OpenRaiser/Intern-Atlas huggingface dataset:
On the Capacity of the Two-Hop Half-Duplex Relay Channel
Venue: arXiv cs.IT 2015
Year: 2015

Although extensively investigated, the capacity of the two-hop half-duplex (HD) relay channel is not fully understood. In particular, a capacity expression which can be evaluated straightforwardly is not available and an explicit coding scheme which achieves the capacity is not known either. In this paper, we derive a new expression for the capacity of the two-hop HD relay channel based on a simplified converse. Compared to previous results, this capacity expression can be easily evaluated. Moreover, we propose an explicit coding scheme which achieves the capacity. To achieve the capacity, the relay does not only send information to the destination by transmitting information-carrying symbols but also with the zero symbols resulting from the relay's silence during reception. As examples, we compute the capacities of the two-hop HD relay channel for the cases when the source-relay and relay-destination links are both binary-symmetric channels (BSCs) and additive white Gaussian noise (AWGN) channels, respectively, and numerically compare the capacities with the rates achieved by conventional relaying where the relay receives and transmits in a codeword-by-codeword fashion and switches between reception and transmission in a strictly alternating manner. Our numerical results show that the capacities of the two-hop HD relay channel for BSC and AWGN links are significantly larger than the rates achieved with conventional relaying.
Further Theoretical Study of Distribution Separation Method for Information Retrieval
Venue: arXiv cs.IR 2015
Year: 2015

Recently, a Distribution Separation Method (DSM) is proposed for relevant feedback in information retrieval, which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While DSM achieved a promising empirical performance, theoretical analysis of DSM is still need further study and comparison with other relative retrieval model. In this article, we first generalize DSM's theoretical property, by proving that its minimum correlation assumption is equivalent to the maximum (original and symmetrized) KL-Divergence assumption. Second, we also analytically show that the EM algorithm in a well-known Mixture Model is essentially a distribution separation process and can be simplified using the linear separation algorithm in DSM. Some empirical results are also presented to support our theoretical analysis.
Performance Analysis of Underlay Cognitive Radio Systems: Estimation-Throughput Tradeoff
Venue: arXiv cs.IT 2015
Year: 2015

In this letter, we study the performance of cognitive Underlay Systems (USs) that employ power control mechanism at the Secondary Transmitter (ST). Existing baseline models considered for the performance analysis either assume the knowledge of involved channels at the ST or retrieve this information by means of a feedback channel, however, such situations hardly exist in practice. Motivated by this fact, we propose a novel approach that incorporates the estimation of the involved channels at the ST, in order to characterize the performance of USs under realistic scenarios. Moreover, we apply an outage constraint that captures the impact of imperfect channel knowledge, particularly on the interference power received at the primary receiver. Besides this, we employ a transmit power constraint at the ST to determine an operating regime for the US. Finally, we analyze an interesting tradeoff between the estimation time and the secondary throughput allowing an optimized performance of the US.
Cyclic Communication and the Inseparability of MIMO Multi-way Relay Channels
Venue: arXiv cs.IT 2015
Year: 2015

The $K$-user MIMO multi-way relay channel (Y-channel) consisting of $K$ users with $M$ antennas each and a common relay node with $N$ antennas is studied in this paper. Each user wants to exchange messages with all the other users via the relay. A transmission strategy is proposed for this channel. The proposed strategy is based on two steps: channel diagonalization and cyclic communication. The channel diagonalization is applied by using zero-forcing beam-forming. After channel diagonalization, the channel is decomposed into parallel sub-channels. Cyclic communication is then applied, where signal-space alignment for network-coding is used over each sub-channel. The proposed strategy achieves the optimal DoF region of the channel if $N\leq M$. To prove this, a new degrees-of-freedom outer bound is derived. As a by-product, we conclude that the MIMO Y-channel is not separable, i.e., independent coding on separate sub-channels is not enough, and one has to code jointly over several sub-channels.
Structured Memory for Neural Turing Machines
Venue: arXiv cs.AI 2015
Year: 2015

Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this paper, we propose several different structures of memory for NTM, and we proved in experiments that two of our proposed structured-memory NTMs could lead to better convergence, in term of speed and prediction accuracy on copy task and associative recall task as in (Graves et al. 2014).
Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor
Venue: arXiv cs.IT 2015
Year: 2015

Spectrum Sensing (SS) is one of the most challenging issues in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance the detection reliability of a Primary User (PU) in fading environments. In this paper, we propose a hard decision based CSS algorithm using energy detection with taking into account the noise uncertainty effect. In the proposed algorithm, two dynamic thresholds are toggled based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. Also, their values are evaluated using an estimated value of the noise uncertainty factor. These dynamic thresholds are used to compensate the noise uncertainty effect and increase (decrease) the probability of detection (false alarm), respectively. Theoretical analysis is performed on the proposed algorithm to deduce its enhanced false alarm and detection probabilities compared to the conventional hard decision CSS. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high performance of the proposed scheme compared to the conventional CSS using different fusion rules.
Persistent Sharing of Fitness App Status on Twitter
Venue: arXiv cs.SI 2015
Year: 2015

As the world becomes more digitized and interconnected, information that was once considered to be private such as one's health status is now being shared publicly. To understand this new phenomenon better, it is crucial to study what types of health information are being shared on social media and why, as well as by whom. In this paper, we study the traits of users who share their personal health and fitness related information on social media by analyzing fitness status updates that MyFitnessPal users have shared via Twitter. We investigate how certain features like user profile, fitness activity, and fitness network in social media can potentially impact the long-term engagement of fitness app users. We also discuss implications of our findings to achieve a better retention of these users and to promote more sharing of their status updates.
BASEL (Buffering Architecture SpEcification Language)
Venue: arXiv cs.NI 2015
Year: 2015

Buffering architectures and policies for their efficient management constitute one of the core ingredients of a network architecture. In this work we introduce a new specification language, BASEL, that allows to express virtual buffering architectures and management policies representing a variety of economic models. BASEL does not require the user to implement policies in a high-level language; rather, the entire buffering architecture and its policy are reduced to several comparators and simple functions. We show examples of buffering architectures in BASEL and demonstrate empirically the impact of various settings on performance.
Dementia assistive system as a dense network
Venue: arXiv cs.CY 2015
Year: 2015

As elderly population increases, portion of dementia patients becomes larger. Thus social cost of caring dementia patients has been a major concern to many nations. This article introduces a dementia assistive system operated by various sensors and devices installed in body area and activity area of patients. Since this system is served based on a network which includes a number of nodes, it requires techniques to reduce the network performance degradation caused by densely composed sensors and devices. This article introduces existing protocols for communications of sensors and devices at both low rate and high rate transmission.
Partitioning Algorithms for Improving Efficiency of Topic Modeling Parallelization
Venue: arXiv cs.DC 2015
Year: 2015

Topic modeling is a very powerful technique in data analysis and data mining but it is generally slow. Many parallelization approaches have been proposed to speed up the learning process. However, they are usually not very efficient because of the many kinds of overhead, especially the load-balancing problem. We address this problem by proposing three partitioning algorithms, which either run more quickly or achieve better load balance than current partitioning algorithms. These algorithms can easily be extended to improve parallelization efficiency on other topic models similar to LDA, e.g., Bag of Timestamps, which is an extension of LDA with time information. We evaluate these algorithms on two popular datasets, NIPS and NYTimes. We also build a dataset containing over 1,000,000 scientific publications in the computer science domain from 1951 to 2010 to experiment with Bag of Timestamps parallelization, which we design to demonstrate the proposed algorithms' extensibility. The results strongly confirm the advantages of these algorithms.
Sketch-based Manga Retrieval using Manga109 Dataset
Venue: arXiv cs.CV 2015
Year: 2015

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, including keyword-based search by title or author, or tag-based categorization. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a content-based manga retrieval system. First, we propose a manga-specific image-describing framework. It consists of efficient margin labeling, edge orientation histogram feature description, and approximate nearest-neighbor search using product quantization. Second, we propose a sketch-based interface as a natural way to interact with manga content. The interface provides sketch-based querying, relevance feedback, and query retouch. For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research. We conducted a comparative study, a localization evaluation, and a large-scale qualitative study. From the experiments, we verified that: (1) the retrieval accuracy of the proposed method is higher than those of previous methods; (2) the proposed method can localize an object instance with reasonable runtime and accuracy; and (3) sketch querying is useful for manga search.
Online Markov decision processes with policy iteration
Venue: arXiv cs.LG 2015
Year: 2015

