OpenRaiser/Intern-Atlas huggingface dataset:
New Results on Multilevel Diversity Coding with Secure Regeneration
Venue: arXiv cs.IT 2018
Year: 2018

The problem of multilevel diversity coding with secure regeneration is revisited. Under the assumption that the eavesdropper can access the repair data for all compromised storage nodes, Shao el al. provided a precise characterization of the minimum-bandwidth-regeneration (MBR) point of the achievable normalized storage-capacity repair-bandwidth tradeoff region. In this paper, it is shown that the MBR point of the achievable normalized storage-capacity repair-bandwidth tradeoff region remains the same even if we assume that the eavesdropper can access the repair data for some compromised storage nodes (type 2 compromised nodes) but only the data contents of the remaining compromised nodes (type 1 compromised nodes), as long as the number of type 1 compromised nodes is no greater than that of type 2 compromised nodes.
How to augment a small learning set for improving the performances of a CNN-based steganalyzer?
Venue: arXiv cs.MM 2018
Year: 2018

Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results. The performances of such networks often rely on the size of their learning database. An obvious preliminary assumption could be considering that "the bigger a database is, the better the results are". However, it appears that cautions have to be taken when increasing the database size if one desire to improve the classification accuracy i.e. enhance the steganalysis efficiency. To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance. What kind of images can be added to the initial learning set? What are the sensitive criteria: the camera models used for acquiring the images, the treatments applied to the images, the cameras proportions in the database, etc? This article continues the work carried out in a previous paper, and explores the ways to improve the performances of CNN. It aims at studying the effects of "base augmentation" on the performance of steganalysis using a CNN. We present the results of this study using various experimental protocols and various databases to define the good practices in base augmentation for steganalysis.
Speculose: Analyzing the Security Implications of Speculative Execution in CPUs
Venue: arXiv cs.CR 2018
Year: 2018

Whenever modern CPUs encounter a conditional branch for which the condition cannot be evaluated yet, they predict the likely branch target and speculatively execute code. Such pipelining is key to optimizing runtime performance and is incorporated in CPUs for more than 15 years. In this paper, to the best of our knowledge, we are the first to study the inner workings and the security implications of such speculative execution. We revisit the assumption that speculatively executed code leaves no traces in case it is not committed. We reveal several measurable side effects that allow adversaries to enumerate mapped memory pages and to read arbitrary memory---all using only speculated code that was never fully executed. To demonstrate the practicality of such attacks, we show how a user-space adversary can probe for kernel pages to reliably break kernel-level ASLR in Linux in under three seconds and reduce the Windows 10 KASLR entropy by 18~bits in less than a second.
Spatio-Temporal Linkage over Location Enhanced Services
Venue: arXiv cs.DB 2018
Year: 2018

We are witnessing an enormous growth in the volume of data generated by various online services. An important portion of this data contains geographic references, since many of these services are \emph{location-enhanced} and thus produce spatio-temporal records of their usage. We postulate that the spatio-temporal usage records belonging to the same real-world entity can be matched across records from different location-enhanced services. Linking spatio-temporal records enables data analysts and service providers to obtain information that they cannot derive by analyzing only one set of usage records. In this paper, we develop a new \emph{linkage model} that can be used to match entities from two sets of spatio-temporal usage records belonging to two different location-enhanced services. This linkage model is based on the concept of $k$-$l$ \emph{diversity} --- that we developed to capture both spatial and temporal aspects of the linkage. To realize this linkage model in practice, we develop a scalable linking algorithm called \emph{ST-Link}, which makes use of effective spatial and temporal filtering mechanisms that significantly reduce the search space for matching users. Furthermore, \emph{ST-Link} utilizes sequential scan procedures to avoid random disk access and thus scales to large datasets. We evaluated our work with respect to accuracy and performance using several datasets. Experiments show that \emph{ST-Link} is effective in practice for performing spatio-temporal linkage and can scale to large datasets.
Multiple Antennas Secure Transmission under Pilot Spoofing and Jamming Attack
Venue: arXiv cs.IT 2018
Year: 2018

