to the target face, number of distractor faces fixated before fixating the target, number of target fixations, and number of distractor faces fixated after fixating the target. Results: Alexithymic patients exhibited in general slower decision latencies compared to non-alexithymic patients in the face-in-the-crowd task. Patient groups did not differ in latency to target, number of target fixations, and number of distractors fixated prior to target fixation. However, after having looked at the target, alexithymic patients fixated more distractors than non-alexithymic patients, regardless of expression condition. Discussion: According to our results, alexithymia goes along with impairments in visual processing of multiple facial emotions in clinical depression. Alexithymia appears to be associated with delayed manual reaction times and prolonged scanning after the first target fixation in depression, but it might have no impact on the early search phase. The observed deficits could indicate difficulties in target identification and/or decision-making when processing multiple emotional facial expressions. Impairments of alexithymic depressed patients in processing emotions in crowds of faces seem not limited to a specific affective valence. In group situations, alexithymic depressed patients might be slowed in processing interindividual differences in emotional expressions compared with non-alexithymic depressed patients. This could represent a disadvantage in understanding non-verbal communication in groups.

The problem of simulating a crowd to see how it would behave in certain situations or just to create a realistic-looking scene to be used in a movie or video game is important and complex, and there are many different methods to solve it. This project is primarily an investigation of the example-based crowd simulation method described in the article "Crowds by Example" by Lerner et al. In the article, traced video footage of crowds are used to create a data set. The simulation program continuously finds situations in the data set that resembles the current situations in the simulation and updates the simulation thereby. We implemented this for around 10 agents using Unity 3D. Example-based crowd simulations does not only, like some other types of crowd simulation methods (for example ORCA), take collision avoidance into account but also the more complex ways the human mind thinks and therefore does not always behave as one would predict. The main conclusion is that this method of simulating crowds has the potential to create more realistic simulations than other forms of crowd simulations. The downsides are that the time the program spends creating simulations can quickly get very high and to make realistic simulations a lot of video footage must be filmed and then traced.

Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. We validated the results of the Market Patrol scenario by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. 3. We modeled the movement of a socially aware mobile robot in a dense crowds, using the concept of a micro-conflict to represent the challenge of giving or not giving way to pedestrians. 4. We developed an approach for the robot behavior in micro-conflicts based on the psychological observation that human opponents will use a consistent strategy. For this, the mobile robot classifies the opponent strategy reflected by the personality and social status of the person and chooses an appropriate counter-strategy that takes into account the urgency of the robots' mission. 5. We developed an alternative approach for the resolution of micro-conflicts based on the imitation of the behavior of the human agent. This approach aims to make the behavior of an autonomous robot closely resemble that of a remotely operated one.

