Env2Vec: accelerating VNF testing with deep learning

The adoption of fast-paced practices for developing virtual network functions (VNFs) allows for continuous software delivery and creates a market advantage for network operators. This adoption, however, is problematic for testing engineers that need to assure, in shorter development cycles, certain quality of highly-configurable product releases running on heterogeneous clouds. Machine learning (ML) can accelerate testing workflows by detecting performance issues in new software builds. However, the overhead of maintaining several models for all combinations of build types, network configurations, and other stack parameters, can quickly become prohibitive and make the application of ML infeasible. We propose Env2Vec, a deep learning architecture that combines contextual features with historical resource usage, and characterizes the various stack parameters that influence the test execution within an embedding space, which allows it to generalize model predictions to previously unseen environments. We integrate a single ML model in the testing workflow to automatically debug errors and pinpoint performance bottlenecks. Results obtained with real testing data show an accuracy between 86.2%-100%, while reducing the false alarm rate by 20.9%-38.1% when reporting performance issues compared to state-of-the-art approaches

Discovering phase and causal dependencies on manufacturing processes

Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.

Tracking beam and location in wireless networks

To meet the stringent requirements in manufacturing, the fifth generation (5G) is integrated with the production process since it supports massive machine type communication (mMTC), ultra-reliable low-latency communications (URLLC), and enhanced mobile broadband (eMBB). Multi-antenna millimeter-wave (mmWave) systems in 5G, promising for higher throughput, struggle with blockage issues in narrow beams, affecting service continuity for URLLC. To mitigate this, we introduce a dual-path beam tracking framework, employing a Recurrent Neural Network-based Constrained Deep Reinforcement Learning algorithm, which efficiently allocates time and frequency resources for beam sweeping, tracking, and data transmission while maintaining URLLC service interruption probability constraints. Next, we further explore beam prediction in multi-antenna systems for mission-critical applications in IIoT using millimeter-wave bands. Continuous service requires periodic beam sweeping by the base station, which leads to resource inefficiency and potential beam misalignment. To address these issues, we propose a beam prediction architecture attention mechanism, with which the next beam direction and signal-to-noise ratio (SNR) are predicted based on different observations of channel state information. Finally, this thesis introduces a novel generative fusion framework for indoor localization, which is crucial for the growing need for precise positioning in IIoT. Utilizing generative neural networks, our approach effectively combines different measurements, making it ideal for new applications. The framework separates round-trip time (RTT) and inertial measurement unit (IMU) measurements into individual generative models. This allows for creating multiple potential positions while maintaining diversity in location predictions. Additionally, we incorporate an attention-based fusion model to merge positions generated from various measurements efficiently, enhancing overall localization precision

Scheduler Designs in Wireless Networks

Wireless networks have undergone significant development in recent years, driven by the increasing demand for wireless connectivity and data services. Radio resource schedulers are developed to assign network users available resources, such as frequency and time, based on network conditions to handle the growing user demands, providing transmission opportunities for each user. Well-designed schedulers optimise wireless resource allocation to ensure that all users receive a fair and high quality of service (QoS) and that the network operates at its maximum performance. However, as new types of wireless network services emerge, the existing schedulers can no longer satisfy their QoS requirements and maximise the network performance objective. Thus, new schedulers are urgently needed in wireless networks. In this thesis, we study scheduler designs in cellular and Wi-Fi networks. We discuss the limitations of the existing scheduler design methods regarding flexibility, convergence rate and network-wise coordination and propose new methods to address these limitations. Specifically, we first develop a deep reinforcement learning algorithm to flexibly design wireless schedulers. We then study the acceleration of the scheduler design's convergence using statistical channel state information. We finally propose a graph representing learning method to enable the network-wise coordinated design of schedulers across multiple base stations. Practical implementations of proposed schedulers are also investigated in this thesis

Statistical viewpoints on network model, PDE Identification, low-rank matrix estimation and deep learning

