Evaluating the relationship between user interaction and financial visual analysis

It has been widely accepted that interactive visualization techniques enable users to more effectively form hypotheses and identify areas for more detailed investigation. There have been numerous empirical user studies testing the effectiveness of specific visual analytical tools. However, there has been limited effort in connecting a user’s interaction with his reasoning for the purpose of extracting the relationship between the two. In this paper, we present an approach for capturing and analyzing user interactions in a financial visual analytical tool and describe an exploratory user study that examines these interaction strategies. To achieve this goal, we created two visual tools to analyze raw interaction data captured during the user session. The results of this study demonstrate one possible strategy for understanding the relationship between interaction and reasoning both operationally and strategically. Index Terms: H.5.2 [Information Interfaces And Presentatio

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Camera-Based Heart Rate Extraction in Noisy Environments

Remote photoplethysmography (rPPG) is a non-invasive technique that benefits from video to measure vital signs such as the heart rate (HR). In rPPG estimation, noise can introduce artifacts that distort rPPG signal and jeopardize accurate HR measurement. Considering that most rPPG studies occurred in lab-controlled environments, the issue of noise in realistic conditions remains open. This thesis aims to examine the challenges of noise in rPPG estimation in realistic scenarios, specifically investigating the effect of noise arising from illumination variation and motion artifacts on the predicted rPPG HR. To mitigate the impact of noise, a modular rPPG measurement framework, comprising data preprocessing, region of interest, signal extraction, preparation, processing, and HR extraction is developed. The proposed pipeline is tested on the LGI-PPGI-Face-Video-Database public dataset, hosting four different candidates and real-life scenarios. In the RoI module, raw rPPG signals were extracted from the dataset using three machine learning-based face detectors, namely Haarcascade, Dlib, and MediaPipe, in parallel. Subsequently, the collected signals underwent preprocessing, independent component analysis, denoising, and frequency domain conversion for peak detection. Overall, the Dlib face detector leads to the most successful HR for the majority of scenarios. In 50% of all scenarios and candidates, the average predicted HR for Dlib is either in line or very close to the average reference HR. The extracted HRs from the Haarcascade and MediaPipe architectures make up 31.25% and 18.75% of plausible results, respectively. The analysis highlighted the importance of fixated facial landmarks in collecting quality raw data and reducing noise

Recognizing and understanding user behaviors from screencasts

User interacts with computers or mobile devices, leading to user behaviors on screen. In the context of software engineering, analyzing user behavior enables many applications such as intelligent bug fix, code completion and knowledge recommendation for developers. Such technique can be extended to more general knowledge worker environment, in which users have to manipulate devices according to specific guidelines. Existing works rely heavily on software instrumentation to obtain user actions from operation systems, which is hard to deploy and maintain. In addition, considering the security and privacy of some scenarios, non-intrusive is the major requirement to be included in the system. In this work, we leverage Computer Vision and Natural Language Processing techniques to recognize and understand user behaviors from screencasts, which is a non-intrusive and cross-platform method. We first recognize 10 categories of low level user actions such as mouse moving and type text, then summarize them to higher level abstractions (i.e. line-granularity coding steps). We also try to interpret user interaction with applications by multi-task learning and generate structured language descriptions (i.e. command, widget and location). Finally, unsupervised learning method is introduced for GUI linting problem, which is taken as a case study of user behavior analysis. To train the deep neural networks, we collect diverse video data from YouTube, Twitch and Bugzilla, and manually label them to build the dataset. The experiment results demonstrate the high performance of proposed method, and the user study validate the practical applications of many downstream tasks

Understanding the bi-directional relationship between analytical processes and interactive visualization systems

Interactive visualizations leverage the human visual and reasoning systems to increase the scale of information with which we can effectively work, therefore improving our ability to explore and analyze large amounts of data. Interactive visualizations are often designed with target domains in mind, such as analyzing unstructured textual information, which is a main thrust in this dissertation. Since each domain has its own existing procedures of analyzing data, a good start to a well-designed interactive visualization system is to understand the domain experts' workflow and analysis processes. This dissertation recasts the importance of understanding domain users' analysis processes and incorporating such understanding into the design of interactive visualization systems. To meet this aim, I first introduce considerations guiding the gathering of general and domain-specific analysis processes in text analytics. Two interactive visualization systems are designed by following the considerations. The first system is Parallel-Topics, a visual analytics system supporting analysis of large collections of documents by extracting semantically meaningful topics. Based on lessons learned from Parallel-Topics, this dissertation further presents a general visual text analysis framework, I-Si, to present meaningful topical summaries and temporal patterns, with the capability to handle large-scale textual information. Both systems have been evaluated by expert users and deemed successful in addressing domain analysis needs. The second contribution lies in preserving domain users' analysis process while using interactive visualizations. Our research suggests the preservation could serve multiple purposes. On the one hand, it could further improve the current system. On the other hand, users often need help in recalling and revisiting their complex and sometimes iterative analysis process with an interactive visualization system. This dissertation introduces multiple types of evidences available for capturing a user's analysis process within an interactive visualization and analyzes cost/benefit ratios of the capturing methods. It concludes that tracking interaction sequences is the most un-intrusive and feasible way to capture part of a user's analysis process. To validate this claim, a user study is presented to theoretically analyze the relationship between interactions and problem-solving processes. The results indicate that constraining the way a user interacts with a mathematical puzzle does have an effect on the problemsolving process. As later evidenced in an evaluative study, a fair amount of high-level analysis can be recovered through merely analyzing interaction logs

