OpenRaiser/Intern-Atlas huggingface dataset:
TrustBase: An Architecture to Repair and Strengthen Certificate-based Authentication
Venue: arXiv cs.CR 2016
Year: 2016

We describe TrustBase, an architecture that provides certificate-based authentication as an operating system service. TrustBase enforces best practices for certificate validation for all applications and transparently enables existing applications to be strengthened against failures of the CA system. The TrustBase system allows simple deployment of authentication systems that harden the CA system. This enables system administrators, for example, to require certificate revocation checks on all TLS connections, or require STARTTLS for email servers that support it. TrustBase is the first system that is able to secure all TLS traffic, using an approach compatible with all operating systems. We design and evaluate a prototype implementation of TrustBase on Linux, evaluate its security, and demonstrate that it has negligible overhead and universal compatibility with applications. To demonstrate the utility of TrustBase, we have developed six authentication services that strengthen certificate validation for all applications.
The Promise and Prejudice of Big Data in Intelligence Community
Venue: arXiv cs.CY 2016
Year: 2016

Big data holds critical importance in the current generation of information technology, with applications ranging from financial, industrial, academic to defense sectors. With the exponential rise of open source data from social media and increasing government monitoring, big data is now also linked with national security, and subsequently to the intelligence community. In this study I review the scope of big data sciences in the functioning of intelligence community. The major part of my study focuses on the inherent limitations of big data, which affects the intelligence agencies from gathering of information to anticipating surprises. The limiting factors range from technical to ethical issues connected with big data. My study concludes the need of experts with domain knowledge from intelligence community to efficiently guide big data analysis for timely filling the knowledge gaps. As a case study on limitations of using big data, I narrate some of the ongoing work in nuclear intelligence using simple analytics and argue on why big data analysis in that case would lead to unnecessary complications. For further investigation, I highlight cases of crowdsource forecasting tournaments and predicting unrest from social media.
A Survey of High Level Frameworks in Block-Structured Adaptive Mesh Refinement Packages
Venue: arXiv cs.DC 2016
Year: 2016

Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific domain areas, others have pursued a more general functionality, providing the building blocks for a larger variety of applications. In this survey paper we examine a representative set of SAMR packages and SAMR-based codes that have been in existence for half a decade or more, have a reasonably sized and active user base outside of their home institutions, and are publicly available. The set consists of a mix of SAMR packages and application codes that cover a broad range of scientific domains. We look at their high-level frameworks, and their approach to dealing with the advent of radical changes in hardware architecture. The codes included in this survey are BoxLib, Cactus, Chombo, Enzo, FLASH, and Uintah.
A Revisit of Block Power Methods for Finite State Markov Chain Applications
Venue: arXiv cs.NA 2016
Year: 2016

In this paper, we revisit the generalized block power methods for approximating the eigenvector associated with $λ_1 = 1$ of a Markov chain transition matrix. Our analysis of the block power method shows that when $s$ linearly independent probability vectors are used as the initial block, the convergence of the block power method to the stationary distribution depends on the magnitude of the $(s+1)$th dominant eigenvalue $λ_{s+1}$ of $P$ instead of that of $λ_2$ in the power method. Therefore, the block power method with block size $s$ is particularly effective for transition matrices where $|λ_{s+1}|$ is well separated from $λ_1 = 1$ but $|λ_2|$ is not. This approach is particularly useful when visiting the elements of a large transition matrix is the main computational bottleneck over matrix--vector multiplications, where the block power method can effectively reduce the total number of times to pass over the matrix. To further reduce the overall computational cost, we combine the block power method with a sliding window scheme, taking advantage of the subsequent vectors of the latest $s$ iterations to assemble the block matrix. The sliding window scheme correlates vectors in the sliding window to quickly remove the influences from the eigenvalues whose magnitudes are smaller than $|λ_{s}|$ to reduce the overall number of matrix--vector multiplications to reach convergence. Finally, we compare the effectiveness of these methods in a Markov chain model representing a stochastic luminal calcium release site.
Domain Specific Distributed Search Engine Based on Semantic P2P Networks
Venue: arXiv cs.DC 2016
Year: 2016

This paper presents a distributed search engine based on semantic P2P Networks. The user's computers join the domains in which user wants to share information in semantic P2P networks which is domain specific virtual tree (VIRGO ). Each user computer contains search engine which indexes the domain specific information on local computer or Internet. We can get all search information through P2P message provided by all joined computers. By companies' effort, we have implemented a prototype of distributed search engine, which demonstrates easily retrieving domain-related information provided by joined computers .
Learnable Visual Markers
Venue: arXiv cs.CV 2016
Year: 2016