Transmitter-side channel state information (CSI) of the legitimate destination plays a critical role in physical layer secure transmissions. However, channel training procedure is vulnerable to the pilot spoofing attack (PSA) or pilot jamming attack (PJA) by an active eavesdropper (Eve), which inevitably results in severe private information leakage. In this paper, we propose a random channel training (RCT) based secure downlink transmission framework for a time division duplex (TDD) multiple antennas base station (BS). In the proposed RCT scheme, multiple orthogonal pilot sequences (PSs) are simultaneously allocated to the legitimate user (LU), and the LU randomly selects one PS from the assigned PS set to transmit. Under either the PSA or PJA, we provide the detailed steps for the BS to identify the PS transmitted by the LU, and to simultaneously estimate channels of the LU and Eve. The probability that the BS makes an incorrect decision on the PS of the LU is analytically investigated. Finally, closed-form secure beamforming (SB) vectors are designed and optimized to enhance the secrecy rates during the downlink transmissions. Numerical results show that the secrecy performance is greatly improved compared to the conventional channel training scheme wherein only one PS is assigned to the LU.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
Venue: arXiv cs.CV 2018
Year: 2018

Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds. To enable training of the complex network with millions of learnable parameters using limited annotated data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are able to use large neuroimaging repositories without manual annotations for training. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior segmentation accuracy and reliability in comparison to state-of-the-art methods, while being orders of magnitude faster. The speed up facilitates processing of large data repositories and supports translation of imaging biomarkers by making them available within seconds for fast clinical decision making.
A Tableaux Calculus for Reducing Proof Size
Venue: arXiv cs.LO 2018
Year: 2018

A tableau calculus is proposed, based on a compressed representation of clauses, where literals sharing a similar shape may be merged. The inferences applied on these literals are fused when possible, which reduces the size of the proof. It is shown that the obtained proof procedure is sound, refutationally complete and allows to reduce the size of the tableau by an exponential factor. The approach is compatible with all usual refinements of tableaux.
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
Venue: arXiv cs.DC 2018
Year: 2018

Grid computing systems require innovative methods and tools to identify cybersecurity incidents and perform autonomous actions i.e. without administrator intervention. They also require methods to isolate and trace job payload activity in order to protect users and find evidence of malicious behavior. We introduce an integrated approach of security monitoring via Security by Isolation with Linux Containers and Deep Learning methods for the analysis of real time data in Grid jobs running inside virtualized High-Throughput Computing infrastructure in order to detect and prevent intrusions. A dataset for malware detection in Grid computing is described. We show in addition the utilization of generative methods with Recurrent Neural Networks to improve the collected dataset. We present Arhuaco, a prototype implementation of the proposed methods. We empirically study the performance of our technique. The results show that Arhuaco outperforms other methods used in Intrusion Detection Systems for Grid Computing. The study is carried out in the ALICE Collaboration Grid, part of the Worldwide LHC Computing Grid.
Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions
Venue: arXiv cs.LG 2018
Year: 2018

Accurate noise modelling is important for training of deep learning reconstruction algorithms. While noise models are well known for traditional imaging techniques, the noise distribution of a novel sensor may be difficult to determine a priori. Therefore, we propose learning arbitrary noise distributions. To do so, this paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly known probability density function. During the training, the Jensen-Shannon divergence between the distribution of the model's output and the target distribution is minimized. We experimentally demonstrate that our model converges towards the desired state. It provides an alternative to existing sampling methods such as inversion sampling, rejection sampling, Gaussian mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.
Optimizing Floating Locations in Hard Disk Drive by Solving Max-min Optimization
Venue: arXiv cs.SY 2018
Year: 2018

Floating operation is very critical in power management in hard disk drive (HDD), during which no control command is applied to the read/write head but a fixed current to counteract actuator flex bias. External disturbance induced drift of head may result in interference of head and bump on the disk during drifting, leading to consequent scratches and head degradation, which is a severe reliability concern in HDD. This paper proposes a unique systematic methodology to minimize the chances of hitting bump on the disk during drive floating. Essentially, it provides a heuristic solution to a class of max-min optimization problem which achieves desirable trade-off between optimality and computation complexity. Multivariable nonlinear optimization problem of this sort is reduced from NP-hard to an arithmetic problem. Also, worst-case is derived for arbitrary bump locations.
Belief Propagation Decoding of Polar Codes on Permuted Factor Graphs
Venue: arXiv cs.IT 2018
Year: 2018

