OpenRaiser/Intern-Atlas huggingface dataset:
Sentiment Recognition in Egocentric Photostreams
Venue: arXiv cs.CV 2017
Year: 2017

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral).
Towards Teleoperation with Human-like Dynamics: Human Use of Elastic Tools
Venue: arXiv cs.HC 2017
Year: 2017

Variable stiffness actuators undergo lower peak force in contacts compared to their rigid counterparts, and are thus safer for human-robot interaction. Furthermore, they can store energy in their elastic element and can release it later to achieve human-like dynamic movements. However, it is not clear how to integrate them in teleoperator systems so that they can be controlled intuitively by a human. We performed an experiment to study human use of elastic tools to determine how a teleoperator system with an elastic slave would need to be designed. For this, we had 13 untrained participants hammer with an elastic tool under different stiffness conditions, asking them to try to find the best timing for a backward-forward swing motion in order to achieve the strongest impact. We found that the participants generally executed the task efficiently after a few trials and they converged to very similar solutions. The stiffness influenced the performance slightly, a stiffness between 2.3 Nm/rad and 4.1 Nm/rad showing the best results. We conclude that humans intuitively know how to efficiently use elastic tools for hammering type tasks. This could facilitate the control of teleoperator systems with elastic slave manipulators for tasks requiring explosive movements like hammering.
Image Restoration using Autoencoding Priors
Venue: arXiv cs.CV 2017
Year: 2017

We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the autoencoder error. A key advantage of our approach is that we do not need to train separate networks for different image restoration tasks, such as non-blind deconvolution with different kernels, or super-resolution at different magnification factors. We demonstrate state of the art results for non-blind deconvolution and super-resolution using the same autoencoding prior.
Modular, Fully-abstract Compilation by Approximate Back-translation
Venue: arXiv cs.PL 2017
Year: 2017

A compiler is fully-abstract if the compilation from source language programs to target language programs reflects and preserves behavioural equivalence. Such compilers have important security benefits, as they limit the power of an attacker interacting with the program in the target language to that of an attacker interacting with the program in the source language. Proving compiler full-abstraction is, however, rather complicated. A common proof technique is based on the back-translation of target-level program contexts to behaviourally-equivalent source-level contexts. However, constructing such a back- translation is problematic when the source language is not strong enough to embed an encoding of the target language. For instance, when compiling from STLC to ULC, the lack of recursive types in the former prevents such a back-translation. We propose a general and elegant solution for this problem. The key insight is that it suffices to construct an approximate back-translation. The approximation is only accurate up to a certain number of steps and conservative beyond that, in the sense that the context generated by the back-translation may diverge when the original would not, but not vice versa. Based on this insight, we describe a general technique for proving compiler full-abstraction and demonstrate it on a compiler from STLC to ULC. The proof uses asymmetric cross-language logical relations and makes innovative use of step-indexing to express the relation between a context and its approximate back-translation. The proof extends easily to common compiler patterns such as modular compilation and it, to the best of our knowledge, it is the first compiler full abstraction proof to have been fully mechanised in Coq. We believe this proof technique can scale to challenging settings and enable simpler, more scalable proofs of compiler full-abstraction.
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs
Venue: arXiv cs.LG 2017
Year: 2017

We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time series. This extended attention model can be deployed on top of any RNN and is shown to yield state-of-the-art performance for time series forecasting on several univariate and multivariate time series.
Is Climate Change Controversial? Modeling Controversy as Contention Within Populations
Venue: arXiv cs.IR 2017
Year: 2017

A growing body of research focuses on computationally detecting controversial topics and understanding the stances people hold on them. Yet gaps remain in our theoretical and practical understanding of how to define controversy, how it manifests, and how to measure it. In this paper, we introduce a novel measure we call "contention", defined with respect to a topic and a population. We model contention from a mathematical standpoint. We validate our model by examining a diverse set of sources: real-world polling data sets, actual voter data, and Twitter coverage on several topics. In our publicly-released Twitter data set of nearly 100M tweets, we examine several topics such as Brexit, the 2016 U.S. Elections, and "The Dress", and cross-reference them with other sources. We demonstrate that the contention measure holds explanatory power for a wide variety of observed phenomena, such as controversies over climate change and other topics that are well within scientific consensus. Finally, we re-examine the notion of controversy, and present a theoretical framework that defines it in terms of population. We present preliminary evidence suggesting that contention is one dimension of controversy, along with others, such as "importance". Our new contention measure, along with the hypothesized model of controversy, suggest several avenues for future work in this emerging interdisciplinary research area.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Venue: arXiv cs.CV 2017
Year: 2017

We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result. First, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to several metrics. Second, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Finally, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well recently published methods based on deep neural networks.
Priv'IT: Private and Sample Efficient Identity Testing
Venue: arXiv cs.DS 2017
Year: 2017

