OpenRaiser/Intern-Atlas huggingface dataset:
Artificial-Noise-Aided Secure Transmission in Wiretap Channels with Transmitter-Side Correlation
Venue: arXiv cs.IT 2016
Year: 2016

This work for the first time examines the impact of transmitter-side correlation on the artificial-noise-aided secure transmission, based on which a new power allocation strategy for artificial noise (AN) is devised for physical layer security enhancement. Specifically, we design a correlation-based power allocation (CPA) for AN, of which the optimality in terms of achieving the minimum secrecy outage probability is analytically proved in the large system regime with the number of transmit antennas approaching infinity. In order to fully reveal the benefits of the CPA, we derive easy-to-evaluate expressions for the secrecy outage probability achieved by the CPA. Our study demonstrates that the CPA is nearly optimal and significantly outperforms the widely-used uniform power allocation (UPA) even for a moderately small number of correlated transmit antennas. Furthermore, our numerical results reveal a fundamental difference between the CPA and UPA. That is when the number of correlated transmit antennas increases, the secrecy outage probability of the CPA always reduces while the secrecy outage probability of the UPA suffers from a saturation point.
Chinese/English mixed Character Segmentation as Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

OCR character segmentation for multilingual printed documents is difficult due to the diversity of different linguistic characters. Previous approaches mainly focus on monolingual texts and are not suitable for multilingual-lingual cases. In this work, we particularly tackle the Chinese/English mixed case by reframing it as a semantic segmentation problem. We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation. Given a wide enough receptive field, FCN can utilize the necessary context around a horizontal position to determinate whether this is a splitting point or not. As a deep neural architecture, FCN can automatically learn useful features from raw text line images. Although trained on synthesized samples with simulated random disturbance, our FCN model generalizes well to real-world samples. The experimental results show that our model significantly outperforms the previous methods.
Sigma Delta Quantized Networks
Venue: arXiv cs.NE 2016
Year: 2016

Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity between neighbouring frames. As a result, it ends up repeatedly doing very similar computations. To put an end to such waste, we introduce Sigma-Delta networks. With each new input, each layer in this network sends a discretized form of its change in activation to the next layer. Thus the amount of computation that the network does scales with the amount of change in the input and layer activations, rather than the size of the network. We introduce an optimization method for converting any pre-trained deep network into an optimally efficient Sigma-Delta network, and show that our algorithm, if run on the appropriate hardware, could cut at least an order of magnitude from the computational cost of processing video data.
:telephone::person::sailboat::whale::okhand:; or "Call me Ishmael" - How do you translate emoji?
Venue: arXiv cs.CL 2016
Year: 2016

We report on an exploratory analysis of Emoji Dick, a project that leverages crowdsourcing to translate Melville's Moby Dick into emoji. This distinctive use of emoji removes textual context, and leads to a varying translation quality. In this paper, we use statistical word alignment and part-of-speech tagging to explore how people use emoji. Despite these simple methods, we observed differences in token and part-of-speech distributions. Experiments also suggest that semantics are preserved in the translation, and repetition is more common in emoji.
Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms
Venue: arXiv cs.LG 2016
Year: 2016

One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different ways to improve MeLiF algorithm performance with parallelization. Experiments show that proposed schemes significantly improves algorithm performance and increase feature selection quality.
A note on Reed's Conjecture about $ω$, $Δ$ and $χ$ with respect to vertices of high degree
Venue: arXiv cs.DM 2016
Year: 2016

Reed conjectured that for every graph, $χ\leq \left \lceil \frac{Δ+ ω+ 1}{2} \right \rceil$ holds, where $χ$, $ω$ and $Δ$ denote the chromatic number, clique number and maximum degree of the graph, respectively. We develop an algorithm which takes a hypothetical counterexample as input. The output discloses some hidden structures closely related to high vertex degrees. Consequently, we deduce two graph classes where Reed's Conjecture holds: One contains all graphs in which the vertices of degree at least $5$ form a stable set. The other contains all graphs in which every induced cycle of odd length contains a vertex of at most degree 3.
A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.
Cubical Type Theory: a constructive interpretation of the univalence axiom
Venue: arXiv cs.LO 2016
Year: 2016

