OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Spectrum Sharing with ARQ based Legacy Users via Chain Decoding
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates the design of access policies in spectrum sharing networks by exploiting the retransmission protocol of legacy primary users (PUs) to improve the spectral efficiency via opportunistic retransmissions at secondary users (SUs) and chain decoding. The optimal policy maximizing the SU throughput under an interference constraint to the PU and its performance are found in closed form. It is shown that the optimal policy randomizes among three modes: Idle, the SU remains idle over the retransmission window of the PU, to avoid causing interference; Interference cancellation, the SU transmits only after decoding the PU packet, to improve its own throughput via interference cancellation; Always transmit, the SU transmits over the retransmission window of the PU to maximize the future potential of interference cancellation via chain decoding. This structure is exploited to design a stochastic optimization algorithm to facilitate learning and adaptation when the model parameters are unknown or vary over time, based on ARQ feedback from the PU and CSI measurements at the SU receiver. It is shown numerically that, for a 10% interference constraint, the optimal access policy yields 15% improvement over a state-of-the-art scheme without SU retransmissions, and up to 2x gain over a scheme using a non-adaptive access policy instead of the optimal one.
vLibOS: Babysitting OS Evolution with a Virtualized Library OS
Venue: arXiv cs.OS 2018
Year: 2018

Many applications have service requirements that are not easily met by existing operating systems. Real-time and security-critical tasks, for example, often require custom OSes to meet their needs. However, development of special purpose OSes is a time-consuming and difficult exercise. Drivers, libraries and applications have to be written from scratch or ported from existing sources. Many researchers have tackled this problem by developing ways to extend existing systems with application-specific services. However, it is often difficult to ensure an adequate degree of separation between legacy and new services, especially when security and timing requirements are at stake. Virtualization, for example, supports logical isolation of separate guest services, but suffers from inadequate temporal isolation of time-critical code required for real-time systems. This paper presents vLibOS, a master-slave paradigm for new systems, whose services are built on legacy code that is temporally and spatially isolated in separate VM domains. Existing OSes are treated as sandboxed libraries, providing legacy services that are requested by inter-VM calls, which execute with the time budget of the caller. We evaluate a real-time implementation of vLibOS. Empirical results show that vLibOS achieves as much as a 50\% reduction in performance slowdown for real-time threads, when competing for a shared memory bus with a Linux VM.
Deep Learning for Sentiment Analysis : A Survey
Venue: arXiv cs.CL 2018
Year: 2018

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
Venue: arXiv cs.LG 2018
Year: 2018

For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of the corresponding row's values in a similarity matrix. The measure is intuitive given the abnormal samples are usually rare and they are dissimilar to the rest of the data. In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem. We show that our analyses guide us to choose fully-connected graphs whose edge weights are calculated via universal kernels. We show that a simple graph degree based unsupervised anomaly detection method with the above properties, achieves higher accuracy compared to other unsupervised anomaly detection methods on average over 10 widely used datasets. We also provide an extensive analysis on the effect of the kernel parameter on the method's accuracy.
Deep Structured Energy-Based Image Inpainting
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a structured image inpainting method employing an energy based model. In order to learn structural relationship between patterns observed in images and missing regions of the images, we employ an energy-based structured prediction method. The structural relationship is learned by minimizing an energy function which is defined by a simple convolutional neural network. The experimental results on various benchmark datasets show that our proposed method significantly outperforms the state-of-the-art methods which use Generative Adversarial Networks (GANs). We obtained 497.35 mean squared error (MSE) on the Olivetti face dataset compared to 833.0 MSE provided by the state-of-the-art method. Moreover, we obtained 28.4 dB peak signal to noise ratio (PSNR) on the SVHN dataset and 23.53 dB on the CelebA dataset, compared to 22.3 dB and 21.3 dB, provided by the state-of-the-art methods, respectively. The code is publicly available.
Threadable Curves
Venue: arXiv cs.CG 2018
Year: 2018

We define a plane curve to be threadable if it can rigidly pass through a point-hole in a line L without otherwise touching L. Threadable curves are in a sense generalizations of monotone curves. We have two main results. The first is a linear-time algorithm for deciding whether a polygonal curve is threadable---O(n) for a curve of n vertices---and if threadable, finding a sequence of rigid motions to thread it through a hole. We also sketch an argument that shows that the threadability of algebraic curves can be decided in time polynomial in the degree of the curve. The second main result is an O(n polylog n)-time algorithm for deciding whether a 3D polygonal curve can thread through hole in a plane in R^3, and if so, providing a description of the rigid motions that achieve the threading.
A New Framework for Centrality Measures in Multiplex Networks
Venue: arXiv cs.SI 2018
Year: 2018

The non-trivial structure of such complex systems makes the analysis of their collective behavior a challenge. The problem is even more difficult when the information is distributed across networks (e.g., communication networks in different media); in this case, it becomes impossible to have a complete, or even partial picture, if situations are analyzed separately within each network due to sparsity. A multiplex network is well-suited to model the complexity of this kind of systems by preserving the semantics associated with each network. Centrality measures are fundamental for the identification of key players, but existing approaches are typically designed to capture a predefined aspect of the system, ignoring or merging the semantics of the individual layers.
SoK: Uncentralisable Ledgers and their Impact on Voting Systems
Venue: arXiv cs.CR 2018
Year: 2018

