OpenRaiser/Intern-Atlas huggingface dataset:
Local Word Vectors Guiding Keyphrase Extraction
Venue: arXiv cs.CL 2018
Year: 2018

Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors), i.e., embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and therefore can help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state-of-the-art unsupervised keyphrase extraction methods.
The Maximum Colorful Arborescence problem parameterized by the structure of its color hierarchy graph
Venue: arXiv cs.CC 2018
Year: 2018

Let G=(V,A) be a vertex-colored arc-weighted directed acyclic graph (DAG) rooted in some vertex r, and let H be its color hierarchy graph, defined as follows: V(H) is the color set C of G, and an arc from color c to color c' exists in H if there is an arc in G from a vertex of color c to a vertex of color c'. In this paper, we study the MAXIMUM COLORFUL ARBORESCENCE problem (or MCA), which takes as input a DAG G with the additional constraint that H is also a DAG, and aims at finding in G an arborescence rooted in r, of maximum weight, and in which no color appears more than once. The MCA problem is motivated by the inference of unknown metabolites from mass spectrometry experiments. However, whereas the problem has been studied for roughly ten years, the crucial property that H is necessarily a DAG has only been pointed out and exploited very recently. In this paper, we further investigate MCA under this new light, by providing algorithmic results for the problem, with a specific focus on fixed-parameterized tractability (FPT) issues, and relatively to different structural parameters of H. In particular, we provide an O*(3^{nhs}) time algorithm for solving MCA, where nhs is the number of vertices of indegree at least two in H, thereby improving the O*(3^{|C|}) algorithm from [Böcker et al. 2008]. We also prove that MCA is W[2]-hard relatively to the treewidth Ht of H, and further show that it is FPT relatively to Ht+lc, where lc = |V| - |C|.
Mobile Social Big Data: WeChat Moments Dataset, Network Applications, and Opportunities
Venue: arXiv cs.SI 2018
Year: 2018

In parallel to the increase of various mobile technologies, the mobile social network (MSN) service has brought us into an era of mobile social big data, where people are creating new social data every second and everywhere. It is of vital importance for businesses, government, and institutes to understand how peoples' behaviors in the online cyberspace can affect the underlying computer network, or their offline behaviors at large. To study this problem, we collect a dataset from WeChat Moments, called WeChatNet, which involves 25,133,330 WeChat users with 246,369,415 records of link reposting on their pages. We revisit three network applications based on the data analytics over WeChatNet, i.e., the information dissemination in mobile cellular networks, the network traffic prediction in backbone networks, and the mobile population distribution projection. Meanwhile, we discuss the potential research opportunities for developing new applications using the released dataset.
Skew cyclic and skew $(α_1 + uα_2 + vα_3 + uvα_4)$-constacyclic codes over $F_q + uF_q + vF_q + uvF_q$
Venue: arXiv cs.IT 2018
Year: 2018

In this note, we study skew cyclic and skew constacyclic codes over the ring $\mathcal{R}=F_{q}+uF_{q}+vF_{q}+uvF_{q}$ where $q=p^{m},$ $p$ is an odd prime, $u^{2}=u,~v^{2}=v,~uv=vu$. We show that Gray images of a skew cyclic and skew $α$-constacyclic code of length $n$ are skew quasi-cyclic code of length $4n$ over $F_{q}$ of index 4. Also, it is shown that skew $α$-constacyclic codes are either equivalent to $α$-constacyclic codes or $α$-quasi-twisted codes over $\mathcal{R}$. Further, structural properties, specially, generating polynomials and idempotent generators for skew cyclic and skew constacyclic codes are determined by decomposition method.
MONROE-Nettest: A Configurable Tool for Dissecting Speed Measurements in Mobile Broadband Networks
Venue: arXiv cs.NI 2018
Year: 2018

As the demand for mobile connectivity continues to grow, there is a strong need to evaluate the performance of Mobile Broadband (MBB) networks. In the last years, mobile "speed", quantified most commonly by data rate, gained popularity as the widely accepted metric to describe their performance. However, there is a lack of consensus on how mobile speed should be measured. In this paper, we design and implement MONROE-Nettest to dissect mobile speed measurements, and investigate the effect of different factors on speed measurements in the complex mobile ecosystem. MONROE-Nettest is built as an Experiment as a Service (EaaS) on top of the MONROE platform, an open dedicated platform for experimentation in operational MBB networks. Using MONROE-Nettest, we conduct a large scale measurement campaign and quantify the effects of measurement duration, number of TCP flows, and server location on measured downlink data rate in 6 operational MBB networks in Europe. Our results indicate that differences in parameter configuration can significantly affect the measurement results. We provide the complete MONROE-Nettest toolset as open source and our measurements as open data.
Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack
Venue: arXiv cs.SE 2018
Year: 2018

