OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis
Venue: arXiv cs.CL 2014
Year: 2014

Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameterization that is more suited to synthesis. In this paper, we use deep learning algorithms to investigate a data-driven parameterization technique that is designed for the specific requirements of synthesis. We create an invertible, low-dimensional, noise-robust encoding of the Mel Log Spectrum by training a tapered Stacked Denoising Autoencoder (SDA). This SDA is then unwrapped and used as the initialization for a Multi-Layer Perceptron (MLP). The MLP is fine-tuned by training it to reconstruct the input at the output layer. This MLP is then split down the middle to form encoding and decoding networks. These networks produce a parameterization of the Mel Log Spectrum that is intended to better fulfill the requirements of synthesis. Results are reported for experiments conducted using this resulting parameterization with the ClusterGen speech synthesizer.
Scheduling Policies for the LTE Downlink Channel: A Performance Comparison
Venue: arXiv cs.NI 2014
Year: 2014

A key feature of the packet scheduler in LTE system is that it can allocate resources both in the time and frequency domain. Furthermore, the scheduler is acquainted with channel state information periodically reported by user equipments either in an aggregate form for the whole downlink channel, or distinguished for each available subchannel. This mechanism allows for wide discretion in resource allocation, thus promoting the flourishing of several scheduling algorithms, with different purposes. It is therefore of great interest to compare the performance of such algorithms in different scenarios. A very common simulation tool that can be used for this purpose is ns-3, which already supports a set of well known scheduling algorithms for LTE downlink, though it still lacks schedulers that provide throughput guarantees. In this work we contribute to fill this gap by implementing a scheduling algorithm that provides long-term throughput guarantees to the different users, while opportunistically exploiting the instantaneous channel fluctuations to increase the cell capacity. We then perform a thorough performance analysis of the different scheduling algorithms by means of extensive ns-3 simulations, both for saturated UDP and TCP traffic sources. The analysis makes it possible to appreciate the difference among the scheduling algorithms, and to assess the performance gain, both in terms of cell capacity and packet service time, obtained by allowing the schedulers to work on the frequency domain.
On Evaluation Validity in Music Autotagging
Venue: arXiv cs.IR 2014
Year: 2014

Music autotagging, an established problem in Music Information Retrieval, aims to alleviate the human cost required to manually annotate collections of recorded music with textual labels by automating the process. Many autotagging systems have been proposed and evaluated by procedures and datasets that are now standard (used in MIREX, for instance). Very little work, however, has been dedicated to determine what these evaluations really mean about an autotagging system, or the comparison of two systems, for the problem of annotating music in the real world. In this article, we are concerned with explaining the figure of merit of an autotagging system evaluated with a standard approach. Specifically, does the figure of merit, or a comparison of figures of merit, warrant a conclusion about how well autotagging systems have learned to describe music with a specific vocabulary? The main contributions of this paper are a formalization of the notion of validity in autotagging evaluation, and a method to test it in general. We demonstrate the practical use of our method in experiments with three specific state-of-the-art autotagging systems --all of which are reproducible using the linked code and data. Our experiments show for these specific systems in a simple and objective two-class task that the standard evaluation approach does not provide valid indicators of their performance.
A Robust MUSIC Based Scheme for Interference Location in Satellite Systems with Multibeam Antennas
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we investigate methods for interference location in satellite communication system using satellite multi-beam antenna with subspace based schemes. A novel MUSIC based approach is proposed for estimating the direction of arrival of the interfering sources. The proposed method provides super resolution and asymptotic maximum likelihood estimates of the direction of arrivals even at low SNR values. Simulations were performed using typical satellite multi-beam antenna configurations and results show that the proposed scheme can effectively estimates the direction of arrival in the azimuth and elevation spectra. Compared to the support vector regression method, the proposed approach offer improved estimation accuracy at low SNR values.
A Monomial-Oriented GVW for Computing Gröbner Bases
Venue: arXiv cs.SC 2014
Year: 2014

