Event detection on streams of short texts for decision-making

L'objectif de cette thèse est de concevoir d'évènements sur les réseaux sociaux permettant d'assister les personnes en charge de prises de décisions dans des contextes industriels. Le but est de créer un système de détection d'évènement permettant de détecter des évènements à la fois ciblés, propres à des domaines particuliers mais aussi des évènements généraux. En particulier, nous nous intéressons à l'application de ce système aux chaînes d'approvisionnements et plus particulièrement celles liées aux matières premières. Le défi est de mettre en place un tel système de détection, mais aussi de déterminer quels sont les évènements potentiellement impactant dans ces contextes. Cette synthèse résume les différentes étapes des recherches menées pour répondre à ces problématiques. Architecture d'un système de détection d'évènements Dans un premier temps, nous introduisons les différents éléments nécessaires à la constitution d'un système de détection d'évènements. Ces systèmes sont classiquement constitués d'une étape de filtrage et de nettoyage des données, permettant de s'assurer de la qualité des données traitées par le reste du système. Ensuite, ces données sont représentées de manière à pouvoir être regroupées par similarité. Une fois ces regroupements de données établis, ils sont analysés de manière à savoir si les documents les constituants traitent d'un évènement ou non. Finalement, l'évolution dans le temps de ces évènements est suivie. Nous avons proposé au cours de cette thèse d'étudier les problématiques propres à chacune de ces étapes. Représentation textuelles de documents issus des réseaux sociaux Nous avons comparé différentes méthodes de représentations des données textuelles, dans le contexte de notre système de détection d'évènements. Nous avons comparé les performances de notre système de détection à l'algorithme First Story Detection (FSD), un algorithme ayant les mêmes objectifs. Nous avons d'abord démontré que le système que nous proposons est plus performant que le FSD, mais aussi que les architectures récentes de réseaux de neurones (transformeur) sont plus performantes que TF-IDF dans notre contexte, contrairement à ce qui avait été montré dans le contexte du FSD. Nous avons ensuite proposé de combiner différentes représentations textuelles afin d'exploiter conjointement leurs forces. Détection d'évènement, suivi et évaluation Nous avons proposé des approches pour les composantes d'analyse de regroupement de documents ainsi que pour le suivi de l'évolution de ces évènements. En particulier, nous utilisons l'entropie et la diversité d'utilisateurs introduits dans [Rajouter les citations] pour évaluer les regroupements. Nous suivons ensuite leur évolution au cours du temps en faisant des comparaisons entre regroupements à des instants différents, afin de créer des chaînes de regroupements. Enfin, nous avons étudié comment évaluer des systèmes de détection d'évènements dans des contextes où seulement peu de données annotées par des humains sont disponibles. Nous avons proposé une méthode permettant d'évaluer automatiquement les systèmes de détection d'évènement en exploitant des données partiellement annotées. Application au contexte des matières premières. Afin de spécifier les types d'évènements à superviser, nous avons mené une étude historique des évènements ayant impacté le cours des matières premières. En particulier, nous nous sommes focalisé sur le phosphate, une matière première stratégique. Nous avons étudié les différents facteurs ayant une influence, proposé une méthode reproductible pouvant être appliquée à d'autres matières premières ou d'autres domaines. Enfin, nous avons dressé une liste d'éléments à superviser pour permettre aux experts d'anticiper les variations des cours.The objective of this thesis is to design an event detection system on social networks to assist people in charge of decision-making in industrial contexts. The event detection system must be able to detect both targeted, domain-specific events and general events. In particular, we are interested in the application of this system to supply chains and more specifically those related to raw materials. The challenge is to build such a detection system, but also to determine which events are potentially influencing the raw materials supply chains. This synthesis summarizes the different stages of research conducted to answer these problems. Architecture of an event detection system First, we introduce the different building blocks of an event detection system. These systems are classically composed of a data filtering and cleaning step, ensuring the quality of the data processed by the system. Then, these data are embedded in such a way that they can be clustered by similarity. Once these data clusters are created, they are analyzed in order to know if the documents constituting them discuss an event or not. Finally, the evolution of these events is tracked. In this thesis, we have proposed to study the problems specific to each of these steps. Textual representation of documents from social networks We compared different text representation models, in the context of our event detection system. We also compared the performances of our event detection system to the First Story Detection (FSD) algorithm, an algorithm with the same objectives. We first demonstrated that our proposed system performs better than FSD, but also that recent neural network architectures perform better than TF-IDF in our context, contrary to what was shown in the context of FSD. We then proposed to combine different textual representations in order to jointly exploit their strengths. Event detection, monitoring, and evaluation We have proposed different approaches for event detection and event tracking. In particular, we use the entropy and user diversity introduced in ... to evaluate the clusters. We then track their evolution over time by making comparisons between clusters at different times, in order to create chains of clusters. Finally, we studied how to evaluate event detection systems in contexts where only few human-annotated data are available. We proposed a method to automatically evaluate event detection systems by exploiting partially annotated data. Application to the commodities context In order to specify the types of events to supervise, we conducted a historical study of events that have impacted the price of raw materials. In particular, we focused on phosphate, a strategic raw material. We studied the different factors having an influence, proposed a reproducible method that can be applied to other raw materials or other fields. Finally, we drew up a list of elements to supervise to enable experts to anticipate price variations