The online Markov decision process (MDP) is a generalization of the classical Markov decision process that incorporates changing reward functions. In this paper, we propose practical online MDP algorithms with policy iteration and theoretically establish a sublinear regret bound. A notable advantage of the proposed algorithm is that it can be easily combined with function approximation, and thus large and possibly continuous state spaces can be efficiently handled. Through experiments, we demonstrate the usefulness of the proposed algorithm.
Polar Codes With Higher-Order Memory
Venue: arXiv cs.IT 2015
Year: 2015

We introduce the design of a set of code sequences $ \{ {\mathscr C}_{n}^{(m)} : n\geq 1, m \geq 1 \}$, with memory order $m$ and code-length $N=O(ϕ^n)$, where $ ϕ\in (1,2]$ is the largest real root of the polynomial equation $F(m,ρ)=ρ^m-ρ^{m-1}-1$ and $ϕ$ is decreasing in $m$. $\{ {\mathscr C}_{n}^{(m)}\}$ is based on the channel polarization idea, where $ \{ {\mathscr C}_{n}^{(1)} \}$ coincides with the polar codes presented by Arıkan and can be encoded and decoded with complexity $O(N \log N)$. $ \{ {\mathscr C}_{n}^{(m)} \}$ achieves the symmetric capacity, $I(W)$, of an arbitrary binary-input, discrete-output memoryless channel, $W$, for any fixed $m$ and its encoding and decoding complexities decrease with growing $m$. We obtain an achievable bound on the probability of block-decoding error, $P_e$, of $\{ {\mathscr C}_{n}^{(m)} \}$ and showed that $P_e = O (2^{-N^β} )$ is achievable for $β< \frac{ϕ-1}{1+m(ϕ-1)}$.
Towards Cleaning-up Open Data Portals: A Metadata Reconciliation Approach
Venue: arXiv cs.IR 2015
Year: 2015

This paper presents an approach for metadata reconciliation, curation and linking for Open Governamental Data Portals (ODPs). ODPs have been lately the standard solution for governments willing to put their public data available for the society. Portal managers use several types of metadata to organize the datasets, one of the most important ones being the tags. However, the tagging process is subject to many problems, such as synonyms, ambiguity or incoherence, among others. As our empiric analysis of ODPs shows, these issues are currently prevalent in most ODPs and effectively hinders the reuse of Open Data. In order to address these problems, we develop and implement an approach for tag reconciliation in Open Data Portals, encompassing local actions related to individual portals, and global actions for adding a semantic metadata layer above individual portals. The local part aims to enhance the quality of tags in a single portal, and the global part is meant to interlink ODPs by establishing relations between tags.
Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition
Venue: arXiv cs.CV 2015
Year: 2015

We address the problem of generating video features for action recognition. The spatial pyramid and its variants have been very popular feature models due to their success in balancing spatial location encoding and spatial invariance. Although it seems straightforward to extend spatial pyramid to the temporal domain (spatio-temporal pyramid), the large spatio-temporal diversity of unconstrained videos and the resulting significantly higher dimensional representations make it less appealing. This paper introduces the space-time extended descriptor, a simple but efficient alternative way to include the spatio-temporal location into the video features. Instead of only coding motion information and leaving the spatio-temporal location to be represented at the pooling stage, location information is used as part of the encoding step. This method is a much more effective and efficient location encoding method as compared to the fixed grid model because it avoids the danger of over committing to artificial boundaries and its dimension is relatively low. Experimental results on several benchmark datasets show that, despite its simplicity, this method achieves comparable or better results than spatio-temporal pyramid.
A Picture is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels
Venue: arXiv cs.CV 2015
Year: 2015

The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels jots, which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator.Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.
Layer-Specific Adaptive Learning Rates for Deep Networks
Venue: arXiv cs.CV 2015
Year: 2015