We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tags) based on the advances in deep generative networks. In our approach, the markers are obtained as color images synthesized by a deep network from input bit strings, whereas another deep network is trained to recover the bit strings back from the photos of these markers. The two networks are trained simultaneously in a joint backpropagation process that takes characteristic photometric and geometric distortions associated with marker fabrication and marker scanning into account. Additionally, a stylization loss based on statistics of activations in a pretrained classification network can be inserted into the learning in order to shift the marker appearance towards some texture prototype. In the experiments, we demonstrate that the markers obtained using our approach are capable of retaining bit strings that are long enough to be practical. The ability to automatically adapt markers according to the usage scenario and the desired capacity as well as the ability to combine information encoding with artistic stylization are the unique properties of our approach. As a byproduct, our approach provides an insight on the structure of patterns that are most suitable for recognition by ConvNets and on their ability to distinguish composite patterns.
Word Embeddings for the Construction Domain
Venue: arXiv cs.CL 2016
Year: 2016

We introduce word vectors for the construction domain. Our vectors were obtained by running word2vec on an 11M-word corpus that we created from scratch by leveraging freely-accessible online sources of construction-related text. We first explore the embedding space and show that our vectors capture meaningful construction-specific concepts. We then evaluate the performance of our vectors against that of ones trained on a 100B-word corpus (Google News) within the framework of an injury report classification task. Without any parameter tuning, our embeddings give competitive results, and outperform the Google News vectors in many cases. Using a keyword-based compression of the reports also leads to a significant speed-up with only a limited loss in performance. We release our corpus and the data set we created for the classification task as publicly available, in the hope that they will be used by future studies for benchmarking and building on our work.
ESCAPE: Efficiently Counting All 5-Vertex Subgraphs
Venue: arXiv cs.SI 2016
Year: 2016

Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex or 5-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. We introduce an algorithmic framework that can be adopted to count any small pattern in a graph and apply this framework to compute exact counts for \emph{all} 5-vertex subgraphs. Our framework is built on cutting a pattern into smaller ones, and using counts of smaller patterns to get larger counts. Furthermore, we exploit degree orientations of the graph to reduce runtimes even further. These methods avoid the combinatorial explosion that typical subgraph counting algorithms face. We prove that it suffices to enumerate only four specific subgraphs (three of them have less than 5 vertices) to exactly count all 5-vertex patterns. We perform extensive empirical experiments on a variety of real-world graphs. We are able to compute counts of graphs with tens of millions of edges in minutes on a commodity machine. To the best of our knowledge, this is the first practical algorithm for $5$-vertex pattern counting that runs at this scale. A stepping stone to our main algorithm is a fast method for counting all $4$-vertex patterns. This algorithm is typically ten times faster than the state of the art $4$-vertex counters.
Cognitive Access Protocol for Alleviating Sensing Errors in Cognitive Multiple-Access Systems
Venue: arXiv cs.NI 2016
Year: 2016

This letter studies a time-slotted multiple-access system with a primary user (PU) and a secondary user (SU) sharing the same channel resource. We propose a novel secondary access protocol which alleviates sensing errors and detects the availability of primary channels with the highest ability of detection. Under the proposed protocol, the SU may access the channel at one of a predefined instants within the time slot each of which associated with a certain access probability that changes based on the sensing outcome. There is also a possibility of accessing the channel at the beginning of the time slot without channel sensing. The optimization problem is stated such that the secondary throughput is maximized under stability of the primary queue and a constraint on the primary queueing delay. Numerical results demonstrate the beneficial gains of the proposed protocol in terms of secondary throughput.
Selective De-noising of Sparse-Coloured Images
Venue: arXiv cs.CV 2016
Year: 2016

Since time immemorial, noise has been a constant source of disturbance to the various entities known to mankind. Noise models of different kinds have been developed to study noise in more detailed fashion over the years. Image processing, particularly, has extensively implemented several algorithms to reduce noise in photographs and pictorial documents to alleviate the effect of noise. Images with sparse colours-lesser number of distinct colours in them-are common nowadays, especially in astronomy and astrophysics where black and white colours form the main components. Additive noise of Gaussian type is the most common form of noise to be studied and analysed in majority of communication channels, namely-satellite links, mobile base station to local cellular tower communication channel,et. al. Most of the time, we encounter images from astronomical sources being distorted with noise maximally as they travel long distance from telescopes in outer space to Earth. Considering Additive White Gaussian Noise(AWGN) to be the common noise in these long distance channels, this paper provides an insight and an algorithmic approach to pixel-specific de-noising of sparse-coloured images affected by AWGN. The paper concludes with some essential future avenues and applications of this de-noising method in industry and academia.
Sparse Signal Recovery for Binary Compressed Sensing by Majority Voting Neural Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we propose majority voting neural networks for sparse signal recovery in binary compressed sensing. The majority voting neural network is composed of several independently trained feedforward neural networks employing the sigmoid function as an activation function. Our empirical study shows that a choice of a loss function used in training processes for the network is of prime importance. We found a loss function suitable for sparse signal recovery, which includes a cross entropy-like term and an $L_1$ regularized term. From the experimental results, we observed that the majority voting neural network achieves excellent recovery performance, which is approaching the optimal performance as the number of component nets grows. The simple architecture of the majority voting neural networks would be beneficial for both software and hardware implementations.
Evangelism in Social Networks: Algorithms and Complexity
Venue: arXiv cs.DS 2016
Year: 2016

