OpenRaiser/Intern-Atlas huggingface dataset:
Trustworthy Graph Algorithms
Venue: arXiv cs.DS 2019
Year: 2019

The goal of the LEDA project was to build an easy-to-use and extendable library of correct and efficient data structures, graph algorithms and geometric algorithms. We report on the use of formal program verification to achieve an even higher level of trustworthiness. Specifically, we report on an ongoing and largely finished verification of the blossom-shrinking algorithm for maximum cardinality matching.
Template-Based Posit Multiplication for Training and Inferring in Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

The posit number system is arguably the most promising and discussed topic in Arithmetic nowadays. The recent breakthroughs claimed by the format proposed by John L. Gustafson have put posits in the spotlight. In this work, we first describe an algorithm for multiplying two posit numbers, even when the number of exponent bits is zero. This configuration, scarcely tackled in literature, is particularly interesting because it allows the deployment of a fast sigmoid function. The proposed multiplication algorithm is then integrated as a template into the well-known FloPoCo framework. Synthesis results are shown to compare with the floating point multiplication offered by FloPoCo as well. Second, the performance of posits is studied in the scenario of Neural Networks in both training and inference stages. To the best of our knowledge, this is the first time that training is done with posit format, achieving promising results for a binary classification problem even with reduced posit configurations. In the inference stage, 8-bit posits are as good as floating point when dealing with the MNIST dataset, but lose some accuracy with CIFAR-10.
Progressive Refinement Imaging
Venue: arXiv cs.GR 2019
Year: 2019

This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory-efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras.
calibDB: enabling web based computer vision through on-the-fly camera calibration
Venue: arXiv cs.CV 2019
Year: 2019

For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it. While calibration data is available on some devices through Augmented Reality (AR) frameworks like ARCore and ARKit, for most cameras this information is not available. Therefore, we propose a web based calibration service that not only aggregates calibration data, but also allows calibrating new cameras on-the-fly. We build upon a novel camera calibration framework that enables even novice users to perform a precise camera calibration in about 2 minutes. This allows general deployment of computer vision algorithms on the web, which was previously not possible due to lack of calibration data.
Historical comparison of gender inequality in scientific careers across countries and disciplines
Venue: arXiv cs.DL 2019
Year: 2019

There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are under-represented in most scientific disciplines, publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender discrepancies in performance through a bibliometric analysis of academic careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in dropout rates and career length explain a large portion of the reported career-wise differences in productivity and impact. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women's careers in academia, with important consequences for institutions and policy makers.
EU H2020 Gauss project. Geo-Fencing Software System
Venue: arXiv cs.OH 2019
Year: 2019

The Geofencing system is the key to operate the Unmanned Aerial Vehicle (UAV) within the safe and appropriate zone to avoid public concerns and other privacy issues. The system is designed to keep the UAV away from geofenced obstacles using the onboard GNSS and IMU location. The Geofencing system is part of the H2020 GAUSS project and facilities other subsystems, for instance, to support the command and control link, which is the security measure to secure the UAV from hijacking and signal spoofing. The regulatory authorities expressed the concern of having UAVs flying in the no-fly zone and causing troubles from offending private privacy to hazards at airport airspace. Hence the geofence system shall provide guidance message, which enables the UAV to evacuate from no-fly-zone, based on real-time updated location. This thesis aims to first illustrate the generation of geofence and then apply the geofence system on UAV operation. This application enables UAV to fly in the designated area without human intervention. The project is built with JAVA using GIS-enabled Database Management System and Open Soured Map data powered by OpenStreetMap and OS map. This method has been tested by simulations which had results of high accuracy.
Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm
Venue: arXiv cs.NE 2019
Year: 2019

Many of the competitive neural network consists of spatially arranged neurons. The weigh matrix that connects cells represents local excitation and long-range inhibition. They are known as soft-winner-take-all networks and shown to exhibit desirable information-processing. The local excitatory connections are many times predefined hand-wired based depending on spatial arrangement which is chosen using the previous knowledge of data. Here we present learning in recurrent network through Haeusslers equation and modified wiring scheme based on biologically based Firefly algorithm. Following results show learning in such network from input patterns without hand-wiring with fixed topology.
A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice
Venue: arXiv cs.NE 2019
Year: 2019