Temporal-spatial profiling of pedunculopontine galanin-cholinergic neurons in the lactacystin rat model of Parkinson’s disease

Parkinson’s disease (PD) is conventionally seen as resulting from single-system neurodegeneration affecting nigrostriatal dopaminergic neurons. However, accumulating evidence indicates a multi-system degeneration and neurotransmitter deficiencies, including cholinergic neurons which degenerate in a brainstem nucleus, the pedunculopontine nucleus (PPN), resulting in motor- and cognitive impairments. The neuropeptide galanin can inhibit cholinergic transmission, whilst being upregulated in degenerating brain regions associated with cognitive decline. Here we determined the temporal-spatial profile of progressive expression of endogenous galanin within degenerating cholinergic neurons, across the rostro-caudal axis of the PPN, by utilising the lactacystin-induced rat model of PD. First, we show progressive neuronal death affecting nigral dopaminergic and PPN cholinergic neurons, reflecting that seen in PD patients, to facilitate use of this model for assessing the therapeutic potential of bioactive peptides. Next, stereological analyses of the lesioned brain hemisphere found that the number of PPN cholinergic neurons expressing galanin increased by 11%, compared to sham-lesioned controls, increasing by a further 5% as the neurodegenerative process evolved. Galanin upregulation within cholinergic PPN neurons was most prevalent closest to the intra-nigral lesion site, suggesting that galanin upregulation in such neurons adapt intrinsically to neurodegeneration, to possibly neuroprotect. This is the first report on the extent and pattern of galanin expression in cholinergic neurons across distinct PPN subregions in both the intact rat CNS and lactacystin lesioned rats. The findings pave the way for future work to target galanin signaling in the PPN, to determine the extent to which upregulated galanin expression could offer a viable treatment strategy for ameliorating PD symptoms associated with cholinergic degeneration

Beyond Human Perception: Challenges in AI Interpretability of Orangutan Artwork

Drawings serve as a profound medium of expression for both humans and apes, offering unique insights into the cognitive and emotional landscapes of the artists, regardless of their species. This study employs artificial intelligence (AI), specifically Convolutional Neural Networks (CNNs) and the interpretability tool Captum, to analyze non-figurative drawings by Molly, an orangutan. The research utilizes VGG19 and ResNet18 models toClick here to view linked References decode seasonal nuances in the drawings, achieving notable accuracy in seasonal classification and revealing complex influences beyond human-centric methods. Techniques such as occlusion, integrated gradients, PCA, t-SNE, and Louvain clustering highlight critical areas and elements influencing seasonal recognition, providing deeper insights into the drawings. This approach not only advances the analysis of non-human art but also demonstrates the potential of AI to enrich our understanding of non-human cognitive and emotional expressions, with significant implications for fields like evolutionary anthropology and comparative psychology.</div

Beyond Human Perception: Challenges in AI Interpretability of Orangutan Artwork

Drawings serve as a profound medium of expression for both humans and apes, offering unique insights into the cognitive and emotional landscapes of the artists, regardless of their species. This study employs artificial intelligence (AI), specifically Convolutional Neural Networks (CNNs) and the interpretability tool Captum, to analyze non-figurative drawings by Molly, an orangutan. The research utilizes VGG19 and ResNet18 models toClick here to view linked References decode seasonal nuances in the drawings, achieving notable accuracy in seasonal classification and revealing complex influences beyond human-centric methods. Techniques such as occlusion, integrated gradients, PCA, t-SNE, and Louvain clustering highlight critical areas and elements influencing seasonal recognition, providing deeper insights into the drawings. This approach not only advances the analysis of non-human art but also demonstrates the potential of AI to enrich our understanding of non-human cognitive and emotional expressions, with significant implications for fields like evolutionary anthropology and comparative psychology.</div

Combinations of Content Representation Models for Event Detection on Social Media

International audienceSocial media are becoming the preferred channel to report and discuss events happening around the world. The data from these channels can be used to detect ongoing events in real-time. A typical approach is to use event detection methods, usually consisting of a clustering phase, in which similar documents are grouped together, and then an analysis of the clusters to decide whether they deal with real-world events. To cluster together similar documents, content representation models are critical. In this paper, we individually compare the performances of different social media documents content representation models used during the clustering phase, exploiting lexical, semantic and social media specific features, like tags and URLs. To the best of our knowledge, these models are usually individually exploited in this context. We investigate their complementarity and propose to combine them

