Tracking Using Continuous Shape Model Learning in the Presence of Occlusion

This paper presents a Bayesian framework for a new model-based learning method, which is able to track nonrigid objects in the presence of occlusions, based on a dynamic shape description in terms of a set of corners. Tracking is done by estimating the new position of the target in a multimodal voting space. However, occlusion events and clutter may affect the model learning, leading to a distraction in the estimation of the new position of the target as well as incorrect updating of the shape model. This method takes advantage of automatic decisions regarding how to learn the model in different environments, by estimating the possible presence of distracters and regulating corner updating on the basis of these estimations. Moreover, by introducing the corner feature vector classification, the method is able to continue learning the model dynamically, even in such situations. Experimental results show a successful tracking along with a more precise estimation of shape and motion during occlusion events

Feature Classification for Robust Shape-Based Collaborative Tracking and Model Updating

Abstract A new collaborative tracking approach is introduced which takes advantage of classified features. The core of this tracker is a single tracker that is able to detect occlusions and classify features contributing in localizing the object. Features are classified in four classes: good, suspicious, malicious, and neutral. Good features are estimated to be parts of the object with a high degree of confidence. Suspicious ones have a lower, yet significantly high, degree of confidence to be a part of the object. Malicious features are estimated to be generated by clutter, while neutral features are characterized with not a sufficient level of uncertainty to be assigned to the tracked object. When there is no occlusion, the single tracker acts alone, and the feature classification module helps it to overcome distracters such as still objects or little clutter in the scene. When more than one desired moving objects bounding boxes are close enough, the collaborative tracker is activated and it exploits the advantages of the classified features to localize each object precisely as well as updating the objects shape models more precisely by assigning again the classified features to the objects. The experimental results show successful tracking compared with the collaborative tracker that does not use the classified features. Moreover, more precise updated object shape models will be shown

Advantages of dynamic analysis in HOG-PCA feature space for video moving object classification

Classification of moving objects for video surveillance applications still remains a challenging problem due to the video inherently changing conditions such as lighting or resolution. This paper proposes a new approach for vehicle/pedestrian object classification based on the learning of a static kNN classifier, a dynamic Hidden Markov Model (HMM)-based classifier, and the definition of a fusion rule that combines the two outputs. The main novelty consists in the study of the dynamic aspects of the moving objects by analysing the trajectories of the features followed in the HOG-PCA feature space, instead of the classical trajectory study based on the frame coordinates. The complete hybrid system was tested on the VIRAT database and worked in real time, yielding up to 100% peak accuracy rate in the tested video sequences

Exploring Parallelism to Improve the Performance of FrodoKEM in Hardware

FrodoKEM is a lattice-based key encapsulation mechanism, currently a semi-finalist in NIST’s post-quantum standardisation effort. A condition for these candidates is to use NIST standards for sources of randomness (i.e. seed-expanding), and as such most candidates utilise SHAKE, an XOF defined in the SHA-3 standard. However, for many of the candidates, this module is a significant implementation bottleneck. Trivium is a lightweight, ISO standard stream cipher which performs well in hardware and has been used in previous hardware designs for lattice-based cryptography. This research proposes optimised designs for FrodoKEM, concentrating on high throughput by parallelising the matrix multiplication operations within the cryptographic scheme. This process is eased by the use of Trivium due to its higher throughput and lower area consumption. The parallelisations proposed also complement the addition of first-order masking to the decapsulation module. Overall, we significantly increase the throughput of FrodoKEM; for encapsulation we see a 16 × speed-up, achieving 825 operations per second, and for decapsulation we see a 14 × speed-up, achieving 763 operations per second, compared to the previous state of the art, whilst also maintaining a similar FPGA area footprint of less than 2000 slices.</p

Abnormality detection using graph matching for multi-task dynamics of autonomous systems

Self-learning abilities in autonomous systems are essential to improve their situational awareness and detection of normal/abnormal situations. In this work, we propose a graph matching technique for activity detection in autonomous agents by using the Gromov-Wasserstein framework. A clustering approach is used to discretise continuous agents' states related to a specific task into a set of nodes with similar objectives. Additionally, a probabilistic transition matrix between nodes is used as edges weights to build a graph. In this paper, we extract an abnormal area based on a sub-graph that encodes the differences between coupled of activities. Such sub-graph is obtained by applying a threshold on the optimal transport matrix, which is obtained through the graph matching procedure. The obtained results are evaluated through experiments performed by a robot in a simulated environment and by a real autonomous vehicle moving within a University Campus

Misure ambientali in mare aperto: sviluppo di tecnologie per l'acquisizione e l'analisi di dati meteo-mareografici misurati da una boa oceanografica in Mar Ligure

