OpenRaiser/Intern-Atlas huggingface dataset:
Formalizing Mathematical Knowledge as a Biform Theory Graph: A Case Study
Venue: arXiv cs.LO 2017
Year: 2017

A biform theory is a combination of an axiomatic theory and an algorithmic theory that supports the integration of reasoning and computation. These are ideal for formalizing algorithms that manipulate mathematical expressions. A theory graph is a network of theories connected by meaning-preserving theory morphisms that map the formulas of one theory to the formulas of another theory. Theory graphs are in turn well suited for formalizing mathematical knowledge at the most convenient level of abstraction using the most convenient vocabulary. We are interested in the problem of whether a body of mathematical knowledge can be effectively formalized as a theory graph of biform theories. As a test case, we look at the graph of theories encoding natural number arithmetic. We used two different formalisms to do this, which we describe and compare. The first is realized in ${\rm CTT}_{\rm uqe}$, a version of Church's type theory with quotation and evaluation, and the second is realized in Agda, a dependently typed programming language.
Comparison of Global Algorithms in Word Sense Disambiguation
Venue: arXiv cs.CL 2017
Year: 2017

This article compares four probabilistic algorithms (global algorithms) for Word Sense Disambiguation (WSD) in terms of the number of scorer calls (local algo- rithm) and the F1 score as determined by a gold-standard scorer. Two algorithms come from the state of the art, a Simulated Annealing Algorithm (SAA) and a Genetic Algorithm (GA) as well as two algorithms that we first adapt from WSD that are state of the art probabilistic search algorithms, namely a Cuckoo search algorithm (CSA) and a Bat Search algorithm (BS). As WSD requires to evaluate exponentially many word sense combinations (with branching factors of up to 6 or more), probabilistic algorithms allow to find approximate solution in a tractable time by sampling the search space. We find that CSA, GA and SA all eventually converge to similar results (0.98 F1 score), but CSA gets there faster (in fewer scorer calls) and reaches up to 0.95 F1 before SA in fewer scorer calls. In BA a strict convergence criterion prevents it from reaching above 0.89 F1.
TransNets: Learning to Transform for Recommendation
Venue: arXiv cs.IR 2017
Year: 2017

Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.
Assessment of Source Code Obfuscation Techniques
Venue: arXiv cs.SE 2017
Year: 2017

Obfuscation techniques are a general category of software protections widely adopted to prevent malicious tampering of the code by making applications more difficult to understand and thus harder to modify. Obfuscation techniques are divided in code and data obfuscation, depending on the protected asset. While preliminary empirical studies have been conducted to determine the impact of code obfuscation, our work aims at assessing the effectiveness and efficiency in preventing attacks of a specific data obfuscation technique - VarMerge. We conducted an experiment with student participants performing two attack tasks on clear and obfuscated versions of two applications written in C. The experiment showed a significant effect of data obfuscation on both the time required to complete and the successful attack efficiency. An application with VarMerge reduces by six times the number of successful attacks per unit of time. This outcome provides a practical clue that can be used when applying software protections based on data obfuscation.
Finding Optimal Sinks for Random Walkers in a Network
Venue: arXiv cs.DM 2017
Year: 2017

In a model of network communication based on a random walk in an undirected graph, what subset of nodes (subject to constraints on the set size), enables the fastest spread of information? In this paper, we assume the dynamics of spread is described by a network consensus process, but to find the most effective seeds we consider the target set of a random walk--the process dual to network consensus spread. Thus an optimal set $A$ minimizes the sum of the expected first hitting times $F(A)$ of random walks that start at nodes outside the set. We introduce a submodular, non-decreasing rank function $ρ$, that permits some comparison between the solution obtained by the classical greedy algorithm and one obtained by our methods. The supermodularity and non-increasing properties of $F$ are used to show that the rank of our solution is at least $(1-\frac{1}{e})$ times the rank of the optimal set. When our approximation has a higher rank than the greedy solution, this can be improved to $(1-\frac{1}{e})(1+χ)$ where $χ>0$ is a constant. A non-zero lower bound for $χ$ can be obtained when the curvature and increments of $ρ$ are known.
AppLP: A Dialogue on Applications of Logic Programming
Venue: arXiv cs.PL 2017
Year: 2017