Using deep learning predictions to study the development of drawing behaviour in children

Drawing behaviour in children provides a unique window into their cognitive development. This study uses Convolutional Neural Networks (CNNs) to examine cognitive development in children's drawing behavior by analyzing 386 drawings from 193 participants, comprising 150 children aged 2 to 10 years and 43 adults from France. CNN models, enhanced by Bayesian optimization, were trained to categorize drawings into ten age groups and to compare children's drawings with adults'. Results showed that model accuracy increases with the child's age, reflecting improvement in drawing skills. Techniques like Grad-CAM and Captum offered insights into key features recognized by CNNs, illustrating the potential of deep learning in evaluating developmental milestones, with significant implications for educational psychology and developmental diagnostics.</div

Using deep learning predictions to study the development of drawing behaviour in children

Drawing behaviour in children provides a unique window into their cognitive development. This study uses Convolutional Neural Networks (CNNs) to examine cognitive development in children's drawing behavior by analyzing 386 drawings from 193 participants, comprising 150 children aged 2 to 10 years and 43 adults from France. CNN models, enhanced by Bayesian optimization, were trained to categorize drawings into ten age groups and to compare children's drawings with adults'. Results showed that model accuracy increases with the child's age, reflecting improvement in drawing skills. Techniques like Grad-CAM and Captum offered insights into key features recognized by CNNs, illustrating the potential of deep learning in evaluating developmental milestones, with significant implications for educational psychology and developmental diagnostics.</div

Étude de l'influence des représentations textuelles sur la détection d'évènements non supervisée dans des flux de données

International audienceDetection of real-world events using online data sources is a trending topic in the information retrieval domain. Multiple data sources are potentially of interest and some of them are data streams. There are multiple data sources that are potentially interesting, and some of them are textual data streams, structured or unstructured. We propose to analyse the problem of event detection from text data stream and to focus particularly on the importance of the representation of the textual data. To do so, we compare multiple approaches in different context: supervised and unsupervised.We focus on the performances of Transformer-based architectures for event detection on short text documents, and we conclude that, contrary to previous studies, these architectures can be competitive compared to classical methods.La détection d’évènements à partir des données postées sur internet est un sujet important de la recherche d’information. Les sources de données potentiellement intéressantes sont multiples et peuvent prendre la forme de flux de données textuelles plus ou moins structurées. Nous étudions dans cet article la détection d’évènements dans les flux de données textuelles et plus particulièrement l’impact de la représentation du texte sur la qualité des évènements détectés. Nous comparons différentes approches de traitement du langage dans deux contextes : supervisé et non supervisé. Nous étudions la question de l’efficacité des modèles basés sur les architectures Transformer pour la détection d’évènements dans les documents courts. Cette étude nous permet de conclure que, contrairement à ce qui avait pu être précédemment montré, les architectures Transformer peuvent être compétitives par rapport aux méthodes classiques

Event detection and time series alignment to improve stock market forecasting

International audienceBuying commodities is a critical issue for multiple industries because the variations of stock prices are induced not only by multiple economic parameters but also by external events. Raw material buyers must keep track of information in numerous fields, which constitutes a major challenge considering the exponential growth of online data. To tackle this issue, we propose an event detection approach in order to assist them in their anticipation process. Indeed, a lot of contextual information is contained in text and exploiting it can allow one to improve its anticipation ability. Thus, we develop a framework of event detection and qualification, then we quantify the impact of these events on stock market to help buyers in their anticipation process. In this paper, we will first introduce our context, then explain the scope of our work and our goals. After detailing the related work, we will present our proposition, conclude and propose some future work possibilities. CCS CONCEPTS • Information systems → Data management systems; Information retrieval; • Computing methodologies → Natural language processing

The investigation of an event-based approach to improve commodities supply chain management

International audienceGoal: Predicting the evolution of commodities price to improve anticipation to supply-chain disruptions is hard. We propose an approach based on an event detection model on data stream to assist experts in such task. The final goal is to report to experts a meaningful description of the most impactful events occurring around the world, to help them in their daily decision-making. Design / Methodology / Approach: This work results from a cross-fertilization between business management, Information Technology and Computer science. This work relies on an expert analysis and advanced AI engines, including a case study on a specific raw material and a literature review to define the parameters to supervise. Results: We propose a general architecture based on IT and business synergy. We conduce a general study on the factors influencing raw materials price fluctuations, namely events influencing supply and demand of the commodity. Finally, we present a case study of the events, which historically affected phosphates prices. Limitations of the investigation: An in-depth knowledge of the domain is needed to analyze and quantify the events impact on the supply chain. Practical implications: This approach was first designed for assisting raw material purchasers but it can potentially be reproduced to assist other decision-makers. Originality / Value: We propose a new approach on how to anticipate the implications of external events on supply chain disruption and raw materials price evolution. This method is multidisciplinary, involving expert domain knowledge and state-of-the-art artificial intelligence