Obiettivo del presente lavoro è lo sviluppo di un metodo di analisi in grado di stimare i parametri fondamentali del moto ondoso, a partire dai dati acquisiti da tre altimetri acustici installati a bordo di una boa meteo-oceanografica operante in mare aperto. Il metodo di analisi sviluppato compie opportune operazioni di filtraggio sulle sequenze temporali delle misure effettuate dai tre altimetri, quindi, elaborando tali sequenze, fornisce le stime di alcuni parametri caratteristici del moto ondoso (tra cui l’altezza e la direzione di propagazione). I diversi tipi di filtraggio ed il metodo di stima sviluppati si basano sul calcolo di alcuni parametri statistici (tra cui media, mediana e deviazione standard) delle serie temporali di dati acquisiti, sulla conoscenza delle loro densità spettrali di potenza (calcolate mediante FFT), e sul calcolo delle funzioni di crosscorrelazione delle sequenze di dati prese a due a due. Il procedimento di stima realizzato è stato sperimentato su una notevole quantità di dati reali acquisiti in Mar Ligure tramite l’utilizzo della stazione di misura fissa su cui sono montati gli altimetri acustici, ed ha fornito risultati soddisfacenti per quanto riguarda affidabilità e precisione. Nell’ambito della valutazione delle prestazioni del sistema di acquisizione dati e del metodo di stima, i risultati ottenuti sono stati confrontati con misure provenienti da altri sensori a bordo della stazione e con stime analoghe effettuate a partire dai dati acquisiti da un’altra stazione di misura, operante anch’essa in Mar Ligure, ma dotata di strumentazione di altro tipo

Jammer detection in M-QAM-OFDM by learning a dynamic Bayesian model for the cognitive radio

Communication and information field has witnessed recent developments in wireless technologies. Among such emerging technologies, the Internet of Things (IoT) is gaining a lot of popularity and attention in almost every field. IoT devices have to be equipped with cognitive capabilities to enhance spectrum utilization by sensing and learning the surrounding environment. IoT network is susceptible to the various jamming attacks which interrupt users communication. In this paper, two systems (Single and Bank-Parallel) have been proposed to implement a Dynamic Bayesian Network (DBN) Model to detect jammer in Orthogonal Frequency Division Multiplexing (OFDM) sub-carriers modulated with different M-QAM. The comparison of the two systems has been evaluated by simulation results after analyzing the effect of self-organizing map's (SOM) size on the performance of the proposed systems in relation to M-QAM modulation

The QARMAv2 Family of Tweakable Block Ciphers

We introduce the QARMAv2 family of tweakable block ciphers. It is a redesign of QARMA (from FSE 2017) to improve its security bounds and allow for longer tweaks, while keeping similar latency and area. The wider tweak input caters to both specific use cases and the design of modes of operation with higher security bounds. This is achieved through new key and tweak schedules, revised S-Box and linear layer choices, and a more comprehensive security analysis. QARMAv2 offers competitive latency and area in fully unrolled hardware implementations. Some of our results may be of independent interest. These include: new MILP models of certain classes of diffusion matrices; the comparative analysis of a full reflection cipher against an iterative half-cipher; our boomerang attack framework; and an improved approach to doubling the width of a block cipher

Data Under Siege: The Quest for the Optimal Convolutional Autoencoder in Side-Channel Attacks

Encryption is a method to keep our data safe from third parties. However, side-channel information may be leaked during encryption due to physical properties. This information can be used in side-channel attacks to recover critical values such as the secret encryption key. To this end, it is necessary to understand the robustness of implementations to assess the security of data handled by a device. Side-channel attacks are one such method which allow researchers to evaluate the robustness of implementations using appropriate metrics.In the security community, machine learning is playing a prominent role in the study of side-channel attacks. A notable example of this is the use of Convolutional Autoencoders (CAE) as a preprocessing step on the measurements. In this work we study in depth the problem of finding the most suitable architecture of such Convolutional Autoencoders. To this end, Optuna is used to explore the CAE hyperparameter space. This process allows us to identify hyperparameters that outperform state-of-the-art autoencoders, reducing the needed traces for a succesful attack by approximately 37% in the presence of Gaussian noise and reducing the trainable parameters needed to attack desynchronization by a factor of 29. In addition to the promising results, experiments carried out in this paper allow a better understanding of the hyperparameter space in the field of side channel attacks, providing a solid base for future use of CAE in this specific domain

Synthesis and application of isotope-labeled carnosine in LCMS/MS

Carnosine is an endogenous dipeptide, composed of \u3b2-alanine and L-histidine, and is highly concentrated in skeletal muscle and other excitable tissues. Its physiological roles, based on its biochemical properties, include pH-buffering, metal-ion chelation and antioxidant capacity as well as the ability to protect against the formation of advanced glycation and lipoxidation end-products.1 For these reasons, besides its nutritional ergogenic application in the sport community,2 carnosine supplementation offers a therapeutic potential for the treatment of numerous diseases in which ischemic or oxidative stress is involved.1 Quantitation of carnosine in biological matrices appears to be crucial for these applications, and LC-MS procedures with isotope-labeled internal standards are the state-of-the-art approach for this analytical need.3 The use of these standards allows to account for variations during the complex sample preparation process, different matrix effects between patient samples, and variations in instrument performance. Figure 1 In this work, we present a fast and highly efficient synthetic route to obtain a deuterated carnosine analogue (Figure 1) starting from the trideuterated L-histidine (\u3b1-d1, imidazole-2,5-d2). Moreover, the use of Carnosine-d3 in the validation of a multiple reaction monitoring (MRM) LC-MS/MS method for the analytical quantitation of carnosine in a biological matrix will be reported. References 1. Boldyrev, A. A.; Aldini, G.; Derave, W. Physiol. Rev. 2013, 93, 1803\u20131845. 2. Brisola, G.; Zagatto, A. J. Strength Cond. Res. 2019, 33, 253-282. 3. Stokvis, E.; Rosing, H.; L\uf3pez-L\ue1zaro, L.; Schellens, J. H. M.; Beijnen, J. H. Biomed. Chromatogr. 2004, 18, 400-402