OpenRaiser/Intern-Atlas huggingface dataset:
Reliability of universal decoding based on vector-quantized codewords
Venue: arXiv cs.IT 2016
Year: 2016

Motivated by applications of biometric identification and content identification systems, we consider the problem of random coding for channels, where each codeword undergoes lossy compression (vector quantization), and where the decoder bases its decision only on the compressed codewords and the channel output, which is in turn, the channel's response to the transmission of an original codeword, before compression. For memoryless sources and memoryless channels with finite alphabets, we propose a new universal decoder and analyze its error exponent, which improves on an earlier result by Dasarathy and Draper (2011), who used the classic maximum mutual information (MMI) universal decoder. Further, we show that our universal decoder provides the same error exponent as that of the optimal, maximum likelihood (ML) decoder, at least as long as all single-letter transition probabilities of the channel are positive. We conjecture that the same argument remains true even without this positivity condition.
A generic framework for the development of geospatial processing pipelines on clusters
Venue: arXiv cs.DC 2016
Year: 2016

The amount of remote sensing data available to applications is constantly growing due to the rise of very-high-resolution sensors and short repeat cycle satellites. Consequently, tackling computational complexity in Earth Observation information extraction is rising as a major challenge. Resorting to High Performance Computing (HPC) is becoming a common practice, since it provides environments and programming facilities able to speed-up processes. In particular, clusters are flexible, cost-effective systems able to perform data-intensive tasks ideally fulfilling any computational requirement. However, their use typically implies a significant coding effort to build proper implementations of specific processing pipelines. This paper presents a generic framework for the development of RS images processing applications targeting cluster computing. It is based on common open sources libraries, and leverages the parallelization of a wide variety of image processing pipelines in a transparent way. Performances on typical RS tasks implemented using the proposed framework demonstrate a great potential for the effective and timely processing of large amount of data.
Statistical comparison of classifiers through Bayesian hierarchical modelling
Venue: arXiv cs.LG 2016
Year: 2016

Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation results obtained by two classifiers on multiple data sets. It returns the posterior probability of the accuracies of the two classifiers being practically equivalent or significantly different. A further strength of the hierarchical model is that, by jointly analyzing the results obtained on all data sets, it reduces the estimation error compared to the usual approach of averaging the cross-validation results obtained on a given data set.
StruClus: Structural Clustering of Large-Scale Graph Databases
Venue: arXiv cs.DB 2016
Year: 2016

We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the clustering process, and helps to interpret the clustering results. The projection-based nature of the clustering approach allows us to bypass dimensionality and feature extraction problems that arise in the context of graph datasets reduced to pairwise distances or feature vectors. While achieving high quality and (human) interpretable clusterings, the runtime of the algorithm only grows linearly with the number of graphs. Furthermore, the approach is easy to parallelize and therefore suitable for very large datasets. Our extensive experimental evaluation on synthetic and real world datasets demonstrates the superiority of our approach over existing structural and subspace clustering algorithms, both, from a runtime and quality point of view.
SWoTSuite: A Development Framework for Prototyping Cross-domain Semantic Web of Things Applications
Venue: arXiv cs.OH 2016
Year: 2016

Semantic Web of Things (SWoT) applications focus on providing a wide-scale interoperability that allows the sharing of IoT devices across domains and the reusing of available knowledge on the web. However, the application development is difficult because developers have to do various tasks such as designing an application, annotating IoT data, interpreting data, and combining application domains. To address the above challenges, this paper demonstrates SWoTSuite, a toolkit for prototyping SWoT applications. It hides the use of semantic web technologies as much as possible to avoid the burden of designing SWoT applications that involves designing ontologies, annotating sensor data, and using reasoning mechanisms to enrich data. Taking inspiration from sharing and reuse approaches, SWoTSuite reuses data and vocabularies. It leverages existing technologies to build applications. We take a hello world naturopathy application as an example and demonstrate an application development process using SWoTSuite. The demo video is available at URL: http://tinyurl.com/zs9flrt.
Learning to Push by Grasping: Using multiple tasks for effective learning
Venue: arXiv cs.RO 2016
Year: 2016