The flawless functioning of a protein is essentially linked to its own three-dimensional structure. Therefore, the prediction of a protein structure from its amino acid sequence is a fundamental problem in many fields that draws researchers attention. This problem can be formulated as a combinatorial optimization problem based on simplified lattice models such as the hydrophobic-polar model. In this paper, we propose a new hybrid algorithm combining three different well-known heuristic algorithms: genetic algorithm, tabu search strategy and local search algorithm in order to solve the PSP problem. Regarding the assessment of suggested algorithm, an experimental study is included, where we considered the quality of the produced solution as the main quality criterion. Furthermore, we compared the suggested algorithm with state-of-the-art algorithms using a selection of well-studied benchmark instances.
Reconstruction under outliers for Fourier-sparse functions
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of learning an unknown $f$ with a sparse Fourier spectrum in the presence of outlier noise. In particular, the algorithm has access to a noisy oracle for (an unknown) $f$ such that (i) the Fourier spectrum of $f$ is $k$-sparse; (ii) at any query point $x$, the oracle returns $y$ such that with probability $1-ρ$, $|y-f(x)| \le ε$. However, with probability $ρ$, the error $y-f(x)$ can be arbitrarily large. We study Fourier sparse functions over both the discrete cube $\{0,1\}^n$ and the torus $[0,1)$ and for both these domains, we design efficient algorithms which can tolerate any $ρ<1/2$ fraction of outliers. We note that the analogous problem for low-degree polynomials has recently been studied in several works~[AK03, GZ16, KKP17] and similar algorithmic guarantees are known in that setting. While our main results pertain to the case where the location of the outliers, i.e., $x$ such that $|y-f(x)|>ε$ is randomly distributed, we also study the case where the outliers are adversarially located. In particular, we show that over the torus, assuming that the Fourier transform satisfies a certain \emph{granularity} condition, there is a sample efficient algorithm to tolerate $ρ=Ω(1)$ fraction of outliers and further, that this is not possible without such a granularity condition. Finally, while not the principal thrust, our techniques also allow us non-trivially improve on learning low-degree functions $f$ on the hypercube in the presence of adversarial outlier noise. Our techniques combine a diverse array of tools from compressive sensing, sparse Fourier transform, chaining arguments and complex analysis.
Prescription AR: A Fully-Customized Prescription-Embedded Augmented Reality Display
Venue: arXiv cs.GR 2019
Year: 2019

In this paper, we present a fully-customized AR display design that considers the user's prescription, interpupillary distance, and taste of fashion. A free-form image combiner embedded inside the prescription lens provides augmented images onto the vision-corrected real world. We establish a prescription-embedded AR display optical design method as well as the customization method for individual users. Our design can cover myopia, hyperopia, astigmatism, and presbyopia, and allows the eye-contact interaction with privacy protection. A 169$g$ dynamic prototype showed a 40$^\circ$ $\times$ 20 $^\circ$ virtual image with a 23 cpd resolution at center field and 6 mm $\times$ 4 mm eye box, with the vision-correction and varifocal (0.5-3$m$) capability.
Making Study Populations Visible through Knowledge Graphs
Venue: arXiv cs.LO 2019
Year: 2019

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations.
Diameter computation on $H$-minor free graphs and graphs of bounded (distance) VC-dimension
Venue: arXiv cs.DS 2019
Year: 2019

We propose to study unweighted graphs of constant distance VC-dimension as a broad generalization of many graph classes for which we can compute the diameter in truly subquadratic-time. In particular for any fixed $H$, the class of $H$-minor free graphs has distance VC-dimension at most $|V(H)|-1$. Our first main result is that on graphs of distance VC-dimension at most $d$, for any fixed $k$ we can either compute the diameter or conclude that it is larger than $k$ in time $\tilde{\cal O}(k\cdot mn^{1-\varepsilon_d})$, where $\varepsilon_d \in (0;1)$ only depends on $d$. Then as a byproduct of our approach, we get the first truly subquadratic-time algorithm for constant diameter computation on all the nowhere dense graph classes. Finally, we show how to remove the dependency on $k$ for any graph class that excludes a fixed graph $H$ as a minor. More generally, our techniques apply to any graph with constant distance VC-dimension and polynomial expansion. As a result for all such graphs one obtains a truly subquadratic-time algorithm for computing their diameter. Our approach is based on the work of Chazelle and Welzl who proved the existence of spanning paths with strongly sublinear stabbing number for every hypergraph of constant VC-dimension. We show how to compute such paths efficiently by combining the best known approximation algorithms for the stabbing number problem with a clever use of $\varepsilon$-nets, region decomposition and other partition techniques.
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Venue: arXiv cs.CL 2019
Year: 2019

Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.
A Novel Contactless Human Machine Interface based on Machine Learning
Venue: arXiv cs.HC 2019
Year: 2019

This paper describes a global framework that enables contactless human machine interaction using computer vision and machine learning techniques. The main originality of our framework is that only a very simple image acquisition device, as a computer camera, is sufficient to establish a rich human machine interaction as traditional devices such as mouse or keyboard. This framework is based on well known computer vision techniques and efficient machine learning techniques are used to detect and track user hand gestures so the end user can control his computer using virtual interfaces with very simple gestures.
An Energy-Efficient Reconfigurable DTLS Cryptographic Engine for Securing Internet-of-Things Applications
Venue: arXiv cs.CR 2019
Year: 2019

This paper presents the first hardware implementation of the Datagram Transport Layer Security (DTLS) protocol to enable end-to-end security for the Internet of Things (IoT). A key component of this design is a reconfigurable prime field elliptic curve cryptography (ECC) accelerator, which is 238x and 9x more energy-efficient compared to software and state-of-the-art hardware respectively. Our full hardware implementation of the DTLS 1.3 protocol provides 438x improvement in energy-efficiency over software, along with code size and data memory usage as low as 8 KB and 3 KB respectively. The cryptographic accelerators are coupled with an on-chip low-power RISC-V processor to benchmark applications beyond DTLS with up to two orders of magnitude energy savings. The test chip, fabricated in 65 nm CMOS, demonstrates hardware-accelerated DTLS sessions while consuming 44.08 uJ per handshake, and 0.89 nJ per byte of encrypted data at 16 MHz and 0.8 V.
Multi-Speaker End-to-End Speech Synthesis
Venue: arXiv cs.CL 2019
Year: 2019

In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i.e., text-to-wave), to generate high-fidelity speech from multiple speakers. To model the unique characteristic of different voices, low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of the model. We demonstrate that the multi-speaker ClariNet outperforms state-of-the-art systems in terms of naturalness, because the whole model is jointly optimized in an end-to-end manner.
A New Benchmark and Approach for Fine-grained Cross-media Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

Cross-media retrieval is to return the results of various media types corresponding to the query of any media type. Existing researches generally focus on coarse-grained cross-media retrieval. When users submit an image of "Slaty-backed Gull" as a query, coarse-grained cross-media retrieval treats it as "Bird", so that users can only get the results of "Bird", which may include other bird species with similar appearance (image and video), descriptions (text) or sounds (audio), such as "Herring Gull". Such coarse-grained cross-media retrieval is not consistent with human lifestyle, where we generally have the fine-grained requirement of returning the exactly relevant results of "Slaty-backed Gull" instead of "Herring Gull". However, few researches focus on fine-grained cross-media retrieval, which is a highly challenging and practical task. Therefore, in this paper, we first construct a new benchmark for fine-grained cross-media retrieval, which consists of 200 fine-grained subcategories of the "Bird", and contains 4 media types, including image, text, video and audio. To the best of our knowledge, it is the first benchmark with 4 media types for fine-grained cross-media retrieval. Then, we propose a uniform deep model, namely FGCrossNet, which simultaneously learns 4 types of media without discriminative treatments. We jointly consider three constraints for better common representation learning: classification constraint ensures the learning of discriminative features, center constraint ensures the compactness characteristic of the features of the same subcategory, and ranking constraint ensures the sparsity characteristic of the features of different subcategories. Extensive experiments verify the usefulness of the new benchmark and the effectiveness of our FGCrossNet. They will be made available at https://github.com/PKU-ICST-MIPL/FGCrossNet_ACMMM2019.
Dynamics of Team Library Adoptions: An Exploration of GitHub Commit Logs
Venue: arXiv cs.SI 2019
Year: 2019

When a group of people strives to understand new information, struggle ensues as various ideas compete for attention. Steep learning curves are surmounted as teams learn together. To understand how these team dynamics play out in software development, we explore Git logs, which provide a complete change history of software repositories. In these repositories, we observe code additions, which represent successfully implemented ideas, and code deletions, which represent ideas that have failed or been superseded. By examining the patterns between these commit types, we can begin to understand how teams adopt new information. We specifically study what happens after a software library is adopted by a project, i.e., when a library is used for the first time in the project. We find that a variety of factors, including team size, library popularity, and prevalence on Stack Overflow are associated with how quickly teams learn and successfully adopt new software libraries.
The Role of Cooperation in Responsible AI Development
Venue: arXiv cs.CY 2019
Year: 2019

In this paper, we argue that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact. Ensuring that AI systems are developed responsibly may therefore require preventing and solving collective action problems between companies. We note that there are several key factors that improve the prospects for cooperation in collective action problems. We use this to identify strategies to improve the prospects for industry cooperation on the responsible development of AI.
Identifying Algorithm Names in Code Comments
Venue: arXiv cs.SE 2019
Year: 2019

For recent machine-learning-based tasks like API sequence generation, comment generation, and document generation, large amount of data is needed. When software developers implement algorithms in code, we find that they often mention algorithm names in code comments. Code annotated with such algorithm names can be valuable data sources. In this paper, we propose an automatic method of algorithm name identification. The key idea is extracting important N-gram words containing the word `algorithm' in the last. We also consider part of speech patterns to derive rules for appropriate algorithm name identification. The result of our rule evaluation produced high precision and recall values (more than 0.70). We apply our rules to extract algorithm names in a large amount of comments from active FLOSS projects written in seven programming languages, C, C++, Java, JavaScript, Python, PHP, and Ruby, and report commonly mentioned algorithm names in code comments.
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning
Venue: arXiv cs.RO 2019
Year: 2019

Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to intractable problems. Furthermore, since the models used in TO have always some level of abstraction, it can be hard to find a realistic set of uncertainties in the model space. In this paper we leverage a sample-efficient learning technique (Bayesian optimization) to robustify TO for humanoid locomotion. The main idea is to use data from full-body simulations to make the TO stage robust by tuning the cost weights. To this end, we split the TO problem into two phases. The first phase solves a convex optimization problem for generating center of mass (CoM) trajectories based on simplified linear dynamics. The second stage employs iterative Linear-Quadratic Gaussian (iLQG) as a whole-body controller to generate full body control inputs. Then we use Bayesian optimization to find the cost weights to use in the first stage that yields robust performance in the simulation/experiment, in the presence of different disturbance/uncertainties. The results show that the proposed approach is able to generate robust motions for different sets of disturbances and uncertainties.