In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore provided an opportunity for knowledge extraction (KE) from a textual document (TD). A typical problem of this kind is the extraction of common characteristics and knowledge from a group of TDs, with the possibility to group such similar TDs in a process known as clustering. In this paper we present a technique for such KE among a group of TDs related to the common characteristics and meaning of their content. Our technique is based on the Spearman's Rank Correlation Coefficient (SRCC), for which the conducted experiments have proven to be comprehensive measure to achieve a high-quality KE.

Abstract--Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity cla ssification and tracking in a noninvasive manner . Existing infrastruct ure makes Wi-Fi a possible candidate, yet, utilizing tradition al signal processing methods to extract information necessary to ful ly characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel en d-to-end deep learning framework that simultaneously predic ts the identity, activity and the location of a user to create user p rofiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervent ion unlike systems that require user-initiated initializatio n, or a user held transmitting device to facilitate the prediction. The system can also predict the trajectory of the user by predicting the location of a user over consecutive time steps. The performa nce of the system is evaluated through experiments. P ARTfrom the applications related to surveillance and defense, user identification, behaviour analysis, localization and user activity recognition have become increasingly crucial tasks due to the popularity of facilities such as cashierless stores and senior citizen residences. Current state-of-the-art techniques for passive user authentication [1], re-identification [2], activity classification [3] and trackin g [4], [5] are primarily based on video feed analysis. However, due to concerns on privacy invasion, camera videos are not deeme d to be the best choice in many practical applications. Hence, there is a growing need for noninvasive alternatives. A possible alternative being considered is ambient Wi-Fi signals, which are widely available and easily accessible.

The composer classification question has been posed for a variety of corpora, from Renaissance composers [2,3], to the narrow (and challenging) case of Haydn and Mozart string quartets [5, 8, 12, 22], and to various collections of classical era composers (most of the other papers discussed in Section 2). In this work we study an expansive collection of scores, from 13th century sacred music by Guillaume Du Fay to 20th century ragtimes by Scott Joplin. A major challenge of this task is learning from limited data. While the corpus considered here is larger than most, this is largely due to the number of composers considered (19): for specific composers, we have at most 466 scores (Bach) and as few as 22 (Japart). Small datasets are an inherent problem for composer classification: the corpus used in this work contains, for example, all of the Bach chorales and all of the Mozart string quartets. We cannot resurrect these composers and have them write us more scores to include in our corpus. This situation contrasts starkly with many learning problems, where substantial progress can be made by collecting massive datasets and exhaustively training an expressive model (usually a deep neural network) with "big data."

Finding appropriate inductive loop invariants for a program is a key challenge in verifying its functional properties. Although the problem is undecidable in general, several heuristics have been proposed to handle practical programs that tend to have simple control-flow structures. However, these heuristics only work well when the space of invariants is small. On the other hand, machine-learned techniques that use continuous optimization have a high sample complexity, i.e., the number of invariant guesses and the associated counterexamples, since the invariant is required to exactly satisfy a specification. We propose a novel technique that is able to solve complex verification problems involving programs with larger number of variables and non-linear specifications. We formulate an invariant as a piecewise low-degree polynomial, and reduce the problem of synthesizing it to a set of integer linear programming (ILP) problems. This enables the use of state-of-the-art ILP techniques that combine enumerative search with continuous optimization; thus ensuring fast convergence for a large class of verification tasks while still ensuring low sample complexity. We instantiate our technique as the open-source oasis tool using an off-the-shelf ILP solver, and evaluate it on more than 300 benchmark tasks collected from the annual SyGuS competition and recent prior work. Our experiments show that oasis outperforms the state-of-the-art tools, including the winner of last year's SyGuS competition, and is able to solve 9 challenging tasks that existing tools fail on.

