OpenRaiser/Intern-Atlas huggingface dataset:
Linear Codes for Broadcasting with Noisy Side Information
Venue: arXiv cs.IT 2018
Year: 2018

We consider network coding for a noiseless broadcast channel where each receiver demands a subset of messages available at the transmitter and is equipped with noisy side information in the form an erroneous version of the message symbols it demands. We view the message symbols as elements from a finite field and assume that the number of symbol errors in the noisy side information is upper bounded by a known constant. This communication problem, which we refer to as 'broadcasting with noisy side information' (BNSI), has applications in the re-transmission phase of downlink networks. We derive a necessary and sufficient condition for a linear coding scheme to satisfy the demands of all the receivers in a given BNSI network, and show that syndrome decoding can be used at the receivers to decode the demanded messages from the received codeword and the available noisy side information. We represent BNSI problems as bipartite graphs, and using this representation, classify the family of problems where linear coding provides bandwidth savings compared to uncoded transmission. We provide a simple algorithm to determine if a given BNSI network belongs to this family of problems, i.e., to identify if linear coding provides an advantage over uncoded transmission for the given BNSI problem. We provide lower bounds and upper bounds on the optimal codelength and constructions of linear coding schemes based on linear error correcting codes. For any given BNSI problem, we construct an equivalent index coding problem. A linear code is a valid scheme for a BNSI problem if and only if it is valid for the constructed index coding problem.
Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, the transmitter (TX) sends a common information symbol to multiple receivers (RXs) and a fusion center (FC) chooses the TX symbol that is more likely, given the likelihood of its observations from all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. Three ML detection variants are proposed according to different RX behaviors and different knowledge at the FC. The system error probabilities for two ML detector variants are derived, one of which is in closed form. The optimal molecule allocation among RXs to minimize the system error probability of one variant is determined by solving a joint optimization problem. Also for this variant, the equal distribution of molecules among two symmetric RXs is analytically shown to achieve the local minimal error probability. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of simpler, non-ML cooperative variants and demonstrate that these simpler cooperative variants have error performance comparable to ML detectors.
FWLBP: A Scale Invariant Descriptor for Texture Classification
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary Pattern (FWLBP). The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct a scale-invariant descriptor. Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples. The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image. The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes. In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors.
Combating Error Propagation in Window Decoding of Braided Convolutional Codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study sliding window decoding of braided convolutional codes (BCCs) in the context of a streaming application, where decoder error propagation can be a serious problem. A window extension algorithm and a resynchronization mechanism are introduced to mitigate the effect of error propagation. In addition, we introduce a soft bit-error-rate stopping rule to reduce computational complexity, and the tradeoff between performance and complexity is examined. Simulation results show that, using the proposed window extension algorithm and resynchronization mechanism, the error performance of BCCs can be improved by up to three orders of magnitude with reduced computational complexity.
Symmetry reduction for dynamic programming
Venue: arXiv cs.SY 2018
Year: 2018

We present a method of exploiting symmetries of discrete-time optimal control problems to reduce the dimensionality of dynamic programming iterations. The results are derived for systems with continuous state variables, and can be applied to systems with continuous or discrete symmetry groups. We prove that symmetries of the state update equation and stage costs induce corresponding symmetries of the optimal cost function and the optimal policies. We then provide a general framework for computing the optimal cost function based on gridding a space of lower dimension than the original state space. This method does not require algebraic manipulation of the state update equations; it only requires knowledge of the symmetries that the state update equations possess. Since the method can be performed without any knowledge of the state update map beyond being able to evaluate it and verify its symmetries, this enables the method to be applied in a wide range of application problems. We illustrate these results on two six-dimensional optimal control problems that are computationally difficult to solve by dynamic programming without symmetry reduction.
Radio-based Traffic Flow Detection and Vehicle Classification for Future Smart Cities
Venue: arXiv cs.NI 2018
Year: 2018

Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and proactive jam avoidance by intelligent traffic control mechanisms. In addition, the monitoring and classification of vehicles can be used in the field of smart parking systems. The required data is measured using networks with a wide range of sensors. Nevertheless, in the context of smart cities no existing solution for traffic flow detection and vehicle classification is able to guarantee high classification accuracy, low deployment and maintenance costs, low power consumption and a weather-independent operation while respecting privacy. In this paper, we propose a radiobased approach for traffic flow detection and vehicle classification using signal attenuation measurements and machine learning algorithms. The results of comprehensive measurements in the field prove its high classification success rate of about 99%.
Fooling End-to-end Speaker Verification by Adversarial Examples
Venue: arXiv cs.LG 2018
Year: 2018

Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems using end-to-end deep neural models. In this paper, we show that such systems are vulnerable to adversarial example attack. Adversarial examples are generated by adding a peculiar noise to original speaker examples, in such a way that they are almost indistinguishable from the original examples by a human listener. Yet, the generated waveforms, which sound as speaker A can be used to fool such a system by claiming as if the waveforms were uttered by speaker B. We present white-box attacks on an end-to-end deep network that was either trained on YOHO or NTIMIT. We also present two black-box attacks: where the adversarial examples were generated with a system that was trained on YOHO, but the attack is on a system that was trained on NTIMIT; and when the adversarial examples were generated with a system that was trained on Mel-spectrum feature set, but the attack is on a system that was trained on MFCC. Results suggest that the accuracy of the attacked system was decreased and the false-positive rate was dramatically increased.
A Polynomial Algorithm for Balanced Clustering via Graph Partitioning
Venue: arXiv cs.CG 2018
Year: 2018

The objective of clustering is to discover natural groups in datasets and to identify geometrical structures which might reside there, without assuming any prior knowledge on the characteristics of the data. The problem can be seen as detecting the inherent separations between groups of a given point set in a metric space governed by a similarity function. The pairwise similarities between all data objects form a weighted graph adjacency matrix which contains all necessary information for the clustering process, which can consequently be formulated as a graph partitioning problem. In this context, we propose a new cluster quality measure which uses the maximum spanning tree and allows us to compute the optimal clustering under the min-max principle in polynomial time. Our algorithm can be applied when a load-balanced clustering is required.
Deep Supervision with Intermediate Concepts
Venue: arXiv cs.CV 2018
Year: 2018

Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). However, decades of work on perceptual organization in both human and machine vision suggests that there are often intermediate representations that are intrinsic to an inference task, and which provide essential structure to improve generalization. In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice. We formulate a probabilistic framework which formalizes these notions and predicts improved generalization via this deep supervision method. One advantage of this approach is that we are able to train only from synthetic CAD renderings of cluttered scenes, where concept values can be extracted, but apply the results to real images. Our implementation achieves the state-of-the-art performance of 2D/3D keypoint localization and image classification on real image benchmarks, including KITTI, PASCAL VOC, PASCAL3D+, IKEA, and CIFAR100. We provide additional evidence that our approach outperforms alternative forms of supervision, such as multi-task networks.
DeepSeek: Content Based Image Search & Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

Most of the internet today is composed of digital media that includes videos and images. With pixels becoming the currency in which most transactions happen on the internet, it is becoming increasingly important to have a way of browsing through this ocean of information with relative ease. YouTube has 400 hours of video uploaded every minute and many million images are browsed on Instagram, Facebook, etc. Inspired by recent advances in the field of deep learning and success that it has gained on various problems like image captioning and, machine translation , word2vec , skip thoughts, etc, we present DeepSeek a natural language processing based deep learning model that allows users to enter a description of the kind of images that they want to search, and in response the system retrieves all the images that semantically and contextually relate to the query. Two approaches are described in the following sections.
Inferring a Third Spatial Dimension from 2D Histological Images
Venue: arXiv cs.CV 2018
Year: 2018

Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specimen that has a three-dimensional structure. In this paper, we propose a method to infer how the stains are distributed in the direction perpendicular to the surface of the slide for a given 2D image in order to obtain a 3D representation of the tissue. This inference is achieved by decomposition of the staining concentration maps under constraints that ensure realistic decomposition and reconstruction of the original 2D images. Our study shows that it is possible to generate realistic 3D images making this method a potential tool for data augmentation when training deep learning models.
Rate Selection and Power Adaptation using Maximal Ratio Combining for the Random Access Gaussian Channel
Venue: arXiv cs.IT 2018
Year: 2018

