First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Audio Visual Language Maps for Robot Navigation

While interacting in the world is a multi-sensory experience, many robots continue to predominantly rely on visual perception to map and navigate in their environments. In this work, we propose Audio-Visual-Language Maps (AVLMaps), a unified 3D spatial map representation for storing cross-modal information from audio, visual, and language cues. AVLMaps integrate the open-vocabulary capabilities of multimodal foundation models pre-trained on Internet-scale data by fusing their features into a centralized 3D voxel grid. In the context of navigation, we show that AVLMaps enable robot systems to index goals in the map based on multimodal queries, e.g., textual descriptions, images, or audio snippets of landmarks. In particular, the addition of audio information enables robots to more reliably disambiguate goal locations. Extensive experiments in simulation show that AVLMaps enable zero-shot multimodal goal navigation from multimodal prompts and provide 50% better recall in ambiguous scenarios. These capabilities extend to mobile robots in the real world - navigating to landmarks referring to visual, audio, and spatial concepts. Videos and code are available at: https://avlmaps.github.io.Comment: Project page: https://avlmaps.github.io

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

FACTS-ON : Fighting Against Counterfeit Truths in Online social Networks : fake news, misinformation and disinformation

L'évolution rapide des réseaux sociaux en ligne (RSO) représente un défi significatif dans l'identification et l'atténuation des fausses informations, incluant les fausses nouvelles, la désinformation et la mésinformation. Cette complexité est amplifiée dans les environnements numériques où les informations sont rapidement diffusées, nécessitant des stratégies sophistiquées pour différencier le contenu authentique du faux. L'un des principaux défis dans la détection automatique de fausses informations est leur présentation réaliste, ressemblant souvent de près aux faits vérifiables. Cela pose de considérables défis aux systèmes d'intelligence artificielle (IA), nécessitant des données supplémentaires de sources externes, telles que des vérifications par des tiers, pour discerner efficacement la vérité. Par conséquent, il y a une évolution technologique continue pour contrer la sophistication croissante des fausses informations, mettant au défi et avançant les capacités de l'IA. En réponse à ces défis, ma thèse introduit le cadre FACTS-ON (Fighting Against Counterfeit Truths in Online Social Networks), une approche complète et systématique pour combattre la désinformation dans les RSO. FACTS-ON intègre une série de systèmes avancés, chacun s'appuyant sur les capacités de son prédécesseur pour améliorer la stratégie globale de détection et d'atténuation des fausses informations. Je commence par présenter le cadre FACTS-ON, qui pose les fondements de ma solution, puis je détaille chaque système au sein du cadre : EXMULF (Explainable Multimodal Content-based Fake News Detection) se concentre sur l'analyse du texte et des images dans les contenus en ligne en utilisant des techniques multimodales avancées, couplées à une IA explicable pour fournir des évaluations transparentes et compréhensibles des fausses informations. En s'appuyant sur les bases d'EXMULF, MythXpose (Multimodal Content and Social Context-based System for Explainable False Information Detection with Personality Prediction) ajoute une couche d'analyse du contexte social en prédisant les traits de personnalité des utilisateurs des RSO, améliorant la détection et les stratégies d'intervention précoce contre la désinformation. ExFake (Explainable False Information Detection Based on Content, Context, and External Evidence) élargit encore le cadre, combinant l'analyse de contenu avec des insights du contexte social et des preuves externes. Il tire parti des données d'organisations de vérification des faits réputées et de comptes officiels, garantissant une approche plus complète et fiable de la détection de la désinformation. La méthodologie sophistiquée d'ExFake évalue non seulement le contenu des publications en ligne, mais prend également en compte le contexte plus large et corrobore les informations avec des sources externes crédibles, offrant ainsi une solution bien arrondie et robuste pour combattre les fausses informations dans les réseaux sociaux en ligne. Complétant le cadre, AFCC (Automated Fact-checkers Consensus and Credibility) traite l'hétérogénéité des évaluations des différentes organisations de vérification des faits. Il standardise ces évaluations et évalue la crédibilité des sources, fournissant une évaluation unifiée et fiable de l'information. Chaque système au sein du cadre FACTS-ON est rigoureusement évalué pour démontrer son efficacité dans la lutte contre la désinformation sur les RSO. Cette thèse détaille le développement, la mise en œuvre et l'évaluation complète de ces systèmes, soulignant leur contribution collective au domaine de la détection des fausses informations. La recherche