Connected and autonomous vehicles can be highly affected by sudden changes in the provided mobile network coverage. Driving safety increases with a high Signal-to-Noise Ratio (SNR) that enhances the Quality of Service (QoS). Therefore, monitoring the QoS for geolocated vehicular mobility from mobile phones is required to detect position and timestamp of QoS changes, and alert the operators to improve the QoS. However, mobility data sharing raises privacy concerns, which, in turn, limits accessibility to the data. This article proposes an adapted Generative Adversarial Network (GAN) model combined with OverPy tool to generate SNR data associated with real vehicle locations, to enrich the dataset while guaranteeing the privacy of users. The dataset used in this paper to train the model is real opt-in data collected from the Signal Map mobile application from volunteers in the region of Nice (France). The training process of the model is iterative and involves updating the parameters of the generator and discriminator in a way that improves the overall performance of the model. The preliminary evaluation results show that the model generates high-quality traffic mobility data correlated to SNR parameter. Specifically, the synthetic generated data have statistical properties similar to real data reflecting real mobility statistics, and therefore protecting the individual’s privacy.

Annotation of biomedical databases by clinicians is a very difficult, sometimes imprecise, and time consuming task. An alternative is to ask the clinician expert for the annotations they are the most confident in, which results in a semi-supervised classification problem. In this paper, we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We train the Semi-Supervised AutoEncoder (SSAE) on labelled data using a double descent algorithm. Then, we classify unlabelled samples using the learned network thanks to a softmax classifier applied to the latent space which provides a classification confidence score for each class. Experiments show that the SSAE outperforms Label Propagation and Spreading and the Fully Connected Neural Network both on a synthetic dataset and on four real-world biological datasets.

Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy computational power.In this paper, we address the problem of lossy image compression using a CAE with a small memory footprint and low computational power usage.In this work, we propose a constrained approach and a new structured sparse learning method. We design an algorithm and test it on three constraints: the classical ℓ 1 constraint, the ℓ 1,∞ and the new ℓ 1,1 constraint. Experimental results show that the ℓ 1,1 constraint provides the best structured sparsity, resulting in a high reduction of memory ( 82 %) and computational cost reduction (25 %), with similar rate-distortion performance as with dense networks.

This paper deals with supervised discriminative and generative modeling. Classical methods are based on variational autoencoders or supervised variational autoencoders encourage the latent space to fit a prior distribution, like a Gaussian. However, they tend to make stronger assumptions on the data, often leading to higher asymptotic bias when the model is wrong. In this paper, we relax the parametric distribution assumption in the latent space and we propose to learn a non-parametric data distribution of the clusters in the latent space. The network encourages the latent space to fit a distribution learned with the labels instead of the parametric prior assumptions. We have built a network architecture that incorporates the labels into an autoencoder latent space to create discriminative and generative models. Thus we define a global criterion combining classification and reconstruction loss. In addition, we have proposed a 1,1 regularization which advantages are a faster convergence of the algorithm and an improvement of the clustering. Finally we propose a tailored algorithm to minimize the criterion with constraint. We demonstrate the effectiveness of our method on two popular image datasets (MNIST and Fashion MNIST) and two biological datasets.

In this paper we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We create a network architecture that encodes labels into the latent space of an autoencoder, and define a global criterion combining classification and reconstruction losses. We train the Semi-Supervised AutoEncoder (SSAE) on labelled data using a double descent algorithm. Then, we classify unlabelled samples using the learned network thanks to a softmax classifier applied to the latent space which provides a classification confidence score for each class. We implemented our SSAE method using the PyTorch framework for the model, optimizer, schedulers, and loss functions. We compare our semi-supervised autoencoder method (SSAE) with classical semi-supervised methods such as Label Propagation and Label Spreading, and with a Fully Connected Neural Network (FCNN). Experiments show that the SSAE outperforms Label Propagation and Spreading and the Fully Connected Neural Network both on a synthetic dataset and on two real-world biological datasets.

•We study the performance of three family of methods, conventional, machine learning and deep learning ones, for anomaly detection in multivariate time series•No family of methods shows significant outperformance over the five datasets considered for evaluation•Deep learning methods seem to perform better on time series containing contextual anomalies•Conventional techniques outperform the other methods when the available training data is small

Deep neural networks (DNNs) are attractive alternatives to more traditional methods for time series anomaly detection thanks to their capacity to automatically learn discriminative features. Despite their demonstrated power, different works have suggested that introducing engineered features in the time series can further improve the performance. In this work, we present a feature engineering strategy to transform univariate time series into a multivariate one by introducing non-local information in the augmented data. In this way, we aim to address an intrinsic limitation of the features learned by DNNs, which is they rely on local information only. We study the performance of our combination compared to each individual method and show that our method achieves better performance without increasing computational time on a set of 250 univariate time series proposed by the University of California, Riverside at the 2021 KDDCup competition.