With the emergence of machine-driven communi- cation, there is a renewed interest in the design of random multiple access schemes for networks with large number of active devices. Many of the recently proposed access paradigms are enhancements to slotted ALOHA. One of the popular schemes, irregular repetition slotted ALOHA (IRSA), is based on an analogy between multiple access with successive interference cancellation and message-passing decoding on bipartite graphs. Most of the results on IRSA and its variants focus on the collision channel and they ignore physical limitations such as transmit power constraints and additive Gaussian noise at the physical layer. As such, naive extensions of IRSA to the Gaussian multiple access channel are not power efficient in the low signal-to-noise- ratio regime. This work introduces a novel paradigm whereby devices adapt their rates and/or transmit powers based on their chosen repetition profiles. The receiver performs maximal ratio combining over all the replicas prior to decoding a message. Numerical results for finite number of users show that the proposed scheme can provide substantial improvements in terms of power efficiency and average rate over standard IRSA.
Secrecy by Witness-Functions on Increasing Protocols
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we present a new formal method to analyze cryptographic protocols statically for the property of secrecy. It consists in inspecting the level of security of every component in the protocol and making sure that it does not diminish during its life cycle. If yes, it concludes that the protocol keeps its secret inputs. We analyze in this paper an amended version of the Woo-Lam protocol using this new method.
RAF: Robust Adaptive Multi-Feedback Channel Estimation for Millimeter Wave MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter wave is a promising technology for the next generation of wireless systems. As it is well-known for its high path loss, the systems working in this spectrum tend to exploit the shorter wavelength to equip the transceivers with a large number of antennas to overcome the path loss issue. The large number of antennas leads to large channel matrices and consequently a challenging channel estimation problem. The channel estimation algorithms that have been proposed so far either neglect the probability of estimation error or require a high feedback overload from receivers to ensure the target probability of estimation error. In this paper, we propose a multi-stage adaptive channel estimation algorithm called robust adaptive multi-feedback (RAF). The algorithm is based on using the estimated channel coefficient to predict a lower bound for the required number of measurements. Our simulations demonstrate that compared with existing algorithms, RAF can achieve the desired probability of estimation error while on average reducing the feedback overhead by 75.5% and the total channel estimation time by 14%.
Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis
Venue: arXiv cs.DC 2018
Year: 2018

In this paper we propose a new parallel architecture based on Big Data technologies for real-time sentiment analysis on microblogging posts. Polypus is a modular framework that provides the following functionalities: (1) massive text extraction from Twitter, (2) distributed non-relational storage optimized for time range queries, (3) memory-based intermodule buffering, (4) real-time sentiment classification, (5) near real-time keyword sentiment aggregation in time series, (6) a HTTP API to interact with the Polypus cluster and (7) a web interface to analyze results visually. The whole architecture is self-deployable and based on Docker containers.
Enhancing Physical Layer Security in AF Relay Assisted Multi-Carrier Wireless Transmission
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study the physical layer security (PLS) problem in the dual hop orthogonal frequency division multiplexing (OFDM) based wireless communication system. First, we consider a single user single relay system and study a joint power optimization problem at the source and relay subject to individual power constraint at the two nodes. The aim is to maximize the end to end secrecy rate with optimal power allocation over different sub-carriers. Later, we consider a more general multi-user multi-relay scenario. Under high SNR approximation for end to end secrecy rate, an optimization problem is formulated to jointly optimize power allocation at the BS, the relay selection, sub-carrier assignment to users and the power loading at each of the relaying node. The target is to maximize the overall security of the system subject to independent power budget limits at each transmitting node and the OFDMA based exclusive sub-carrier allocation constraints. A joint optimization solution is obtained through duality theory. Dual decomposition allows to exploit convex optimization techniques to find the power loading at the source and relay nodes. Further, an optimization for power loading at relaying nodes along with relay selection and sub carrier assignment for the fixed power allocation at the BS is also studied. Lastly, a sub-optimal scheme that explores joint power allocation at all transmitting nodes for the fixed subcarrier allocation and relay assignment is investigated. Finally, simulation results are presented to validate the performance of the proposed schemes.
Hybrid NOMA-TDMA for Multiple Access Channels with Non-Ideal Batteries and Circuit Cost
Venue: arXiv cs.IT 2018
Year: 2018

