OpenRaiser/Intern-Atlas huggingface dataset:
New families of Strictly optimal Frequency hopping sequence sets
Venue: arXiv cs.IT 2016
Year: 2016

Frequency hopping sequences (FHSs) with favorable partial Hamming correlation properties have important applications in many synchronization and multiple-access systems. In this paper, we investigate constructions of FHS sets with optimal partial Hamming correlation. We present several direct constructions for balanced nested cyclic difference packings (BNCDPs) and balanced nested cyclic relative difference packings (BNCRDPs) such that both of them have a special property by using trace functions and discrete logarithm. We also show three recursive constructions for FHS sets with partial Hamming correlation, which are based on cyclic difference matrices and discrete logarithm. Combing these BNCDPs, BNCRDPs and three recursive constructions, we obtain infinitely many new strictly optimal FHS sets with respect to the Peng-Fan bounds.
Efficient Probabilistic Collision Detection for Non-Convex Shapes
Venue: arXiv cs.RO 2016
Year: 2016

We present new algorithms to perform fast probabilistic collision queries between convex as well as non-convex objects. Our approach is applicable to general shapes, where one or more objects are represented using Gaussian probability distributions. We present a fast new algorithm for a pair of convex objects, and extend the approach to non-convex models using hierarchical representations. We highlight the performance of our algorithms with various convex and non-convex shapes on complex synthetic benchmarks and trajectory planning benchmarks for a 7-DOF Fetch robot arm.
Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes
Venue: arXiv cs.CV 2016
Year: 2016

Recognising detailed clothing characteristics (fine-grained attributes) in unconstrained images of people in-the-wild is a challenging task for computer vision, especially when there is only limited training data from the wild whilst most data available for model learning are captured in well-controlled environments using fashion models (well lit, no background clutter, frontal view, high-resolution). In this work, we develop a deep learning framework capable of model transfer learning from well-controlled shop clothing images collected from web retailers to in-the-wild images from the street. Specifically, we formulate a novel Multi-Task Curriculum Transfer (MTCT) deep learning method to explore multiple sources of different types of web annotations with multi-labelled fine-grained attributes. Our multi-task loss function is designed to extract more discriminative representations in training by jointly learning all attributes, and our curriculum strategy exploits the staged easy-to-complex transfer learning motivated by cognitive studies. We demonstrate the advantages of the MTCT model over the state-of-the-art methods on the X-Domain benchmark, a large scale clothing attribute dataset. Moreover, we show that the MTCT model has a notable advantage over contemporary models when the training data size is small.
Discovering Small Target Sets in Social Networks: A Fast and Effective Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

Given a network represented by a graph $G=(V,E)$, we consider a dynamical process of influence diffusion in $G$ that evolves as follows: Initially only the nodes of a given $S\subseteq V$ are influenced; subsequently, at each round, the set of influenced nodes is augmented by all the nodes in the network that have a sufficiently large number of already influenced neighbors. The question is to determine a small subset of nodes $S$ (\emph{a target set}) that can influence the whole network. This is a widely studied problem that abstracts many phenomena in the social, economic, biological, and physical sciences. It is known that the above optimization problem is hard to approximate within a factor of $2^{\log^{1-ε}|V|}$, for any $ε>0$. In this paper, we present a fast and surprisingly simple algorithm that exhibits the following features: 1) when applied to trees, cycles, or complete graphs, it always produces an optimal solution (i.e, a minimum size target set); 2) when applied to arbitrary networks, it always produces a solution of cardinality which improves on the previously known upper bound; 3) when applied to real-life networks, it always produces solutions that substantially outperform the ones obtained by previously published algorithms (for which no proof of optimality or performance guarantee is known in any class of graphs).
Burst Transmission Symbol Synchronization in the Presence of Cycle Slip Arising from Different Clock Frequencies
Venue: arXiv cs.IT 2016
Year: 2016

In digital communication systems different clock frequencies of transmitter and receiver usually is translated into cycle slips. Receivers might experience different sampling frequencies from transmitter due to manufacturing imperfection, Doppler Effect introduced by channel or wrong estimation of symbol rate. Timing synchronization in presence of cycle slip for a burst sequence of received information, leads to severe degradation in system performance that represents as shortening or prolonging of bit stream. Therefor the necessity of prior detection and elimination of cycle slip is unavoidable. Accordingly, the main idea introduced in this paper is to employ the Gardner Detector (GAD) not only to recover a fixed timing offset, its output is also processed in a way such that timing drifts can be estimated and corrected. Deriving a two steps algorithm, eliminates the cycle slips arising from wrong estimation of symbol rate firstly, and then iteratively synchronize symbol timing of a burst received signal by applying GAD to a feed forward structure with the additional benefits that convergence and stability problems are avoided, as they are typical for feedback schemes normally used by GAD. The proposed algorithm is able to compensate considerable symbol rate offsets at the receiver side. Considerable results in terms of BER confirm the algorithm proficiency.
Deep disentangled representations for volumetric reconstruction
Venue: arXiv cs.CV 2016
Year: 2016