This paper presents a type theory in which it is possible to directly manipulate $n$-dimensional cubes (points, lines, squares, cubes, etc.) based on an interpretation of dependent type theory in a cubical set model. This enables new ways to reason about identity types, for instance, function extensionality is directly provable in the system. Further, Voevodsky's univalence axiom is provable in this system. We also explain an extension with some higher inductive types like the circle and propositional truncation. Finally we provide semantics for this cubical type theory in a constructive meta-theory.
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization
Venue: arXiv cs.NE 2016
Year: 2016

Artificial neural networks have gone through a recent rise in popularity, achieving state-of-the-art results in various fields, including image classification, speech recognition, and automated control. Both the performance and computational complexity of such models are heavily dependant on the design of characteristic hyper-parameters (e.g., number of hidden layers, nodes per layer, or choice of activation functions), which have traditionally been optimized manually. With machine learning penetrating low-power mobile and embedded areas, the need to optimize not only for performance (accuracy), but also for implementation complexity, becomes paramount. In this work, we present a multi-objective design space exploration method that reduces the number of solution networks trained and evaluated through response surface modelling. Given spaces which can easily exceed 1020 solutions, manually designing a near-optimal architecture is unlikely as opportunities to reduce network complexity, while maintaining performance, may be overlooked. This problem is exacerbated by the fact that hyper-parameters which perform well on specific datasets may yield sub-par results on others, and must therefore be designed on a per-application basis. In our work, machine learning is leveraged by training an artificial neural network to predict the performance of future candidate networks. The method is evaluated on the MNIST and CIFAR-10 image datasets, optimizing for both recognition accuracy and computational complexity. Experimental results demonstrate that the proposed method can closely approximate the Pareto-optimal front, while only exploring a small fraction of the design space.
Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement
Venue: arXiv cs.GR 2016
Year: 2016

The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. In this paper, we design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error δ, minimal interior angle θ and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound δ as a hard constraint and the other two criteria as optimization goals. More specifically, we iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. In this way our optimization framework greedily searches for the coarsest mesh with minimal interior angle above θ and approximation error bounded by δ. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.
Proceedings of the First International Workshop on Argumentation in Logic Programming and Non-Monotonic Reasoning (Arg-LPNMR 2016)
Venue: arXiv cs.AI 2016
Year: 2016

This volume contains the papers presented at Arg-LPNMR 2016: First International Workshop on Argumentation in Logic Programming and Nonmonotonic Reasoning held on July 8-10, 2016 in New York City, NY.
Single-Tap Precoders and Decoders for Multi-User MIMO FBMC-OQAM under Strong Channel Frequency Selectivity
Venue: arXiv cs.IT 2016
Year: 2016

The design of linear precoders or decoders for multiuser (MU) multiple-input multiple-output (MIMO) filterbank multicarrier (FBMC) modulations in the case of strong channel frequency selectivity is presented. The users and the base station (BS) communicate using space division multiple access (SDMA). The low complexity proposed solution is based on a single tap per-subcarrier precoding/decoding matrix at the base station (BS) in the downlink/uplink. As opposed to classical approaches that assume flat channel frequency selectivity at the subcarrier level, the BS does not make this assumption and takes into account the distortion caused by channel frequency selectivity. The expression of the FBMC asymptotic mean squared error (MSE) in the case of strong channel selectivity derived in earlier works is developed and extended. The linear precoders and decoders are found by optimizing the MSE formula under two design criteria, namely zero forcing (ZF) or minimum mean squared error (MMSE). Finally, simulation results demonstrate the performance of the optimized design. As long as the number of BS antennas is larger than the number of users, it is shown that those extra degrees of freedom can be used to compensate for the channel frequency selectivity.
Arc diagrams, flip distances, and Hamiltonian triangulations
Venue: arXiv cs.CG 2016
Year: 2016