ne met pas seulement en évidence les capacités actuelles dans la lutte contre la désinformation, mais prépare également le terrain pour de futures avancées dans ce domaine critique d'étude.The rapid evolution of online social networks (OSN) presents a significant challenge in identifying and mitigating false information, which includes Fake News, Disinformation, and Misinformation. This complexity is amplified in digital environments where information is quickly disseminated, requiring sophisticated strategies to differentiate between genuine and false content. One of the primary challenges in automatically detecting false information is its realistic presentation, often closely resembling verifiable facts. This poses considerable challenges for artificial intelligence (AI) systems, necessitating additional data from external sources, such as third-party verifications, to effectively discern the truth. Consequently, there is a continuous technological evolution to counter the growing sophistication of false information, challenging and advancing the capabilities of AI. In response to these challenges, my dissertation introduces the FACTS-ON framework (Fighting Against Counterfeit Truths in Online Social Networks), a comprehensive and systematic approach to combat false information in OSNs. FACTS-ON integrates a series of advanced systems, each building upon the capabilities of its predecessor to enhance the overall strategy for detecting and mitigating false information. I begin by introducing the FACTS-ON framework, which sets the foundation for my solution, and then detail each system within the framework: EXMULF (Explainable Multimodal Content-based Fake News Detection) focuses on analyzing both text and image in online content using advanced multimodal techniques, coupled with explainable AI to provide transparent and understandable assessments of false information. Building upon EXMULF’s foundation, MythXpose (Multimodal Content and Social Context-based System for Explainable False Information Detection with Personality Prediction) adds a layer of social context analysis by predicting the personality traits of OSN users, enhancing the detection and early intervention strategies against false information. ExFake (Explainable False Information Detection Based on Content, Context, and External Evidence) further expands the framework, combining content analysis with insights from social context and external evidence. It leverages data from reputable fact-checking organizations and official social accounts, ensuring a more comprehensive and reliable approach to the detection of false information. ExFake's sophisticated methodology not only evaluates the content of online posts but also considers the broader context and corroborates information with external, credible sources, thereby offering a well-rounded and robust solution for combating false information in online social networks. Completing the framework, AFCC (Automated Fact-checkers Consensus and Credibility) addresses the heterogeneity of ratings from various fact-checking organizations. It standardizes these ratings and assesses the credibility of the sources, providing a unified and trustworthy assessment of information. Each system within the FACTS-ON framework is rigorously evaluated to demonstrate its effectiveness in combating false information on OSN. This dissertation details the development, implementation, and comprehensive evaluation of these systems, highlighting their collective contribution to the field of false information detection. The research not only showcases the current capabilities in addressing false information but also sets the stage for future advancements in this critical area of study

Deep learning for health outcome prediction

Modern medical data contains rich information that allows us to make new types of inferences to predict health outcomes. However, the complexity of modern medical data has rendered many classical analysis approaches insufficient. Machine learning with deep neural networks enables computational models to process raw data and learn useful representations with multiple levels of abstraction. In this thesis, I present novel deep learning methods for health outcome prediction from brain MRI and genomic data. I show that a deep neural network can learn a biomarker from structural brain MRI and that this biomarker provides a useful measure for investigating brain and systemic health, can augment neuroradiological research and potentially serve as a decision-support tool in clinical environments. I also develop two tensor methods for deep neural networks: the first, tensor dropout, for improving the robustness of deep neural networks, and the second, Kronecker machines, for combining multiple sources of data to improve prediction accuracy. Finally, I present a novel deep learning method for predicting polygenic risk scores from genome sequences by leveraging both local and global interactions between genetic variants. These contributions demonstrate the benefits of using deep learning for health outcome prediction in both research and clinical settings.Open Acces