A student learning to speak Mandarin wanders into a marketplace on the streets of China on a sunny summer afternoon. Before long, two vendors approach and begin hawking products, trying to outbid one another. The student must now grasp what's being said and formulate an appropriate response using proper pronunciation to avoid being misunderstood. It's a challenging, yet common, scenario for anyone trying to learn a new language by interacting with native speakers and immersing themselves in a foreign culture. Fortunately, the student in this case is able to pause the unfolding scenario to check the accuracy and tone of her planned response.

Emotional Intelligence(also known as Emotional Quotient, or EQ) training courses and workshops in Dubai, the UAE have become very popular recently among managers of all levels. What reason stands behind the popularity of emotional intelligence trainings? A lot of people dream about taking on a managerial or leadership role, but only a few think about the drawbacks and consequences of this role. The responsibility for success or failure always puts a heavy burden on the shoulders of the leader. To manage that pressure and successfully accomplish daily tasks, each leader must have a certain set of characteristics, skills, and traits.

So is it possible to interpret how someone is feeling based on their gait alone? That's exactly what scientists at the University of North Carolina at Chapel Hill and the University of Maryland at College Park have taught a computer to do. Using deep learning, their software can analyze a video of someone walking, turn it into a 3D model, and extract their gait. A neural network then determines the dominant motion and how it matches up to a particular feeling, based on the data on which it's trained. According to their research paper, published in June on arXiv, their deep learning model can guess four different emotions--happy, sad, angry, and neutral--with 80% accuracy.

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...]

However, neural networks' powerful abilities to induce complex representations have come at the cost of data efficiency. Indeed, compared to earlier logical form-based methods, neural networks can sometimes require orders of magnitude more data. The data-hungriness of neural approaches is not surprising - starting with classic logical forms improves data efficiency by presenting a system with pre-made abstractions, where end-to-end neural approaches must do the hard work of inducing abstractions on their own. In this paper, we aim to combine the power of neural networks with the data-efficiency of logical forms by pre-learning abstractions in a semi-supervised way, satiating part of the network's data hunger on cheaper unlabeled data from the environment. When neural nets have only limited data that Figure 1: After seeing this transition, a neural net might generalize this action as stack red blocks to the right of blue blocks except for on brown blocks, but a generalization like stack red blocks on orange blocks is more plausible and generally applicable. We aim to guide our model towards more plausible generalizations by pre-learning inductive biases from observations of the environment.

Decentralized consensus-based optimization is a general computational framework where a network of nodes cooperatively minimizes a sum of locally available cost functions via only local computation and communication. In this article, we survey recent advances on this topic, particularly focusing on decentralized, consensus-based, first-order gradient methods for large-scale stochastic optimization. The class of consensus-based stochastic optimization algorithms is communication-efficient, able to exploit data parallelism, robust in random and adversarial environments, and simple to implement, thus providing scalable solutions to a wide range of large-scale machine learning problems. We review different state-of-the-art decentralized stochastic optimization formulations, different variants of consensus-based procedures, and demonstrate how to obtain decentralized counterparts of centralized stochastic first-order methods. We provide several intuitive illustrations of the main technical ideas as well as applications of the algorithms in the context of decentralized training of machine learning models.

In this paper we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and learning from data streams. We combine the TWSVM with a fuzzy membership function, so that each input has a different contribution to each hyperplane in a binary classifier. To solve the pair of quadratic programming problems (QPPs) we use a dual coordinate descent algorithm with a shrinking strategy, and to obtain a robust classification with a fast training we propose the use of a Fourier Gaussian approximation function with our linear FBTWSVM. Inspired by the shrinking technique, the incremental algorithm re-utilizes part of the training method with some heuristics, while the decremental procedure is based on a scored window. The FBTWSVM is also extended for multi-class problems by combining binary classifiers using a Directed Acyclic Graph (DAG) approach. Moreover, we analyzed the theoretical foundations properties of the proposed approach and its extension, and the experimental results on benchmark datasets indicate that the FBTWSVM has a fast training and retraining process while maintaining a robust classification performance.

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.

In the last decade, we have seen a qualitative jump in the performance of machine learning (ML) methods directed at narrow, well-defined tasks. For example, there has been marked progress in object recognition [57], game-playing [73], and generative models of images [40] and text [39]. Some of these methods have achieved superhuman performance within their domain [73, 64]. In each of these cases, a powerful ML model was trained using large amounts of data on a highly complex task to surpass what was commonly believed possible. Here we consider the transpose of this situation.

In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks. We first introduce the sign operation of stochastic gradients (as in sign-based methods, e.g., SIGN-SGD) into ADAM, which is called as signADAM. Moreover, in order to make the rate of fitting each feature closer, we define a confidence function to distinguish different components of gradients and apply it to our algorithm. It can generate more sparse gradients than existing algorithms do. We call this new algorithm signADAM++. In particular, both our algorithms are easy to implement and can speed up training of various deep neural networks. The motivation of signADAM++ is preferably learning features from the most different samples by updating large and useful gradients regardless of useless information in stochastic gradients. We also establish theoretical convergence guarantees for our algorithms. Empirical results on various datasets and models show that our algorithms yield much better performance than many state-of-the-art algorithms including SIGN-SGD, SIGNUM and ADAM. We also analyze the performance from multiple perspectives including the loss landscape and develop an adaptive method to further improve generalization. The source code is available at https://github.com/DongWanginxdu/signADAM-Learn-by-Confidence.