Deep neural networks (DNN) have been applied recently to different domains and perform better than classical state-of-the-art methods. However the high level of performances of DNNs is most often obtained with networks containing millions of parameters and for which training requires substantial computational power. To deal with this computational issue proximal regularization methods have been proposed in the literature but they are time consuming. In this paper, we propose instead a constrained approach. We provide the general framework for this new projection gradient method. Our algorithm iterates a gradient step and a projection on convex constraints. We studied algorithms for different constraints: the classical ℓ 1 unstructured constraint and structured constraints such as the £2,1 constraint (Group LASSO). We propose a new ℓ 1,1 structured constraint for which we provide a new projection algorithm. Finally, we used the recent “Lottery optimizer” replacing the threshold by our ℓ 1,1 projection. We demonstrate the effectiveness of this method with three popular datasets (MNIST, Fashion MNIST and CIFAR). Experiments with these datasets show that our projection method using this new ℓ 1,1 structured constraint provides the best decrease in memory and computational power.

This paper concerns the supervised generative non parametric autoencoder. Classical methods are based on variational non supervised autoencoders (VAE). Variational autoencoders encourage the latent space to fit a prior distribution, like a Gaussian. However, they tend to draw stronger assumptions for the data, often leading to higher asymptotic bias when the model is wrong.In this paper, we relax the parametric distribution assumption in the latent space and we propose to learn a non-parametric data distribution of the clusters in the latent space. The network encourages the latent space to fit a distribution learned with the labels instead of the parametric prior assumptions. We have built a network architecture that uses the labels to compute the latent space. Thus we define a global criterion combining classification and reconstruction loss. In addition, we have proposed a ℓ1,1 regularization which has the advantage of sparsifying the network and improving the clustering. Finally we propose a tailored algorithm to minimize the criterion with constraint. We demonstrate the effectiveness of our method using the popular image dataset MNIST and two biological datasets.

The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.

ObjectiveFlares in rheumatoid arthritis (RA) and axial spondyloarthritis (SpA) may influence physical activity. The aim of this study was to assess longitudinally the association between patient‐reported flares and activity‐tracker–provided steps per minute, using machine learning.MethodsThis prospective observational study (ActConnect) included patients with definite RA or axial SpA. For a 3‐month time period, physical activity was assessed continuously by number of steps/minute, using a consumer grade activity tracker, and flares were self‐assessed weekly. Machine‐learning techniques were applied to the data set. After intrapatient normalization of the physical activity data, multiclass Bayesian methods were used to calculate sensitivities, specificities, and predictive values of the machine‐generated models of physical activity in order to predict patient‐reported flares.ResultsOverall, 155 patients (1,339 weekly flare assessments and 224,952 hours of physical activity assessments) were analyzed. The mean ± SD age for patients with RA (n = 82) was 48.9 ± 12.6 years and was 41.2 ± 10.3 years for those with axial SpA (n = 73). The mean ± SD disease duration was 10.5 ± 8.8 years for patients with RA and 10.8 ± 9.1 years for those with axial SpA. Fourteen patients with RA (17.1%) and 41 patients with axial SpA (56.2%) were male. Disease was well‐controlled (Disease Activity Score in 28 joints mean ± SD 2.2 ± 1.2; Bath Ankylosing Spondylitis Disease Activity Index score mean ± SD 3.1 ± 2.0), but flares were frequent (22.7% of all weekly assessments). The model generated by machine learning performed well against patient‐reported flares (mean sensitivity 96% [95% confidence interval (95% CI) 94–97%], mean specificity 97% [95% CI 96–97%], mean positive predictive value 91% [95% CI 88–96%], and negative predictive value 99% [95% CI 98–100%]). Sensitivity analyses were confirmatory.ConclusionAlthough these pilot findings will have to be confirmed, the correct detection of flares by machine‐learning processing of activity tracker data provides a framework for future studies of remote‐control monitoring of disease activity, with great precision and minimal patient burden.