The phenomenal advancements in modern computational infrastructure enable the massive amounts of data acquisition in high-dimensional feature space possible. To put it more specific, the largest datasets available in the industry which often involve up to billions of samples and millions of features. The nature of datasets arising in modern science and engineering are sometimes even larger, often with the dimension of the same order as, or possibly even larger than, the sample size. The cornerstone of modern statistics and machine learning has been a precise characterization of how well we can estimate the objects of interests under these huge high-dimensional datasets. While it remains impossible to consistently estimate in such a high-dimensional regime in general, a large body of research has investigated various structural assumptions under which statistical recovery is possible even in these seemingly ill-posed scenarios. Examples include a large line of works on sparsity, low-rank assumptions and more abstract generalizations of these. These structural assumptions on signals are often realized through specially designed norms; i.e., for inducing sparsity of either vector or matrix, entry-wise L1-norm is used; for inducing low-rank matrix, nuclear norm is used. Not only in parametric, but in non-parametric models, high-dimensional dataset is common in real world applications. A deep neural network, one of the most successful models in modern machine learning in various tasks, is a primary example of non-parametric model for function estimations. Tasks such as image classification or speech recognition often require a dataset in high-dimensional space. For the accurate function estimation avoiding the commonly known curse of dimensionality phenomena, some special structural assumptions on regression functions are imposed. Under some specific structural assumptions imposed on problems, the main emphasis in this thesis proposal is on exploring how various regularizing penalties can be utilized for estimating parameters and functions in parametric and non-parametric statistical problems. Specifically, our main focus will be the problems in network science, PDE identification, and neural network.Ph.D

Statistical and deep learning methods for geoscience problems

Machine learning is the new frontier for technology development in geosciences and has developed extremely fast in the past decade. With the increased compute power provided by distributed computing and Graphics Processing Units (GPUs) and their exploitation provided by machine learning (ML) frameworks such as Keras, Pytorch, and Tensorflow, ML algorithms can now solve complex scientific problems. Although powerful, ML algorithms need to be applied to suitable problems conditioned for optimal results. For this reason ML algorithms require not only a deep understanding of the problem but also of the algorithm’s ability. In this dissertation, I show that Simple statistical techniques can often outperform ML-based models if applied correctly. In this dissertation, I show the success of deep learning in addressing two difficult problems. In the first application I use deep learning to auto-detect the leaks in a carbon capture project using pressure field data acquired from the DOE Cranfield site in Mississippi. I use the history of pressure, rates, and cumulative injection volumes to detect leaks as pressure anomaly. I use a different deep learning workflow to forecast high-energy electrons in Earth’s outer radiation belt using in situ measurements of different space weather parameters such as solar wind density and pressure. I focus on predicting electron fluxes of 2 MeV and higher energy and introduce the ensemble of deep learning models to further improve the results as compared to using a single deep learning architecture. I also show an example where a carefully constructed statistical approach, guided by the human interpreter, outperforms deep learning algorithms implemented by others. Here, the goal is to correlate multiple well logs across a survey area in order to map not only the thickness, but also to characterize the behavior of stacked gamma ray parasequence sets. Using tools including maximum likelihood estimation (MLE) and dynamic time warping (DTW) provides a means of generating quantitative maps of upward fining and upward coarsening across the oil field. The ultimate goal is to link such extensive well control with the spectral attribute signature of 3D seismic data volumes to provide a detailed maps of not only the depositional history, but also insight into lateral and vertical variation of mineralogy important to the effective completion of shale resource plays

Recurrent neural networks for structured data

A key challenge in machine learning is to explore and incorporate the complex nature of real-world data structures into the training models. The contributions of this thesis are novel RNN architectures for different types of structured data

Bayesian unit-level modeling of non-Gaussian survey data under informative sampling with application to small area estimation

Unit-level models are an alternative to the traditional area-level models used in small area estimation, characterized by the direct modeling of survey responses rather than aggregated direct estimates. These unit-level approaches offer many benefits over area-level modeling, such as potential for more precise estimates, construction of estimates at multiple spatial resolutions through a single model, and elimination of the need for benchmarking techniques, among others. Furthermore, many recent surveys collect interesting and complex data types at the unit level, such as text and functional data. Yet, unit-level models present two primary challenges that have limited their widespread use. First, when surveys have been sampled in an informative manner, it is critical to account for the design in some fashion when utilizing a model at the unit level. Second, unit-level datasets are inherently much larger than area-level ones, with responses that are typically non-Gaussian, leading to computational constraints. After providing a comprehensive review on the problem of informative sampling, this dissertation provides four computationally efficient methodologies for non-Gaussian survey data under informative sampling. This methodology relies on the Bayesian pseudo-likelihood to adjust for the survey design, as well as Bayesian hierarchical modeling to characterize various dependence structures. First, a count data model is developed and applied to small area estimation of housing vacancies. Second, modeling approaches for both binary and categorical data are developed, along with a variational Bayes procedure that may be used in extremely high-dimensional settings. This approach is applied to the problem of small area estimation of health insurance rates using the American Community Survey. Third, a nonlinear model is developed to allow for complex covariates, with application to text data contained within the American National Election Studies. Finally, a model is developed for functional covariates and applied to physical activity monitor data from the National Health and Nutrition Examination Survey.Includes bibliographical references

