OpenRaiser/Intern-Atlas huggingface dataset:
e-Counterfeit: a mobile-server platform for document counterfeit detection
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.
Optimally Gathering Two Robots
Venue: arXiv cs.DC 2017
Year: 2017

We present an algorithm that ensures in finite time the gathering of two robots in the non-rigid ASYNC model. To circumvent established impossibility results, we assume robots are equipped with 2-colors lights and are able to measure distances between one another. Aside from its light, a robot has no memory of its past actions, and its protocol is deterministic. Since, in the same model, gathering is impossible when lights have a single color, our solution is optimal with respect to the number of used colors.
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator
Venue: arXiv cs.CL 2017
Year: 2017

The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre-trained word embedding features, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task on Emotion Intensity.
Analysis of Statistical QoS in Half Duplex and Full Duplex Dense Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

Statistical QoS provisioning as an important performance metric in analyzing next generation mobile cellular network, aka 5G, is investigated. In this context, by quantifying the performance in terms of the effective capacity, we introduce a lower bound for the system performance that facilitates an efficient analysis. Based on the proposed lower bound, which is mainly built on a per resource block analysis, we build a basic mathematical framework to analyze effective capacity in an ultra dense heterogeneous cellular network. We use our proposed scalable approach to give insights about the possible enhancements of the statistical QoS experienced by the end users if heterogeneous cellular networks migrate from a conventional half duplex to an imperfect full duplex mode of operation. Numerical results and analysis are provided, where the network is modeled as a Matern point process. The results demonstrate the accuracy and computational efficiency of the proposed scheme, especially in large scale wireless systems. Moreover, the minimum level of self interference cancellation for the full duplex system to start outperforming its half duplex counterpart is investigated.
Pillar Networks++: Distributed non-parametric deep and wide networks
Venue: arXiv cs.CV 2017
Year: 2017

In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks that used hand-crafted features in addition to the deep convolutional feature extractors. In the present work, we show that combining distributed Gaussian Processes with multi-stream deep convolutional neural networks (CNN) alleviate the need to augment a neural network with hand-crafted features. In contrast to prior work, we treat each deep neural convolutional network as an expert wherein the individual predictions (and their respective uncertainties) are combined into a Product of Experts (PoE) framework.
Neville's algorithm revisited
Venue: arXiv cs.OH 2017
Year: 2017

Neville's algorithm is known to provide an efficient and numerically stable solution for polynomial interpolations. In this paper, an extension of this algorithm is presented which includes the derivatives of the interpolating polynomial.
Online Coherence Identification Using Dynamic Time Warping for Controlled Islanding
Venue: arXiv cs.SY 2017
Year: 2017

Controlled islanding is considered to be the last countermeasure to prevent system-wide blackouts in case of cascading failures. It splits the system into self-sustained islands to maintain transient stability at the expense of possible loss of load. Generator coherence identification is critical to controlled islanding scheme as it helps identify the optimal cut-set to maintain system transient stability. This paper presents a novel approach for online generator coherency identification using phasor measurement unit (PMU) data and dynamic time warping (DTW). Results from the coherence identification are used to further cluster non-generator buses using spectral clustering with the objective of minimizing power flow disruption. The proposed approach is validated and compared to existing methods on the IEEE 39-bus system, through which its advantages are demonstrated.
Unavoidable Sets of Partial Words of Uniform Length
Venue: arXiv cs.FL 2017
Year: 2017

A set X of partial words over a finite alphabet A is called unavoidable if every two-sided infinite word over A has a factor compatible with an element of X. Unlike the case of a set of words without holes, the problem of deciding whether or not a given finite set of n partial words over a k-letter alphabet is avoidable is NP-hard, even when we restrict to a set of partial words of uniform length. So classifying such sets, with parameters k and n, as avoidable or unavoidable becomes an interesting problem. In this paper, we work towards this classification problem by investigating the maximum number of holes we can fill in unavoidable sets of partial words of uniform length over an alphabet of any fixed size, while maintaining the unavoidability property.
Device-Aware Routing and Scheduling in Multi-Hop Device-to-Device Networks
Venue: arXiv cs.NI 2017
Year: 2017