As we observe a trend towards the recentralisation of the Internet, this paper raises the question of guaranteeing an everlasting decentralisation. We introduce the properties of strong and soft uncentralisability in order to describe systems in which all authorities can be untrusted at any time without affecting the system. We link the soft uncentralisability to another property called perfect forkability. Using that knowledge, we introduce a new cryptographic primitive called uncentralisable ledger and study its properties. We use those properties to analyse what an uncentralisable ledger may offer to classic electronic voting systems and how it opens up the realm of possibilities for completely new voting mechanisms. We review a list of selected projects that implement voting systems using blockchain technol- ogy. We then conclude that the true revolutionary feature enabled by uncentralisable ledgers is a self-sovereign and distributed identity provider.
Non-parametric Estimation of Mutual Information with Application to Nonlinear Optical Fibers
Venue: arXiv cs.IT 2018
Year: 2018

This paper compares and evaluates a set of non-parametric mutual information estimators with the goal of providing a novel toolset to progress in the analysis of the capacity of the nonlinear optical channel, which is currently an open problem. In the first part of the paper, the methods of the study are presented. The second part details their application to several optically-related channels to highlight their features.
Pointer-Chase Prefetcher for Linked Data Structures
Venue: arXiv cs.AR 2018
Year: 2018

Caches only exploit spatial and temporal locality in a set of address referenced in a program. Due to dynamic construction of linked data-structures, they are difficult to cache as the spatial locality between the nodes is highly dependent on the data layout. Prefetching can play an important role in improving the performance of linked data-structures. In this project, a pointer chase mechanism along with compiler hints is adopted to design a prefetcher for linked data-structures. The design is evaluated against the baseline of processor with cache in terms of performance, area and energy
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents
Venue: arXiv cs.AI 2018
Year: 2018

Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial agents. Psychlab has a simple and flexible API that enables users to easily create their own tasks. As examples, we are releasing Psychlab implementations of several classical experimental paradigms including visual search, change detection, random dot motion discrimination, and multiple object tracking. We also contribute a study of the visual psychophysics of a specific state-of-the-art deep reinforcement learning agent: UNREAL (Jaderberg et al. 2016). This study leads to the surprising conclusion that UNREAL learns more quickly about larger target stimuli than it does about smaller stimuli. In turn, this insight motivates a specific improvement in the form of a simple model of foveal vision that turns out to significantly boost UNREAL's performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By open-sourcing Psychlab we hope to facilitate a range of future such studies that simultaneously advance deep reinforcement learning and improve its links with cognitive science.
Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0
Venue: arXiv cs.AI 2018
Year: 2018

The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM's). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate quantification of savings. A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology's ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances.
Exposing Vulnerabilities in Mobile Networks: A Mobile Data Consumption Attack
Venue: arXiv cs.CR 2018
Year: 2018

Smartphone carrier companies rely on mobile networks for keeping an accurate record of customer data usage for billing purposes. In this paper, we present a vulnerability that allows an attacker to force the victim's smartphone to consume data through the cellular network by starting the data download on the victim's cell phone without the victim's knowledge. The attack is based on switching the victim's smartphones from the Wi-Fi network to the cellular network while downloading a large data file. This attack has been implemented in real-life scenarios where the test's outcomes demonstrate that the attack is feasible and that mobile networks do not record customer data usage accurately.
Towards Per-user Flexible Management in 5G
Venue: arXiv cs.NI 2018
Year: 2018

Flexible management is one of the key components of next-generation 5G networks. Currently, many approaches focus on network functionality (services) and translate it afterward into end-user requirements, which slightly constrains the flexibility for both management and end users. Furthermore, moving the intelligence of the network towards the edge (i.e. the users) has already proven its benefits, such as computational offloading, lower latency and higher bandwidth utilization. In this article, we try to move management as close as possible to final users, providing per-user flexibility and unique user to service paths, enabling custom paths adapted for each user requirements instead of users adapting to service requirements. To validate our ideas, we work on two different use cases, implemented as proof-of-concepts in the ONOS platform. From the results obtained we conclude that there is still work to be done regarding the integration of SDN in the radio access and evolved packet core functions to provide the desired flexibility.
Active Neural Localization
Venue: arXiv cs.LG 2018
Year: 2018

Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.
Analysis and Design of Serially Concatenated LDGM Codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we first present the asymptotic performance of serially concatenated low-density generator-matrix (SCLDGM) codes for binary input additive white Gaussian noise channels using discretized density evolution (DDE). We then provide a necessary condition for the successful decoding of these codes. The error-floor analysis along with the lower bound formulas for both LDGM and SCLDGM codes are also provided and verified. We further show that by concatenating inner LDGM codes with a high-rate outer LDPC code instead of concatenating two LDGM codes as in SCLDGM codes, good codes without error floors can be constructed. Finally, with an efficient DDE-based optimization approach that utilizes the necessary condition for the successful decoding, we construct optimized SCLDGM codes that approach the Shannon limit. The improved performance of our optimized SCLDGM codes is demonstrated through both asymptotic and simulation results.
A Comprehensive Survey of Watermarking Relational Databases Research
Venue: arXiv cs.CR 2018
Year: 2018