Adaptive Automated Machine Learning

The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances

Time series data mining: preprocessing, analysis, segmentation and prediction. Applications

Currently, the amount of data which is produced for any information system is increasing exponentially. This motivates the development of automatic techniques to process and mine these data correctly. Specifically, in this Thesis, we tackled these problems for time series data, that is, temporal data which is collected chronologically. This kind of data can be found in many fields of science, such as palaeoclimatology, hydrology, financial problems, etc. TSDM consists of several tasks which try to achieve different objectives, such as, classification, segmentation, clustering, prediction, analysis, etc. However, in this Thesis, we focus on time series preprocessing, segmentation and prediction. Time series preprocessing is a prerequisite for other posterior tasks: for example, the reconstruction of missing values in incomplete parts of time series can be essential for clustering them. In this Thesis, we tackled the problem of massive missing data reconstruction in SWH time series from the Gulf of Alaska. It is very common that buoys stop working for different periods, what it is usually related to malfunctioning or bad weather conditions. The relation of the time series of each buoy is analysed and exploited to reconstruct the whole missing time series. In this context, EANNs with PUs are trained, showing that the resulting models are simple and able to recover these values with high precision. In the case of time series segmentation, the procedure consists in dividing the time series into different subsequences to achieve different purposes. This segmentation can be done trying to find useful patterns in the time series. In this Thesis, we have developed novel bioinspired algorithms in this context. For instance, for paleoclimate data, an initial genetic algorithm was proposed to discover early warning signals of TPs, whose detection was supported by expert opinions. However, given that the expert had to individually evaluate every solution given by the algorithm, the evaluation of the results was very tedious. This led to an improvement in the body of the GA to evaluate the procedure automatically. For significant wave height time series, the objective was the detection of groups which contains extreme waves, i.e. those which are relatively large with respect other waves close in time. The main motivation is to design alert systems. This was done using an HA, where an LS process was included by using a likelihood-based segmentation, assuming that the points follow a beta distribution. Finally, the analysis of similarities in different periods of European stock markets was also tackled with the aim of evaluating the influence of different markets in Europe. When segmenting time series with the aim of reducing the number of points, different techniques have been proposed. However, it is an open challenge given the difficulty to operate with large amounts of data in different applications. In this work, we propose a novel statistically-driven CRO algorithm (SCRO), which automatically adapts its parameters during the evolution, taking into account the statistical distribution of the population fitness. This algorithm improves the state-of-the-art with respect to accuracy and robustness. Also, this problem has been tackled using an improvement of the BBPSO algorithm, which includes a dynamical update of the cognitive and social components in the evolution, combined with mathematical tricks to obtain the fitness of the solutions, which significantly reduces the computational cost of previously proposed coral reef methods. Also, the optimisation of both objectives (clustering quality and approximation quality), which are in conflict, could be an interesting open challenge, which will be tackled in this Thesis. For that, an MOEA for time series segmentation is developed, improving the clustering quality of the solutions and their approximation. The prediction in time series is the estimation of future values by observing and studying the previous ones. In this context, we solve this task by applying prediction over high-order representations of the elements of the time series, i.e. the segments obtained by time series segmentation. This is applied to two challenging problems, i.e. the prediction of extreme wave height and fog prediction. On the one hand, the number of extreme values in SWH time series is less with respect to the number of standard values. In this way, the prediction of these values cannot be done using standard algorithms without taking into account the imbalanced ratio of the dataset. For that, an algorithm that automatically finds the set of segments and then applies EANNs is developed, showing the high ability of the algorithm to detect and predict these special events. On the other hand, fog prediction is affected by the same problem, that is, the number of fog events is much lower tan that of non-fog events, requiring a special treatment too. A preprocessing of different data coming from sensors situated in different parts of the Valladolid airport are used for making a simple ANN model, which is physically corroborated and discussed. The last challenge which opens new horizons is the estimation of the statistical distribution of time series to guide different methodologies. For this, the estimation of a mixed distribution for SWH time series is then used for fixing the threshold of POT approaches. Also, the determination of the fittest distribution for the time series is used for discretising it and making a prediction which treats the problem as ordinal classification. The work developed in this Thesis is supported by twelve papers in international journals, seven papers in international conferences, and four papers in national conferences