We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The network comprises an encoder and a twin-tailed decoder. The encoder generates a disentangled graphics code. The first decoder generates a volume, and the second decoder reconstructs the input image using a novel training regime that allows the graphics code to learn a separate representation of the 3D object and a description of its lighting and pose conditions. We demonstrate this method by generating volumes and disentangled graphical descriptions from images and videos of faces and chairs.
SNR-Walls in Eigenvalue-based Spectrum Sensing
Venue: arXiv cs.IT 2016
Year: 2016

Various spectrum sensing approaches have been shown to suffer from a so-called SNR-wall, an SNR value below which a detector cannot perform robustly no matter how many observations are used. Up to now, the eigenvalue-based maximum-minimum-eigenvalue (MME) detector has been a notable exception. For instance, the model uncertainty of imperfect knowledge of the receiver noise power, which is known to be responsible for the energy detector's fundamental limits, does not adversely affect the MME detector's performance. While additive white Gaussian noise (AWGN) is a standard assumption in wireless communications, it is not a reasonable one for the MME detector. In fact, in this work we prove that uncertainty in the amount of noise coloring does lead to an SNR-wall for the MME detector. We derive a lower bound on this SNR-wall and evaluate it for example scenarios. The findings are supported by numerical simulations.
A Fuzzy Logic System to Analyze a Student's Lifestyle
Venue: arXiv cs.CY 2016
Year: 2016

A college student's life can be primarily categorized into domains such as education, health, social and other activities which may include daily chores and travelling time. Time management is crucial for every student. A self realisation of one's daily time expenditure in various domains is therefore essential to maximize one's effective output. This paper presents how a mobile application using Fuzzy Logic and Global Positioning System (GPS) analyzes a student's lifestyle and provides recommendations and suggestions based on the results.
Multi-view Consistency in UML
Venue: arXiv cs.SE 2016
Year: 2016

We study the question of consistency of multi-view models in UML and OCL. We first critically survey the large amount of literature that already exists. We find that only limited subsets of the UML/OCL have been covered so far and that consistency checks mostly only cover structural aspects, whereas only few methods also address behaviour. We also give a classification of different techniques for multi-view UML/OCL consistency: consistency rules, the system model approach, dynamic meta-modelling, universal logic, and heterogeneous transformation. Finally, we elaborate cornerstones of a comprehensive distributed semantics approach to consistency using OMG's Distributed Ontology, Model and Specification Language (DOL).
An Efficient and Robust Social Network De-anonymization Attack
Venue: arXiv cs.CR 2016
Year: 2016

Releasing connection data from social networking services can pose a significant threat to user privacy. In our work, we consider structural social network de-anonymization attacks, which are used when a malicious party uses connections in a public or other identified network to re-identify users in an anonymized social network release that he obtained previously. In this paper we design and evaluate a novel social de-anonymization attack. In particular, we argue that the similarity function used to re-identify nodes is a key component of such attacks, and we design a novel measure tailored for social networks. We incorporate this measure in an attack called Bumblebee. We evaluate Bumblebee in depth, and show that it significantly outperforms the state-of-the-art, for example it has higher re-identification rates with high precision, robustness against noise, and also has better error control.
An arbiter PUF secured by remote random reconfigurations of an FPGA
Venue: arXiv cs.CR 2016
Year: 2016

We present a practical and highly secure method for the authentication of chips based on a new concept for implementing strong Physical Unclonable Function (PUF) on field programmable gate arrays (FPGA). Its qualitatively novel feature is a remote reconfiguration in which the delay stages of the PUF are arranged to a random pattern within a subset of the FPGA's gates. Before the reconfiguration is performed during authentication the PUF simply does not exist. Hence even if an attacker has the chip under control previously she can gain no useful information about the PUF. This feature, together with a strict renunciation of any error correction and challenge selection criteria that depend on individual properties of the PUF that goes into the field make our strong PUF construction immune to all machine learning attacks presented in the literature. More sophisticated attacks on our strong-PUF construction will be difficult, because they require the attacker to learn or directly measure the properties of the complete FPGA. A fully functional reference implementation for a secure "chip biometrics" is presented. We remotely configure ten 64-stage arbiter PUFs out of 1428 lookup tables within a time of 25 seconds and then receive one "fingerprint" from each PUF within 1 msec.
Scale Stain: Multi-Resolution Feature Enhancement in Pathology Visualization
Venue: arXiv cs.HC 2016
Year: 2016

