Convolutional neural networks (CNNs) allow the computer to classify images. Apart from classifying objects they also are able to give us insights on what makes a picture. What is the essence of a picture? By visualizing the layers of CNN architectures we dive into the understanding of how machines process images. This gives provides also insights into how the human "sees" pictures.

Recent advances in machine learning, deep learning techniques, and sensors are greatly impacting how humans and computers and robots interact. For instance, surface electromyography sensors combined with deep-learning-based algorithms are currently being used to operate robotic prosthetic limbs or 3D pose estimation methods to control an avatar in Virtual Reality. Thus, the combination of sensors and machine learning techniques is enabling a range of novel and interesting applications. This Special Issue is intended to cover cutting-edge applications and research on new sensors, machine learning methods or their combination to perform human–computer and human–robot interaction. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics will also be considered.

AI researchers developing reinforcement learning agents could learn a lot from animals. In a decades-long venture to advance machine intelligence, the AI research community has often looked to neuroscience and behavioral science for inspiration and to better understand how intelligence is formed. But this effort has focused primarily on human intelligence, specifically that of babies and children. "This is especially true in a reinforcement learning context, where, thanks to progress in deep learning, it is now possible to bring the methods of comparative cognition directly to bear," the researchers' paper reads. "Animal cognition supplies a compendium of well-understood, nonlinguistic, intelligent behavior; it suggests experimental methods for evaluation and benchmarking; and it can guide environment and task design." DeepMind introduced some of the first forms of AI to combine deep learning and reinforcement learning, like the deep Q-network (DQN) algorithm, a system that played numerous Atari games at superhuman levels.

To develop a deep neural network–based computational workflow for inline myocardial perfusion analysis that automatically delineates the myocardium, which improves the clinical workflow and offers a "one-click" solution. In this retrospective study, consecutive adenosine stress and rest perfusion scans were acquired from three hospitals between October 1, 2018 and February 27, 2019. The training and validation set included 1825 perfusion series from 1034 patients (mean age, 60.6 years 14.2 [standard deviation]). The independent test set included 200 scans from 105 patients (mean age, 59.1 years 12.5). A convolutional neural network (CNN) model was trained to segment the left ventricular cavity, myocardium, and right ventricle by processing an incoming time series of perfusion images.

In deep learning, Convolutional Neural Networks(CNNs or Convnets) take up a major role. CNNs are widely used in computer vision-based problems, natural language processing, time series analysis, recommendation systems. ConvNet architecture mainly has 3 layers – convolutional layer, pooling layer and fully connected layer. All these layers bring out features of the input by finding some pattern using mathematical calculations. Unlike other neural networks architecture, CNNs have a backpropagation algorithm.

This article reviews meta-learning also known as learning-to-learn which seeks rapid and accurate model adaptation to unseen tasks with applications in highly automated AI, few-shot learning, natural language processing and robotics. Unlike deep learning, meta-learning can be applied to few-shot high-dimensional datasets and considers further improving model generalization to unseen tasks. Deep learning is focused upon in-sample prediction and meta-learning concerns model adaptation for out-of-sample prediction. Meta-learning can continually perform self-improvement to achieve highly autonomous AI. Meta-learning may serve as an additional generalization block complementary for original deep learning model. Meta-learning seeks adaptation of machine learning models to unseen tasks which are vastly different from trained tasks. Meta-learning with coevolution between agent and environment provides solutions for complex tasks unsolvable by training from scratch. Meta-learning methodology covers a wide range of great minds and thoughts. We briefly introduce meta-learning methodologies in the following categories: black-box meta-learning, metric-based meta-learning, layered meta-learning and Bayesian meta-learning framework. Recent applications concentrate upon the integration of meta-learning with other machine learning framework to provide feasible integrated problem solutions. We briefly present recent meta-learning advances and discuss potential future research directions.