The GVW algorithm, presented by Gao et al., is a signature-based algorithm for computing Gröbner bases. In this paper, a variant of GVW is presented. This new algorithm is called a monomial-oriented GVW algorithm or mo-GVW algorithm for short. The mo-GVW algorithm presents a new frame of GVW and regards {\em labeled monomials} instead of {\em labeled polynomials} as basic elements of the algorithm. Being different from the original GVW algorithm, for each labeled monomial, the mo-GVW makes efforts to find the smallest signature that can generate this monomial. The mo-GVW algorithm also avoids generating J-pairs, and uses efficient methods of searching reducers and checking criteria. Thus, the mo-GVW algorithm has a better performance during practical implementations.
Deep Tempering
Venue: arXiv cs.LG 2014
Year: 2014

Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work we propose a novel method of sampling from Boltzmann machines that demonstrates a computationally efficient way to promote mixing. Our approach leverages an under-appreciated property of deep generative models such as the Deep Belief Network (DBN), where Gibbs sampling from deeper levels of the latent variable hierarchy results in dramatically increased ergodicity. Our approach is thus to train an auxiliary latent hierarchical model, based on the DBN. When used in conjunction with parallel-tempering, the method is asymptotically guaranteed to simulate samples from the target RBM. Experimental results confirm the effectiveness of this sampling strategy in the context of RBM training.
LAF-Fabric: a data analysis tool for Linguistic Annotation Framework with an application to the Hebrew Bible
Venue: arXiv cs.CL 2014
Year: 2014

The Linguistic Annotation Framework (LAF) provides a general, extensible stand-off markup system for corpora. This paper discusses LAF-Fabric, a new tool to analyse LAF resources in general with an extension to process the Hebrew Bible in particular. We first walk through the history of the Hebrew Bible as text database in decennium-wide steps. Then we describe how LAF-Fabric may serve as an analysis tool for this corpus. Finally, we describe three analytic projects/workflows that benefit from the new LAF representation: 1) the study of linguistic variation: extract cooccurrence data of common nouns between the books of the Bible (Martijn Naaijer); 2) the study of the grammar of Hebrew poetry in the Psalms: extract clause typology (Gino Kalkman); 3) construction of a parser of classical Hebrew by Data Oriented Parsing: generate tree structures from the database (Andreas van Cranenburgh).
Using social network graph analysis for interest detection
Venue: arXiv cs.SI 2014
Year: 2014

A person's interests exist as an internal state and are difficult to define. Since only external actions are observable, a proxy must be used that represents someone's interests. Techniques like collaborative filtering, behavioral targeting, and hashtag analysis implicitly model an individual's interests. I argue that these models are limited to shallow, temporary interests, which do not reflect people's deeper interests or passions. I propose an alternative model of interests that takes advantage of a user's social graph. The basic principle is that people only follow those that interest them, so the social graph is an effective and robust proxy for people's interests.
Elicitation for Aggregation
Venue: arXiv cs.GT 2014
Year: 2014

We study the problem of eliciting and aggregating probabilistic information from multiple agents. In order to successfully aggregate the predictions of agents, the principal needs to elicit some notion of confidence from agents, capturing how much experience or knowledge led to their predictions. To formalize this, we consider a principal who wishes to elicit predictions about a random variable from a group of Bayesian agents, each of whom have privately observed some independent samples of the random variable, and hopes to aggregate the predictions as if she had directly observed the samples of all agents. Leveraging techniques from Bayesian statistics, we represent confidence as the number of samples an agent has observed, which is quantified by a hyperparameter from a conjugate family of prior distributions. This then allows us to show that if the principal has access to a few samples, she can achieve her aggregation goal by eliciting predictions from agents using proper scoring rules. In particular, if she has access to one sample, she can successfully aggregate the agents' predictions if and only if every posterior predictive distribution corresponds to a unique value of the hyperparameter. Furthermore, this uniqueness holds for many common distributions of interest. When this uniqueness property does not hold, we construct a novel and intuitive mechanism where a principal with two samples can elicit and optimally aggregate the agents' predictions.
Learning to Transfer Privileged Information
Venue: arXiv cs.CV 2014
Year: 2014