We develop differentially private hypothesis testing methods for the small sample regime. Given a sample $\cal D$ from a categorical distribution $p$ over some domain $Σ$, an explicitly described distribution $q$ over $Σ$, some privacy parameter $\varepsilon$, accuracy parameter $α$, and requirements $β_{\rm I}$ and $β_{\rm II}$ for the type I and type II errors of our test, the goal is to distinguish between $p=q$ and $d_{\rm{TV}}(p,q) \geq α$. We provide theoretical bounds for the sample size $|{\cal D}|$ so that our method both satisfies $(\varepsilon,0)$-differential privacy, and guarantees $β_{\rm I}$ and $β_{\rm II}$ type I and type II errors. We show that differential privacy may come for free in some regimes of parameters, and we always beat the sample complexity resulting from running the $χ^2$-test with noisy counts, or standard approaches such as repetition for endowing non-private $χ^2$-style statistics with differential privacy guarantees. We experimentally compare the sample complexity of our method to that of recently proposed methods for private hypothesis testing.
Bio-inspired Tensegrity Soft Modular Robots
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop icosahedron tensegrity structures with programmable variable stiffness that can deform in a three-dimensional space. We also describe a tendon-driven contraction mechanism to actively control the deformation of the tensegrity mod-ules. Finally, we validate the approach in a modular locomotory worm as a proof of concept.
Recursive Method for the Solution of Systems of Linear Equations
Venue: arXiv cs.DS 2017
Year: 2017

New solution method for the systems of linear equations in commutative integral domains is proposed. Its complexity is the same that the complexity of the matrix multiplication.
Análise comparativa de pesquisas de origens e destinos: uma abordagem baseada em Redes Complexas
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, a comparative study was conducted between complex networks representing origin and destination survey data. Similarities were found between the characteristics of the networks of Brazilian cities with networks of foreign cities. Power laws were found in the distributions of edge weights and this scale - free behavior can occur due to the economic characteristics of the cities.
A Survey of Riccati Equation Results in Negative Imaginary Systems Theory and Quantum Control Theory
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a survey of some new applications of algebraic Riccati equations. In particular, the paper surveys some recent results on the use of algebraic Riccati equations in testing whether a system is negative imaginary and in synthesizing state feedback controllers which make the closed loop system negative imaginary. The paper also surveys the use of Riccati equation methods in the control of quantum linear systems including coherent $H^\infty$ control.
Finding News Citations for Wikipedia
Venue: arXiv cs.IR 2017
Year: 2017

An important editing policy in Wikipedia is to provide citations for added statements in Wikipedia pages, where statements can be arbitrary pieces of text, ranging from a sentence to a paragraph. In many cases citations are either outdated or missing altogether. In this work we address the problem of finding and updating news citations for statements in entity pages. We propose a two-stage supervised approach for this problem. In the first step, we construct a classifier to find out whether statements need a news citation or other kinds of citations (web, book, journal, etc.). In the second step, we develop a news citation algorithm for Wikipedia statements, which recommends appropriate citations from a given news collection. Apart from IR techniques that use the statement to query the news collection, we also formalize three properties of an appropriate citation, namely: (i) the citation should entail the Wikipedia statement, (ii) the statement should be central to the citation, and (iii) the citation should be from an authoritative source. We perform an extensive evaluation of both steps, using 20 million articles from a real-world news collection. Our results are quite promising, and show that we can perform this task with high precision and at scale.
Automated News Suggestions for Populating Wikipedia Entity Pages
Venue: arXiv cs.IR 2017
Year: 2017

Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20\% of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources. We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the \emph{salience} and \emph{relative authority} of entities, and the \emph{novelty} of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93\% in the \emph{article-entity} suggestion stage and upto 84\% for the \emph{article-section placement}. Finally, we compare our approach against competitive baselines and show significant improvements.
Improving Entity Retrieval on Structured Data
Venue: arXiv cs.IR 2017
Year: 2017

The increasing amount of data on the Web, in particular of Linked Data, has led to a diverse landscape of datasets, which make entity retrieval a challenging task. Explicit cross-dataset links, for instance to indicate co-references or related entities can significantly improve entity retrieval. However, only a small fraction of entities are interlinked through explicit statements. In this paper, we propose a two-fold entity retrieval approach. In a first, offline preprocessing step, we cluster entities based on the \emph{x--means} and \emph{spectral} clustering algorithms. In the second step, we propose an optimized retrieval model which takes advantage of our precomputed clusters. For a given set of entities retrieved by the BM25F retrieval approach and a given user query, we further expand the result set with relevant entities by considering features of the queries, entities and the precomputed clusters. Finally, we re-rank the expanded result set with respect to the relevance to the query. We perform a thorough experimental evaluation on the Billions Triple Challenge (BTC12) dataset. The proposed approach shows significant improvements compared to the baseline and state of the art approaches.
Joining Extractions of Regular Expressions
Venue: arXiv cs.DB 2017
Year: 2017