We show that the performance of iterative belief propagation (BP) decoding of polar codes can be enhanced by decoding over different carefully chosen factor graph realizations. With a genie-aided stopping condition, it can achieve the successive cancellation list (SCL) decoding performance which has already been shown to achieve the maximum likelihood (ML) bound provided that the list size is sufficiently large. The proposed decoder is based on different realizations of the polar code factor graph with randomly permuted stages during decoding. Additionally, a different way of visualizing the polar code factor graph is presented, facilitating the analysis of the underlying factor graph and the comparison of different graph permutations. In our proposed decoder, a high rate Cyclic Redundancy Check (CRC) code is concatenated with a polar code and used as an iteration stopping criterion (i.e., genie) to even outperform the SCL decoder of the plain polar code (without the CRC-aid). Although our permuted factor graph-based decoder does not outperform the SCL-CRC decoder, it achieves, to the best of our knowledge, the best performance of all iterative polar decoders presented thus far.
The DCS Theorem
Venue: arXiv cs.DC 2018
Year: 2018

Blockchain design involves many tradeoffs, and much debate has focused on tradeoffs related to scaling parameters such as blocksize. To address some of the confusion around this subject, we present a probability proof of the DCS Triangle. We use the triangle to show decentralized consensus systems, like blockchains, can have Decentralization, Consensus, or Scale, but not all three properties simultaneously. We then describe two methods for getting around the limitations suggested by the triangle.
Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge
Venue: arXiv cs.DC 2018
Year: 2018

Cooperative computation is a promising approach for localized data processing at the edge, e.g. for Internet of Things (IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than $99\%$, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.
Asymptotic Distribution of Multilevel Channel Polarization for a Certain Class of Erasure Channels
Venue: arXiv cs.IT 2018
Year: 2018

This study examines multilevel channel polarization for a certain class of erasure channels that the input alphabet size is an arbitrary composite number. We derive limiting proportions of partially noiseless channels for such a class. The results of this study are proved by an argument of convergent sequences, inspired by Alsan and Telatar's simple proof of polarization, and without martingale convergence theorems for polarization process.
Longest Common Prefixes with $k$-Errors and Applications
Venue: arXiv cs.DS 2018
Year: 2018

Although real-world text datasets, such as DNA sequences, are far from being uniformly random, average-case string searching algorithms perform significantly better than worst-case ones in most applications of interest. In this paper, we study the problem of computing the longest prefix of each suffix of a given string of length $n$ over a constant-sized alphabet that occurs elsewhere in the string with $k$-errors. This problem has already been studied under the Hamming distance model. Our first result is an improvement upon the state-of-the-art average-case time complexity for non-constant $k$ and using only linear space under the Hamming distance model. Notably, we show that our technique can be extended to the edit distance model with the same time and space complexities. Specifically, our algorithms run in $\mathcal{O}(n \log^k n \log \log n)$ time on average using $\mathcal{O}(n)$ space. We show that our technique is applicable to several algorithmic problems in computational biology and elsewhere.
Scalable De Novo Genome Assembly Using Pregel
Venue: arXiv cs.DC 2018
Year: 2018

De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throughput genome sequencing and thus accelerates the discovery of new genomes. In this paper, we present a toolkit, called PPA-assembler, for de novo genome assembly in a distributed setting. The operations in our toolkit provide strong performance guarantees, and can be assembled to implement various sequencing strategies. PPA-assembler adopts the popular {\em de Bruijn graph} based approach for sequencing, and each operation is implemented as a program in Google's Pregel framework for big graph processing. Experiments on large real and simulated datasets demonstrate that PPA-assembler is much more efficient than the state-of-the-arts and provides good sequencing quality.
Size-to-depth: A New Perspective for Single Image Depth Estimation
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we consider the problem of single monocular image depth estimation. It is a challenging problem due to its ill-posedness nature and has found wide application in industry. Previous efforts belongs roughly to two families: learning-based method and interactive method. Learning-based method, in which deep convolutional neural network (CNN) is widely used, can achieve good result. But they suffer low generalization ability and typically perform poorly for unfamiliar scenes. Besides, data-hungry nature for such method makes data aquisition expensive and time-consuming. Interactive method requires human annotation of depth which, however, is errorneous and of large variance. To overcome these problems, we propose a new perspective for single monocular image depth estimation problem: size to depth. Our method require sparse label for real-world size of object rather than raw depth. A Coarse depth map is then inferred following geometric relationships according to size labels. Then we refine the depth map by doing energy function optimization on conditional random field(CRF). We experimentally demonstrate that our method outperforms traditional depth-labeling methods and can produce satisfactory depth maps.
Lattice Erasure Codes of Low Rank with Noise Margins
Venue: arXiv cs.IT 2018
Year: 2018

