OpenRaiser/Intern-Atlas huggingface dataset:
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Venue: arXiv cs.SD 2016
Year: 2016

The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep convolutional neural network architecture for environmental sound classification. Second, we propose the use of audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture. Combined with data augmentation, the proposed model produces state-of-the-art results for environmental sound classification. We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation. Finally, we examine the influence of each augmentation on the model's classification accuracy for each class, and observe that the accuracy for each class is influenced differently by each augmentation, suggesting that the performance of the model could be improved further by applying class-conditional data augmentation.
A Productivity Checker for Logic Programming
Venue: arXiv cs.PL 2016
Year: 2016

Automated analysis of recursive derivations in logic programming is known to be a hard problem. Both termination and non-termination are undecidable problems in Turing-complete languages. However, some declarative languages offer a practical work-around for this problem, by making a clear distinction between whether a program is meant to be understood inductively or coinductively. For programs meant to be understood inductively, termination must be guaranteed, whereas for programs meant to be understood coinductively, productive non-termination (or "productivity") must be ensured. In practice, such classification helps to better understand and implement some non-terminating computations. Logic programming was one of the first declarative languages to make this distinction: in the 1980's, Lloyd and van Emden's "computations at infinity" captured the big-step operational semantics of derivations that produce infinite terms as answers. In modern terms, computations at infinity describe "global productivity" of computations in logic programming. Most programming languages featuring coinduction also provide an observational, or small-step, notion of productivity as a computational counterpart to global productivity. This kind of productivity is ensured by checking that finite initial fragments of infinite computations can always be observed to produce finite portions of their infinite answer terms. In this paper we introduce a notion of observational productivity for logic programming as an algorithmic approximation of global productivity, give an effective procedure for semi-deciding observational productivity, and offer an implemented automated observational productivity checker for logic programs.
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Venue: arXiv cs.CL 2016
Year: 2016

Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500x, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).
A Pathophysiological Model-Driven Communication for Dynamic Distributed Medical Best Practice Guidance Systems
Venue: arXiv cs.CY 2016
Year: 2016

There is a great divide between rural and urban areas, particularly in medical emergency care. Although medical best practice guidelines exist in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. In this paper, we propose the concept of distributed executable medical best practice guidance systems to assist adherence to best practice from the time that a patient first presents at a rural hospital, through diagnosis and ambulance transfer to arrival and treatment at a regional tertiary hospital center. We codify complex medical knowledge in the form of simplified distributed executable disease automata, from the thin automata at rural hospitals to the rich automata in the regional center hospitals. However, a main challenge is how to efficiently and safely synchronize distributed best practice models as the communication among medical facilities, devices, and professionals generates a large number of messages. This complex problem of patient diagnosis and transport from rural to center facility is also fraught with many uncertainties and changes resulting in a high degree of dynamism. To address this situation, we propose a pathophysiological model-driven message exchange communication architecture that ensures the real-time and dynamic requirements of synchronization among distributed emergency best-practice models are met in a reliable and safe manner. Taking the signs, symptoms, and progress of stroke patients transported across a geographically distributed healthcare network as the motivating use case, we implement our communication system and apply it to our developed best practice automata using laboratory simulations. Our proof-of-concept experiments shows there is potential for the use of our system in a wide variety of domains.
Tuning Fuzzy Logic Programs with Symbolic Execution
Venue: arXiv cs.PL 2016
Year: 2016

Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming. One of the most difficult tasks when specifying a fuzzy logic program is determining the right weights for each rule, as well as the most appropriate fuzzy connectives and operators. In this paper, we introduce a symbolic extension of fuzzy logic programs in which some of these parameters can be left unknown, so that the user can easily see the impact of their possible values. Furthermore, given a number of test cases, the most appropriate values for these parameters can be automatically computed.
Evaluating Causal Models by Comparing Interventional Distributions
Venue: arXiv cs.AI 2016
Year: 2016

The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure. We present an alternative method for evaluating causal models that directly measures the accuracy of estimated interventional distributions. We contrast such distributional measures with structural measures, such as structural Hamming distance and structural intervention distance, showing that structural measures often correspond poorly to the accuracy of estimated interventional distributions. We use a number of real and synthetic datasets to illustrate various scenarios in which structural measures provide misleading results with respect to algorithm selection and parameter tuning, and we recommend that distributional measures become the new standard for evaluating causal models.
On Strategic Multi-Antenna Jamming in Centralized Detection Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we model a complete-information zero-sum game between a centralized detection network with a multiple access channel (MAC) between the sensors and the fusion center (FC), and a jammer with multiple transmitting antennas. We choose error probability at the FC as the performance metric, and investigate pure strategy equilibria for this game, and show that the jammer has no impact on the FC's error probability by employing pure strategies at the Nash equilibrium. Furthermore, we also show that the jammer has an impact on the expected utility if it employs mixed strategies.
Know Your Enemy: Stealth Configuration-Information Gathering in SDN
Venue: arXiv cs.CR 2016
Year: 2016

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideration
A Functional Package for Automatic Solution of Ordinary Differential Equations with Spectral Methods
Venue: arXiv cs.MS 2016
Year: 2016

We present a Python module named PyCheb, to solve the ordinary differential equations by using spectral collocation method. PyCheb incorporates discretization using Chebyshev points, barycentric interpolation and iterate methods. With this Python module, users can initialize the ODEsolver class by passing attributes, including the both sides of a given differential equation, boundary conditions, and the number of Chebyshev points, which can also be generated automatically by the ideal precision, to the constructor of ODEsolver class. Then, the instance of the ODEsolver class can be used to automatically determine the resolution of the differential equation as well as generate the graph of the high-precision approximate solution. (If you have any questions, please send me an email and I will reply ASAP. e-mail:shaohui_liu@qq.com/2013141482143@stu.scu.edu.cn)
Hard Clusters Maximize Mutual Information
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate mutual information as a cost function for clustering, and show in which cases hard, i.e., deterministic, clusters are optimal. Using convexity properties of mutual information, we show that certain formulations of the information bottleneck problem are solved by hard clusters. Similarly, hard clusters are optimal for the information-theoretic co-clustering problem that deals with simultaneous clustering of two dependent data sets. If both data sets have to be clustered using the same cluster assignment, hard clusters are not optimal in general. We point at interesting and practically relevant special cases of this so-called pairwise clustering problem, for which we can either prove or have evidence that hard clusters are optimal. Our results thus show that one can relax the otherwise combinatorial hard clustering problem to a real-valued optimization problem with the same global optimum.
An image compression and encryption scheme based on deep learning
Venue: arXiv cs.CV 2016
Year: 2016

Stacked Auto-Encoder (SAE) is a kind of deep learning algorithm for unsupervised learning. Which has multi layers that project the vector representation of input data into a lower vector space. These projection vectors are dense representations of the input data. As a result, SAE can be used for image compression. Using chaotic logistic map, the compression ones can further be encrypted. In this study, an application of image compression and encryption is suggested using SAE and chaotic logistic map. Experiments show that this application is feasible and effective. It can be used for image transmission and image protection on internet simultaneously.
MT3S: Mobile Turkish Scene Text-to-Speech System for the Visually Impaired
Venue: arXiv cs.MM 2016
Year: 2016

Reading text is one of the essential needs of the visually impaired people. We developed a mobile system that can read Turkish scene and book text, using a fast gradient-based multi-scale text detection algorithm for real-time operation and Tesseract OCR engine for character recognition. We evaluated the OCR accuracy and running time of our system on a new, publicly available mobile Turkish scene text dataset we constructed and also compared with state-of-the-art systems. Our system proved to be much faster, able to run on a mobile device, with OCR accuracy comparable to the state-of-the-art.
A Holistic Approach for Data-Driven Object Cutout
Venue: arXiv cs.CV 2016
Year: 2016

Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerable background clutter. In contrast to existing cutout methods, which are based mainly on low-level image analysis, we propose a more holistic approach, which considers the entire shape of the object of interest by leveraging higher-level image analysis and learnt global shape priors. Specifically, we leverage a deep neural network (DNN) trained for objects of a particular class (chairs) for realizing this mechanism. Given a rectangular image region, the DNN outputs a probability map (P-map) that indicates for each pixel inside the rectangle how likely it is to be contained inside an object from the class of interest. We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask. This amounts to an automatic end-to-end pipeline for catergory-specific object cutout. We evaluate our approach on segmentation benchmark datasets, and show that it significantly outperforms the state-of-the-art on them.
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
Venue: arXiv cs.LG 2016
Year: 2016

We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating responses at the individual-level is valuable for personalizing decision-making. We refer to this as the problem of estimating individualized treatment response (ITR) curves. In statistics, G-computation formula (Robins, 1986) has been commonly used for estimating treatment responses from observational data containing sequential treatment assignments. However, past studies have focused predominantly on obtaining point-in-time estimates at the population level. We leverage the G-computation formula and develop a novel Bayesian nonparametric (BNP) method that can flexibly model functional data and provide posterior inference over the treatment response curves at both the individual and population level. On a challenging dataset containing time series from patients admitted to a hospital, we estimate responses to treatments used in managing kidney function and show that the resulting fits are more accurate than alternative approaches. Accurate methods for obtaining ITRs from observational data can dramatically accelerate the pace at which personalized treatment plans become possible.
Coinductive Soundness of Corecursive Type Class Resolution
Venue: arXiv cs.PL 2016
Year: 2016

