OpenRaiser/Intern-Atlas huggingface dataset:
Deep Discriminant Analysis for i-vector Based Robust Speaker Recognition
Venue: arXiv cs.SD 2018
Year: 2018

Linear Discriminant Analysis (LDA) has been used as a standard post-processing procedure in many state-of-the-art speaker recognition tasks. Through maximizing the inter-speaker difference and minimizing the intra-speaker variation, LDA projects i-vectors to a lower-dimensional and more discriminative sub-space. In this paper, we propose a neural network based compensation scheme(termed as deep discriminant analysis, DDA) for i-vector based speaker recognition, which shares the spirit with LDA. Optimized against softmax loss and center loss at the same time, the proposed method learns a more compact and discriminative embedding space. Compared with the Gaussian distribution assumption of data and the learnt linear projection in LDA, the proposed method doesn't pose any assumptions on data and can learn a non-linear projection function. Experiments are carried out on a short-duration text-independent dataset based on the SRE Corpus, noticeable performance improvement can be observed against the normal LDA or PLDA methods.
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
Venue: arXiv cs.LG 2018
Year: 2018

Mapping complex input data into suitable lower dimensional manifolds is a common procedure in machine learning. This step is beneficial mainly for two reasons: (1) it reduces the data dimensionality and (2) it provides a new data representation possibly characterised by convenient geometric properties. Euclidean spaces are by far the most widely used embedding spaces, thanks to their well-understood structure and large availability of consolidated inference methods. However, recent research demonstrated that many types of complex data (e.g., those represented as graphs) are actually better described by non-Euclidean geometries. Here, we investigate how embedding graphs on constant-curvature manifolds (hyper-spherical and hyperbolic manifolds) impacts on the ability to detect changes in sequences of attributed graphs. The proposed methodology consists in embedding graphs into a geometric space and perform change detection there by means of conventional methods for numerical streams. The curvature of the space is a parameter that we learn to reproduce the geometry of the original application-dependent graph space. Preliminary experimental results show the potential capability of representing graphs by means of curved manifold, in particular for change and anomaly detection problems.
Optimal Resource Allocation in Full-Duplex Ambient Backscatter Communication Networks for Green IoT
Venue: arXiv cs.IT 2018
Year: 2018

Ambient backscatter communication (AmBC) enables wireless-powered backscatter devices (BDs) to transmit information over ambient radio-frequency (RF) carriers without using an RF transmitter, and thus has emerged as a promising technology for green Internet-of-Things. This paper considers an AmBC network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing (OFDM) signals to its legacy user (LU) and receives uplink signals backscattered from multiple BDs in a time-division-multiple-access manner. To enhance the system performance from multiple design dimensions and ensure fairness, we maximize the minimum throughput among all BDs by jointly optimizing the BDs' backscatter time portions, the BDs' power reflection coefficients, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. As such, we propose an efficient iterative algorithm for solving the formulated non-convex problem by leveraging the block coordinated decent and successive convex optimization techniques. We further show the convergence of the proposed algorithm, and analyze its complexity. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gains as compared to the benchmark scheme with equal resource allocation.
Prevalence of web trackers on hospital websites in Illinois
Venue: arXiv cs.CY 2018
Year: 2018

Web tracking technologies are pervasive and operated by a few large technology companies. This technology, and the use of the collected data has been implicated in influencing elections, fake news, discrimination, and even health decisions. Little is known about how this technology is deployed on hospital or other health related websites. The websites of the 210 public hospitals in the state of Illinois, USA were evaluated with a web tracker identification tool. Web trackers were identified on 94% of hospital webs sites, with an average of 3.5 trackers on the websites of general hospitals. The websites of smaller critical access hospitals used an average of 2 web trackers. The most common web tracker identified was Google Analytics, found on 74% of Illinois hospital websites. Of the web trackers discovered, 88% were operated by Google and 26% by Facebook. In light of revelations about how web browsing profiles have been used and misused, search bubbles, and the potential for algorithmic discrimination hospital leadership and policy makers must carefully consider if it is appropriate to use third party tracking technology on hospital web sites.
Dynamic Structural Similarity on Graphs
Venue: arXiv cs.SI 2018
Year: 2018

One way of characterizing the topological and structural properties of vertices and edges in a graph is by using structural similarity measures. Measures like Cosine, Jaccard and Dice compute the similarities restricted to the immediate neighborhood of the vertices, bypassing important structural properties beyond the locality. Others measures, such as the generalized edge clustering coefficient, go beyond the locality but with high computational complexity, making them impractical in large-scale scenarios. In this paper we propose a novel similarity measure that determines the structural similarity by dynamically diffusing and capturing information beyond the locality. This new similarity is modeled as an iterated function that can be solved by fixed point iteration in super-linear time and memory complexity, so it is able to analyze large-scale graphs. In order to show the advantages of the proposed similarity in the community detection task, we replace the local structural similarity used in the SCAN algorithm with the proposed similarity measure, improving the quality of the detected community structure and also reducing the sensitivity to the parameter $ε$ of the SCAN algorithm.
Robustness of sentence length measures in written texts
Venue: arXiv cs.CL 2018
Year: 2018

Hidden structural patterns in written texts have been subject of considerable research in the last decades. In particular, mapping a text into a time series of sentence lengths is a natural way to investigate text structure. Typically, sentence length has been quantified by using measures based on the number of words and the number of characters, but other variations are possible. To quantify the robustness of different sentence length measures, we analyzed a database containing about five hundred books in English. For each book, we extracted six distinct measures of sentence length, including number of words and number of characters (taking into account lemmatization and stop words removal). We compared these six measures for each book by using i) Pearson's coefficient to investigate linear correlations; ii) Kolmogorov--Smirnov test to compare distributions; and iii) detrended fluctuation analysis (DFA) to quantify auto-correlations. We have found that all six measures exhibit very similar behavior, suggesting that sentence length is a robust measure related to text structure.
Improved decoding of Folded Reed-Solomon and Multiplicity Codes
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we show new and improved error-correcting properties of folded Reed-Solomon codes and multiplicity codes. Both of these families of codes are based on polynomials over finite fields, and both have been the sources of recent advances in coding theory. Folded Reed-Solomon codes were the first explicit constructions of codes known to achieve list-decoding capacity; multivariate multiplicity codes were the first constructions of high-rate locally correctable codes; and univariate multiplicity codes are also known to achieve list-decoding capacity. However, previous analyses of the error-correction properties of these codes did not yield optimal results. In particular, in the list-decoding setting, the guarantees on the list-sizes were polynomial in the block length, rather than constant; and for multivariate multiplicity codes, local list-decoding algorithms could not go beyond the Johnson bound. In this paper, we show that Folded Reed-Solomon codes and multiplicity codes are in fact better than previously known in the context of list-decoding and local list-decoding. More precisely, we first show that Folded RS codes achieve list-decoding capacity with constant list sizes, independent of the block length; and that high-rate univariate multiplicity codes can also be list-recovered with constant list sizes. Using our result on univariate multiplicity codes, we show that multivariate multiplicity codes are high-rate, locally list-recoverable codes. Finally, we show how to combine the above results with standard tools to obtain capacity achieving locally list decodable codes with query complexity significantly lower than was known before.
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing
Venue: arXiv cs.CR 2018
Year: 2018

Anti-phishing aims to detect phishing content/documents in a pool of textual data. This is an important problem in cybersecurity that can help to guard users from fraudulent information. Natural language processing (NLP) offers a natural solution for this problem as it is capable of analyzing the textual content to perform intelligent recognition. In this work, we investigate state-of-the-art techniques for text categorization in NLP to address the problem of anti-phishing for emails (i.e, predicting if an email is phishing or not). These techniques are based on deep learning models that have attracted much attention from the community recently. In particular, we present a framework with hierarchical long short-term memory networks (H-LSTMs) and attention mechanisms to model the emails simultaneously at the word and the sentence level. Our expectation is to produce an effective model for anti-phishing and demonstrate the effectiveness of deep learning for problems in cybersecurity.
OMG Emotion Challenge - ExCouple Team
Venue: arXiv cs.SD 2018
Year: 2018

The proposed model is only for the audio module. All videos in the OMG Emotion Dataset are converted to WAV files. The proposed model makes use of semi-supervised learning for the emotion recognition. A GAN is trained with unsupervised learning, with another database (IEMOCAP), and part of the GAN structure (part of the autoencoder) will be used for the audio representation. The audio spectrogram will be extracted in 1-second windows of 16khz frequency, and this will serve as input to the model of audio representation trained with another database in an unsupervised way. This audio representation will serve as input to a convolutional network and a Dense layer with 'tanh' activation that performs the prediction of Arousal and Valence values. For joining the 1-second pieces of audio, the median of the predicted values of a given utterance will be taken.
Insight into Insiders and IT: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures
Venue: arXiv cs.CR 2018
Year: 2018

Insider threats are one of today's most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. Despite several scientific works published in this domain, we argue that the field can benefit from the proposed structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research, while leveraging existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include: 1) Incidents and datasets, 2) Analysis of attackers, 3) Simulations, and 4) Defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents, which is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers' efforts in the domain of insider threat, because it provides: a) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, b) an updated overview on publicly available datasets that can be used to test new detection solutions against other works, c) references of existing case studies and frameworks modeling insiders' behaviors for the purpose of reviewing defense solutions or extending their coverage, and d) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
Business Processes of High-Tech Enterprises
Venue: arXiv cs.CY 2018
Year: 2018

This paper analyzes the results of Russia's current innovative activities. It shows the need to increase the level of return on investments in the innovative capacities of high-technology enterprises (HTEs). The paper describes the methods to help increase the competitiveness of HTEs based on the implementation of modern control methods for innovative HTEs. It analyzes HTE KPIs, and also describes the characteristics of the organizational structure of HTEs. HTEs are studied as an innovative self-training HTE, and their characteristics and system of competencies are analyzed. The paper likewise describes the management of information support for managerial decisions in self-training HTEs. It shows that a considerable share of innovative products in a highly competitive market requires accelerated promotion and new approaches to building the system of business processes (BPs) for self-training HTEs. It also shows the dependency of self-training HTE competitiveness on innovative management methods for manufacturing and technological processes (MTP).
Intracranial Error Detection via Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks' learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.
Lower and Upper Bound for Computing the Size of All Second Neighbourhoods
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem of computing the size of each $r$-neighbourhood for every vertex of a graph. Specifically, we ask whether the size of the closed second neighbourhood can be computed in subquadratic time. Adapting the SETH reductions by Abboud et al. (2016) that exclude subquadratic algorithms to compute the radius of a graph, we find that a subquadratic algorithm would violate the SETH. On the other hand, a linear fpt-time algorithm by Demaine et al. (2014) parameterized by a certain `sparseness parameter' of the graph is known, where the dependence on the parameter is exponential. We show here that a better dependence is unlikely: for any~$δ< 2$, no algorithm running in time $O(2^{o({\rm vc}(G))} \, n^δ)$, where~${\rm vc}(G)$ is the vertex cover number, is possible unless the SETH fails. We supplement these lower bounds with algorithms that solve the problem in time~$O(2^{{\rm vc}(G)/2} {\rm vc}(G)^2 \cdot n)$ and $O(2^w w \cdot n)$.
Sustainable Cloud Computing: Foundations and Future Directions
Venue: arXiv cs.DC 2018
Year: 2018

Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters to support the ever-increasing demand for their computational and application services. However, the financial and carbon footprint related costs of running such large infrastructure negatively impacts the sustainability of cloud services. Most of existing efforts primarily focus on minimizing the energy consumption of servers. In this paper, we devise a conceptual model and practical design guidelines for holistic management of all resources (including servers, networks, storage, cooling systems) to improve the energy efficiency and reduce carbon footprints in Cloud Data Centers (CDCs). Furthermore, we discuss the intertwined relationship between energy and reliability for sustainable cloud computing, where we highlight the associated research issues. Finally, we propose a set of future research directions in the field and setup grounds for further practical developments.
Manifold Geometry with Fast Automatic Derivatives and Coordinate Frame Semantics Checking in C++
Venue: arXiv cs.RO 2018
Year: 2018