We show that every triangulation (maximal planar graph) on $n\ge 6$ vertices can be flipped into a Hamiltonian triangulation using a sequence of less than $n/2$ combinatorial edge flips. The previously best upper bound uses $4$-connectivity as a means to establish Hamiltonicity. But in general about $3n/5$ flips are necessary to reach a $4$-connected triangulation. Our result improves the upper bound on the diameter of the flip graph of combinatorial triangulations on $n$ vertices from $5.2n-33.6$ to $5n-23$. We also show that for every triangulation on $n$ vertices there is a simultaneous flip of less than $2n/3$ edges to a $4$-connected triangulation. The bound on the number of edges is tight, up to an additive constant. As another application we show that every planar graph on $n$ vertices admits an arc diagram with less than $n/2$ biarcs, that is, after subdividing less than $n/2$ (of potentially $3n-6$) edges the resulting graph admits a $2$-page book embedding.
On the Uplink Spectral Efficiency of Full-Duplex Cooperative OFDMA Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we develop a resource allocation algorithm for uplink of in-band full-duplex (FD) cellular networks. The FD cellular network is assumed to be based on orthogonal frequency division multiple access (OFDMA) and consists of a base station communicating with multiple users. Some of the users in the network act as relay for other users and help them to transmit their data to the base station. These relays are FD and work based on amplify and forward (AF) protocol. By appropriate selection of the relays and optimized allocation of subcarriers and powers to all users, we try to maximize the total sum rate of the network. During this optimization, we also impose some constraints on the users' quality of service (QoS) and power. We propose a new algorithm to select the best relays based on the users' maximum data rate and also use Linear Assignment Problem Jonker-Volgenant (LAPJV) algorithm for subcarrier assignment. It is proved that the resulting optimization problem can be converted to a convex problem, and hence it can be solved by standard numerical methods. The simulation results demonstrate the effect of the proposed scheme on the sum rate and coverage of the network.
Veracity Computing from Lexical Cues and Perceived Certainty Trends
Venue: arXiv cs.CL 2016
Year: 2016

We present a data-driven method for determining the veracity of a set of rumorous claims on social media data. Tweets from different sources pertaining to a rumor are processed on three levels: first, factuality values are assigned to each tweet based on four textual cue categories relevant for our journalism use case; these amalgamate speaker support in terms of polarity and commitment in terms of certainty and speculation. Next, the proportions of these lexical cues are utilized as predictors for tweet certainty in a generalized linear regression model. Subsequently, lexical cue proportions, predicted certainty, as well as their time course characteristics are used to compute veracity for each rumor in terms of the identity of the rumor-resolving tweet and its binary resolution value judgment. The system operates without access to extralinguistic resources. Evaluated on the data portion for which hand-labeled examples were available, it achieves .74 F1-score on identifying rumor resolving tweets and .76 F1-score on predicting if a rumor is resolved as true or false.
Optimization and parallelization of B-spline based orbital evaluations in QMC on multi/many-core shared memory processors
Venue: arXiv cs.DC 2016
Year: 2016

B-spline based orbital representations are widely used in Quantum Monte Carlo (QMC) simulations of solids, historically taking as much as 50% of the total run time. Random accesses to a large four-dimensional array make it challenging to efficiently utilize caches and wide vector units of modern CPUs. We present node-level optimizations of B-spline evaluations on multi/many-core shared memory processors. To increase SIMD efficiency and bandwidth utilization, we first apply data layout transformation from array-of-structures to structure-of-arrays (SoA). Then by blocking SoA objects, we optimize cache reuse and get sustained throughput for a range of problem sizes. We implement efficient nested threading in B-spline orbital evaluation kernels, paving the way towards enabling strong scaling of QMC simulations. These optimizations are portable on four distinct cache-coherent architectures and result in up to 5.6x performance enhancements on Intel Xeon Phi processor 7250P (KNL), 5.7x on Intel Xeon Phi coprocessor 7120P, 10x on an Intel Xeon processor E5v4 CPU and 9.5x on BlueGene/Q processor. Our nested threading implementation shows nearly ideal parallel efficiency on KNL up to 16 threads. We employ roofline performance analysis to model the impacts of our optimizations. This work combined with our current efforts of optimizing other QMC kernels, result in greater than 4.5x speedup of miniQMC on KNL.
Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress
Venue: arXiv cs.HC 2016
Year: 2016