Regular expressions with capture variables, also known as "regex formulas," extract relations of spans (interval positions) from text. These relations can be further manipulated via Relational Algebra as studied in the context of document spanners, Fagin et al.'s formal framework for information extraction. We investigate the complexity of querying text by Conjunctive Queries (CQs) and Unions of CQs (UCQs) on top of regex formulas. We show that the lower bounds (NP-completeness and W[1]-hardness) from the relational world also hold in our setting; in particular, hardness hits already single-character text! Yet, the upper bounds from the relational world do not carry over. Unlike the relational world, acyclic CQs, and even gamma-acyclic CQs, are hard to compute. The source of hardness is that it may be intractable to instantiate the relation defined by a regex formula, simply because it has an exponential number of tuples. Yet, we are able to establish general upper bounds. In particular, UCQs can be evaluated with polynomial delay, provided that every CQ has a bounded number of atoms (while unions and projection can be arbitrary). Furthermore, UCQ evaluation is solvable with FPT (Fixed-Parameter Tractable) delay when the parameter is the size of the UCQ.
Light spanners for bounded treewidth graphs imply light spanners for $H$-minor-free graphs
Venue: arXiv cs.DS 2017
Year: 2017

Grigni and Hung~\cite{GH12} conjectured that H-minor-free graphs have $(1+ε)$-spanners that are light, that is, of weight $g(|H|,ε)$ times the weight of the minimum spanning tree for some function $g$. This conjecture implies the {\em efficient} polynomial-time approximation scheme (PTAS) of the traveling salesperson problem in $H$-minor free graphs; that is, a PTAS whose running time is of the form $2^{f(ε)}n^{O(1)}$ for some function $f$. The state of the art PTAS for TSP in H-minor-free-graphs has running time $n^{1/ε^c}$. We take a further step toward proving this conjecture by showing that if the bounded treewidth graphs have light greedy spanners, then the conjecture is true. We also prove that the greedy spanner of a bounded pathwidth graph is light and discuss the possibility of extending our proof to bounded treewidth graphs.
Algorithm/Architecture Co-design of Proportionate-type LMS Adaptive Filters for Sparse System Identification
Venue: arXiv cs.OH 2017
Year: 2017

This paper investigates the problem of implementing proportionate-type LMS family of algorithms in hardware for sparse adaptive filtering applications especially the network echo cancelation. We derive a re-formulated proportionate type algorithm through algorithm-architecture co-design methodology that can be pipelined and has an efficient architecture for hardware implementation. We study the convergence, steady-state and tracking performances of these re-formulated algorithms for white, color and speech inputs before implementing them in hardware. To the best of our knowledge this is the first attempt to implement proportionate-type algorithms in hardware. We show that Delayed $μ$-law Proportionate LMS (DMPLMS) algorithm for white input and Delayed Wavelet MPLMS (DWMPLMS) for colored input are the robust VLSI solutions for network echo cancellation where the sparsity of the echo paths can vary with time. We implemented all the designs considering $16$-bit fixed point representation in hardware, synthesized the designs and synthesis results show that DMPLMS algorithm with $\approx25\%$ increase in hardware over conventional DLMS architecture, achieves $3X$ improvement in convergence rate for white input and DWMPLMS algorithm with $\approx58\%$ increase in hardware achieves $15X$ improvement in convergence rate for correlated input conditions.
Applying Ricci Flow to High Dimensional Manifold Learning
Venue: arXiv cs.LG 2017
Year: 2017

Traditional manifold learning algorithms often bear an assumption that the local neighborhood of any point on embedded manifold is roughly equal to the tangent space at that point without considering the curvature. The curvature indifferent way of manifold processing often makes traditional dimension reduction poorly neighborhood preserving. To overcome this drawback we propose a new algorithm called RF-ML to perform an operation on the manifold with help of Ricci flow before reducing the dimension of manifold.
UNBIAS PUF: A Physical Implementation Bias Agnostic Strong PUF
Venue: arXiv cs.AR 2017
Year: 2017

The Physical Unclonable Function (PUF) is a promising hardware security primitive because of its inherent uniqueness and low cost. To extract the device-specific variation from delay-based strong PUFs, complex routing constraints are imposed to achieve symmetric path delays; and systematic variations can severely compromise the uniqueness of the PUF. In addition, the metastability of the arbiter circuit of an Arbiter PUF can also degrade the quality of the PUF due to the induced instability. In this paper we propose a novel strong UNBIAS PUF that can be implemented purely by Register Transfer Language (RTL), such as verilog, without imposing any physical design constraints or delay characterization effort to solve the aforementioned issues. Efficient inspection bit prediction models for unbiased response extraction are proposed and validated. Our experimental results of the strong UNBIAS PUF show 5.9% intra-Fractional Hamming Distance (FHD) and 45.1% inter-FHD on 7 Field Programmable Gate Array (FPGA) boards without applying any physical layout constraints or additional XOR gates. The UNBIAS PUF is also scalable because no characterization cost is required for each challenge to compensate the implementation bias. The averaged intra-FHD measured at worst temperature and voltage variation conditions is 12%, which is still below the margin of practical Error Correction Code (ECC) with error reduction techniques for PUFs.
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
Venue: arXiv cs.NI 2017
Year: 2017

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.