Computer vision and robotics problems often require representation and estimation of poses on the SE(3) manifold. Developers of algorithms that must run in real time face several time-consuming programming tasks, including deriving and computing analytic derivatives and avoiding mathematical errors when handling poses in multiple coordinate frames. To support rapid and error-free development, we present wave_geometry, a C++ manifold geometry library with two key contributions: expression template-based automatic differentiation and compile-time enforcement of coordinate frame semantics. We contrast the library with existing open source packages and show that it can evaluate Jacobians in forward and reverse mode with little to no runtime overhead compared to hand-coded derivatives. The library is available at https://github.com/wavelab/wave_geometry .
Simplified SPARQL REST API - CRUD on JSON Object Graphs via URI Paths
Venue: arXiv cs.DB 2018
Year: 2018

Within the Semantic Web community, SPARQL is one of the predominant languages to query and update RDF knowledge. However, the complexity of SPARQL, the underlying graph structure and various encodings are common sources of confusion for Semantic Web novices. In this paper we present a general purpose approach to convert any given SPARQL endpoint into a simple to use REST API. To lower the initial hurdle, we represent the underlying graph as an interlinked view of nested JSON objects that can be traversed by the API path.
Automatic Estimation of Modulation Transfer Functions
Venue: arXiv cs.CV 2018
Year: 2018

The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Instead, MTFs based on simulations or, at best, MTFs measured on other specimens of the same lens are used. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a data-driven supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available.
The repair problem for Reed-Solomon codes: Optimal repair of single and multiple erasures, asymptotically optimal node size
Venue: arXiv cs.IT 2018
Year: 2018

The repair problem in distributed storage addresses recovery of the data encoded using an erasure code, for instance, a Reed-Solomon (RS) code. We consider the problem of repairing a single node or multiple nodes in RS-coded storage systems using the smallest possible amount of inter-nodal communication. According to the cut-set bound, communication cost of repairing $h\ge 1$ failed nodes for an $(n,k=n-r)$ MDS code using $d$ helper nodes is at least $dhl/(d+h-k),$ where $l$ is the size of the node. Guruswami and Wootters (2016) initiated the study of efficient repair of RS codes, showing that they can be repaired using a smaller bandwidth than under the trivial approach. At the same time, their work as well as follow-up papers stopped short of constructing RS codes (or any scalar MDS codes) that meet the cut-set bound with equality. In this paper we construct families of RS codes that achieve the cutset bound for repair of one or several nodes. In the single-node case, we present RS codes of length $n$ over the field $\mathbb{F}_{q^l},l=\exp((1+o(1))n\log n)$ that meet the cut-set bound. We also prove an almost matching lower bound on $l$, showing that super-exponential scaling is both necessary and sufficient for scalar MDS codes to achieve the cut-set bound using linear repair schemes. For the case of multiple nodes, we construct a family of RS codes that achieve the cut-set bound universally for the repair of any $h=2,3,\dots$ failed nodes from any subset of $d$ helper nodes, $k\le d\le n-h.$ For a fixed number of parities $r$ the node size of the constructed codes is close to the smallest possible node size for codes with such properties.
Superconducting Optoelectronic Neurons V: Networks and Scaling
Venue: arXiv cs.NE 2018
Year: 2018

Networks of superconducting optoelectronic neurons are investigated for their near-term technological potential and long-term physical limitations. Networks with short average path length, high clustering coefficient, and power-law degree distribution are designed using a growth model that assigns connections between new and existing nodes based on spatial distance as well as degree of existing nodes. The network construction algorithm is scalable to arbitrary levels of network hierarchy and achieves systems with fractal spatial properties and efficient wiring. By modeling the physical size of superconducting optoelectronic neurons, we calculate the area of these networks. A system with 8100 neurons and 330,430 total synapses will fit on a 1\,cm $\times$ 1\,cm die. Systems of millions of neurons with hundreds of millions of synapses will fit on a 300\,mm wafer. For multi-wafer assemblies, communication at light speed enables a neuronal pool the size of a large data center comprising 100 trillion neurons with coherent oscillations at 1\,MHz. Assuming a power law frequency distribution, as is necessary for self-organized criticality, we calculate the power consumption of the networks. We find the use of single photons for communication and superconducting circuits for computation leads to power density low enough to be cooled by liquid $^4$He for networks of any scale.