This document describes the contributions of the 2016 Applications of Logic Programming Workshop (AppLP), which was held on October 17 and associated with the International Conference on Logic Programming (ICLP) in Flushing, New York City.
GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild
Venue: arXiv cs.CV 2017
Year: 2017

Facial landmark localization is a fundamental module for pose-invariant face recognition. The most common approach for facial landmark detection is cascaded regression, which is composed of two steps: feature extraction and facial shape regression. Recent methods employ deep convolutional networks to extract robust features for each step, while the whole system could be regarded as a deep cascaded regression architecture. In this work, instead of employing a deep regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture is proposed to identify the target pixels through solving a cascaded pixel labeling problem without resorting to high-level inference models or complex stacked architecture. The proposed end-to-end system relies on distance-aware softmax functions and dual-pathway proposal-refinement architecture. Results show that it outperforms the state-of-the-art cascaded regression-based methods on multiple in-the-wild face alignment databases. The model achieves 1.84 normalized mean error (NME) on the AFLW database, which outperforms 3DDFA by 61.8%. Experiments on face identification demonstrate that GoDP, coupled with DPM-headhunter, is able to improve rank-1 identification rate by 44.2% compared to Dlib toolbox on a challenging database.
Audit Analysis Models, Security Frameworks and Their Relevance for VoIP
Venue: arXiv cs.CR 2017
Year: 2017

Voice over IP (VoIP) is the transmission of voice and multimedia content over Internet Protocol (IP) networks, this paper reviews models, frameworks and auditing standards proposed to this date to manage VoIP security through a literature review, with descriptions of both the historical and philosophical evolution reflecting an adequate knowledge of related research. Three research questions are raised here: RQ1. What are the requirements to be met by a model of security audit in VoIP systems to achieve their goals? RQ2. Today, are there additional attacks that previous works have not considered? RQ3. Which security requirements in the VoIP systems are covered (and which are not covered) by security frameworks? After some discussion about VoIP Protocols, Attacks on VoIP, Information Technology (IT) audit, IT security audits, Frameworks and auditing standards, we present a unified view of VoIP Security Requirements; as well as considering the contributions and disadvantages of frameworks and auditing standards toward achieving those requirements through a comparative evaluation. It was determined that there is no security framework which considers social engineering attacks in spite of being an important aspect to consider in security management VoIP; also there is no specific framework that covers all categories of security requirements for VoIP system, therefore, a more extensive model is needed.
Dual polynomials and communication complexity of $\textsf{XOR}$ functions
Venue: arXiv cs.CC 2017
Year: 2017

We show a new duality between the polynomial margin complexity of $f$ and the discrepancy of the function $f \circ \textsf{XOR}$, called an $\textsf{XOR}$ function. Using this duality, we develop polynomial based techniques for understanding the bounded error ($\textsf{BPP}$) and the weakly-unbounded error ($\textsf{PP}$) communication complexities of $\textsf{XOR}$ functions. We show the following. A weak form of an interesting conjecture of Zhang and Shi (Quantum Information and Computation, 2009) (The full conjecture has just been reported to be independently settled by Hatami and Qian (Arxiv, 2017). However, their techniques are quite different and are not known to yield many of the results we obtain here). Zhang and Shi assert that for symmetric functions $f : \{0, 1\}^n \rightarrow \{-1, 1\}$, the weakly unbounded-error complexity of $f \circ \textsf{XOR}$ is essentially characterized by the number of points $i$ in the set $\{0,1, \dots,n-2\}$ for which $D_f(i) \neq D_f(i+2)$, where $D_f$ is the predicate corresponding to $f$. The number of such points is called the odd-even degree of $f$. We show that the $\textsf{PP}$ complexity of $f \circ \textsf{XOR}$ is $Ω(k/ \log(n/k))$. We resolve a conjecture of a different Zhang characterizing the Threshold of Parity circuit size of symmetric functions in terms of their odd-even degree. We obtain a new proof of the exponential separation between $\textsf{PP}^{cc}$ and $\textsf{UPP}^{cc}$ via an $\textsf{XOR}$ function. We provide a characterization of the approximate spectral norm of symmetric functions, affirming a conjecture of Ada et al. (APPROX-RANDOM, 2012) which has several consequences. Additionally, we prove strong $\textsf{UPP}$ lower bounds for $f \circ \textsf{XOR}$, when $f$ is symmetric and periodic with period $O(n^{1/2-ε})$, for any constant $ε> 0$.
Embedded Collaborative Filtering for "Cold Start" Prediction
Venue: arXiv cs.IR 2017
Year: 2017

Using only implicit data, many recommender systems fail in general to provide a precise set of recommendations to users with limited interaction history. This issue is regarded as the "Cold Start" problem and is typically resolved by switching to content-based approaches where extra costly information is required. In this paper, we use a dimensionality reduction algorithm, Word2Vec (W2V), originally applied in Natural Language Processing problems under the framework of Collaborative Filtering (CF) to tackle the "Cold Start" problem using only implicit data. This combined method is named Embedded Collaborative Filtering (ECF). An experiment is conducted to determine the performance of ECF on two different implicit data sets. We show that the ECF approach outperforms other popular and state-of-the-art approaches in "Cold Start" scenarios.
Prosody: The Rhythms and Melodies of Speech
Venue: arXiv cs.CL 2017
Year: 2017

The present contribution is a tutorial on selected aspects of prosody, the rhythms and melodies of speech, based on a course of the same name at the Summer School on Contemporary Phonetics and Phonology at Tongji University, Shanghai, China in July 2016. The tutorial is not intended as an introduction to experimental methodology or as an overview of the literature on the topic, but as an outline of observationally accessible aspects of fundamental frequency and timing patterns with the aid of computational visualisation, situated in a semiotic framework of sign ranks and interpretations. After an informal introduction to the basic concepts of prosody in the introduction and a discussion of the place of prosody in the architecture of language, a selection of acoustic phonetic topics in phonemic tone and accent prosody, word prosody, phrasal prosody and discourse prosody are discussed, and a stylisation method for visualising aspects of prosody is introduced. Examples are taken from a number of typologically different languages: Anyi/Agni (Niger-Congo>Kwa, Ivory Coast), English, Kuki-Thadou (Sino-Tibetan, North-East India and Myanmar), Mandarin Chinese, Tem (Niger-Congo>Gur, Togo) and Farsi. The main focus is on fundamental frequency patterns, but issues of timing and rhythm are also discussed. In the final section, further reading and possible future research directions are outlined.
Quantized Innovations Bayesian Filtering
Venue: arXiv cs.SY 2017
Year: 2017

The paper provides simple formulas of Bayesian filtering for the exact recursive computation of state conditional probability density functions given quantized innovations signal measurements of a linear stochastic system. This is a topic of current interest because the innovations signal should be white and therefore efficient in its use of channel capacity and in the design and optimization of the quantizer. Earlier approaches, which we reexamine and characterize here, have relied on assumptions concerning densities or approximations to yield recursive solutions, which include the sign-of-innovations Kalman filter and a Particle filtering technique. Our approach uses the Kalman filter innovations at the transmitter side and provides a point of comparison for the other methods, since it is based on the Bayesian filter. Computational examples are provided.
Quaternion Based Camera Pose Estimation From Matched Feature Points
Venue: arXiv cs.CV 2017
Year: 2017

We present a novel solution to the camera pose estimation problem, where rotation and translation of a camera between two views are estimated from matched feature points in the images. The camera pose estimation problem is traditionally solved via algorithms that are based on the essential matrix or the Euclidean homography. With six or more feature points in general positions in the space, essential matrix based algorithms can recover a unique solution. However, such algorithms fail when points are on critical surfaces (e.g., coplanar points) and homography should be used instead. By formulating the problem in quaternions and decoupling the rotation and translation estimation, our proposed algorithm works for all point configurations. Using both simulated and real world images, we compare the estimation accuracy of our algorithm with some of the most commonly used algorithms. Our method is shown to be more robust to noise and outliers. For the benefit of community, we have made the implementation of our algorithm available online and free.
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Venue: arXiv cs.CV 2017
Year: 2017

Object detection in wide area motion imagery (WAMI) has drawn the attention of the computer vision research community for a number of years. WAMI proposes a number of unique challenges including extremely small object sizes, both sparse and densely-packed objects, and extremely large search spaces (large video frames). Nearly all state-of-the-art methods in WAMI object detection report that appearance-based classifiers fail in this challenging data and instead rely almost entirely on motion information in the form of background subtraction or frame-differencing. In this work, we experimentally verify the failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a heatmap-based fully convolutional neural network (CNN), and propose a novel two-stage spatio-temporal CNN which effectively and efficiently combines both appearance and motion information to significantly surpass the state-of-the-art in WAMI object detection. To reduce the large search space, the first stage (ClusterNet) takes in a set of extremely large video frames, combines the motion and appearance information within the convolutional architecture, and proposes regions of objects of interest (ROOBI). These ROOBI can contain from one to clusters of several hundred objects due to the large video frame size and varying object density in WAMI. The second stage (FoveaNet) then estimates the centroid location of all objects in that given ROOBI simultaneously via heatmap estimation. The proposed method exceeds state-of-the-art results on the WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped objects, as well as being the first proposed method in wide area motion imagery to detect completely stationary objects.
Implementing a Cloud Platform for Autonomous Driving
Venue: arXiv cs.DC 2017
Year: 2017

Autonomous driving clouds provide essential services to support autonomous vehicles. Today these services include but not limited to distributed simulation tests for new algorithm deployment, offline deep learning model training, and High-Definition (HD) map generation. These services require infrastructure support including distributed computing, distributed storage, as well as heterogeneous computing. In this paper, we present the details of how we implement a unified autonomous driving cloud infrastructure, and how we support these services on top of this infrastructure.
Secure Mode Distinguishability for Switching Systems Subject to Sparse Attacks
Venue: arXiv cs.SY 2017
Year: 2017

Switching systems are an important mathematical formalism when dealing with Cyber-Physical Systems (CPSs). In this paper we provide conditions for the exact reconstruction of the initial discrete state of a switching system, when only the continuous output is measurable, and the discrete output signal is not available. In particular, assuming that the continuous input and output signals may be corrupted by additive malicious attacks, we provide conditions for the secure mode distinguishability for linear switching systems. As illustrative example, we consider the hybrid model of a DC/DC boost converter.
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
Venue: arXiv cs.CV 2017
Year: 2017

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.
Fine-graind Image Classification via Combining Vision and Language
Venue: arXiv cs.CV 2017
Year: 2017

Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combining vision and language (CVL) for learning latent semantic representations. The vision stream learns deep representations from the original visual information via deep convolutional neural network. The language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combining the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.
Serving Distance and Coverage in a Closed Access PHP-Based Heterogeneous Cellular Network
Venue: arXiv cs.IT 2017
Year: 2017

Heterogeneous cellular networks (HCNs) usually exhibit spatial separation amongst base stations (BSs) of different types (termed tiers in this paper). For instance, operators will usually not deploy a picocell in close proximity to a macrocell, thus inducing separation amongst the locations of pico and macrocells. This separation has recently been captured by modeling the small cell locations by a Poisson Hole Process (PHP) with the hole centers being the locations of the macrocells. Due to the presence of exclusion zones, the analysis of the resulting model is significantly more complex compared to the more popular Poisson Point Process (PPP) based models. In this paper, we derive a tight bound on the distribution of the distance of a typical user to the closest point of a PHP. Since the exact distribution of this distance is not known, it is often approximated in the literature. For this model, we then provide tight characterization of the downlink coverage probability for a typical user in a two-tier closed-access HCN under two cases: (i) typical user is served by the closest macrocell, and (ii) typical user is served by its closest small cell. The proposed approach can be extended to analyze other relevant cases of interest, e.g., coverage in a PHP-based open access HCN.
Update-tolerant and Revocable Password Backup (Extended Version)
Venue: arXiv cs.CR 2017
Year: 2017

It is practically impossible for users to memorize a large portfolio of strong and individual passwords for their online accounts. A solution is to generate passwords randomly and store them. Yet, storing passwords instead of memorizing them bears the risk of loss, e.g., in situations where the device on which the passwords are stored is damaged, lost, or stolen. This makes the creation of backups of the passwords indispensable. However, placing such backups at secure locations to protect them as well from loss and unauthorized access and keeping them up-to-date at the same time is an unsolved problem in practice. We present PASCO, a backup solution for passwords that solves this challenge. PASCO backups need not to be updated, even when the user's password portfolio is changed. PASCO backups can be revoked without having physical access to them. This prevents password leakage, even when a user loses control over a backup. Additionally, we show how to extend PASCO to enable a fully controllable emergency access. It allows a user to give someone else access to his passwords in urgent situations. We also present a security evaluation and an implementation of PASCO.