ShakeMe: Key Generation From Shared Motion

Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy ($76$%) and key strength (entropy approximately $15$ bits).Comment: The paper is accepted to the 13th IEEE International Conference on Pervasive Intelligence and Computing (PIComp-2015

d-Path Laplacians and Quantum Transport on Graphs

We generalize the Schrödinger equation on graphs to include long-range interactions (LRI) by means of the Mellin-transformed d-path Laplacian operators. We find analytical expressions for the transition and return probabilities of a quantum particle at the nodes of a ring graph. We show that the average return probability in ring graphs decays as a power law with time when LRI is present. In contrast, we prove analytically that the transition and return probabilities on a complete and start graphs oscillate around a constant value. This allowed us to infer that in a barbell graph-a graph consisting of two cliques separated by a path-the quantum particle get trapped and oscillates across the nodes of the path without visiting the nodes of the cliques. We then compare the use of the Mellin-transformed d-path Laplacian operators versus the use of fractional powers of the combinatorial Laplacian to account for LRI. Apart from some important differences observed at the limit of the strongest LRI, the d-path Laplacian operators produces the emergence of new phenomena related to the location of the wave packet in graphs with barriers, which are not observed neither for the Schrödinger equation without LRI nor for the one using fractional powers of the Laplacian

Data Augmentation for Graph Neural Networks

Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data augmentation for graphs. This is largely due to the complex, non-Euclidean structure of graphs, which limits possible manipulation operations. Augmentation operations commonly used in vision and language have no analogs for graphs. Our work studies graph data augmentation for graph neural networks (GNNs) in the context of improving semi-supervised node-classification. We discuss practical and theoretical motivations, considerations and strategies for graph data augmentation. Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction. Extensive experiments on multiple benchmarks show that augmentation via GAug improves performance across GNN architectures and datasets.Comment: AAAI 2021. This complete version contains the Appendi

Addressing class imbalance in deep learning for acoustic target classification

Acoustic surveys provide important data for fisheries management. During the surveys, ship-mounted echo sounders send acoustic signals into the water and measure the strength of the reflection, so-called backscatter. Acoustic target classification (ATC) aims to identify backscatter signals by categorizing them into specific groups, e.g. sandeel, mackerel, and background (as bottom and plankton). Convolutional neural networks typically perform well for ATC but fail in cases where the background class is similar to the foreground class. In this study, we discuss how to address the challenge of class imbalance in the sampling of training and validation data for deep convolutional neural networks. The proposed strategy seeks to equally sample areas containing all different classes while prioritizing background data that have similar characteristics to the foreground class. We investigate the performance of the proposed sampling methodology for ATC using a previously published deep convolutional neural network architecture on sandeel data. Our results demonstrate that utilizing this approach enables accurate target classification even when dealing with imbalanced data. This is particularly relevant for pixel-wise semantic segmentation tasks conducted on extensive datasets. The proposed methodology utilizes state-of-the-art deep learning techniques and ensures a systematic approach to data balancing, avoiding ad hoc methods.Addressing class imbalance in deep learning for acoustic target classificationpublishedVersio

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Flow time series clustering for demand pattern recognition in drinking water distribution systems: New insights about the most adequate methods

This study presents a proposal of clustering methodologies for demand pattern recognition using network flow data collected from a large set of drinking water distribution networks in Portugal. Most of the existing studies about clustering in flow time series rely on hierarchical or k-Means clustering algorithms with inelastic measures distances. This study explores alternative clustering algorithms, distance measures, comparison time windows, internal index metrics and clustering prototypes. The performance of the alternative clustering methodology was assessed in terms of multiple internal index metrics and the characterization of the cluster centroids. The methods with the best performance were Partition Algorithm with DTW distance, PAM prototype with 15 minutes time window and the Partition Algorithm with GAK distance, PAM prototype and 15 minutes time window because they allow a clear partition of flow time series in three clusters. The first method identifies a night consumption pattern, a typical weekend pattern and a typical working day pattern, whereas the second one identifies a pattern with small variability between night and daily consumption. To improve knowledge extraction, in terms of typical and anomalous existing patterns, additional clustering operations were performed with the flow data set that belongs to the cluster with small variability between night and daily consumption. New clusters were identified and characterized regarding weekday, geographical location, and dry months and wet months, showing that patterns associated with garden irrigation are independent of the period of the day and season of the year, which indicates an inefficient water use.Este estudo apresenta uma proposta de metodologias de clustering para reconhecimento de padrões de consumo usando um conjunto de dados de caudal coletados em redes de distribuição de água em Portugal. A maioria dos estudos existentes sobre clustering em séries temporais de caudal baseia-se em algoritmos de clustering hierárquicos ou de k-Means com medidas de distâncias inelásticas. Este estudo explora alternativas de algoritmos de clustering, medidas de distância, janelas temporais de comparação, medidas de índice interno e protótipos de clustering. O desempenho das metodologias de clustering foi avaliado em termos de medidas de índice interno e também através da caracterização dos centroides dos clusters. As metodologias com melhor desempenho foram o Algoritmo de Partição com distância DTW, protótipo PAM e janela de temporal de 15 minutos e o Algoritmo de Partição com distância GAK, protótipo PAM e janela de temporal de 15 minutos, pois permitiram a formação três clusters. O primeiro método identifica um padrão de consumo noturno, um padrão típico de fim-de-semana e um padrão típico de dia útil, enquanto o segundo método destaca-se por apresentar um padrão com pequena variabilidade entre o consumo noturno e diurno. Para melhorar a extração de conhecimento, operações adicionais de clustering foram realizadas ao conjunto de dados que pertence ao cluster com pequena variabilidade entre consumo noturno e diurno. Novos clusters foram identificados e caracterizados, mostrando que os padrões associados à irrigação são independentes do período do dia e da época do ano, o que indica um uso ineficiente da água

Interactive Pattern Recognition applied to Natural Language Processing

This thesis is about Pattern Recognition. In the last decades, huge efforts have been made to develop automatic systems able to rival human capabilities in this field. Although these systems achieve high productivity rates, they are not precise enough in most situations. Humans, on the contrary, are very accurate but comparatively quite slower. This poses an interesting question: the possibility of benefiting from both worlds by constructing cooperative systems. This thesis presents diverse contributions to this kind of collaborative approach. The point is to improve the Pattern Recognition systems by properly introducing a human operator into the system. We call this Interactive Pattern Recognition (IPR). Firstly, a general proposal for IPR will be stated. The aim is to develop a framework to easily derive new applications in this area. Some interesting IPR issues are also introduced. Multi-modality or adaptive learning are examples of extensions that can naturally fit into IPR. In the second place, we will focus on a specific application. A novel method to obtain high quality speech transcriptions (CAST, Computer Assisted Speech Transcription). We will start by proposing a CAST formalization and, next, we will cope with different implementation alternatives. Practical issues, as the system response time, will be also taken into account, in order to allow for a practical implementation of CAST. Word graphs and probabilistic error correcting parsing are tools that will be used to reach an alternative formulation that allows for the use of CAST in a real scenario. Afterwards, a special application within the general IPR framework will be discussed. This is intended to test the IPR capabilities in an extreme environment, where no input pattern is available and the system only has access to the user actions to produce a hypothesis. Specifically, we will focus here on providing assistance in the problem of text generation.Rodríguez Ruiz, L. (2010). Interactive Pattern Recognition applied to Natural Language Processing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8479Palanci