The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to vanishing gradients, in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for shallow layers (near the input layer); this results in slow learning in the shallow layers. Additionally, it has also been shown that in highly non-convex problems, such as deep neural networks, there is a proliferation of high-error low curvature saddle points, which slows down learning dramatically. In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points. This enables us to speed up learning in the shallow layers of the network and quickly escape high-error low curvature saddle points. We test our method on standard image classification datasets such as MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy as well as reduces the required training time over standard algorithms.
Evaluating the Non-Intrusive Room Acoustics Algorithm with the ACE Challenge
Venue: arXiv cs.SD 2015
Year: 2015

We present a single channel data driven method for non-intrusive estimation of full-band reverberation time and full-band direct-to-reverberant ratio. The method extracts a number of features from reverberant speech and builds a model using a recurrent neural network to estimate the reverberant acoustic parameters. We explore three configurations by including different data and also by combining the recurrent neural network estimates using a support vector machine. Our best method to estimate DRR provides a Root Mean Square Deviation (RMSD) of 3.84 dB and a RMSD of 43.19 % for T60 estimation.
Joint Estimation of Reverberation Time and Direct-to-Reverberation Ratio from Speech using Auditory-Inspired Features
Venue: arXiv cs.SD 2015
Year: 2015

Blind estimation of acoustic room parameters such as the reverberation time $T_\mathrm{60}$ and the direct-to-reverberation ratio ($\mathrm{DRR}$) is still a challenging task, especially in case of blind estimation from reverberant speech signals. In this work, a novel approach is proposed for joint estimation of $T_\mathrm{60}$ and $\mathrm{DRR}$ from wideband speech in noisy conditions. 2D Gabor filters arranged in a filterbank are exploited for extracting features, which are then used as input to a multi-layer perceptron (MLP). The MLP output neurons correspond to specific pairs of $(T_\mathrm{60}, \mathrm{DRR})$ estimates; the output is integrated over time, and a simple decision rule results in our estimate. The approach is applied to single-microphone fullband speech signals provided by the Acoustic Characterization of Environments (ACE) Challenge. Our approach outperforms the baseline systems with median errors of close-to-zero and -1.5 dB for the $T_\mathrm{60}$ and $\mathrm{DRR}$ estimates, respectively, while the calculation of estimates is 5.8 times faster compared to the baseline.
Improving the Competency of First-Order Ontologies
Venue: arXiv cs.AI 2015
Year: 2015

We introduce a new framework to evaluate and improve first-order (FO) ontologies using automated theorem provers (ATPs) on the basis of competency questions (CQs). Our framework includes both the adaptation of a methodology for evaluating ontologies to the framework of first-order logic and a new set of non-trivial CQs designed to evaluate FO versions of SUMO, which significantly extends the very small set of CQs proposed in the literature. Most of these new CQs have been automatically generated from a small set of patterns and the mapping of WordNet to SUMO. Applying our framework, we demonstrate that Adimen-SUMO v2.2 outperforms TPTP-SUMO. In addition, using the feedback provided by ATPs we have set an improved version of Adimen-SUMO (v2.4). This new version outperforms the previous ones in terms of competency. For instance, "Humans can reason" is automatically inferred from Adimen-SUMO v2.4, while it is neither deducible from TPTP-SUMO nor Adimen-SUMO v2.2.
Measurement of Road Traffic Parameters Based on Multi-Vehicle Tracking
Venue: arXiv cs.CV 2015
Year: 2015

Development of computing power and cheap video cameras enabled today's traffic management systems to include more cameras and computer vision applications for transportation system monitoring and control. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow volume, origin-destination matrices, classify vehicles, etc. In this paper we propose a system for measurement of road traffic parameters (basic motion model parameters and macro-scopic traffic parameters). The system is based on Local Binary Pattern (LBP) image features classification with a cascade of Gentle Adaboost (GAB) classifiers to determine vehicle existence and its location in an image. Additionally, vehicle tracking and counting in a road traffic video is performed by using Extended Kalman Filter (EKF) and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by the means of execution time and accuracy.