Recently, end-to-end learning frameworks are gaining prevalence in the field of robot control. These frameworks input states/images and directly predict the torques or the action parameters. However, these approaches are often critiqued due to their huge data requirements for learning a task. The argument of the difficulty in scalability to multiple tasks is well founded, since training these tasks often require hundreds or thousands of examples. But do end-to-end approaches need to learn a unique model for every task? Intuitively, it seems that sharing across tasks should help since all tasks require some common understanding of the environment. In this paper, we attempt to take the next step in data-driven end-to-end learning frameworks: move from the realm of task-specific models to joint learning of multiple robot tasks. In an astonishing result we show that models with multi-task learning tend to perform better than task-specific models trained with same amounts of data. For example, a deep-network learned with 2.5K grasp and 2.5K push examples performs better on grasping than a network trained on 5K grasp examples.
Universum Learning for Multiclass SVM
Venue: arXiv cs.LG 2016
Year: 2016

We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose a span bound for MU-SVM that can be used for model selection thereby avoiding resampling. Empirical results demonstrate the effectiveness of MU-SVM and the proposed bound.
A comparative study of complexity of handwritten Bharati characters with that of major Indian scripts
Venue: arXiv cs.CV 2016
Year: 2016

We present Bharati, a simple, novel script that can represent the characters of a majority of contemporary Indian scripts. The shapes/motifs of Bharati characters are drawn from some of the simplest characters of existing Indian scripts. Bharati characters are designed such that they strictly reflect the underlying phonetic organization, thereby attributing to the script qualities of simplicity, familiarity, ease of acquisition and use. Thus, employing Bharati script as a common script for a majority of Indian languages can ameliorate several existing communication bottlenecks in India. We perform a complexity analysis of handwritten Bharati script and compare its complexity with that of 9 major Indian scripts. The measures of complexity are derived from a theory of handwritten characters based on Catastrophe theory. Bharati script is shown to be simpler than the 9 major Indian scripts in most measures of complexity.
Tensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition
Venue: arXiv cs.NA 2016
Year: 2016

Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model which represents data as an ordered network of sub-tensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network decomposition has been long studied in quantum physics and scientific computing. In this study, we present novel algorithms and applications of tensor network decompositions, with a particular focus on the tensor train decomposition and its variants. The novel algorithms developed for the tensor train decomposition update, in an alternating way, one or several core tensors at each iteration, and exhibit enhanced mathematical tractability and scalability to exceedingly large-scale data tensors. The proposed algorithms are tested in classic paradigms of blind source separation from a single mixture, denoising, and feature extraction, and achieve superior performance over the widely used truncated algorithms for tensor train decomposition.
Measurement of Sound Fields Using Moving Microphones
Venue: arXiv cs.SD 2016
Year: 2016

The sampling of sound fields involves the measurement of spatially dependent room impulse responses, where the Nyquist-Shannon sampling theorem applies in both the temporal and spatial domain. Therefore, sampling inside a volume of interest requires a huge number of sampling points in space, which comes along with further difficulties such as exact microphone positioning and calibration of multiple microphones. In this paper, we present a method for measuring sound fields using moving microphones whose trajectories are known to the algorithm. At that, the number of microphones is customizable by trading measurement effort against sampling time. Through spatial interpolation of the dynamic measurements, a system of linear equations is set up which allows for the reconstruction of the entire sound field inside the volume of interest.
Micropolis Interdependency Modeling using Open Hybrid Automata
Venue: arXiv cs.SY 2016
Year: 2016

Micropolis is a virtual city that is used for various studies, such as modeling and analyzing water networks. In this paper we model the various interdependencies between three major infrastructures in Micropolis, the power system, the communication network and the water network, in order to study cascades effects between them. Specifically, we develop open hybrid automata models for the main components of the three infrastructures and then composed them together based on their interdependencies.
On the statistical properties of viral misinformation in online social media
Venue: arXiv cs.SI 2016
Year: 2016

