4 research outputs found

    Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines

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    Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to making documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable phrases, successfully measuring novelty.publishedVersionPaid Open Acces

    Scalable Algorithms for the Analysis of Massive Networks

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    Die Netzwerkanalyse zielt darauf ab, nicht-triviale Erkenntnisse aus vernetzten Daten zu gewinnen. Beispiele für diese Erkenntnisse sind die Wichtigkeit einer Entität im Verhältnis zu anderen nach bestimmten Kriterien oder das Finden des am besten geeigneten Partners für jeden Teilnehmer eines Netzwerks - bekannt als Maximum Weighted Matching (MWM). Da der Begriff der Wichtigkeit an die zu betrachtende Anwendung gebunden ist, wurden zahlreiche Zentralitätsmaße eingeführt. Diese Maße stammen hierbei aus Jahrzehnten, in denen die Rechenleistung sehr begrenzt war und die Netzwerke im Vergleich zu heute viel kleiner waren. Heute sind massive Netzwerke mit Millionen von Kanten allgegenwärtig und eine triviale Berechnung von Zentralitätsmaßen ist oft zu zeitaufwändig. Darüber hinaus ist die Suche nach der Gruppe von k Knoten mit hoher Zentralität eine noch kostspieligere Aufgabe. Skalierbare Algorithmen zur Identifizierung hochzentraler (Gruppen von) Knoten in großen Graphen sind von großer Bedeutung für eine umfassende Netzwerkanalyse. Heutigen Netzwerke verändern sich zusätzlich im zeitlichen Verlauf und die effiziente Aktualisierung der Ergebnisse nach einer Änderung ist eine Herausforderung. Effiziente dynamische Algorithmen sind daher ein weiterer wesentlicher Bestandteil moderner Analyse-Pipelines. Hauptziel dieser Arbeit ist es, skalierbare algorithmische Lösungen für die zwei oben genannten Probleme zu finden. Die meisten unserer Algorithmen benötigen Sekunden bis einige Minuten, um diese Aufgaben in realen Netzwerken mit bis zu Hunderten Millionen von Kanten zu lösen, was eine deutliche Verbesserung gegenüber dem Stand der Technik darstellt. Außerdem erweitern wir einen modernen Algorithmus für MWM auf dynamische Graphen. Experimente zeigen, dass unser dynamischer MWM-Algorithmus Aktualisierungen in Graphen mit Milliarden von Kanten in Millisekunden bewältigt.Network analysis aims to unveil non-trivial insights from networked data by studying relationship patterns between the entities of a network. Among these insights, a popular one is to quantify the importance of an entity with respect to the others according to some criteria. Another one is to find the most suitable matching partner for each participant of a network knowing the pairwise preferences of the participants to be matched with each other - known as Maximum Weighted Matching (MWM). Since the notion of importance is tied to the application under consideration, numerous centrality measures have been introduced. Many of these measures, however, were conceived in a time when computing power was very limited and networks were much smaller compared to today's, and thus scalability to large datasets was not considered. Today, massive networks with millions of edges are ubiquitous, and a complete exact computation for traditional centrality measures are often too time-consuming. This issue is amplified if our objective is to find the group of k vertices that is the most central as a group. Scalable algorithms to identify highly central (groups of) vertices on massive graphs are thus of pivotal importance for large-scale network analysis. In addition to their size, today's networks often evolve over time, which poses the challenge of efficiently updating results after a change occurs. Hence, efficient dynamic algorithms are essential for modern network analysis pipelines. In this work, we propose scalable algorithms for identifying important vertices in a network, and for efficiently updating them in evolving networks. In real-world graphs with hundreds of millions of edges, most of our algorithms require seconds to a few minutes to perform these tasks. Further, we extend a state-of-the-art algorithm for MWM to dynamic graphs. Experiments show that our dynamic MWM algorithm handles updates in graphs with billion edges in milliseconds