We consider a population of interconnected individuals that, with respect to a piece of information, at each time instant can be subdivided into three (time-dependent) categories: agnostics, influenced, and evangelists. A dynamical process of information diffusion evolves among the individuals of the population according to the following rules. Initially, all individuals are agnostic. Then, a set of people is chosen from the outside and convinced to start evangelizing, i.e., to start spreading the information. When a number of evangelists, greater than a given threshold, communicate with a node v, the node v becomes influenced, whereas, as soon as the individual v is contacted by a sufficiently much larger number of evangelists, it is itself converted into an evangelist and consequently it starts spreading the information. The question is: How to choose a bounded cardinality initial set of evangelists so as to maximize the final number of influenced individuals? We prove that the problem is hard to solve, even in an approximate sense. On the positive side, we present exact polynomial time algorithms for trees and complete graphs. For general graphs, we derive exact parameterized algorithms. We also investigate the problem when the objective is to select a minimum number of evangelists capable of influencing the whole network. Our motivations to study these problems come from the areas of Viral Marketing and the analysis of quantitative models of spreading of influence in social networks.
Serious Games for Cyber Security Education
Venue: arXiv cs.CY 2016
Year: 2016

Phishing is an online identity theft that aims to steal sensitive information such as username, passwords and online banking details from its victims. Phishing education needs to be considered as a means to combat this threat. This book focuses on a design and development of a mobile game prototype as an educational tool helping computer users to protect themselves against phishing attacks. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues. The mobile game design aimed to enhance the user's avoidance behaviour through motivation to protect themselves against phishing threats. A think-aloud study was conducted, along with a pre- and post-test, to assess the game design framework through the developed mobile game prototype. The study results showed a significant improvement of participants' phishing avoidance behaviour in their post-test assessment. Furthermore, the study findings suggest that participants' threat perception, safeguard effectiveness, self-efficacy, perceived severity and perceived susceptibility elements positively impact threat avoidance behaviour, whereas safeguard cost had a negative impact on it.
Multi-Camera Occlusion and Sudden-Appearance-Change Detection Using Hidden Markovian Chains
Venue: arXiv cs.CV 2016
Year: 2016

This paper was originally submitted to Xinova as a response to a Request for Invention (RFI) on new event monitoring methods. In this paper, a new object tracking algorithm using multiple cameras for surveillance applications is proposed. The proposed system can detect sudden-appearance-changes and occlusions using a hidden Markovian statistical model. The experimental results confirm that our system detect the sudden-appearance changes and occlusions reliably.
Solving Large-scale Systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow
Venue: arXiv cs.IT 2016
Year: 2016

A novel approach termed \emph{stochastic truncated amplitude flow} (STAF) is developed to reconstruct an unknown $n$-dimensional real-/complex-valued signal $\bm{x}$ from $m$ `phaseless' quadratic equations of the form $ψ_i=|\langle\bm{a}_i,\bm{x}\rangle|$. This problem, also known as phase retrieval from magnitude-only information, is \emph{NP-hard} in general. Adopting an amplitude-based nonconvex formulation, STAF leads to an iterative solver comprising two stages: s1) Orthogonality-promoting initialization through a stochastic variance reduced gradient algorithm; and, s2) A series of iterative refinements of the initialization using stochastic truncated gradient iterations. Both stages involve a single equation per iteration, thus rendering STAF a simple, scalable, and fast approach amenable to large-scale implementations that is useful when $n$ is large. When $\{\bm{a}_i\}_{i=1}^m$ are independent Gaussian, STAF provably recovers exactly any $\bm{x}\in\mathbb{R}^n$ exponentially fast based on order of $n$ quadratic equations. STAF is also robust in the presence of additive noise of bounded support. Simulated tests involving real Gaussian $\{\bm{a}_i\}$ vectors demonstrate that STAF empirically reconstructs any $\bm{x}\in\mathbb{R}^n$ exactly from about $2.3n$ magnitude-only measurements, outperforming state-of-the-art approaches and narrowing the gap from the information-theoretic number of equations $m=2n-1$. Extensive experiments using synthetic data and real images corroborate markedly improved performance of STAF over existing alternatives.
Diversity Promoting Online Sampling for Streaming Video Summarization
Venue: arXiv cs.CV 2016
Year: 2016

Many applications benefit from sampling algorithms where a small number of well chosen samples are used to generalize different properties of a large dataset. In this paper, we use diverse sampling for streaming video summarization. Several emerging applications support streaming video, but existing summarization algorithms need access to the entire video which requires a lot of memory and computational power. We propose a memory efficient and computationally fast, online algorithm that uses competitive learning for diverse sampling. Our algorithm is a generalization of online K-means such that the cost function reduces clustering error, while also ensuring a diverse set of samples. The diversity is measured as the volume of a convex hull around the samples. Finally, the performance of the proposed algorithm is measured against human users for 50 videos in the VSUMM dataset. The algorithm performs better than batch mode summarization, while requiring significantly lower memory and computational requirements.
Mobile Millimeter Wave Channel Acquisition, Tracking, and Abrupt Change Detection
Venue: arXiv cs.IT 2016
Year: 2016

Millimeter wave provides a promising approach for meeting the ever-growing traffic demand in next generation wireless networks. It is crucial to obtain relatively accurate channel state information so that beamforming/combining can be performed to compensate for severe path loss in this band. In contrast to lower frequencies, a typical mobile millimeter wave channel consists of a few dominant paths. It is generally sufficient to estimate the path gains, angles of departure (AoD), and angles of arrival (AoA) of those paths. In this paper, multiple transmit and receive antennas and beamforming with a single baseband processing chain are assumed. We propose a framework for estimating millimeter wave channels with intermittent abrupt changes (e.g., blockage or emergence of dominant paths) and slow variations of AoDs and AoAs. The solution consists of three components: tracking of the slow channel variations, detection of abrupt changes, followed by (re-)acquisition of channel (and back to the tracking stage). For acquisition, we formulate a least squares problem and find its solution based on the Levenberg-Marquardt algorithm. To track slow variations of AoDs and AoAs, we propose a new approach using Kalman filtering. Finally, an algorithm based on a likelihood test is devised for detecting abrupt changes. Simulation results show that, with moderate signal-to-noise ratios, the proposed scheme can achieve more than 8 dB higher estimation accuracy than several other methods using the same number of pilots.
A New Distance Measure for Non-Identical Data with Application to Image Classification
Venue: arXiv cs.CV 2016
Year: 2016

Distance measures are part and parcel of many computer vision algorithms. The underlying assumption in all existing distance measures is that feature elements are independent and identically distributed. However, in real-world settings, data generally originate from heterogeneous sources even if they do possess a common data-generating mechanism. Since these sources are not identically distributed by necessity, the assumption of identical distribution is inappropriate. Here, we use statistical analysis to show that feature elements of local image descriptors are indeed non-identically distributed. To test the effect of omitting the unified distribution assumption, we created a new distance measure called the Poisson-Binomial Radius (PBR). PBR is a bin-to-bin distance which accounts for the dispersion of bin-to-bin information. PBR's performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications: texture, material, leaf, scene, ear biometrics and category-level image classification. Results from these experiments demonstrate that PBR outperforms state-of-the-art distance measures for most of the data sets and achieves comparable performance on the rest, suggesting that accounting for different distributions in distance measures can improve performance in classification and recognition tasks.
On Sequential Locally Repairable Codes
Venue: arXiv cs.IT 2016
Year: 2016

We consider the locally repairable codes (LRC), aiming at sequential recovering multiple erasures. We define the (n,k,r,t)-SLRC (Sequential Locally Repairable Codes) as an [n,k] linear code where any t'(>= t) erasures can be sequentially recovered, each one by r (2<=r<k) other code symbols. Sequential recovering means that the erased symbols are recovered one by one, and an already recovered symbol can be used to recover the remaining erased symbols. This important recovering method, in contrast with the vastly studied parallel recovering, is currently far from understanding, say, lacking codes constructed for arbitrary t>=3 erasures and bounds to evaluate the performance of such codes. We first derive a tight upper bound on the code rate of (n, k, r, t)-SLRC for t=3 and r>=2. We then propose two constructions of binary (n, k, r, t)-SLRCs for general r,t>=2 (Existing constructions are dealing with t<=7 erasures. The first construction generalizes the method of direct product construction. The second construction is based on the resolvable configurations and yields SLRCs for any r>=2 and odd t>=3. For both constructions, the rates are optimal for t in {2,3} and are higher than most of the existing LRC families for arbitrary t>=4.