Digital whole-slide images of pathological tissue samples have recently become feasible for use within routine diagnostic practice. These gigapixel sized images enable pathologists to perform reviews using computer workstations instead of microscopes. Existing workstations visualize scanned images by providing a zoomable image space that reproduces the capabilities of the microscope. This paper presents a novel visualization approach that enables filtering of the scale-space according to color preference. The visualization method reveals diagnostically important patterns that are otherwise not visible. The paper demonstrates how this approach has been implemented into a fully functional prototype that lets the user navigate the visualization parameter space in real time. The prototype was evaluated for two common clinical tasks with eight pathologists in a within-subjects study. The data reveal that task efficiency increased by 15% using the prototype, with maintained accuracy. By analyzing behavioral strategies, it was possible to conclude that efficiency gain was caused by a reduction of the panning needed to perform systematic search of the images. The prototype system was well received by the pathologists who did not detect any risks that would hinder use in clinical routine.
Multi-Layer Precoding: A Potential Solution for Full-Dimensional Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

Massive multiple-input multiple-output (MIMO) systems achieve high sum spectral efficiency by offering an order of magnitude increase in multiplexing gains. In time division duplexing systems, however, the reuse of uplink training pilots among cells results in additional channel estimation error, which causes downlink inter-cell interference, even when large numbers of antennas are employed. Handling this interference with conventional network MIMO techniques is challenging due to the large channel dimensionality. Further, the implementation of large antenna precoding/combining matrices is associated with high hardware complexity and power consumption. In this paper, we propose multi-layer precoding to enable efficient and low complexity operation in full-dimensional massive MIMO, where a large number of antennas is used in two dimensions. In multi-layer precoding, the precoding matrix of each base station is written as a product of a number of precoding matrices, each one called a layer. Multi-layer precoding (i) leverages the directional characteristics of large-scale MIMO channels to manage inter-cell interference with low channel knowledge requirements, and (ii) allows for an efficient implementation using low-complexity hybrid analog/digital architectures. We present a specific multi-layer precoding design for full-dimensional massive MIMO systems. The performance of this precoding design is analyzed and the per-user achievable rate is characterized for general channel models. The asymptotic optimality of the proposed multi-layer precoding design is then proved for some special yet important channels. Numerical simulations verify the analytical results and illustrate the potential gains of multi-layer precoding compared to traditional pilot-contaminated massive MIMO solutions.
Partial Strong Converse for the Non-Degraded Wiretap Channel
Venue: arXiv cs.IT 2016
Year: 2016

We prove the partial strong converse property for the discrete memoryless \emph{non-degraded} wiretap channel, for which we require the leakage to the eavesdropper to vanish but allow an asymptotic error probability $ε\in [0,1)$ to the legitimate receiver. We show that when the transmission rate is above the secrecy capacity, the probability of correct decoding at the legitimate receiver decays to zero exponentially. Therefore, the maximum transmission rate is the same for $ε\in [0,1)$, and the partial strong converse property holds. Our work is inspired by a recently developed technique based on information spectrum method and Chernoff-Cramer bound for evaluating the exponent of the probability of correct decoding.
Generalized and Extended Product Codes
Venue: arXiv cs.IT 2016
Year: 2016

Generalized Product (GPC) Codes, an unification of Product Codes and Integrated Interleaved (II) Codes, are presented. Applications for approaches requiring local and global parities are described. The more general problem of extending product codes by adding global parities is studied and an upper bound on the minimum distance of such codes is obtained. Codes with one, two and three global parities whose minimum distances meet the bound are presented. Tradeoffs between optimality and field size are discussed.
Delay Minimization in Real-time Communications with Joint Buffering and Coding
Venue: arXiv cs.IT 2016
Year: 2016

We present a closed-form expression for the minimal delay that is achievable in a setting that combines a buffer and an erasure code, used to mitigate the packet delay variance. The erasure code is modeled according to the recent information-theoretic results on finite block length codes. Evaluations reveal that accurate knowledge of the network parameters is essential for optimal operation. Moreover, it is shown that, when the network packet delay variance is large, the buffer delay becomes negligible. Therefore, in this case the delay budget should be spent mainly on the erasure code.
Fractal Properties of Multiagent News Diffusion Model
Venue: arXiv cs.SI 2016
Year: 2016

The paper deals with fractal characteristics (Hurst exponent) and wavelet-scaleograms of the information distribution model, suggested by the authors. The authors have studied the effect of Hurst exponent change depending upon the model parameters, which have semantic meaning. The paper also considers fractal characteristics of real information streams. It is described, how the Hurst exponent dynamics depends on these information streams state in practice
On the computational complexity of read once resolution decidability in 2CNF formulas
Venue: arXiv cs.CC 2016
Year: 2016