    Adaptive multi-classifier systems for face re-identification applications

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    In video surveillance, decision support systems rely more and more on face recognition (FR) to rapidly determine if facial regions captured over a network of cameras correspond to individuals of interest. Systems for FR in video surveillance are applied in a range of scenarios, for instance in watchlist screening, face re-identification, and search and retrieval. The focus of this Thesis is video-to-video FR, as found in face re-identification applications, where facial models are designed on reference data, and update is archived on operational captures from video streams. Several challenges emerge from the task of recognizing individuals of interest from faces captured with video cameras. Most notably, it is often assumed that the facial appearance of target individuals do not change over time, and the proportions of faces captured for target and non-target individuals are balanced, known a priori and remain fixed. However, faces captured during operations vary due to several factors, including illumination, blur, resolution, pose expression, and camera interoperability. In addition, facial models used matching are commonly not representative since they are designed a priori, with a limited amount of reference samples that are collected and labeled at a high cost. Finally, the proportions of target and non-target individuals continuously change during operations. In literature, adaptive multiple classifier systems (MCSs) have been successfully applied to video-to-video FR, where the facial model for each target individual is designed using an ensemble of 2-class classifiers (trained using target vs. non-target reference samples). Recent approaches employ ensembles of 2-class Fuzzy ARTMAP classifiers, with a DPSO strategy to generate a pool of classifiers with optimized hyperparameters, and Boolean combination to merge their responses in the ROC space. Besides, the skew-sensitive ensembles were recently proposed to adapt the fusion function of an ensemble according to class imbalance measured on operational data. These active approaches estimate target vs. non-target proportions periodically during operations distance, and the fusion of classifier ensembles are adapted to such imbalance. Finally, face tracking can be used to regroup the system responses linked to a facial trajectory (facial captures from a single person in the scene) for robust spatio-temporal recognition, and to update facial models over time using operational data. In this Thesis, new techniques are proposed to adapt the facial models for individuals enrolled to a video-to-video FR system. Trajectory-based self-updating is proposed to update the system, considering gradual and abrupt changes in the classification environment. Then, skew-sensitive ensembles are proposed to adapt the system to the operational imbalance. In Chapter 2, an adaptive framework is proposed for partially-supervised learning of facial models over time based on facial trajectories. During operations, information from a face tracker and individual-specific ensembles is integrated for robust spatio-temporal recognition and for self-update of facial models. The tracker defines a facial trajectory for each individual in video. Recognition of a target individual is done if the positive predictions accumulated along a trajectory surpass a detection threshold for an ensemble. If the accumulated positive predictions surpass a higher update threshold, then all target face samples from the trajectory are combined with non-target samples (selected from the cohort and universal models) to update the corresponding facial model. A learn-and-combine strategy is employed to avoid knowledge corruption during self-update of ensembles. In addition, a memory management strategy based on Kullback-Leibler divergence is proposed to rank and select the most relevant target and non-target reference samples to be stored in memory as the ensembles evolves. The proposed system was validated with synthetic data and real videos from Face in Action dataset, emulating a passport checking scenario. Initially, enrollment trajectories were used for supervised learning of ensembles, and videos from three capture sessions were presented to the system for FR and self-update. Transaction-level analysis shows that the proposed approach outperforms baseline systems that do not adapt to new trajectories, and provides comparable performance to ideal systems that adapt to all relevant target trajectories, through supervised learning. Subject-level analysis reveals the existence of individuals for which self-updated ensembles provide a considerable benefit. Trajectory-level analysis indicates that the proposed system allows for robust spatio-temporal video-to-video FR. In Chapter 3, an extension and a particular implementation of the ensemble-based system for spatio-temporal FR is proposed, and is characterized in scenarios with gradual and abrupt changes in the classification environment. Transaction-level results show that the proposed system allows to increase AUC accuracy by about 3% in scenarios with abrupt changes, and by about 5% in scenarios with gradual changes. Subject-based analysis reveals the difficulties of FR with different poses, affecting more significantly the lamb- and goat-like individuals. Compared to reference spatio-temporal fusion approaches, the proposed accumulation scheme produces the highest discrimination. In Chapter 4, adaptive skew-sensitive ensembles are proposed to combine classifiers trained by selecting data with varying levels of imbalance and complexity, to sustain a high level the performance for video-to-video FR. During operations, the level of imbalance is periodically estimated from the input trajectories using the HDx quantification method, and pre-computed histogram representations of imbalanced data distributions. Ensemble scores are accumulated of trajectories for robust skew-sensitive spatio-temporal recognition. Results on synthetic data show that adapting the fusion function with the proposed approach can significantly improve performance. Results on real data show that the proposed method can outperform reference techniques in imbalanced video surveillance environments
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