The dramatic increase in data and connectivity demand, in addition to heterogeneous device capabilities, poses a challenge for future wireless networks. One of the promising solutions is Device-to-Device (D2D) networking. D2D networking, advocating the idea of connecting two or more devices directly without traversing the core network, is promising to address the increasing data and connectivity demand. In this paper, we consider D2D networks, where devices with heterogeneous capabilities including computing power, energy limitations, and incentives participate in D2D activities heterogeneously. We develop (i) a device-aware routing and scheduling algorithm (DARS) by taking into account device capabilities, and (ii) a multi-hop D2D testbed using Android-based smartphones and tablets by exploiting Wi-Fi Direct and legacy Wi-Fi connections. We show that DARS significantly improves throughput in our testbed as compared to state-of-the-art.
Trajectory Optimization for Completion Time Minimization in UAV-Enabled Multicasting
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a number of geographically distributed ground terminals (GTs). Our objective is to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT is able to successfully recover the file with a high probability required. We consider the use of practical random linear network coding (RLNC) for UAV multicasting, so that each GT is able to recover the file as long as it receives a sufficiently large number of coded packets. However, the formulated UAV trajectory optimization problem is non-convex and difficult to be directly solved. To tackle this issue, we first derive an analytical lower bound for the success probability of each GT's file recovery. Based on this result, we then reformulate the problem into a more tractable form, where the UAV trajectory only needs to be designed to meet a set of constraints each on the minimum connection time with a GT, during which their distance is below a designed threshold. We show that the optimal UAV trajectory only needs to constitute connected line segments, thus it can be obtained by determining first the optimal set of waypoints and then UAV speed along the lines connecting the waypoints. We propose practical schemes for the waypoints design based on a novel concept of virtual base station (VBS) placement and by applying convex optimization techniques. Furthermore, for given set of waypoints, we obtain the optimal UAV speed over the resulting path efficiently by solving a linear programming (LP) problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains as compared to benchmark schemes.
Game Efficiency through Linear Programming Duality
Venue: arXiv cs.GT 2017
Year: 2017

The efficiency of a game is typically quantified by the price of anarchy (PoA), defined as the worst ratio of the objective function value of an equilibrium --- solution of the game --- and that of an optimal outcome. Given the tremendous impact of tools from mathematical programming in the design of algorithms and the similarity of the price of anarchy and different measures such as the approximation and competitive ratios, it is intriguing to develop a duality-based method to characterize the efficiency of games. In the paper, we present an approach based on linear programming duality to study the efficiency of games. We show that the approach provides a general recipe to analyze the efficiency of games and also to derive concepts leading to improvements. The approach is particularly appropriate to bound the PoA. Specifically, in our approach the dual programs naturally lead to competitive PoA bounds that are (almost) optimal for several classes of games. The approach indeed captures the smoothness framework and also some current non-smooth techniques/concepts. We show the applicability to the wide variety of games and environments, from congestion games to Bayesian welfare, from full-information settings to incomplete-information ones.
Sparsity Invariant CNNs
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings and will be made available upon publication.
Recover Feasible Solutions for SOCP Relaxation of Optimal Power Flow Problems in Mesh Networks
Venue: arXiv cs.SY 2017
Year: 2017

Convex relaxation methods have been studied and used extensively to obtain an optimal solution to the optimal power flow (OPF) problem. Meanwhile, convex relaxed power flow equations are also prerequisites for efficiently solving a wide range of problems in power systems including mixed-integer nonlinear programming (MINLP) and distributed optimization. When the exactness of convex relaxations is not guaranteed, it is important to recover a feasible solution for the convex relaxation methods. This paper presents an alternative convex optimization (ACP) approach that can efficiently recover a feasible solution from the result of second-order cone programming (SOCP) relaxed OPF in mesh networks. The OPF problem is first formulated as a difference-of-convex (DC) programming problem, then efficiently solved by a penalty convex concave procedure (CCP). CCP iteratively linearizes the concave parts of the power flow constraints and solves a convex approximation of the DCP problem. Numerical tests show that the proposed method can find a global or near-global optimal solution to the AC OPF problem, and outperforms those semidefinite programming (SDP) based algorithms.
Simplified Cooperative Detection for Multi-Receiver Molecular Communication
Venue: arXiv cs.IT 2017
Year: 2017

Diffusion-based molecular communication (MC) systems experience significant reliability losses. To boost the reliability, an MC scheme where multiple receivers (RXs) work cooperatively to decide the signal of a transmitter (TX) by sending the same type of molecules to a fusion center (FC) is proposed in this paper. The FC observes the total number of molecules received and compares this number with a threshold to determine the TX's signal. The proposed scheme is more bio-realistic and requires relatively low computational complexity compared to existing cooperative schemes where the RXs send and the FC recognizes different types of molecules. Asymmetric and symmetric topologies are considered, and closed-form expressions are derived for the global error probability for both topologies. Results show that the trade-off for simplified computations leads to a slight reduction in error performance, compared to the existing cooperative schemes.
Parametric identification of parallel Wiener-Hammerstein systems
Venue: arXiv cs.SY 2017
Year: 2017

Block-oriented nonlinear models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. To increase the flexibility of single branch block-oriented models, such as Hammerstein, Wiener, and Wiener-Hammerstein models, parallel block-oriented models can be considered. This paper presents a method to identify parallel Wiener-Hammerstein systems starting from input-output data only. In the first step, the best linear approximation is estimated for different input excitation levels. In the second step, the dynamics are decomposed over a number of parallel orthogonal branches. Next, the dynamics of each branch are partitioned into a linear time invariant subsystem at the input and a linear time invariant subsystem at the output. This is repeated for each branch of the model. The static nonlinear part of the model is also estimated during this step. The consistency of the proposed initialization procedure is proven. The method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.
Finding Regressions in Projects under Version Control Systems
Venue: arXiv cs.SE 2017
Year: 2017

