Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Real-time multimodal emotion classification system in E-Learning context

Emotions of learners are crucial and important in e-learning as they promote learning. To investigate the effects of emotions on improving and optimizing the outcomes of e-learning, machine learning models have been proposed in the literature. However, proposed models so far are suitable for offline mode, where data for emotion classification is stored and can be accessed boundlessly. In contrast, when data arrives in a stream, the model can see the data once and real-time response is required for real-time emotion classification. Additionally, researchers have identified that single data modality is incapable of capturing the complete insight of the learning experience and emotions. So, multi-modal data streams such as electroencephalogram (EEG), Respiratory Belt (RB), electrodermal activity data (EDA), etc., are utilized to improve the accuracy and provide deeper insights in learners’ emotion and learning experience. In this paper, we propose a Real-time Multimodal Emotion Classification System (ReMECS) based on Feed-Forward Neural Network, trained in an online fashion using the Incremental Stochastic Gradient Descent algorithm. To validate the performance of ReMECS, we have used the popular multimodal benchmark emotion classification dataset called DEAP. The results (accuracy and F1-score) show that the ReMECS can adequately classify emotions in real-time from the multimodal data stream in comparison to the state-of-the-art approaches.Work partially funded by ACCIO under the project TutorIA.Peer ReviewedPostprint (author's final draft

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.Comment: This paper has been accepted for publication in Information Science

Towards Efficient Lifelong Machine Learning in Deep Neural Networks

Humans continually learn and adapt to new knowledge and environments throughout their lifetimes. Rarely does learning new information cause humans to catastrophically forget previous knowledge. While deep neural networks (DNNs) now rival human performance on several supervised machine perception tasks, when updated on changing data distributions, they catastrophically forget previous knowledge. Enabling DNNs to learn new information over time opens the door for new applications such as self-driving cars that adapt to seasonal changes or smartphones that adapt to changing user preferences. In this dissertation, we propose new methods and experimental paradigms for efficiently training continual DNNs without forgetting. We then apply these methods to several visual and multi-modal perception tasks including image classification, visual question answering, analogical reasoning, and attribute and relationship prediction in visual scenes

NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

We introduce the Never Ending VIsual-classification Stream (NEVIS'22), a benchmark consisting of a stream of over 100 visual classification tasks, sorted chronologically and extracted from papers sampled uniformly from computer vision proceedings spanning the last three decades. The resulting stream reflects what the research community thought was meaningful at any point in time. Despite being limited to classification, the resulting stream has a rich diversity of tasks from OCR, to texture analysis, crowd counting, scene recognition, and so forth. The diversity is also reflected in the wide range of dataset sizes, spanning over four orders of magnitude. Overall, NEVIS'22 poses an unprecedented challenge for current sequential learning approaches due to the scale and diversity of tasks, yet with a low entry barrier as it is limited to a single modality and each task is a classical supervised learning problem. Moreover, we provide a reference implementation including strong baselines and a simple evaluation protocol to compare methods in terms of their trade-off between accuracy and compute. We hope that NEVIS'22 can be useful to researchers working on continual learning, meta-learning, AutoML and more generally sequential learning, and help these communities join forces towards more robust and efficient models that efficiently adapt to a never ending stream of data. Implementations have been made available at https://github.com/deepmind/dm_nevis

Ensemble and continual federated learning for classifcation tasks

Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real world problems, it is common to have a continual data stream, which may be non stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphonesOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has received financial support from AEI/FEDER (European Union) Grant Number PID2020-119367RB-I00, as well as the Consellería de Cultura, Educación e Universitade of Galicia (accreditation ED431G-2019/04, ED431G2019/01, and ED431C2018/29), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S

Machine Learning for Signal Reconstruction from Streaming Time-series Data

Papers I and II are extracted as separate files to meet IEEE publication policy for accepted manuscripts.Paper IV is extracted from the dissertation pending publication.Nowadays, deploying cyber-physical networked systems generates tremendous streams of data, with data rates increasing as time goes by. This trend is especially noticeable in several fairly automated sectors, such as energy or telecommunications. Compared to the last decades, this represents not only an additional large volume of data to explore and the need for more efficient and scalable data analysis methods but also raises additional challenges in the design and analysis of real-time streaming data processing algorithms. In many applications of interest, it is required to process a sequence of samples from multiple, possibly correlated, data time series that are acquired at different sampling rates and which may be quantized in amplitude at different resolutions. A commonly sought goal is to obtain a low-error signal reconstruction that can be uniformly resampled with a temporal resolution as fine as desired, hence facilitating subsequent data analyses. This Ph.D. thesis consists of a compendium of four papers that incrementally investigate the task of sequentially reconstructing a signal from a stream of multivariate time series of quantization intervals under several requirements encountered in practice and detailed next. First, we investigate how to track signals from streams of quantization intervals while enforcing low model complexity in the function estimation. Specifically, we explore the use of reproducing kernel Hilbert space-based online regression techniques expressly tailored for such a task. More specifically, the core techniques we devise and employ are influenced by the abundant theoretical and practical benefits in the literature about proximal operators and multiple kernel approaches. Second, we require the signal to be sequentially reconstructed, subject to smoothness constraints, and as soon as a data sample is available (zero-delay response). These well-motivated requirements appear in many practical problems, including online trajectory planning, real-time control systems, and high-speed digital-to-analog conversion. We address this challenge through a novel spline-based approach underpinned by a sequential decision-making framework and assisted with deep learning techniques. Specifically, we use recurrent neural networks to capture the temporal dependencies among data, helping to reduce the roughness of the reconstruction on average. Finally, we analyze the requirement of consistency, which amounts to exploiting all available information about the signal source and acquisition system to optimize some figure of reconstruction merit. In our context, consistency means guaranteeing that the reconstruction lies within the acquired quantization intervals. Consistency has been proven to entail a profitable-in-practice asymptotic error-rate decay as the sampling rate increases. Particularly, we investigate the impact of consistency on zero-delay reconstruction and also incorporate the idea of exploiting the spatiotemporal dependencies among multivariate signals.publishedVersio