We consider a multiple-access channel where the users are powered from batteries having non-negligible internal resistance. When power is drawn from the battery, a variable fraction of the power, which is a function of the power drawn from the battery, is lost across the internal resistance. Hence, the power delivered to the load is less than the power drawn from the battery. The users consume a constant power for the circuit operation during transmission but do not consume any power when not transmitting. In this setting, we obtain the maximum sum-rates and achievable rate regions under various cases. We show that, unlike in the ideal battery case, the TDMA (time-division multiple access) strategy, wherein the users transmit orthogonally in time, may not always achieve the maximum sum-rate when the internal resistance is non-zero. The users may need to adopt a hybrid NOMA-TDMA strategy which combines the features of NOMA (non-orthogonal multiple access) and TDMA, wherein a set of users are allocated fixed time windows for orthogonal single-user and non-orthogonal joint transmissions, respectively. We also numerically show that the maximum achievable rate regions in NOMA and TDMA strategies are contained within the maximum achievable rate region of the hybrid NOMA-TDMA strategy.
Enhancing Translation Language Models with Word Embedding for Information Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

In this paper, we explore the usage of Word Embedding semantic resources for Information Retrieval (IR) task. This embedding, produced by a shallow neural network, have been shown to catch semantic similarities between words (Mikolov et al., 2013). Hence, our goal is to enhance IR Language Models by addressing the term mismatch problem. To do so, we applied the model presented in the paper Integrating and Evaluating Neural Word Embedding in Information Retrieval by Zuccon et al. (2015) that proposes to estimate the translation probability of a Translation Language Model using the cosine similarity between Word Embedding. The results we obtained so far did not show a statistically significant improvement compared to classical Language Model.
Construction and Performance of Quantum Burst Error Correction Codes for Correlated Errors
Venue: arXiv cs.IT 2018
Year: 2018

In practical communication and computation systems, errors occur predominantly in adjacent positions rather than in a random manner. In this paper, we develop a stabilizer formalism for quantum burst error correction codes (QBECC) to combat such error patterns in the quantum regime. Our contributions are as follows. Firstly, we derive an upper bound for the correctable burst errors of QBECCs, the quantum Reiger bound (QRB). This bound generalizes the quantum Singleton bound for standard quantum error correction codes (QECCs). Secondly, we propose two constructions of QBECCs: one by heuristic computer search and the other by concatenating two quantum tensor product codes (QTPCs). We obtain several new QBECCs with better parameters than existing codes with the same coding length. Moreover, some of the constructed codes can saturate the quantum Reiger bounds. Finally, we perform numerical experiments for our constructed codes over Markovian correlated depolarizing quantum memory channels, and show that QBECCs indeed outperform standard QECCs in this scenario.
A parallel workload has extreme variability in a production environment
Venue: arXiv cs.DC 2018
Year: 2018

Writing data in parallel is a common operation in some computing environments and a good proxy for a number of other parallel processing patterns. The duration of time taken to write data in large-scale compute environments can vary considerably. This variation comes from a number of sources, both systematic and transient. The result is a highly complex behavior that is difficult to characterize. This paper further develops the model for parallel task variability proposed in the paper "A parallel workload has extreme variability" (Henwood et. al 2016). This model is the Generalized Extreme Value (GEV) distribution. This paper further develops the systematic analysis that leads to the GEV model with the addition of a traffic congestion term. Observations of a parallel workload are presented from a High Performance Computing environment under typical production conditions, which include traffic congestion. An analysis of the workload is performed and shows the variability tends towards GEV as the order of parallelism is increased. The results are presented in the context of Amdahl's law and the predictive properties of a GEV models are discussed. A optimization for certain machine designs is also suggested.