Networks are one of the most powerful structures for modeling problems in the real world. Downstream machine learning tasks defined on networks have the potential to solve a variety of problems. With link prediction, for instance, one can predict whether two persons will become friends on a social network. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding encompasses various methods for unsupervised, and sometimes supervised, learning of feature representations of nodes and links in a network. Typically, embedding methods are based on the assumption that the similarity between nodes in the network should be reflected in the learned feature representations. In this paper, we review significant contributions to network embedding in the last decade. In particular, we look at four methods: Spectral Clustering, DeepWalk, Large-scale Information Network Embedding (LINE), and node2vec. We describe each method and list its advantages and shortcomings. In addition, we give examples of real-world machine learning problems on networks in which the embedding is critical in order to maximize the predictive performance of the machine learning task. Finally, we take a look at research trends and state-of-the art methods in the research on network embedding.

The excessively increased volume of data in modern data management systems demands an improved system performance, frequently provided by data distribution, system scalability and performance optimization techniques. Optimized horizontal data partitioning has a significant influence of distributed data management systems. An optimally partitioned schema found in the early phase of logical database design without loading of real data in the system and its adaptation to changes of business environment are very important for a successful implementation, system scalability and performance improvement. In this paper we present a novel approach for finding an optimal horizontally partitioned schema that manifests a minimal total execution cost of a given database workload. Our approach is based on a formal model that enables abstraction of the predicates in the workload queries, and are subsequently used to define all relational fragments. This approach has predictive features acquired by simulation of horizontal partitioning, without loading any data into the partitions, but instead, altering the statistics in the database catalogs. We define an optimization problem and employ a genetic algorithm (GA) to find an approximately optimal horizontally partitioned schema. The solutions to the optimization problem are evaluated using PostgreSQL's query optimizer. The initial experimental evaluation of our approach confirms its efficiency and correctness, and the numbers imply that the approach is effective in reducing the workload execution cost.

Head motion prediction is an important problem with 360\degree\ videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article we first show the startling result that all such existing methods, which attempt to benefit both from the history of past positions and knowledge of the video content, perform worse than a simple no-motion baseline. We then propose an LSTM-based architecture which processes the positional information only. It is able to establish state-of-the-art performance and we consider it our position-only baseline. Through a thorough root cause analysis, we first show that the content can indeed inform the head position prediction for horizons longer than 2 to 3s, the trajectory inertia being predominant earlier. We also identify that a sequence-to-sequence auto-regressive framework is crucial to improve the prediction accuracy over longer prediction windows, and that a dedicated recurrent network handling the time series of positions is necessary to reach the performance of the position-only baseline in the early prediction steps. This allows to make the most of the positional information and ground-truth saliency. Finally we show how the level of noise in the estimated saliency impacts the architecture's performance, and we propose a new architecture establishing state-of-the-art performance with estimated saliency, supporting its assets with an ablation study.

Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime, we introduce the Unstructured Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent. We show empirically that many of the different neural network structures commonly used in practice today (including fully connected, locally connected and residual networks of different depths and widths) can emerge dynamically from the same URN. These different structures can be derived using gradient descent on a single general loss function where the structure of the data and the relative strengths of various regulator terms determine the structure of the emergent network. We show that this loss function and the regulators arise naturally when considering the symmetries of the network as well as the geometric properties of the input data.

In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in environments that seem trivial, but the reason for such failures is still poorly understood. In this paper, we contribute a formal explanation of these failures in the particular case of sparse reward and deterministic environments. First, using a very elementary control problem, we illustrate that the learning process can get stuck into a fixed point corresponding to a poor solution. Then, generalizing from the studied example, we provide a detailed analysis of the underlying mechanisms which results in a new understanding of one of the convergence regimes of these algorithms. The resulting perspective casts a new light on already existing solutions to the issues we have highlighted, and suggests other potential approaches.

The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as the Gaia catalog containing noisy observations of a billion stars. We propose two minibatch variants of extreme deconvolution, based on an online variation of the EM algorithm, and direct gradient-based optimisation of the log-likelihood, both of which can run on GPUs. We demonstrate that these methods provide faster fitting, whilst being able to scale to much larger models for use with larger datasets.