The massive diffusion of online social media allows for the rapid and uncontrolled spreading of conspiracy theories, hoaxes, unsubstantiated claims, and false news. Such an impressive amount of misinformation can influence policy preferences and encourage behaviors strongly divergent from recommended practices. In this paper, we study the statistical properties of viral misinformation in online social media. By means of methods belonging to Extreme Value Theory, we show that the number of extremely viral posts over time follows a homogeneous Poisson process, and that the interarrival times between such posts are independent and identically distributed, following an exponential distribution. Moreover, we characterize the uncertainty around the rate parameter of the Poisson process through Bayesian methods. Finally, we are able to derive the predictive posterior probability distribution of the number of posts exceeding a certain threshold of shares over a finite interval of time.
A Cross Entropy based Stochastic Approximation Algorithm for Reinforcement Learning with Linear Function Approximation
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we provide a new algorithm for the problem of prediction in Reinforcement Learning, \emph{i.e.}, estimating the Value Function of a Markov Reward Process (MRP) using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The algorithm is a multi-timescale variant of the very popular Cross Entropy (CE) method which is a model based search method to find the global optimum of a real-valued function. This is the first time a model based search method is used for the prediction problem. The application of CE to a stochastic setting is a completely unexplored domain. A proof of convergence using the ODE method is provided. The theoretical results are supplemented with experimental comparisons. The algorithm achieves good performance fairly consistently on many RL benchmark problems. This demonstrates the competitiveness of our algorithm against least squares and other state-of-the-art algorithms in terms of computational efficiency, accuracy and stability.
Distributed Navigation of Multi-Robot Systems For Sensing Coverage
Venue: arXiv cs.RO 2016
Year: 2016

This research investigates decentralized control of mobile robots specifically for coverage problems. There are different approaches associated with decentralized control strategy for coverage control problems. We perform a comparative review of these approaches and use the approach based on simple local coordination rules. We investigate this extensively used nearest neighbour rule based approach for developing coverage control algorithms. In this approach, a mobile robot gives an equal importance to every neighbour robot coming under its communication range. We develop our control approach by making some of the mobile robots playing a more influential role than other members in the team. The approach based on this control strategy becomes efficient in terms of achieving a consensus on control inputs, say heading angle, velocity, etc. The decentralized control of mobile robots can also exhibit a cyclic behaviour under some physical constraints like a quantized orientation of mobile robot. We further investigate the cyclic behaviour appearing due to the quantized control of mobile robots under some conditions. Our nearest neighbour rule based approach offers a biased strategy in case of cyclic behaviour appearing in the team of mobile robots. We consider a clustering technique inside the team of mobile robots. Our decentralized control strategy calculates the similarity measure among the neighbours of a mobile robot. The team of mobile robots with the similarity measure based approach becomes efficient in achieving a fast consensus like on heading angle or velocity. We perform a rigorous mathematical analysis of our developed approach. We also develop a condition based on relaxed criteria for achieving consensus on velocity or heading angle of the mobile robots. Our validation approach is based on mathematical arguments and extensive computer simulations.
Knowledge management metrics for Public Organizations: A literature review-based proposal
Venue: arXiv cs.CY 2016
Year: 2016

Knowledge Management (KM) is a relatively new phenomenon that appears in the field of Public Sector Organizations (PSO) bringing new paradigms of organizational management, challenges, risks and opportunities for its implementation, development and evaluation. KM can be seen as a systematic and deliberate effort to coordinate people, technology, organizational structures and its environment through knowledge reuse and innovation. This management approach has been established in parallel with the development and use of information and communications technologies (ICT). Nowadays more PSO are embodying KM practices in their core processes for support them, and as an advanced management strategy to create a new culture based on technology and resources efficiency. In this paper, we observed that KM can support organizational goals in PSO. The aim of this paper is to understand KM factors and its associated components, and propose KM metrics for measure KM programs in PSO. Through a critical literature review we analysed diverse studies related with KM performance indicators in PSO, then based on previous works we summarized the more convenient this purpose. We found that, in academic literature, studies about KM measurement in PSO are uncommon and emerging. As well, in the last section of this paper, we present a proposal of KM metrics for PSO, and some recommendations and practical implications for KM metrics development in PSO. This academic endeavour seeks to contribute to theoretical debate about KM measure development for KM initiatives in PSO.
Algorithms for item categorization based on ordinal ranking data
Venue: arXiv cs.LG 2016
Year: 2016

