The increasing availability of traffic data from sensor networks has created new opportunities for understanding vehicular dynamics and identifying anomalies. In this study, we employ clustering techniques to analyse traffic flow data with the dual objective of uncovering meaningful traffic patterns and detecting anomalies, including sensor failures and irregular congestion events. We explore multiple clustering approaches, i.e. partitioning and hierarchical methods, combined with various time-series representations and similarity measures. Our methodology is applied to real-world data from highway sensors, enabling us to assess the impact of different clustering frameworks on traffic pattern recognition. We also introduce a clustering-driven anomaly detection methodology that identifies deviations from expected traffic behaviour based on distance-based anomaly scores. Results indicate that hierarchical clustering with symbolic representations provides robust segmentation of traffic patterns, while partitioning methods such as k -means and fuzzy c-means yield meaningful results when paired with Dynamic Time Warping. The proposed anomaly detection strategy successfully identifies sensor malfunctions and abnormal traffic conditions with minimal false positives, demonstrating its practical utility for real-time monitoring. Real-world vehicular traffic data are provided by Autostrade Alto Adriatico S.p.A. Keywords.

Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification. In this paper, we examine the adequacy of the leading approach to generating adversarial samples---the gradient descent approach. In particular (1) we perform extensive experiments on three datasets, MNIST, USPS and Spambase, in order to analyse the effectiveness of the gradient-descent method against non-linear support vector machines, and conclude that carefully reduced kernel smoothness can significantly increase robustness to the attack; (2) we demonstrate that separated inter-class support vectors lead to more secure models, and propose a quantity similar to margin that can efficiently predict potential susceptibility to gradient-descent attacks, before the attack is launched; and (3) we design a new adversarial sample construction algorithm based on optimising the multiplicative ratio of class decision functions.

Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we prove the efficacy of temporal coherence for semi-supervised incremental tuning. We show that a deep architecture, just mildly trained in a supervised manner, can progressively improve its classification accuracy, if exposed to video sequences of unlabeled data. The extent to which, in some cases, a semi-supervised tuning allows to improve classification accuracy (approaching the supervised one) is somewhat surprising. A number of control experiments pointed out the fundamental role of temporal coherence.

But this premature paving is not very useful if the searchtree is explored depth-first (DFS) or breadth-first (BFS): DFS When applied to numerical CSPs, the branch and quickly converges to ɛ-boxes that are too close to one another prune algorithm (BPA) computes a sharp covering to be representative of the solution set (see the left part of of the solution set. The BPA is therefore impractical Figure 1); BFS computes a homogeneous paving but finds no when the solution set is large, typically when ɛ-box at all if stopped too early (see the center graphic of Figure it has a dimension larger than four or five which is 1; note that such a sharp paving cannot be computed for often met in underconstrained problems. The purpose larger solution sets, making BFS useless in such cases). of this paper is to present a new search tree The search strategy used in an anytime BPA should quickly exploration strategy for BPA that hybridizes depthfirst find ɛ-boxes that are representative of the solution set: ɛ- and breadth-first searches. This search strategy boxes should be discovered uniformly on a continuous connected allows the BPA discovering potential solutions component in the solution set, while every connected in different areas of the search space in early stages components should be reached by some ɛ-boxes in early of the exploration, hence allowing an anytime usage stages of the search. Two such strategies are introduced in of the BPA. The merits of the proposed search the present paper. The most distant-first strategy (MDFS) strategy are experimentally evaluated.