Horn clauses and first-order resolution are commonly used to implement type classes in Haskell. Several corecursive extensions to type class resolution have recently been proposed, with the goal of allowing (co)recursive dictionary construction where resolution does not termi- nate. This paper shows, for the first time, that corecursive type class resolution and its extensions are coinductively sound with respect to the greatest Herbrand models of logic programs and that they are induc- tively unsound with respect to the least Herbrand models. We establish incompleteness results for various fragments of the proof system.
Generation of the Single Precision BLAS library for the Parallella platform, with Epiphany co-processor acceleration, using the BLIS framework
Venue: arXiv cs.DC 2016
Year: 2016

The Parallella is a hybrid computing platform that came into existence as the result of a Kickstarter project by Adapteva. It is composed of the high performance, energy-efficient, manycore architecture, Epiphany chip (used as co-processor) and one Zynq-7000 series chip, which normally runs a regular Linux OS version, serves as the main processor, and implements "glue logic" in its internal FPGA to communicate with the many interfaces in the Parallella. In this paper an Epiphany-accelerated BLAS library for the Parallella platform was created (which could be suitable, also, for similar hybrid platforms that include the Epiphany chip as a coprocessor). For the actual instantiation of the BLAS, the BLIS framework was used. There have been previous implementations of Matrix-Matrix multiplication, on this platform, that achieved very good performances inside the Epiphany chip (up to 85% of peak), but not so good ones for the complete Parallella platform (due to inter-chip data transfer bandwidth limitations). The main purpose of this work was to get closer to practical Linear Algebra aplications for the entire Parallella platform, with scientific computing in view.
Diversified Top-k Similarity Search in Large Attributed Networks
Venue: arXiv cs.IR 2016
Year: 2016

Given a large network and a query node, finding its top-k similar nodes is a primitive operation in many graph-based applications. Recently enhancing search results with diversification have received much attention. In this paper, we explore an novel problem of searching for top-k diversified similar nodes in attributed networks, with the motivation that modeling diversification in an attributed network should consider both the emergence of network links and the attribute features of nodes such as user profile information. We formulate this practical problem as two optimization problems: the Attributed Coverage Diversification (ACD) problem and the r-Dissimilar Attributed Coverage Diversification (r-DACD) problem. Based on the submodularity and the monotonicity of ACD, we propose an efficient greedy algorithm achieving a tight approximation guarantee of 1-1/e. Unlike the expension based methods only considering nodes' neighborhood, ACD generalize the definition of diversification to nodes' own features. To capture diversification in topological structure of networks, the r-DACD problem introduce a dissimilarity constraint. We refer to this problem as the Dissimilarity Constrained Non-monotone Submodular Maximization (DCNSM) problem. We prove that there is no constant-factor approximation for DCNSM, and also present an efficient greedy algorithms achieving $1/ρ$ approximation, where $ρ\leΔ$, $Δ$ is the maximum degree of its dissimilarity based graph. To the best of our knowledge, it is the first approximation algorithm for the Submodular Maximization problem with a distance constraint. The experimental results on real-world attributed network datasets demonstrate the effectiveness of our methods, and confirm that adding dissimilarity constraint can significantly enhance the performance of diversification.
Probabilistic Data Analysis with Probabilistic Programming
Venue: arXiv cs.AI 2016
Year: 2016

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.
Multi-Person Tracking by Multicut and Deep Matching
Venue: arXiv cs.CV 2016
Year: 2016

In [1], we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem. In this paper, we modify and extend [1] in three ways: 1) We introduce a novel local pairwise feature based on local appearance matching that is robust to partial occlusion and camera motion. 2) We perform extensive experiments to compare different pairwise potentials and to analyze the robustness of the tracking formulation. 3) We consider a plain multicut problem and remove outlying clusters from its solution. This allows us to employ an efficient primal feasible optimization algorithm that is not applicable to the subgraph multicut problem of [1]. Unlike the branch-and-cut algorithm used there, this efficient algorithm used here is applicable to long videos and many detections. Together with the novel feature, it eliminates the need for the intermediate tracklet representation of [1]. We demonstrate the effectiveness of our overall approach on the MOT16 benchmark [2], achieving state-of-art performance.
Channel Estimation and Uplink Achievable Rates in One-Bit Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers channel estimation and achievable rates for the uplink of a massive multiple-input multiple-output (MIMO) system where the base station is equipped with one-bit analog-to-digital converters (ADCs). By rewriting the nonlinear one-bit quantization using a linear expression, we first derive a simple and insightful expression for the linear minimum mean-square-error (LMMSE) channel estimator. Then employing this channel estimator, we derive a closed-form expression for the lower bound of the achievable rate for the maximum ratio combiner (MRC) receiver. Numerical results are presented to verify our analysis and show that our proposed LMMSE channel estimator outperforms the near maximum likelihood (nML) estimator proposed previously.