Watermarking and fingerprinting of relational databases are quite proficient for ownership protection, tamper proofing, and proving data integrity. In past few years several such techniques have been proposed. A survey of almost all the work done, till date, in these fields has been presented in this paper. The techniques have been classified on the basis of how and where they embed the watermark. The analysis and comparison of these techniques on different merits has also been provided. In the end, this paper points out the direction of future research in these fields.
Continuous Space Reordering Models for Phrase-based MT
Venue: arXiv cs.CL 2018
Year: 2018

Bilingual sequence models improve phrase-based translation and reordering by overcoming phrasal independence assumption and handling long range reordering. However, due to data sparsity, these models often fall back to very small context sizes. This problem has been previously addressed by learning sequences over generalized representations such as POS tags or word clusters. In this paper, we explore an alternative based on neural network models. More concretely we train neuralized versions of lexicalized reordering and the operation sequence models using feed-forward neural network. Our results show improvements of up to 0.6 and 0.5 BLEU points on top of the baseline German->English and English->German systems. We also observed improvements compared to the systems that used POS tags and word clusters to train these models. Because we modify the bilingual corpus to integrate reordering operations, this allows us to also train a sequence-to-sequence neural MT model having explicit reordering triggers. Our motivation was to directly enable reordering information in the encoder-decoder framework, which otherwise relies solely on the attention model to handle long range reordering. We tried both coarser and fine-grained reordering operations. However, these experiments did not yield any improvements over the baseline Neural MT systems.
A Statistical Approach for RF Exposure Compliance Boundary Assessment in Massive MIMO Systems
Venue: arXiv cs.NI 2018
Year: 2018

Massive multiple-input multiple-output (MIMO) is a fundamental enabler to provide high data throughput in next generation cellular networks. By equipping the base stations (BSs) with tens or hundreds of antenna elements, narrow and high gain beams can be used to spatially multiplex several user equipment (UE) devices. While increasing the achievable performance, focusing the transmit power into specific UE directions also poses new issues when performing the radio frequency (RF) exposure assessment. In fact, the spatial distribution of the actual BS transmit power strongly depends on the deployment scenario and on the position of the UEs. Traditional methods for assessing the RF exposure compliance boundaries around BS sites are generally based on maximum transmit power and static beams. In massive MIMO systems, these approaches tend to be very conservative, in particular when time averaging is properly considered. In this work, we propose to leverage the three dimensional spatial channel model standardized by the Third Generation Partnership Project in order to assess reasonably foreseeable compliance boundaries of massive MIMO BSs. The analysis is performed by considering BSs fully loaded and different configurations of active UEs per cell. Numerical results show that the statistical approach developed in this paper allows reducing to nearly half the compliance distance when compared to the traditional method.
Scenarios for Development, Test and Validation of Automated Vehicles
Venue: arXiv cs.SE 2018
Year: 2018

The ISO 26262 standard from 2016 represents the state of the art for a safety-guided development of safety-critical electric/electronic vehicle systems. These vehicle systems include advanced driver assistance systems and vehicle guidance systems. The development process proposed in the ISO 26262 standard is based upon multiple V-models, and defines activities and work products for each process step. In many of these process steps, scenario based approaches can be applied to achieve the defined work products for the development of automated driving functions. To accomplish the work products of different process steps, scenarios have to focus on various aspects like a human understandable notation or a description via time-space variables. This leads to contradictory requirements regarding the level of detail and way of notation for the representation of scenarios. In this paper, the authors present requirements for the representation of scenarios in different process steps defined by the ISO 26262 standard, propose a consistent terminology based on prior publications for the identified levels of abstraction, and demonstrate how scenarios can be systematically evolved along the phases of the development process outlined in the ISO 26262 standard.
MmWave Channel Estimation via Atomic Norm Minimization for Multi-User Hybrid Precoding
Venue: arXiv cs.IT 2018
Year: 2018

To perform multi-user multiple-input and multiple-output transmission in millimeter-wave(mmWave) cellular systems, the high-dimensional channels need to be estimated for designing the multi-user precoder. Conventional grid-based compressed sensing (CS) methods for mmWave channel estimation suffer from the basis mismatch problem, which prevents accurate channel reconstruction and degrades the precoding performance. This paper formulates mmWave channel estimation as an Atomic Norm Minimization (ANM) problem. In contrast to grid-based CS methods which use discrete dictionaries, ANM uses a continuous dictionary for representing the mmWave channel. We consider a continuous dictionary based on sub-sampling in the antenna domain via a small number of radio frequency chains. We show that mmWave channel estimation using ANM can be formulated as a semidefinite programming (SDP) problem, and the user channel can be accurately estimated via off-the-shelf SDP solvers in polynomial time. Simulation results indicate that ANM can achieve much better estimation accuracy compared to grid-based CS, and significantly improves the spectral efficiency provided by multi-user precoding.