In this paper, we take a theory-oriented approach to systematically study the privacy properties of gradients from a broad class of neural networks with rectified linear units (ReLU), probably the most popular activation function used in current deep learning practices. By utilizing some intrinsic properties of neural networks with ReLU, we prove the existence of exclusively activated neurons is critical to the separability of the activation patterns of different samples. Intuitively, an activation pattern is like the fingerprint of the corresponding sample during the training process. With the separated activation patterns, we for the first time show the equivalence of data reconstruction attacks with a sparse linear equation system. In practice, we propose a novel data reconstruction attack on fully-connected neural networks and extend the attack to more commercial convolutional neural network architectures. Our systematic evaluations cover more than $10$ representative neural network architectures (e.g., GoogLeNet, VGGNet and $6$ more), on various real-world scenarios related with healthcare, medical imaging, location, face recognition and shopping behaviors. In the majority of test cases, our proposed attack is able to infer ground-truth labels in the training batch with near $100\%$ accuracy, reconstruct the input data to fully-connected neural networks with lower than $10^{-6}$ MSE error, and provide better reconstruction results on both shallow and deep convolutional neural networks than previous attacks.

Literature analysis facilitates researchers to acquire a good understanding of the development of science and technology. The traditional literature analysis focuses largely on the literature metadata such as topics, authors, abstracts, keywords, references, etc., and little attention was paid to the main content of papers. In many scientific domains such as science, computing, engineering, etc., the methods and datasets involved in the scientific papers published in those domains carry important information and are quite useful for domain analysis as well as algorithm and dataset recommendation. In this paper, we propose a novel entity recognition model, called MDER, which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model utilizes rule embedding and adopts a parallel structure of CNN and Bi-LSTM with the self-attention mechanism. We evaluate the proposed model on datasets which are constructed from the published papers of four research areas in computer science, i.e., NLP, CV, Data Mining and AI. The experimental results demonstrate that our model performs well in all the four areas and it features a good learning capacity for cross-area learning and recognition. We also conduct experiments to evaluate the effectiveness of different building modules within our model which indicate that the importance of different building modules in collectively contributing to the good entity recognition performance as a whole. The data augmentation experiments on our model demonstrated that data augmentation positively contributes to model training, making our model much more robust in dealing with the scenarios where only small number of training samples are available. We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.

In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network). Most previous studies extract the feature vectors that characterize the cover song relation from a pair of songs and used it to compute the (dis)similarity between the two songs. Based on the observation that there is a meaningful pattern between cover songs and that this can be learned, we have reformulated the cover song identification problem in a machine learning framework. To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs. We then construct the data set composed of cover song pairs and non-cover song pairs, which are used as positive and negative training samples, respectively. The trained CNN outputs the probability of being in the cover song relation given a cross-similarity matrix generated from any two pieces of music and identifies the cover song by ranking on the probability. Experimental results show that the proposed algorithm achieves performance better than or comparable to the state-of-the-art.

Solving optimization problems is the key to decision making in many real-life analytics applications. However, the coefficients of the optimization problems are often uncertain and dependent on external factors, such as future demand or energy or stock prices. Machine learning (ML) models, especially neural networks, are increasingly being used to estimate these coefficients in a datadriven way. Hence, end-to-end predict-and-optimize approaches, which consider how effective the predicted values are to solve the optimization problem, have received increasing attention. In case of integer linear programming problems, a popular approach to overcome their non-differentiabilty is to add a quadratic penalty term to the continuous relaxation, such that results from differentiating over quadratic programs can be used. Instead we investigate the use of the more principled logarithmic barrier term, as widely used in interior point solvers for linear programming. Specifically, instead of differentiating the KKT conditions, we consider the homogeneous self-dual formulation of the LP and we show the relation between the interior point step direction and corresponding gradients needed for learning. Finally our empirical experiments demonstrate our approach performs as good as if not better than the state-of-the-art QPTL (Quadratic Programming task loss) formulation of Wilder et al. [29] and SPO approach of Elmachtoub and Grigas [12].