In this dissertation, we address the problem of analyzing the activities of people in a variety of scenarios, this is commonly encountered in vision applications. The overarching goal is to devise new representations for the activities, in settings where individuals or a number of people may take a part in specific activities. Different types of activities can be performed by either an individual at the fine level or by several people constituting a crowd at the coarse level. We take into account the domain specific information for modeling these activities. The summary of the proposed solutions is presented in the following. The holistic description of videos is appealing for visual detection and classification tasks for several reasons including capturing the spatial relations between the scene components, simplicity, and performance [1, 2, 3]. First, we present a holistic (global) frequency spectrum based descriptor for representing the atomic actions performed by individuals such as: bench pressing, diving, hand waving, boxing, playing guitar, mixing, jumping, horse riding, hula hooping etc. We model and learn these individual actions for classifying complex user uploaded videos. Our method bypasses the detection of interest points, the extraction of local video descriptors and the quantization of local descriptors into a code book; it represents each video sequence as a single feature vector. This holistic feature vector is computed by applying a bank of 3-D spatio-temporal filters on the frequency spectrum of a video sequence; hence it integrates the information about the motion and scene structure. We tested our approach on two of the most challenging datasets, UCF50 [4] and HMDB51 [5], and obtained promising results which demonstrates the robustness and the discriminative power of our holistic video descriptor for classifying videos of various realistic actions. In the above approach, a holistic feature vector of a video clip is acquired by dividing the video into spatio-temporal blocks then concatenating the features of the individual blocks together. However, such a holistic representation blindly incorporates all the video regions regardless of iii their contribution in classification. Next, we present an approach which improves the performance of the holistic descriptors for activity recognition. In our novel method, we improve the holistic descriptors by discovering the discriminative video blocks. We measure the discriminativity of a block by examining its response to a pre-learned support vector machine model. In particular, a block is considered discriminative if it responds positively for positive training samples, and negatively for negative training samples. We pose the problem of finding the optimal blocks as a problem of selecting a sparse set of blocks, which maximizes the total classifier discriminativity. Through a detailed set of experiments on benchmark datasets [6, 7, 8, 9, 5, 10], we show that our method discovers the useful regions in the videos and eliminates the ones which are confusing for classification, which results in significant performance improvement over the state-of-the-art. In contrast to the scenes where an individual performs a primitive action, there may be scenes with several people, where crowd behaviors may take place. For these types of scenes the traditional approaches for recognition will not work due to severe occlusion and computational requirements. The number of videos is limited and the scenes are complicated, hence learning these behaviors is not feasible. For this problem, we present a novel approach, based on the optical flow in a video sequence, for identifying five specific and common crowd behaviors in visual scenes. In the algorithm, the scene is overlaid by a grid of particles, initializing a dynamical system which is derived from the optical flow. Numerical integration of the optical flow provides particle trajectories that represent the motion in the scene. Linearization of the dynamical system allows a simple and practical analysis and classification of the behavior through the Jacobian matrix. Essentially, the eigenvalues of this matrix are used to determine the dynamic stability of points in the flow and each type of stability corresponds to one of the five crowd behaviors. The identified crowd behaviors are (1) bottlenecks: where many pedestrians/vehicles from various points in the scene are entering through one narrow passage, (2) fountainheads: where many pedestrians/vehicles are emerging from a narrow passage only to separate in many directions, (3) lanes: where many pedestrians/vehicles are moving at the same speeds in the same direction, (4) arches or rings: where the iv collective motion is curved or circular, and (5) blocking: where there is a opposing motion and desired movement of groups of pedestrians is somehow prohibited. The implementation requires identifying a region of interest in the scene, and checking the eigenvalues of the Jacobian matrix in that region to determine the type of flow, that corresponds to various well-defined crowd behaviors. The eigenvalues are only considered in these regions of interest, consistent with the linear approximation and the implied behaviors. Since changes in eigenvalues can mean changes in stability, corresponding to changes in behavior, we can repeat the algorithm over clips of long video sequences to locate changes in behavior. This method was tested on over real videos representing crowd and traffic scenes.

In the area of steering behaviours of autonomous agents and crowd simulations, there is a plethora of methods for executing the simulations. A very hard-to-achieve goal of crowd simulations is to make them seem natural and accurately reflect real-life crowds. A very important criterion for this goal is to have the agents avoid collisions, both with each other and with the environment. A less important, but important nonetheless, criterion is to not let the time taken or distance covered to reach the goal in the simulation be too high, compared with when not implementing collision avoidance. This paper proposes and explores a novel method of enhancing vector field-based steering with rule-based deviations to implement collision avoidance. This method is called ’DevVec’ (’Deviation + Vector Field steering’). The rules which are used for the deviations are extracted from a user survey, and they describe what the agent should do in different collision avoidance scenarios. The viability of DevVec is tested by comparing it with another already established method, called ’Gradient-based Steering’, in terms of fulfilling the criteria mentioned above. Both methods are used to simulate pedestrians moving throughout different scenes. The results suggest that DevVec has potential, but would require additional time and resources, and perhaps a few changes in future works to be presented in its best possible version. / Inom ämnesområdet för styrbeteenden hos autonoma agenter och simuleringar av folkmassor finns det många metoder för att framställa dessa simuleringar. Ett väldigt svåruppnåeligt mål för denna typ av simuleringar är få dem att verka naturliga och verklighetstrogna. Ett viktigt kriterie för detta mål är att få agenterna att undvika kollisioner, både med varandra och med den kringliggande omgivningen. Ett mindre viktigt, men viktigt oavsett, kriterie är att inte låta en agent ta för lång tid eller gå för långt för att nå sitt mål i simuleringen, i jämförelse med när de inte försöka undvika hinder. Denna studie presenterar och utforskar en ny metod som utökar en vektorfältsbaserat styralgoritm med regelbaserade avvikelser för att ta hänsyn till att undvika kollisioner. Denna nya metod kallas för ’DevVec’ (’Deviation + Vector Field steering’). Reglerna som används för avvikelserna är framtagna från en enkät, och de beskriver vad en agent borde göra vid olika kollision-scenarion. Användbarheten av DevVec prövas genom att jämföra den med en redan etablerad metod som kallas för ’Gradientbaserad styrning’, med avseende på de ovan nämnda kriterierna. Båda metoderna används för att simulera fotgängare i olika omgivningar. Resultaten antyder att DevVec har potential, men att det krävs ytterligare tid och resurser, och troligtvis några ändringar i framtiden för att framställa den bästa möjliga versionen.