Version Control Systems (VCS) are frequently used to support development of large-scale software projects. A typical VCS repository of a large project can contain various intertwined branches consisting of a large number of commits. If some kind of unwanted behaviour (e.g. a bug in the code) is found in the project, it is desirable to find the commit that introduced it. Such commit is called a regression point. There are two main issues regarding the regression points. First, detecting whether the project after a certain commit is correct can be very expensive as it may include large-scale testing and/or some other forms of verification. It is thus desirable to minimise the number of such queries. Second, there can be several regression points preceding the actual commit; perhaps a bug was introduced in a certain commit, inadvertently fixed several commits later, and then reintroduced in a yet later commit. In order to fix the actual commit it is usually desirable to find the latest regression point. The currently used distributed VCS contain methods for regression identification, see e.g. the git bisect tool. In this paper, we present a new regression identification algorithm that outperforms the current tools by decreasing the number of validity queries. At the same time, our algorithm tends to find the latest regression points which is a feature that is missing in the state-of-the-art algorithms. The paper provides an experimental evaluation of the proposed algorithm and compares it to the state-of-the-art tool git bisect on a real data set.
Wireless Network-Level Partial Relay Cooperation: A Stable Throughput Analysis
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we study the benefit of partial relay cooperation. We consider a two-node system consisting of one source and one relay node transmitting information to a common destination. The source and the relay have external traffic and in addition, the relay is equipped with a flow controller to regulate the incoming traffic from the source node. The cooperation is performed at the network level. A collision channel with erasures is considered. We provide an exact characterization of the stability region of the system and we also prove that the system with partial cooperation is always better or at least equal to the system without the flow controller.
Locally Differentially Private Heavy Hitter Identification
Venue: arXiv cs.CR 2017
Year: 2017

The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions while preserving their privacy. The basic LDP frequent oracle protocol enables the aggregator to estimate the frequency of any value. But when the domain of input values is large, finding the most frequent values, also known as the heavy hitters, by estimating the frequencies of all possible values, is computationally infeasible. In this paper, we propose an LDP protocol for identifying heavy hitters. In our proposed protocol, which we call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value. We analyze how to choose optimal parameters for the protocol and identify two design principles for designing LDP protocols with high utility. Experiments on both synthetic and real-world datasets demonstrate the advantage of our proposed protocol.
Herding Vulnerable Cats: A Statistical Approach to Disentangle Joint Responsibility for Web Security in Shared Hosting
Venue: arXiv cs.CR 2017
Year: 2017

Hosting providers play a key role in fighting web compromise, but their ability to prevent abuse is constrained by the security practices of their own customers. {\em Shared} hosting, offers a unique perspective since customers operate under restricted privileges and providers retain more control over configurations. We present the first empirical analysis of the distribution of web security features and software patching practices in shared hosting providers, the influence of providers on these security practices, and their impact on web compromise rates. We construct provider-level features on the global market for shared hosting -- containing 1,259 providers -- by gathering indicators from 442,684 domains. Exploratory factor analysis of 15 indicators identifies four main latent factors that capture security efforts: content security, webmaster security, web infrastructure security and web application security. We confirm, via a fixed-effect regression model, that providers exert significant influence over the latter two factors, which are both related to the software stack in their hosting environment. Finally, by means of GLM regression analysis of these factors on phishing and malware abuse, we show that the four security and software patching factors explain between 10\% and 19\% of the variance in abuse at providers, after controlling for size. For web-application security for instance, we found that when a provider moves from the bottom 10\% to the best-performing 10\%, it would experience 4 times fewer phishing incidents. We show that providers have influence over patch levels--even higher in the stack, where CMSes can run as client-side software--and that this influence is tied to a substantial reduction in abuse levels.
Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN with Incomplete CSI
Venue: arXiv cs.NI 2017
Year: 2017

This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), etc. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower-bound. Then, based on the successive convex approximation (SCA) technique and the relationship between the data rate and the mean square error (MSE), we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.
D3NOC: Dynamic Data-Driven Network On Chip in Photonic Electronic Hybrids
Venue: arXiv cs.OH 2017
Year: 2017

In this paper, we present a reconfigurable hybrid Photonic-Plasmonic Network-on-Chip (NoC) based on the Dynamic Data Driven Application System (DDDAS) paradigm. In DDDAS computations and measurements form a dynamic closed feedback loop in which they tune one another in response to changes in the environment. Our proposed system enables dynamic augmentation of a base electrical mesh topology with an optical express bus during the run-time. In addition, the measurement process itself adjusts to the environment. In order to achieve lower latencies, lower dynamic power, and higher throughput, we take advantage of a Configurable Hybrid Photonic Plasmonic Interconnect (CHyPPI) for our reconfigurable connections. We evaluate the performance and power of our system against kernels from NAS Parallel Benchmark (NPB) in addition to some synthetically generated traffic. In comparison to a 16x16 base electrical mesh, D3NOC shows up to 89% latency and 67% dynamic power net improvements beyond overhead-corrected performance. It should be noted that the design-space of NoC reconfiguration is vast and the goal of this study is not design-space exploration. Our goal is to show the potentials of adaptive dynamic measurements when coupled with other reconfiguration techniques in the NoC context.