Leveraging eXtented Reality & Human-Computer Interaction for User Experi- ence in 360◦ Video

EXtended Reality systems have resurged as a medium for work and entertainment. While 360o video has been characterized as less immersive than computer-generated VR, its realism, ease of use and affordability mean it is in widespread commercial use. Based on the prevalence and potential of the 360o video format, this research is focused on improving and augmenting the user experience of watching 360o video. By leveraging knowledge from Extented Reality (XR) systems and Human-Computer Interaction (HCI), this research addresses two issues affecting user experience in 360o video: Attention Guidance and Visually Induced Motion Sickness (VIMS). This research work relies on the construction of multiple artifacts to answer the de- fined research questions: (1) IVRUX, a tool for analysis of immersive VR narrative expe- riences; (2) Cue Control, a tool for creation of spatial audio soundtracks for 360o video, as well as enabling the collection and analysis of captured metrics emerging from the user experience; and (3) VIMS mitigation pipeline, a linear sequence of modules (including optical flow and visual SLAM among others) that control parameters for visual modi- fications such as a restricted Field of View (FoV). These artifacts are accompanied by evaluation studies targeting the defined research questions. Through Cue Control, this research shows that non-diegetic music can be spatialized to act as orientation for users. A partial spatialization of music was deemed ineffective when used for orientation. Addi- tionally, our results also demonstrate that diegetic sounds are used for notification rather than orientation. Through VIMS mitigation pipeline, this research shows that dynamic restricted FoV is statistically significant in mitigating VIMS, while mantaining desired levels of Presence. Both Cue Control and the VIMS mitigation pipeline emerged from a Research through Design (RtD) approach, where the IVRUX artifact is the product of de- sign knowledge and gave direction to research. The research presented in this thesis is of interest to practitioners and researchers working on 360o video and helps delineate future directions in making 360o video a rich design space for interaction and narrative.Sistemas de Realidade EXtendida ressurgiram como um meio de comunicação para o tra- balho e entretenimento. Enquanto que o vídeo 360o tem sido caracterizado como sendo menos imersivo que a Realidade Virtual gerada por computador, o seu realismo, facili- dade de uso e acessibilidade significa que tem uso comercial generalizado. Baseado na prevalência e potencial do formato de vídeo 360o, esta pesquisa está focada em melhorar e aumentar a experiência de utilizador ao ver vídeos 360o. Impulsionado por conhecimento de sistemas de Realidade eXtendida (XR) e Interacção Humano-Computador (HCI), esta pesquisa aborda dois problemas que afetam a experiência de utilizador em vídeo 360o: Orientação de Atenção e Enjoo de Movimento Induzido Visualmente (VIMS). Este trabalho de pesquisa é apoiado na construção de múltiplos artefactos para res- ponder as perguntas de pesquisa definidas: (1) IVRUX, uma ferramenta para análise de experiências narrativas imersivas em VR; (2) Cue Control, uma ferramenta para a criação de bandas sonoras de áudio espacial, enquanto permite a recolha e análise de métricas capturadas emergentes da experiencia de utilizador; e (3) canal para a mitigação de VIMS, uma sequência linear de módulos (incluindo fluxo ótico e SLAM visual entre outros) que controla parâmetros para modificações visuais como o campo de visão restringido. Estes artefactos estão acompanhados por estudos de avaliação direcionados para às perguntas de pesquisa definidas. Através do Cue Control, esta pesquisa mostra que música não- diegética pode ser espacializada para servir como orientação para os utilizadores. Uma espacialização parcial da música foi considerada ineficaz quando usada para a orientação. Adicionalmente, os nossos resultados demonstram que sons diegéticos são usados para notificação em vez de orientação. Através do canal para a mitigação de VIMS, esta pesquisa mostra que o campo de visão restrito e dinâmico é estatisticamente significante ao mitigar VIMS, enquanto mantem níveis desejados de Presença. Ambos Cue Control e o canal para a mitigação de VIMS emergiram de uma abordagem de Pesquisa através do Design (RtD), onde o artefacto IVRUX é o produto de conhecimento de design e deu direcção à pesquisa. A pesquisa apresentada nesta tese é de interesse para profissionais e investigadores tra- balhando em vídeo 360o e ajuda a delinear futuras direções em tornar o vídeo 360o um espaço de design rico para a interação e narrativa