Online creative communities allow creators to share their work with a large audience, maximizing opportunities to showcase their work and connect with fans and peers. However, sharing in-progress work can be technically and socially challenging in environments designed for sharing completed pieces. We propose an online creative community where sharing process, rather than showcasing outcomes, is the main method of sharing creative work. Based on this, we present Mosaic---an online community where illustrators share work-in-progress snapshots showing how an artwork was completed from start to finish. In an online deployment and observational study, artists used Mosaic as a vehicle for reflecting on how they can improve their own creative process, developed a social norm of detailed feedback, and became less apprehensive of sharing early versions of artwork. Through Mosaic, we argue that communities oriented around sharing creative process can create a collaborative environment that is beneficial for creative growth.
ZeroTouch Provisioning (ZTP) Model and Infrastructure Components for Multi-provider Cloud Services Provisioning
Venue: arXiv cs.DC 2016
Year: 2016

This paper presents results of the ongoing development of the Cloud Services Delivery Infrastructure (CSDI) that provides a basis for infrastructure centric cloud services provisioning, operation and management in multi-cloud multi-provider environment defined as a Zero Touch Provisioning, Operation and Management (ZTP/ZTPOM) model. The presented work refers to use cases from data intensive research that require high performance computation resources and large storage volumes that are typically distributed between datacenters often involving multiple cloud providers. Automation for large scale scientific (and industrial) applications should include provisioning of both inter-cloud network infrastructure and intra-cloud application resources. It should provide support for the complete application operation workflow together with the possible application infrastructure and resources changes that can occur during the application lifecycle. The authors investigate existing technologies for automation of the service provisioning and management processes aiming to cross-pollinate best practices from currently disconnected domains such as cloud based applications provisioning and multi-domain high-performance network provisioning. The paper refers to the previous and legacy research by authors, the Open Cloud eXchange (OCX), that has been proposed to address the last mile problem in cloud services delivery to campuses over trans-national backbone networks such as GEANT. OCX will serve as an integral component of the prospective ZTP infrastructure over the GEANT network. Another important component, the Marketplace, is defined for providing cloud services and applications discovery (in generally intercloud environment) and may also support additional services such as services composition and trust brokering for establishing customer-provider federations.
Supporting Education in Math Sciences with a Low-budget LMS SAKAI
Venue: arXiv cs.CY 2016
Year: 2016

In this paper we present the Low-budget LMS Sakai, the analysis conducted and its selection from a list of few popular Low-budget LMSs, as well as its capability in presentation of math texts and formulas. The MATHEIS e-learning system for learning mathematics and informatics and its empowering with few software agent based features including the new developed GUPA (Graphic User Presentation Agent) software agent is emphasized. The plugins offered for Sakai allow powerful mathematics presentation capabilities, one of them being the WIRIS plugin. The paper is focused on the Sakai WIRIS plugin implementation.
Catching Worms, Trojan Horses and PUPs: Unsupervised Detection of Silent Delivery Campaigns
Venue: arXiv cs.CR 2016
Year: 2016

The growing commoditization of the underground economy has given rise to malware delivery networks, which charge fees for quickly delivering malware or unwanted software to a large number of hosts. To provide this service, a key method is the orchestration of silent delivery campaigns, which involve a group of downloaders that receive remote commands and that deliver their payloads without any user interaction. These campaigns have not been characterized systematically, unlike other aspects of malware delivery networks. Moreover, silent delivery campaigns can evade detection by relying on inconspicuous downloaders on the client side and on disposable domain names on the server side. We describe Beewolf, a system for detecting silent delivery campaigns from Internet-wide records of download events. The key observation behind our system is that the downloaders involved in these campaigns frequently retrieve payloads in lockstep. Beewolf identifies such locksteps in an unsupervised and deterministic manner. By exploiting novel techniques and empirical observations, Beewolf can operate on streaming data. We utilize Beewolf to study silent delivery campaigns at scale, on a data set of 33.3 million download events. This investigation yields novel findings, e.g. malware distributed through compromised software update channels, a substantial overlap between the delivery ecosystems for malware and unwanted software, and several types of business relationships within these ecosystems. Beewolf achieves over 92% true positives and fewer than 5% false positives. Moreover, Beewolf can detect suspicious downloaders a median of 165 days ahead of existing anti-virus products and payload-hosting domains a median of 196 days ahead of existing blacklists.
Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach
Venue: arXiv cs.SI 2016
Year: 2016

Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates' Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through two case studies, one on the `woman card' controversy and one on Sanders followers, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections.