OpenRaiser/Intern-Atlas huggingface dataset:
Qsmodels: ASP Planning in Interactive Gaming Environment
Venue: arXiv cs.AI 2015
Year: 2015

Qsmodels is a novel application of Answer Set Programming to interactive gaming environment. We describe a software architecture by which the behavior of a bot acting inside the Quake 3 Arena can be controlled by a planner. The planner is written as an Answer Set Program and is interpreted by the Smodels solver.
Experience Report: Developing the Servo Web Browser Engine using Rust
Venue: arXiv cs.PL 2015
Year: 2015

All modern web browsers - Internet Explorer, Firefox, Chrome, Opera, and Safari - have a core rendering engine written in C++. This language choice was made because it affords the systems programmer complete control of the underlying hardware features and memory in use, and it provides a transparent compilation model. Servo is a project started at Mozilla Research to build a new web browser engine that preserves the capabilities of these other browser engines but also both takes advantage of the recent trends in parallel hardware and is more memory-safe. We use a new language, Rust, that provides us a similar level of control of the underlying system to C++ but which builds on many concepts familiar to the functional programming community, forming a novelty - a useful, safe systems programming language. In this paper, we show how a language with an affine type system, regions, and many syntactic features familiar to functional language programmers can be successfully used to build state-of-the-art systems software. We also outline several pitfalls encountered along the way and describe some potential areas for future research.
Training a Convolutional Neural Network for Appearance-Invariant Place Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of recognizing a revisited location increases with appearance changes caused, for instance, by weather or illumination variations, which hinders the long-term application of such algorithms in real environments. In this paper we present a convolutional neural network (CNN), trained for the first time with the purpose of recognizing revisited locations under severe appearance changes, which maps images to a low dimensional space where Euclidean distances represent place dissimilarity. In order for the network to learn the desired invariances, we train it with triplets of images selected from datasets which present a challenging variability in visual appearance. The triplets are selected in such way that two samples are from the same location and the third one is taken from a different place. We validate our system through extensive experimentation, where we demonstrate better performance than state-of-art algorithms in a number of popular datasets.
Threshold Effects in Parameter Estimation from Compressed Data
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we investigate threshold effects associated with swapping of signal and noise subspaces in estimating signal parameters from compressed noisy data. The term threshold effect refers to a sharp departure of mean-squared error from the Cramer-Rao bound when the signal-to-noise ratio falls below a threshold SNR. In many cases, the threshold effect is caused by a subspace swap event, when the measured data (or its sample covariance) is better approximated by a subset of components of an orthogonal subspace than by the components of a signal subspace. We derive analytical lower bounds on the probability of a subspace swap in compressively measured noisy data. These bounds guide our understanding of threshold effects and performance breakdown for parameter estimation using compression. As a case study, we investigate threshold effects in maximum likelihood (ML) estimation of directions of arrival of two closely-spaced sources using co-prime subsampling. Our results show the impact of compression on threshold SNR. A rule of thumb is that every doubling of compression ratio brings a penalty in threshold SNR of 3 dB.
Like Partying? Your Face Says It All. Predicting the Ambiance of Places with Profile Pictures
Venue: arXiv cs.HC 2015
Year: 2015

To choose restaurants and coffee shops, people are increasingly relying on social-networking sites. In a popular site such as Foursquare or Yelp, a place comes with descriptions and reviews, and with profile pictures of people who frequent them. Descriptions and reviews have been widely explored in the research area of data mining. By contrast, profile pictures have received little attention. Previous work showed that people are able to partly guess a place's ambiance, clientele, and activities not only by observing the place itself but also by observing the profile pictures of its visitors. Here we further that work by determining which visual cues people may have relied upon to make their guesses; showing that a state-of-the-art algorithm could make predictions more accurately than humans at times; and demonstrating that the visual cues people relied upon partly differ from those of the algorithm.
Impact of Multipath Reflections on the Performance of Indoor Visible Light Positioning Systems
Venue: arXiv cs.IT 2015
Year: 2015

Visible light communication (VLC) using light-emitting-diodes (LEDs) has been a popular research area recently. VLC can provide a practical solution for indoor positioning. In this paper, the impact of multipath reflections on indoor VLC positioning is investigated, considering a complex indoor environment with walls, floor and ceiling. For the proposed positioning system, an LED bulb is the transmitter and a photo-diode (PD) is the receiver to detect received signal strength (RSS) information. Combined deterministic and modified Monte Carlo (CDMMC) method is applied to compute the impulse response of the optical channel. Since power attenuation is applied to calculate the distance between the transmitter and receiver, the received power from each reflection order is analyzed. Finally, the positioning errors are estimated for all the locations over the room and compared with the previous works where no reflections considered. Three calibration approaches are proposed to decrease the effect of multipath reflections.
A novel normalized sign algorithm for system identification under impulsive noise interference
Venue: arXiv cs.SY 2015
Year: 2015

To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies.
An impulsive dynamical systems framework for reset control systems
Venue: arXiv cs.SY 2015
Year: 2015

Impulsive dynamical systems is a well-established area of dynamical systems theory, and it is used in this work to analyze several basic properties of reset control systems: existence and uniqueness of solutions, and continuous dependence on the initial condition (well-posedness). The work scope is about reset control systems with a linear and time-invariant base system, and a zero-crossing resetting law. A necessary and sufficient condition for existence and uniqueness of solutions, based on the well-posedness of reset instants, is developed. As a result, it is shown that reset control systems (with strictly proper plants) do no have Zeno solutions. It is also shown that full reset and partial reset (with a special structure) always produce well-posed reset instants. Moreover, a definition of continuous dependence on the initial condition is developed, and also a sufficient condition for reset control systems to satisfy that property. Finally, this property is used to analyze sensitivity of reset control systems to sensor noise. This work also includes a number of illustrative examples motivating the key concepts and main results.
The Spatial-Perceptual Design Space: a new comprehension for Data Visualization
Venue: arXiv cs.GR 2015
Year: 2015

We revisit the design space of visualizations aiming at identifying and relating its components. In this sense, we establish a model to examine the process through which visualizations become expressive for users. This model has leaded us to a taxonomy oriented to the human visual perception, a conceptualization that provides natural criteria in order to delineate a novel understanding for the visualization design space. The new organization of concepts that we introduce is our main contribution: a grammar for the visualization design based on the review of former works and of classical and state-of-the-art techniques. Like so, the paper is presented as a survey whose structure introduces a new conceptualization for the space of techniques concerning visual analysis.
A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning
Venue: arXiv cs.NE 2015
Year: 2015

Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18 $μ$m CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01 mm$^2$ and has an energy-efficiency of 9.3 pJ$/$spike$/$synapse.
Supervised Fine Tuning for Word Embedding with Integrated Knowledge
Venue: arXiv cs.CL 2015
Year: 2015

Learning vector representation for words is an important research field which may benefit many natural language processing tasks. Two limitations exist in nearly all available models, which are the bias caused by the context definition and the lack of knowledge utilization. They are difficult to tackle because these algorithms are essentially unsupervised learning approaches. Inspired by deep learning, the authors propose a supervised framework for learning vector representation of words to provide additional supervised fine tuning after unsupervised learning. The framework is knowledge rich approacher and compatible with any numerical vectors word representation. The authors perform both intrinsic evaluation like attributional and relational similarity prediction and extrinsic evaluations like the sentence completion and sentiment analysis. Experiments results on 6 embeddings and 4 tasks with 10 datasets show that the proposed fine tuning framework may significantly improve the quality of the vector representation of words.
Relationships, Paths and Principal Matching: A New Approach to Access Control
Venue: arXiv cs.CR 2015
Year: 2015

Recent work on relationship-based access control has begun to show how it can be applied to general computing systems, as opposed to simply being employed for social networking applications. The use of relationships to determine authorization policies enables more powerful policies to be defined than those based solely on the commonly used concept of role membership. The relationships, paths and principal matching (RPPM) model described here is a formal access control model using relationships and a two-stage request evaluation process. We make use of path conditions, which are similar to regular expressions, to define policies. We then employ non-deterministic finite automata to determine which policies are applicable to a request. The power and robustness of the RPPM model allows us to include contextual information in the authorization process (through the inclusion of logical entities) and allows us to support desirable policy foundations such as separation of duty and Chinese Wall. Additionally, the RPPM model naturally supports a caching mechanism which has significant impact on request evaluation performance.
Learning to count with deep object features
Venue: arXiv cs.CV 2015
Year: 2015

Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.
Graph Watermarks
Venue: arXiv cs.CR 2015
Year: 2015

From network topologies to online social networks, many of today's most sensitive datasets are captured in large graphs. A significant challenge facing owners of these datasets is how to share sensitive graphs with collaborators and authorized users, e.g. network topologies with network equipment vendors or Facebook's social graphs with academic collaborators. Current tools can provide limited node or edge privacy, but require modifications to the graph that significantly reduce its utility. In this work, we propose a new alternative in the form of graph watermarks. Graph watermarks are small graphs tailor-made for a given graph dataset, a secure graph key, and a secure user key. To share a sensitive graph G with a collaborator C, the owner generates a watermark graph W using G, the graph key, and C's key as input, and embeds W into G to form G'. If G' is leaked by C,its owner can reliably determine if the watermark W generated for C does in fact reside inside G', thereby proving C is responsible for the leak. Graph watermarks serve both as a deterrent against data leakage and a method of recourse after a leak. We provide robust schemes for creating, embedding and extracting watermarks, and use analysis and experiments on large, real graphs to show that they are unique and difficult to forge. We study the robustness of graph watermarks against both single and powerful colluding attacker models, then propose and empirically evaluate mechanisms to dramatically improve resilience.
A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a SLaT (Smoothing, Lifting and Thresholding) method with three stages for multiphase segmentation of color images corrupted by different degradations: noise, information loss, and blur. At the first stage, a convex variant of the Mumford-Shah model is applied to each channel to obtain a smooth image. We show that the model has unique solution under the different degradations. In order to properly handle the color information, the second stage is dimension lifting where we consider a new vector-valued image composed of the restored image and its transform in the secondary color space with additional information. This ensures that even if the first color space has highly correlated channels, we can still have enough information to give good segmentation results. In the last stage, we apply multichannel thresholding to the combined vector-valued image to find the segmentation. The number of phases is only required in the last stage, so users can choose or change it all without the need of solving the previous stages again. Experiments demonstrate that our SLaT method gives excellent results in terms of segmentation quality and CPU time in comparison with other state-of-the-art segmentation methods.
On the computational complexity of evolution
Venue: arXiv cs.CC 2015
Year: 2015

It is well-known that the problem of recognizing an ESS in a symmetric bimatrix game is coNP-complete. In this paper, we show that recognizing an ESS even in doubly symmetric bimatrix games is also coNP-complete. Our result further implies that recognizing asymptotically stable equilibria of the replicator dynamic in this class of games is also a coNP-complete problem.
Discovering similar Twitter accounts using semantics
Venue: arXiv cs.SI 2015
Year: 2015

On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be. Towards this direction, we introduce a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of Twitter entities used. The methodology is based exclusively on semantic representation protocols and related technologies. An ontological schema is also described towards the semantification of the Twitter accounts and their entities.
Design and implementation for automated network troubleshooting using data mining
Venue: arXiv cs.NI 2015
Year: 2015

The efficient and effective monitoring of mobile networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this paper is to present a monitoring scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of fault isolation and localisation. The data mining techniques described in this paper are intended to allow a system to be trained to actually learn network fault rules. The results of the tests that were conducted allowed for the conclusion that the rules were highly effective to improve network troubleshooting.
Robust and Efficient Elimination of Cache and Timing Side Channels
Venue: arXiv cs.CR 2015
Year: 2015

Timing and cache side channels provide powerful attacks against many sensitive operations including cryptographic implementations. Existing defenses cannot protect against all classes of such attacks without incurring prohibitive performance overhead. A popular strategy for defending against all classes of these attacks is to modify the implementation so that the timing and cache access patterns of every hardware instruction is independent of the secret inputs. However, this solution is architecture-specific, brittle, and difficult to get right. In this paper, we propose and evaluate a robust low-overhead technique for mitigating timing and cache channels. Our solution requires only minimal source code changes and works across multiple languages/platforms. We report the experimental results of applying our solution to protect several C, C++, and Java programs. Our results demonstrate that our solution successfully eliminates the timing and cache side-channel leaks while incurring significantly lower performance overhead than existing approaches.
OR-Benchmark: An Open and Reconfigurable Digital Watermarking Benchmarking Framework
Venue: arXiv cs.MM 2015
Year: 2015

Benchmarking digital watermarking algorithms is not an easy task because different applications of digital watermarking often have very different sets of requirements and trade-offs between conflicting requirements. While there have been some general-purpose digital watermarking benchmarking systems available, they normally do not support complicated benchmarking tasks and cannot be easily reconfigured to work with different watermarking algorithms and testing conditions. In this paper, we propose OR-Benchmark, an open and highly reconfigurable general-purpose digital watermarking benchmarking framework, which has the following two key features: 1) all the interfaces are public and general enough to support all watermarking applications and benchmarking tasks we can think of; 2) end users can easily extend the functionalities and freely configure what watermarking algorithms are tested, what system components are used, how the benchmarking process runs, and what results should be produced. We implemented a prototype of this framework as a MATLAB software package and used it to benchmark a number of digital watermarking algorithms involving two types of watermarks for content authentication and self-restoration purposes. The benchmarking results demonstrated the advantages of the proposed benchmarking framework, and also gave us some useful insights about existing image authentication and self-restoration watermarking algorithms which are an important but less studied topic in digital watermarking.
Efficient Iterative Processing in the SciDB Parallel Array Engine
Venue: arXiv cs.DB 2015
Year: 2015

Many scientific data-intensive applications perform iterative computations on array data. There exist multiple engines specialized for array processing. These engines efficiently support various types of operations, but none includes native support for iterative processing. In this paper, we develop a model for iterative array computations and a series of optimizations. We evaluate the benefits of an optimized, native support for iterative array processing on the SciDB engine and real workloads from the astronomy domain.
Energy efficient D2D communications in dynamic TDD systems
Venue: arXiv cs.IT 2015
Year: 2015

Network-assisted device-to-device communication is a promising technology for improving the performance of proximity-based services. This paper demonstrates how the integration of device-to-device communications and dynamic time-division duplex can improve the energy efficiency of future cellular networks, leading to a greener system operation and a prolonged battery lifetime of mobile devices. We jointly optimize the mode selection, transmission period and power allocation to minimize the energy consumption (from both a system and a device perspective) while satisfying a certain rate requirement. The radio resource management problems are formulated as mixed-integer nonlinear programming problems. Although they are known to be NP-hard in general, we exploit the problem structure to design efficient algorithms that optimally solve several problem cases. For the remaining cases, a heuristic algorithm that computes near-optimal solutions while respecting practical constraints on execution times and signaling overhead is also proposed. Simulation results confirm that the combination of device-to-device and flexible time-division-duplex technologies can significantly enhance spectrum and energy-efficiency of next generation cellular systems.