We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching computers to recognize objects in images. We want the computers to be able to learn faster at the expense of providing extra information during training time. As additional information about the image data, we look at several scenarios that have been studied in computer vision before: attributes, bounding boxes and image tags. The information is privileged as it is available at training time but not at test time. We explore two maximum-margin techniques that are able to make use of this additional source of information, for binary and multiclass object classification. We interpret these methods as learning easiness and hardness of the objects in the privileged space and then transferring this knowledge to train a better classifier in the original space. We provide a thorough analysis and comparison of information transfer from privileged to the original data spaces for both LUPI methods. Our experiments show that incorporating privileged information can improve the classification accuracy. Finally, we conduct user studies to understand which samples are easy and which are hard for human learning, and explore how this information is related to easy and hard samples when learning a classifier.
Risk Dynamics in Trade Networks
Venue: arXiv cs.GT 2014
Year: 2014

We introduce a new framework to model interactions among agents which seek to trade to minimize their risk with respect to some future outcome. We quantify this risk using the concept of risk measures from finance, and introduce a class of trade dynamics which allow agents to trade contracts contingent upon the future outcome. We then show that these trade dynamics exactly correspond to a variant of randomized coordinate descent. By extending the analysis of these coordinate descent methods to account for our more organic setting, we are able to show convergence rates for very general trade dynamics, showing that the market or network converges to a unique steady state. Applying these results to prediction markets, we expand on recent results by adding convergence rates and general aggregation properties. Finally, we illustrate the generality of our framework by applying it to agent interactions on a scale-free network.
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Venue: arXiv cs.LG 2014
Year: 2014

Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental study shows that its computation/prediction tradeoff ability compares very favorably against strong baselines.
Strong Converse for a Degraded Wiretap Channel via Active Hypothesis Testing
Venue: arXiv cs.IT 2014
Year: 2014

We establish an upper bound on the rate of codes for a wiretap channel with public feedback for a fixed probability of error and secrecy parameter. As a corollary, we obtain a strong converse for the capacity of a degraded wiretap channel with public feedback. Our converse proof is based on a reduction of active hypothesis testing for discriminating between two channels to coding for wiretap channel with feedback.
PinView: Implicit Feedback in Content-Based Image Retrieval
Venue: arXiv cs.IR 2014
Year: 2014

This paper describes PinView, a content-based image retrieval system that exploits implicit relevance feedback collected during a search session. PinView contains several novel methods to infer the intent of the user. From relevance feedback, such as eye movements or pointer clicks, and visual features of images, PinView learns a similarity metric between images which depends on the current interests of the user. It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user. We have integrated PinView to the content-based image retrieval system PicSOM, which enables applying PinView to real-world image databases. With the new algorithms PinView outperforms the original PicSOM, and in online experiments with real users the combination of implicit and explicit feedback gives the best results.
Factors Affecting QoS in Tanzania Cellular Networks
Venue: arXiv cs.CY 2014
Year: 2014

Quality of service in cellular communication system is a topic that recently has raised much interest for many researchers. This paper presents the findings obtained from the study on factors affecting QoS in Tanzania cellular networks. The study was carried out in Dodoma Municipal, Tanzania. The study employed cross sectional research design. Information was gathered from structured questionnaire of 240 subscribers during the study of quality of service for the four leading cellular networks in Tanzania. Both qualitative and quantitative data from field survey were collected and analyzed using Statistical Package for Social Sciences and Excel software. The study findings show that the major factors that degrade QoS in Tanzania cellular networks are inadequate network infrastructure, lack of fairness from service providers and little efforts taken by the government in enforcing the national agreed standards. Other factors are lack of reliable end to end systems, geographical terrain, low quality handsets, poor government monitoring on standards and lack of subscriber skills and training.
Is Twitter a Public Sphere for Online Conflicts? A Cross-Ideological and Cross-Hierarchical Look
Venue: arXiv cs.SI 2014
Year: 2014