We present a new method for identifying the latent categorization of items based on their rankings. Complimenting a recent work that uses a Dirichlet prior on preference vectors and variational inference, we show that this problem can be effectively dealt with using existing community detection algorithms, with the communities corresponding to item categories. In particular we convert the bipartite ranking data to a unipartite graph of item affinities, and apply community detection algorithms. In this context we modify an existing algorithm - namely the label propagation algorithm to a variant that uses the distance between the nodes for weighting the label propagation - to identify the categories. We propose and analyze a synthetic ordinal ranking model and show its relation to the recently much studied stochastic block model. We test our algorithms on synthetic data and compare performance with several popular community detection algorithms. We also test the method on real data sets of movie categorization from the Movie Lens database. In all of the cases our algorithm is able to identify the categories for a suitable choice of tuning parameter.
NP vs PSPACE
Venue: arXiv cs.CC 2016
Year: 2016

We present a proof of the conjecture $\mathcal{NP}$ = $\mathcal{PSPACE}$ by showing that arbitrary tautologies of Johansson's minimal propositional logic admit "small" polynomial-size dag-like natural deductions in Prawitz's system for minimal propositional logic. These "small" deductions arise from standard "large"\ tree-like inputs by horizontal dag-like compression that is obtained by merging distinct nodes labeled with identical formulas occurring in horizontal sections of deductions involved. The underlying "geometric" idea: if the height, $h\left( \partial \right) $ , and the total number of distinct formulas, $ϕ\left( \partial \right) $ , of a given tree-like deduction $\partial$ of a minimal tautology $ρ$ are both polynomial in the length of $ρ$, $\left| ρ\right|$, then the size of the horizontal dag-like compression is at most $h\left( \partial \right) \times ϕ\left( \partial \right) $, and hence polynomial in $\left| ρ\right|$. The attached proof is due to the first author, but it was the second author who proposed an initial idea to attack a weaker conjecture $\mathcal{NP}= \mathcal{\mathit{co}NP}$ by reductions in diverse natural deduction formalisms for propositional logic. That idea included interactive use of minimal, intuitionistic and classical formalisms, so its practical implementation was too involved. The attached proof of $ \mathcal{NP}=\mathcal{PSPACE}$ runs inside the natural deduction interpretation of Hudelmaier's cutfree sequent calculus for minimal logic.
Optimized motor imagery paradigm based on imagining Chinese characters writing movement
Venue: arXiv cs.HC 2016
Year: 2016

Motor imagery (MI) is a mental representation of motor behavior that has been widely used as a control method for a brain-computer interface (BCI), allowing communication for the physically impaired. The performance of MI based BCI mainly depends on the subject's ability to self-modulate EEG signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involves abstract motor tasks and is time-consuming. To improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 years, mean 23.6+/-1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements following the sequence of writing strokes in the Chinese character. This paradigm was meant to find an effective and familiar action for most Chinese people, to provide them with a specific, extensively practiced task and help them modulate brain activity. Results showed that the writing task paradigm yielded significantly better performance than the traditional arrow paradigm (p<0.001). Questionnaire replies indicated that most subjects thought the new paradigm was easier and more comfortable. The proposed new motor imagery paradigm could guide subjects to help them modulate brain activity effectively. Results showed that there were significant improvements using new paradigm, both in classification accuracy and usability.
Joint Channel Estimation / Data Detection in MIMO-FBMC/OQAM Systems - A Tensor-Based Approach
Venue: arXiv cs.IT 2016
Year: 2016

Filter bank-based multicarrier (FBMC) systems are currently being considered as a prevalent candidate for replacing the long established cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) in the physical layer of next generation communications systems. In particular, FBMC/OQAM has received increasing attention due to, among other features, its potential for maximum spectral efficiency. It suffers, however, from an intrinsic self-interference effect, which complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-)blind FBMC/OQAM receivers have been little studied so far and mainly for single-antenna systems. The problem of joint channel estimation and data detection in a MIMO-FBMC/OQAM system, given limited or no training information, is studied in this paper through a tensor-based approach in the light of the success of such techniques in OFDM applications. Simulation-based comparisons with CP-OFDM are included, for realistic transmission models.