OpenRaiser/Intern-Atlas huggingface dataset:
Stochastic Configuration Networks Ensemble for Large-Scale Data Analytics
Venue: arXiv cs.LG 2017
Year: 2017

This paper presents a fast decorrelated neuro-ensemble with heterogeneous features for large-scale data analytics, where stochastic configuration networks (SCNs) are employed as base learner models and the well-known negative correlation learning (NCL) strategy is adopted to evaluate the output weights. By feeding a large number of samples into the SCN base models, we obtain a huge sized linear equation system which is difficult to be solved by means of computing a pseudo-inverse used in the least squares method. Based on the group of heterogeneous features, the block Jacobi and Gauss-Seidel methods are employed to iteratively evaluate the output weights, and a convergence analysis is given with a demonstration on the uniqueness of these iterative solutions. Experiments with comparisons on two large-scale datasets are carried out, and the system robustness with respect to the regularizing factor used in NCL is given. Results indicate that the proposed ensemble learning techniques have good potential for resolving large-scale data modelling problems.
Sampling-based Planning of In-Hand Manipulation with External Pushes
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents a sampling-based planning algorithm for in-hand manipulation of a grasped object using a series of external pushes. A high-level sampling-based planning framework, in tandem with a low-level inverse contact dynamics solver, effectively explores the space of continuous pushes with discrete pusher contact switch-overs. We model the frictional interaction between gripper, grasped object, and pusher, by discretizing complex surface/line contacts into arrays of hard frictional point contacts. The inverse dynamics problem of finding an instantaneous pusher motion that yields a desired instantaneous object motion takes the form of a mixed nonlinear complementarity problem. Building upon this dynamics solver, our planner generates a sequence of pushes that steers the object to a goal grasp. We evaluate the performance of the planner for the case of a parallel-jaw gripper manipulating different objects, both in simulation and with real experiments. Through these examples, we highlight the important properties of the planner: respecting and exploiting the hybrid dynamics of contact sticking/sliding/rolling and a sense of efficiency with respect to discrete contact switch-overs.
Adaptive Stimulus Selection in ERP-Based Brain-Computer Interfaces by Maximizing Expected Discrimination Gain
Venue: arXiv cs.HC 2017
Year: 2017

Brain-computer interfaces (BCIs) can provide an alternative means of communication for individuals with severe neuromuscular limitations. The P300-based BCI speller relies on eliciting and detecting transient event-related potentials (ERPs) in electroencephalography (EEG) data, in response to a user attending to rarely occurring target stimuli amongst a series of non-target stimuli. However, in most P300 speller implementations, the stimuli to be presented are randomly selected from a limited set of options and stimulus selection and presentation are not optimized based on previous user data. In this work, we propose a data-driven method for stimulus selection based on the expected discrimination gain metric. The data-driven approach selects stimuli based on previously observed stimulus responses, with the aim of choosing a set of stimuli that will provide the most information about the user's intended target character. Our approach incorporates knowledge of physiological and system constraints imposed due to real-time BCI implementation. Simulations were performed to compare our stimulus selection approach to the row-column paradigm, the conventional stimulus selection method for P300 spellers. Results from the simulations demonstrated that our adaptive stimulus selection approach has the potential to significantly improve performance from the conventional method: up to 34% improvement in accuracy and 43% reduction in the mean number of stimulus presentations required to spell a character in a 72-character grid. In addition, our greedy approach to stimulus selection provides the flexibility to accommodate design constraints.
Learning Deep Latent Spaces for Multi-Label Classification
Venue: arXiv cs.LG 2017
Year: 2017

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.
Privacy-preserving data outsourcing in the cloud via semantic data splitting
Venue: arXiv cs.CR 2017
Year: 2017

Even though cloud computing provides many intrinsic benefits, privacy concerns related to the lack of control over the storage and management of the outsourced data still prevent many customers from migrating to the cloud. Several privacy-protection mechanisms based on a prior encryption of the data to be outsourced have been proposed. Data encryption offers robust security, but at the cost of hampering the efficiency of the service and limiting the functionalities that can be applied over the (encrypted) data stored on cloud premises. Because both efficiency and functionality are crucial advantages of cloud computing, in this paper we aim at retaining them by proposing a privacy-protection mechanism that relies on splitting (clear) data, and on the distributed storage offered by the increasingly popular notion of multi-clouds. We propose a semantically-grounded data splitting mechanism that is able to automatically detect pieces of data that may cause privacy risks and split them on local premises, so that each chunk does not incur in those risks; then, chunks of clear data are independently stored into the separate locations of a multi-cloud, so that external entities cannot have access to the whole confidential data. Because partial data are stored in clear on cloud premises, outsourced functionalities are seamlessly and efficiently supported by just broadcasting queries to the different cloud locations. To enforce a robust privacy notion, our proposal relies on a privacy model that offers a priori privacy guarantees; to ensure its feasibility, we have designed heuristic algorithms that minimize the number of cloud storage locations we need; to show its potential and generality, we have applied it to the least structured and most challenging data type: plain textual documents.
Speeding Up String Matching by Weak Factor Recognition
Venue: arXiv cs.DS 2017
Year: 2017

String matching is the problem of finding all the substrings of a text which match a given pattern. It is one of the most investigated problems in computer science, mainly due to its very diverse applications in several fields. Recently, much research in the string matching field has focused on the efficiency and flexibility of the searching procedure and quite effective techniques have been proposed for speeding up the existing solutions. In this context, algorithms based on factors recognition are among the best solutions. In this paper, we present a simple and very efficient algorithm for string matching based on a weak factor recognition and hashing. Our algorithm has a quadratic worst-case running time. However, despite its quadratic complexity, experimental results show that our algorithm obtains in most cases the best running times when compared, under various conditions, against the most effective algorithms present in literature. In the case of small alphabets and long patterns, the gain in running times reaches 28%. This makes our proposed algorithm one of the most flexible solutions in practical cases.
A note on computing range space bases of rational matrices
Venue: arXiv cs.SY 2017
Year: 2017

We discuss computational procedures based on descriptor state-space realizations to compute proper range space bases of rational matrices. The main computation is the orthogonal reduction of the system matrix pencil to a special Kronecker-like form, which allows to extract a full column rank factor, whose columns form a proper rational basis of the range space. The computation of several types of bases can be easily accommodated, such as minimum-degree bases, stable inner minimum-degree bases, etc. Several straightforward applications of the range space basis computation are discussed, such as, the computation of full rank factorizations, normalized coprime factorizations, pseudo-inverses, and inner-outer factorizations.
Interference Coordination via Power Domain Channel Estimation
Venue: arXiv cs.NI 2017
Year: 2017

A novel technique is proposed which enables each transmitter to acquire global channel state information (CSI) from the sole knowledge of individual received signal power measurements, which makes dedicated feedback or inter-transmitter signaling channels unnecessary. To make this possible, we resort to a completely new technique whose key idea is to exploit the transmit power levels as symbols to embed information and the observed interference as a communication channel the transmitters can use to exchange coordination information. Although the used technique allows any kind of {low-rate} information to be exchanged among the transmitters, the focus here is to exchange local CSI. The proposed procedure also comprises a phase which allows local CSI to be estimated. Once an estimate of global CSI is acquired by the transmitters, it can be used to optimize any utility function which depends on it. While algorithms which use the same type of measurements such as the iterative water-filling algorithm (IWFA) implement the sequential best-response dynamics (BRD) applied to individual utilities, here, thanks to the availability of global CSI, the BRD can be applied to the sum-utility. Extensive numerical results show that significant gains can be obtained and, this, by requiring no additional online signaling.
MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission
Venue: arXiv cs.IT 2017
Year: 2017

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts both as a radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.
An Active Sequential Xampling Detector for Spectrum Sensing
Venue: arXiv cs.IT 2017
Year: 2017

Enabling low power wireless devices to adopt Nyquist sampling at high carriers is prohibitive. In spectrum sensing, this limit calls for an analog front-end that can sweep different bands quickly, in order to use the available spectrum opportunistically. In this paper we propose a new framework that allows to optimize a sub-Nyquist sampling front-end to combine the benefits of optimum sequential sensing with those of Compressive Spectrum Sensing (CSS). The sensing strategy we propose is formulated as a sequential utility optimization problem. The utility function decreases linearly with the number of measurements (accrued to make a decision) and increases as a function of the signal components binary states. The optimization selects the best linear combinations of sub-bands to mix, in order to accrue maximum utility. The structure of the utility represents the trade-off between exploration, exploitation and risk of making an error, that is characteristic of the spectrum sensing problem. We first present the analog front-end architecture, and then map the measurement model into the abstract optimization problem proposed, and analyzed, in the remainder of the paper. We characterize the optimal policy under constraints on the sensing matrix and derive the approximation factor of the greedy approach. Numerical simulations showcase the benefits of combining opportunistic spectrum sensing with sub-Nyquist sampling.
Submodular Function Maximization for Group Elevator Scheduling
Venue: arXiv cs.AI 2017
Year: 2017

We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.
Effect of Pipelining and Multiplexing in Estimating HTTP/2.0 Web Object Sizes
Venue: arXiv cs.CR 2017
Year: 2017

HTTP response size is a well-known side channel attack. With the deployment of HTTP/2.0, response size estimation attacks are generally dismissed with the argument that pipelining and response multiplexing prevent eavesdroppers from finding out response sizes. Yet the impact that pipelining and response multiplexing actually have in estimating HTTP response sizes has not been adequately investigated. In this paper we set out to help understand the effect of pipelining and response multiplexing in estimating the size of web objects on the Internet. We conduct an experiment that collects HTTP response sizes and TLS record sizes from 10k popular web sites. We gather evidence on and discuss reasons for the limited amount of pipelining and response multiplexing used on the Internet today: only 29% of the HTTP2 web objects we observe are pipelined and only 5% multiplexed. We also provide worst case results under different attack assumptions and show how effective a simple model for estimating response sizes from TLS record sizes can be. Our conclusion is that pipelining and especially response multiplexing can yield, as expected, a perceivable increase in relative object size estimation error yet the limited extent of multiplexing observed on the Internet today and the relative simplicity of attacks to the current pipelining mechanisms hinder their ability to help prevent web object size estimation.
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
Venue: arXiv cs.CV 2017
Year: 2017

We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart. Our method is able to jointly predict the visibility, viewing plane, location and orientation of the fetal heart at the frame level. The contributions of the paper are three-fold: (i) a convolutional neural network architecture is developed for a multi-task prediction, which is computed by sliding a 3x3 window spatially through convolutional maps. (ii) an anchor mechanism and Intersection over Union (IoU) loss are applied for improving localization accuracy. (iii) a recurrent architecture is designed to recursively compute regional convolutional features temporally over sequential frames, allowing each prediction to be conditioned on the whole video. This results in a spatial-temporal model that precisely describes detailed heart parameters in challenging US videos. We report results on a real-world clinical dataset, where our method achieves performance on par with expert annotations.
Speaker Identification in a Shouted Talking Environment Based on Novel Third-Order Circular Suprasegmental Hidden Markov Models
Venue: arXiv cs.SD 2017
Year: 2017

It is well known that speaker identification yields very high performance in a neutral talking environment, on the other hand, the performance has been sharply declined in a shouted talking environment. This work aims at proposing, implementing, and evaluating novel Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s) to improve the low performance of text-independent speaker identification in a shouted talking environment. CSPHMM3s possess combined characteristics of: Circular Hidden Markov Models (CHMMs), Third-Order Hidden Markov Models (HMM3s), and Suprasegmental Hidden Markov Models (SPHMMs). Our results show that CSPHMM3s are superior to each of: First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s), Third-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM3s), First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s), and Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) in a shouted talking environment. Using our collected speech database, average speaker identification performance in a shouted talking environment based on LTRSPHMM1s, LTRSPHMM2s, LTRSPHMM3s, CSPHMM1s, CSPHMM2s, and CSPHMM3s is 74.6%, 78.4%, 81.7%, 78.7%, 83.4%, and 85.8%, respectively. Speaker identification performance that has been achieved based on CSPHMM3s is close to that attained based on subjective assessment by human listeners.
Version 0.1 of the BigDAWG Polystore System
Venue: arXiv cs.DB 2017
Year: 2017

A polystore system is a database management system (DBMS) composed of integrated heterogeneous database engines and multiple programming languages. By matching data to the storage engine best suited to its needs, complex analytics run faster and flexible storage choices helps improve data organization. BigDAWG (Big Data Working Group) is our reference implementation of a polystore system. In this paper, we describe the current BigDAWG software release which supports PostgreSQL, Accumulo and SciDB. We describe the overall architecture, API and initial results of applying BigDAWG to the MIMIC II medical dataset.
Polar Codes for SCMA Systems
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we design and compare multilevel polar coding (MLPC) and bit-interleaved polar coded modulation (BIPCM) for uplink sparse code multiple access (SCMA) systems that operate over fast and block fading channels. Both successive cancellation (SC) and successive cancellation list (SCL) decoding algorithms are considered. Simulation results show that, with either decoder, BIPCM performs better than its MLPC counterpart. Also, both BIPCM and MLPC exhibit a performance advantage over LTE turbo-coded and WiMAX LDPC SCMA systems when the SCL technique is used for decoding.
Senior Programmers: Characteristics of Elderly Users from Stack Overflow
Venue: arXiv cs.CY 2017
Year: 2017

In this paper we presents results of research about elderly users of Stack Overflow (Question and Answer portal for programmers). They have different roles, different main activities and different habits. They are an important part of the community, as they tend to have higher reputation and they like to share their knowledge. This is a great example of possible way of keeping elderly people active and helpful for society.
Appearance invariance in convolutional networks with neighborhood similarity
Venue: arXiv cs.CV 2017
Year: 2017

We present a neighborhood similarity layer (NSL) which induces appearance invariance in a network when used in conjunction with convolutional layers. We are motivated by the observation that, even though convolutional networks have low generalization error, their generalization capability does not extend to samples which are not represented by the training data. For instance, while novel appearances of learned concepts pose no problem for the human visual system, feedforward convolutional networks are generally not successful in such situations. Motivated by the Gestalt principle of grouping with respect to similarity, the proposed NSL transforms its input feature map using the feature vectors at each pixel as a frame of reference, i.e. center of attention, for its surrounding neighborhood. This transformation is spatially varying, hence not a convolution. It is differentiable; therefore, networks including the proposed layer can be trained in an end-to-end manner. We analyze the invariance of NSL to significant changes in appearance that are not represented in the training data. We also demonstrate its advantages for digit recognition, semantic labeling and cell detection problems.
A Complete Classification of Partial-MDS (Maximally Recoverable) Codes with One Global Parity
Venue: arXiv cs.IT 2017
Year: 2017

Partial-MDS (PMDS) codes are a family of locally repairable codes, mainly used for distributed storage. They are defined to be able to correct any pattern of $s$ additional erasures, after a given number of erasures per locality group have occurred. This makes them also maximally recoverable (MR) codes, another class of locally repairable codes. It is known that MR codes in general, and PMDS codes in particular, exist for any set of parameters, if the field size is large enough. Moreover, some explicit constructions of PMDS codes are known, mostly (but not always) with a strong restriction on the number of erasures that can be corrected per locality group. In this paper we generalize the notion of PMDS codes to allow locality groups of different sizes. We give a general construction of such PMDS codes with $s=1$ global parity, i.e., one additional erasure can be corrected. Furthermore, we show that all PMDS codes for the given parameters are of this form, i.e., we give a classification of these codes. This implies a necessary and sufficient condition on the underlying field size for the existence of these codes (assuming that the MDS conjecture is true). For some parameter sets our generalized construction gives rise to PMDS codes with a smaller field size than any other known construction.