В данной работе изучена специфика краудфандинга в сфере книгоиздания, а также описаны особенности продвижения краудфандингового издательского проекта. Объект настоящей магистерской диссертации – краудфандинговый проект на переиздание архитектурного путеводителя по Екатеринбургу. Предметом исследования являются способы продвижения, использованные в проекте краудфандинга. Магистерская диссертация состоит из введения, двух глав, заключения, библиографического списка, включающего 141 наименования. В первой части магистерской диссертации исследованы понятие, виды и преимущества краудфандинга, описаны особенности краудфандинга в сфере книгоиздания, а также перечислены основные способы продвижения проектов. Во второй части исследования подробно описаны все этапы создания краудфандингового проекта на переиздание путеводителя по Екатеринбургу, перечислены способы его продвижения. Благодаря статистическим данным дана оценка эффективности тех способов, результаты которых можно отследить и зафиксировать. Также были подведены итоги исследуемого проекта. Данная работа может послужить источником для других исследований, в том числе для сравнения эффективности кампаний по продвижению краудфандинговых проектов, что приведет к качественному и полному изучению данной темы. / In this paper, we studied the specifics of crowdfunding in the field of book publishing, and also described the features of promoting a crowdfunding publishing project. The object of this master's thesis is a crowdfunding project to reprint the architectural guide to Yekaterinburg. The subject of the research is the promotion methods used in the crowdfunding project. Master's thesis consists of introduction, two chapters, conclusion, bibliography, which includes 141 titles. The first part of the master's thesis explores the concept, types and advantages of crowdfunding, describes the features of crowdfunding in the field of book publishing, and also lists the main ways to promote projects. The second part of the study describes in detail all the stages of creating a crowdfunding project for the reissue of a guide to Yekaterinburg, lists ways to promote it. Thanks to statistical data, an assessment is given of the effectiveness of those methods, the results of which can be tracked and recorded. The results of the research project were also summed up. This work can serve as a source for other studies, including comparing the effectiveness of campaigns to promote crowdfunding projects, which will lead to a qualitative and complete study of this topic.

La cancel culture (culture de l'annulation) est depuis quelques années une pratique en ligne populaire, mais controversée. Souvent par milliers, les utilisateurs de réseaux sociaux se rassemblent pour dénoncer publiquement et tenter de minimiser la présence en ligne d’une personnalité publique ou d’une entreprise qu'ils jugent problématique, leur fureur atteignant parfois des sommets impressionnants. Considérant l'impact de cette pratique, nous nous sommes interrogées sur la manière dont elle est perçue et vécue par les personnes qui y participent. Cette étude exploratoire définit d'abord la culture de l'annulation à travers diverses modalités numériques et sociohistoriques, puis analyse les réponses de 19 individus ayant participé à des annulations en ligne. Ces étapes ont permis de découvrir pourquoi les gens décident « d’annuler » et d'explorer les expressions de leurs participations. Finalement, c'est surtout le désir de défendre leurs propres valeurs et les communautés marginalisées qui sont ressorties comme les principales motivations de leurs annulations. / Cancel culture has been a popular, but controversial online practice for the past few years. Often by thousands, social media users gather to publicly denounce and attempt to minimise the online presence of a public figure or company they deem problematic, their fury sometimes attaining impressive heights. Considering the impact of this practice, it is worth asking how it is perceived and experienced by the people concerned. This exploratory study first defines cancel culture through diverse digital and sociohistorical modalities, and then analyses the responses of 19 subjects who have participated in online cancellations. These steps served to discover exactly why people canceled and to explore the expressions of their participations. In the end, it was mostly the desire to stand up for their own values and for marginalized communities that stood out as their main motivators for canceling.