The rise in popularity of Twitter has led to a debate on its impact on public opinions. The optimists foresee an increase in online participation and democratization due to social media's personal and interactive nature. Cyber-pessimists, on the other hand, explain how social media can lead to selective exposure and can be used as a disguise for those in power to disseminate biased information. To investigate this debate empirically, we evaluate Twitter as a public sphere using four metrics: equality, diversity, reciprocity and quality. Using these measurements, we analyze the communication patterns between individuals of different hierarchical levels and ideologies. We do this within the context of three diverse conflicts: Israel-Palestine, US Democrats-Republicans, and FC Barcelona-Real Madrid. In all cases, we collect data around a central pair of Twitter accounts representing the two main parties. Our results show in a quantitative manner that Twitter is not an ideal public sphere for democratic conversations and that hierarchical effects are part of the reason why it is not.
Fast, memory efficient low-rank approximation of SimRank
Venue: arXiv cs.DS 2014
Year: 2014

SimRank is a well-known similarity measure between graph vertices. In this paper novel low-rank approximation of SimRank is proposed.
An introduction to Docker for reproducible research, with examples from the R environment
Venue: arXiv cs.SE 2014
Year: 2014

As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a `DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.
Minimax Analysis of Active Learning
Venue: arXiv cs.LG 2014
Year: 2014

This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models. The results reveal a number of surprising facts. In particular, under the noise model of Tsybakov (2004), the minimax label complexity of active learning with a VC class is always asymptotically smaller than that of passive learning, and is typically significantly smaller than the best previously-published upper bounds in the active learning literature. In high-noise regimes, it turns out that all active learning problems of a given VC dimension have roughly the same minimax label complexity, which contrasts with well-known results for bounded noise. In low-noise regimes, we find that the label complexity is well-characterized by a simple combinatorial complexity measure we call the star number. Interestingly, we find that almost all of the complexity measures previously explored in the active learning literature have worst-case values exactly equal to the star number. We also propose new active learning strategies that nearly achieve these minimax label complexities.
Alltagstaugliche Biometrie: Entwicklungen, Herausforderungen und Chancen
Venue: arXiv cs.CR 2014
Year: 2014

This article summarizes recent trends in mobile biometrics.
Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis
Venue: arXiv cs.NE 2014
Year: 2014

In this article, we propose a combination of an noise-reduction algorithm based on Singular Spectrum Analysis (SSA) and a standard feedforward neural prediction model. Basically, the proposed algorithm consists of two different steps: data preprocessing based on the SSA filtering method and step-by-step training procedure in which we use a simple feedforward multilayer neural network with backpropagation learning. The proposed noise-reduction procedure successfully removes most of the noise. That increases long-term predictability of the processed dataset comparison with the raw dataset. The method was applied to predict the International sunspot number RZ time series. The results show that our combined technique has better performances than those offered by the same network directly applied to raw dataset.
Understanding Class-level Testability Through Dynamic Analysis
Venue: arXiv cs.SE 2014
Year: 2014

It is generally acknowledged that software testing is both challenging and time-consuming. Understanding the factors that may positively or negatively affect testing effort will point to possibilities for reducing this effort. Consequently there is a significant body of research that has investigated relationships between static code properties and testability. The work reported in this paper complements this body of research by providing an empirical evaluation of the degree of association between runtime properties and class-level testability in object-oriented (OO) systems. The motivation for the use of dynamic code properties comes from the success of such metrics in providing a more complete insight into the multiple dimensions of software quality. In particular, we investigate the potential relationships between the runtime characteristics of production code, represented by Dynamic Coupling and Key Classes, and internal class-level testability. Testability of a class is considered here at the level of unit tests and two different measures are used to characterise those unit tests. The selected measures relate to test scope and structure: one is intended to measure the unit test size, represented by test lines of code, and the other is designed to reflect the intended design, represented by the number of test cases. In this research we found that Dynamic Coupling and Key Classes have significant correlations with class-level testability measures. We therefore suggest that these properties could be used as indicators of class-level testability. These results enhance our current knowledge and should help researchers in the area to build on previous results regarding factors believed to be related to testability and testing. Our results should also benefit practitioners in future class testability planning and maintenance activities.