In this paper, we analyze 2CNF formulas from the perspectives of Read-Once resolution (ROR) refutation schemes. We focus on two types of ROR refutations, viz., variable-once refutation and clause-once refutation. In the former, each variable may be used at most once in the derivation of a refutation, while in the latter, each clause may be used at most once. We show that the problem of checking whether a given 2CNF formula has an ROR refutation under both schemes is NP-complete. This is surprising in light of the fact that there exist polynomial refutation schemes (tree-resolution and DAG-resolution) for 2CNF formulas. On the positive side, we show that 2CNF formulas have copy-complexity 2, which means that any unsatisfiable 2CNF formula has a refutation in which any clause needs to be used at most twice.
Computing the K-terminal Reliability of Circle Graphs
Venue: arXiv cs.DM 2016
Year: 2016

Let G denote a graph and let K be a subset of vertices that are a set of target vertices of G. The K-terminal reliability of G is defined as the probability that all target vertices in K are connected, considering the possible failures of non-target vertices of G. The problem of computing K-terminal reliability is known to be #P-complete for polygon-circle graphs, and can be solved in polynomial-time for t-polygon graphs, which are a subclass of polygon-circle graphs. The class of circle graphs is a subclass of polygon-circle graphs and a superclass of t-polygon graphs. Therefore, the problem of computing K-terminal reliability for circle graphs is of particular interest. This paper proves that the problem remains #P-complete even for circle graphs. Additionally, this paper proposes a linear-time algorithm for solving the problem for proper circular-arc graphs, which are a subclass of circle graphs and a superclass of proper interval graphs.
Comparing Face Detection and Recognition Techniques
Venue: arXiv cs.CV 2016
Year: 2016

This paper implements and compares different techniques for face detection and recognition. One is find where the face is located in the images that is face detection and second is face recognition that is identifying the person. We study three techniques in this paper: Face detection using self organizing map (SOM), Face recognition by projection and nearest neighbor and Face recognition using SVM.
Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits
Venue: arXiv cs.LG 2016
Year: 2016

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not assume data distribution, but rather relying on the asymptotic distribution of extremal values, and reduce the problem to estimate three parameters of the extreme value distributions and the extremal index. We specially take care of data dependence via estimating extremal index since in many settings, such as scan statistics, change-point detection, and extreme bandits, where dependence in the sequence of statistics can be significant. Key features of our DTM also include robustness and the computational efficiency, and it only requires one sample path to form a reliable estimate of the threshold, in contrast to the Monte Carlo sampling approach which requires drawing a large number of sample paths. We demonstrate the good performance of DTM via numerical examples in various dependent settings.
Three-dimensional nonlinear micro/meso-mechanical response of the fibre-reinforced polymer composites
Venue: arXiv cs.CE 2016
Year: 2016

A three-dimensional multi-scale computational homogenisation framework is developed for the prediction of nonlinear micro/meso-mechanical response of the fibre-reinforced polymer (FRP) composites. Two dominant damage mechanisms, i.e. matrix elasto-plastic response and fibre-matrix decohesion are considered and modelled using a non-associative pressure dependent paraboloidal yield criterion and cohesive interface elements respectively. A linear-elastic transversely isotropic material model is used to model yarns/fibres within the representative volume element (RVE). A unified approach is used to impose the RVE boundary conditions, which allows convenient switching between linear displacement, uniform traction and periodic boundary conditions. The computational model is implemented within the framework of the hierarchic finite element, which permits the use of arbitrary orders of approximation. Furthermore, the computational framework is designed to take advantage of distributed memory high-performance computing. The accuracy and performance of the computational framework are demonstrated with a variety of numerical examples, including unidirectional FRP composite, a composite comprising a multi-fibre and multi-layer RVE, with randomly generated fibres, and a single layered plain weave textile composite. Results are validated against the reference experimental/numerical results from the literature. The computational framework is also used to study the effect of matrix and fibre-matrix interfaces properties on the homogenised stress-strain responses.
Incremental One-Class Models for Data Classification
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we outline a PhD research plan. This research contributes to the field of one-class incremental learning and classification in case of non-stationary environments. The goal of this PhD is to define a new classification framework able to deal with very small learning dataset at the beginning of the process and with abilities to adjust itself according to the variability of the incoming data which create large scale datasets. As a preliminary work, incremental Covariance-guided One-Class Support Vector Machine is proposed to deal with sequentially obtained data. It is inspired from COSVM which put more emphasis on the low variance directions while keeping the basic formulation of incremental One-Class Support Vector Machine untouched. The incremental procedure is introduced by controlling the possible changes of support vectors after the addition of new data points, thanks to the Karush-Kuhn-Tucker conditions, that have to be maintained on all previously acquired data. Comparative experimental results with contemporary incremental and non-incremental one-class classifiers on numerous artificial and real data sets show that our method results in significantly better classification performance.