Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012\_100, Events2018\_100, and CrisisLexT\_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.Comment: Accepted by TKDE 202

Resource theories of knowledge

How far can we take the resource theoretic approach to explore physics? Resource theories like LOCC, reference frames and quantum thermodynamics have proven a powerful tool to study how agents who are subject to certain constraints can act on physical systems. This approach has advanced our understanding of fundamental physical principles, such as the second law of thermodynamics, and provided operational measures to quantify resources such as entanglement or information content. In this work, we significantly extend the approach and range of applicability of resource theories. Firstly we generalize the notion of resource theories to include any description or knowledge that agents may have of a physical state, beyond the density operator formalism. We show how to relate theories that differ in the language used to describe resources, like micro and macroscopic thermodynamics. Finally, we take a top-down approach to locality, in which a subsystem structure is derived from a global theory rather than assumed. The extended framework introduced here enables us to formalize new tasks in the language of resource theories, ranging from tomography, cryptography, thermodynamics and foundational questions, both within and beyond quantum theory.Comment: 28 pages featuring figures, examples, map and neatly boxed theorems, plus appendi

Recognition Situations Using Extended Dempster-Shafer Theory

Weiser’s [111] vision of pervasive computing describes a world where technology seamlessly integrates into the environment, automatically responding to peoples’ needs. Underpinning this vision is the ability of systems to automatically track the situation of a person. The task of situation recognition is critical and complex: noisy and unreliable sensor data, dynamic situations, unpredictable human behaviour and changes in the environment all contribute to the complexity. No single recognition technique is suitable in all environments. Factors such as availability of training data, ability to deal with uncertain information and transparency to the user will determine which technique to use in any particular environment. In this thesis, we propose the use of Dempster-Shafer theory as a theoretically sound basis for situation recognition - an approach that can reason with uncertainty, but which does not rely on training data. We use existing operations from Dempster-Shafer theory and create new operations to establish an evidence decision network. The network is used to generate and assess situation beliefs based on processed sensor data for an environment. We also define two specific extensions to Dempster-Shafer theory to enhance the knowledge that can be used for reasoning: 1) temporal knowledge about situation time patterns 2) quality of evidence sources (sensors) into the reasoning process. To validate the feasibility of our approach, this thesis creates evidence decision networks for two real-world data sets: a smart home data set and an officebased data set. We analyse situation recognition accuracy for each of the data sets, using the evidence decision networks with temporal/quality extensions. We also compare the evidence decision networks against two learning techniques: Naïve Bayes and J48 Decision Tree

Semantic Decision Support for Information Fusion Applications

La thèse s'inscrit dans le domaine de la représentation des connaissances et la modélisation de l'incertitude dans un contexte de fusion d'informations. L'idée majeure est d'utiliser les outils sémantiques que sont les ontologies, non seulement pour représenter les connaissances générales du domaine et les observations, mais aussi pour représenter les incertitudes que les sources introduisent dans leurs observations. Nous proposons de représenter ces incertitudes au travers d'une méta-ontologie (DS-ontology) fondée sur la théorie des fonctions de croyance. La contribution de ce travail porte sur la définition d'opérateurs d'inclusion et d'intersection sémantique et sur lesquels s'appuie la mise en œuvre de la théorie des fonctions de croyance, et sur le développement d'un outil appelé FusionLab permettant la fusion d'informations sémantiques à partir du développement théorique précédent. Une application de ces travaux a été réalisée dans le cadre d'un projet de surveillance maritime.This thesis is part of the knowledge representation domain and modeling of uncertainty in a context of information fusion. The main idea is to use semantic tools and more specifically ontologies, not only to represent the general domain knowledge and observations, but also to represent the uncertainty that sources may introduce in their own observations. We propose to represent these uncertainties and semantic imprecision trough a metaontology (called DS-Ontology) based on the theory of belief functions. The contribution of this work focuses first on the definition of semantic inclusion and intersection operators for ontologies and on which relies the implementation of the theory of belief functions, and secondly on the development of a tool called FusionLab for merging semantic information within ontologies from the previous theorical development. These works have been applied within a European maritime surveillance project.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

Data mining for decision support with uncertainty on the airplane

This study describes the formalization of the medical decision-making process under uncertainty underpinned by conditional preferences, the theory of evidence and the exploitation of high-utility patterns in data mining. To assist a decision maker, the medical process (clinical pathway) was implemented using a Conditional Preferences Base (CPB). Then for knowledge engineering, a Dempster-Shafer ontology integrating uncertainty underpinned by evidence theory was built. Beliefs from different sources are established with the use of data mining. The result is recorded in an In-flight Electronic Health Records (IEHR). The IEHR contains evidential items corresponding to the variables determining the management of medical incidents. Finally, to manage tolerance to uncertainty, a belief fusion algorithm was developed. There is an inherent risk in the practice of medicine that can affect the conditions of medical activities (diagnostic or therapeutic purposes). The management of uncertainty is also an integral part of decision-making processes in the medical field. Different models of medical decisions under uncertainty have been proposed. Much of the current literature on these models pays particular attention to health economics inspired by how to manage uncertainty in economic decisions. However, these models fail to consider the purely medical aspect of the decision that always remains poorly characterized. Besides, the models achieving interesting decision outcomes are those considering the patient's health variable and other variables such as the costs associated with the care services. These models are aimed at defining health policy (health economics) without a deep consideration for the uncertainty surrounding the medical practices and associated technologies. Our approach is to integrate the management of uncertainty into clinical reasoning models such as Clinical Pathway and to exploit the relationships between the determinants of incident management using data mining tools. To this end, how healthcare professionals see and conceive uncertainty has been investigated. This allowed for the identification of the characteristics determining people under uncertainty and to understand the different forms and representations of uncertainty. Furthermore, what an in-flight medical incident is and how its management is a decision under uncertainty issues was defined. This is the first phase of common data mining that will provide an evidential transaction basis. Subsequently an evidential and ontological rea-soning to manage this uncertainty has been established in order to support decision making processes on the airplane