In this paper we propose a methodological framework for modeling Quality of Experience (QoE) for media services in a generic manner. We consider QoE as a multi-dimensional concept dependent on several factors related to the service itself, its resource requirements, its users, and its context of use. As a first step, we group these factors into four factor spaces and propose a mapping of them into a QoE space. We then focus on the application of this mapping in the context of networked media services by adhering to a layered approach for modeling QoE dimensions in relation to the aforementioned QoE-affecting factors. Such an approach facilitates understanding a service’s QoE as acomposite function of the performance of the underlying network, and the actual service implementation, under constraints imposed by some of the QoEaffecting factors. In order to illustrate the applicability of the proposed methodology, we present a case study for mobile video.

Chapter 11 Quality of Experience Estimators in Networks Frédéric Guyard, Frédéric GuyardSearch for more papers by this authorMartin Varela, Martin VarelaSearch for more papers by this authorLea Skorin-Kapov, Lea Skorin-KapovSearch for more papers by this authorAntonio Cuadra-Sanchez, Antonio Cuadra-SanchezSearch for more papers by this authorPedro J. Sevilla-Ramos, Pedro J. Sevilla-RamosSearch for more papers by this author Frédéric Guyard, Frédéric GuyardSearch for more papers by this authorMartin Varela, Martin VarelaSearch for more papers by this authorLea Skorin-Kapov, Lea Skorin-KapovSearch for more papers by this authorAntonio Cuadra-Sanchez, Antonio Cuadra-SanchezSearch for more papers by this authorPedro J. Sevilla-Ramos, Pedro J. Sevilla-RamosSearch for more papers by this author Book Editor(s):Abdelhamid Mellouk, Abdelhamid MelloukSearch for more papers by this authorAntonio Cuadra-Sanchez, Antonio Cuadra-SanchezSearch for more papers by this author First published: 17 June 2014 https://doi.org/10.1002/9781118984352.ch11Citations: 2 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary The Celtic Plus Quality of Experience Estimators in Networks (QuEEN) project was conceived to create a suitable conceptual framework for Quality of Experience (QoE). This chapter presents some of the conceptual results produced so far within QuEEN (and other related activities, such as COST Action IC1003 Qualinet). It provides an overview of the QuEEN project's approach for estimating QoE for generic services, and exploiting these estimates in various ways. The chapter proposes a conceptual framework for understanding QoE, for different services and in different timescales, as well as a model to make this conceptual framework operational. The QuEEN agent provides a flexible distributed implementation of the QuEEN layered model, allowing us to estimate the quality of different services in different locations, and to feed those estimates to QoE-aware applications, such as monitoring, network management, or service level management, to name a few. Citing Literature Quality of Experience Engineering for Customer Added Value Services: From Evaluation to Monitoring RelatedInformation

Brief episodes of network faults and performance issues adversely affect the user Quality of Experience (QoE). Besides damaging the current opinions of users, these events may also shape user's future perception of the service. Therefore, it is important to quantify the impact of such events on QoE over time. In this paper, we present our findings on the temporal aspects of user feedback to disturbances on networks. These findings are based on subjective user tests performed in the context of web browsing on an e-commerce website. The results of this study suggest that the QoE drops significantly every time the page load time grows. The after-effects of network disturbances on user QoE remain visible even when the network problems are over, i.e., users do not immediately return to the same level of opinion scores as compared to the corresponding pre-disturbance phase. They tend to remember their recent experiences. Our results also show that there are four segments of users that exist with regards to their feedback to page load times. Network operators may customize their services according to each segment of users to raise the overall QoE. Finally, we show that the exponential relationship provides best fits of QoE and page load times for all segments of users.

Anomaly detection and characterization is a main topic for network managers. Although quality-of-service (QoS) indicators can help to infer problem occurrence, they do not provide immediate insight on the user’s perceived quality. Evolved service-level agreements (SLA) will likely be established in terms of quality of experience (QoE) indicators. QoS metrics composing the QoE indicators need to be monitored on a real-time basis by the SLA management tools in order to detect anomalies driving to contract violations. Monitoring SLA contracts may involve the surveillance of individual application sessions for several users. In this work, we address the problem of anomaly detection with impact on a relatively large number of users, either on one or on several types of applications simultaneously. We propose a method to characterize the state of the network, representing QoE indicators as time series and reducing the dimension of the data set. The singular spectrum analysis (SSA) method, using a combination of geometric and statistical methods, is proposed as an analysis tool in order to detect anomalies on QoE indicator evolution.

We are concerned with the integer multiommodity flows which are feasible on a given a network topology. We characterize the demand-capacity vectors for which there exists an integral routing. We show the Hilbert basis role associated to a matrix, and characterize right handside corresponding to non-decomposable integral routing sets.