We consider the following generalization of an $(n,k)$ MDS code for application to an erasure channel with additive noise. Like an MDS code, our code is required to be decodable from any $k$ received symbols, in the absence of noise. In addition, we require that the noise margin for every allowable erasure pattern be as large as possible and that the code satisfy a power constraint. In this paper we derive performance bounds and present a few designs for low rank lattice codes for an additive noise channel with erasures.
MAC-Oriented Programmable Terahertz PHY via Graphene-based Yagi-Uda Antennas
Venue: arXiv cs.ET 2018
Year: 2018

Graphene is enabling a plethora of applications in a wide range of fields due to its unique electrical, mechanical, and optical properties. In the realm of wireless communications, graphene shows great promise for the implementation of miniaturized and tunable antennas in the terahertz band. These unique advantages open the door to new reconfigurable antenna structures which, in turn, enable novel communication protocols at different levels of the stack. This paper explores both aspects by, first, presenting a terahertz Yagi-Uda-like antenna concept that achieves reconfiguration both in frequency and beam direction simultaneously. Then, a programmable antenna controller design is proposed to expose the reconfigurability to the PHY and MAC layers, and several examples of its applicability are given. The performance and cost of the proposed scheme is evaluated through full-wave simulations and comparative analysis, demonstrating reconfigurability at nanosecond granularity with overheads below 0.02 mm$^{2}$ and 0.2 mW.
Can Computers Create Art?
Venue: arXiv cs.AI 2018
Year: 2018

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.
Voltage Control of DC Islanded Microgrids: Scalable Decentralised L1 Adaptive Controllers
Venue: arXiv cs.SY 2018
Year: 2018

Voltage stability is a critical feature of an efficiently operating power distribution system such as a DC islanded microgrid. Large-scale autonomous power systems can be defined by heterogeneous elements, uncertainty and changing conditions. This paper proposes a novel scalable decentralised control scheme at the primary level of the typical hierarchical control architecture of DC islanded microgrids with arbitrary topology. Local state-feedback $\mathcal{L}_1$ adaptive controllers are retrofitted to existing baseline voltage controllers of DC-DC boost converters, which interface distributed generation units with loads. The use of $\mathcal{L}_1$ adaptive controllers achieves fast and robust microgrid voltage stability in the presence of dynamic uncertainty and plug-and-play operations. Furthermore, local controller synthesis is modular as it only requires approximate information about the line parameters that couple neighbouring units. The performance of the proposed architecture is evaluated using a heterogeneous DC islanded-microgrid that consists of 6 DC-DC boost converters configured in a radial and meshed topology. The use of $\mathcal{L}_1$ adaptive controllers achieves fast and robust microgrid voltage stability in the presence of plug-and-play operations, unknown load and voltage reference changes, and unmodelled dynamics. Finally, sufficient conditions for global stability of the overall system are provided.
Poisson Cox Point Processes for Vehicular Networks
Venue: arXiv cs.IT 2018
Year: 2018

This paper analyzes statistical properties of the Poisson line Cox point process useful in the modeling of vehicular networks. The point process is created by a two-stage construction: a Poisson line process to model road infrastructure and independent Poisson point processes, conditionally on the Poisson lines, to model vehicles on the roads. We derive basic properties of the point process, including the general quadratic position of the points, the nearest distance distribution, the Laplace functional, the densities of facets of the Cox-Voronoi tessellation, and the asymptotic behavior of the typical Voronoi cell under vehicular densification. These properties are closely linked to features that are important in vehicular networks.