Many approaches for testing configurable software systems start from the same assumption: it is impossible to test all configurations. This motivated the definition of variability-aware abstractions and sampling techniques to cope with large configuration spaces. Yet, there is no theoretical barrier that prevents the exhaustive testing of all configurations by simply enumerating them, if the effort required to do so remains acceptable. Not only this: we believe there is lots to be learned by systematically and exhaustively testing a configurable system. In this case study, we report on the first ever endeavour to test all possible configurations of an industry-strength, open source configurable software system, JHipster, a popular code generator for web applications. We built a testing scaffold for the 26,000+ configurations of JHipster using a cluster of 80 machines during 4 nights for a total of 4,376 hours (182 days) CPU time. We find that 35.70% configurations fail and we identify the feature interactions that cause the errors. We show that sampling strategies (like dissimilarity and 2-wise): (1) are more effective to find faults than the 12 default configurations used in the JHipster continuous integration; (2) can be too costly and exceed the available testing budget. We cross this quantitative analysis with the qualitative assessment of JHipster's lead developers.
VGGFace2: A dataset for recognising faces across pose and age
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS- Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A, IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin. Datasets and models are publicly available.
Symbolic Computations of First Integrals for Polynomial Vector Fields
Venue: arXiv cs.SC 2018
Year: 2018

In this article we show how to generalize to the Darbouxian, Liouvillian and Riccati case the extactic curve introduced by J. Pereira. With this approach, we get new algorithms for computing, if it exists, a rational, Darbouxian, Liouvillian or Riccati first integral with bounded degree of a polynomial planar vector field. We give probabilistic and deterministic algorithms. The arithmetic complexity of our probabilistic algorithm is in $\tilde{\mathcal{O}}(N^{ω+1})$, where $N$ is the bound on the degree of a representation of the first integral and $ω\in [2;3]$ is the exponent of linear algebra. This result improves previous algorithms. Our algorithms have been implemented in Maple and are available on authors' websites. In the last section, we give some examples showing the efficiency of these algorithms.
Nauticle: a general-purpose particle-based simulation tool
Venue: arXiv cs.MS 2018
Year: 2018

Nauticle is a general-purpose simulation tool for the flexible and highly configurable application of particle-based methods of either discrete or continuum phenomena. It is presented that Nauticle has three distinct layers for users and developers, then the top two layers are discussed in detail. The paper introduces the Symbolic Form Language (SFL) of Nauticle, which facilitates the formulation of user-defined numerical models at the top level in text-based configuration files and provides simple application examples of use. On the other hand, at the intermediate level, it is shown that the SFL can be intuitively extended with new particle methods without tedious recoding or even the knowledge of the bottom level. Finally, the efficiency of the code is also tested through a performance benchmark.
Fitch-Style Modal Lambda Calculi
Venue: arXiv cs.LO 2018
Year: 2018

Fitch-style modal deduction, in which modalities are eliminated by opening a subordinate proof, and introduced by shutting one, were investigated in the 1990s as a basis for lambda calculi. We show that such calculi have good computational properties for a variety of intuitionistic modal logics. Semantics are given in cartesian closed categories equipped with an adjunction of endofunctors, with the necessity modality interpreted by the right adjoint. Where this functor is an idempotent comonad, a coherence result on the semantics allows us to present a calculus for intuitionistic S4 that is simpler than others in the literature. We show the calculi can be extended à la tense logic with the left adjoint of necessity, and are then complete for the categorical semantics.
BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking
Venue: arXiv cs.CL 2018
Year: 2018

The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present BENGAL, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by BENGAL and on 16benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at http://faturl.com/bengalexpinlg and the code at https://github.com/dice-group/BENGAL.
Algorithmic detectability threshold of the stochastic block model
Venue: arXiv cs.SI 2018
Year: 2018

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation--maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure, but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.
We Don't Need Another Hero? The Impact of "Heroes" on Software Development
Venue: arXiv cs.SE 2018
Year: 2018

A software project has "Hero Developers" when 80% of contributions are delivered by 20% of the developers. Are such heroes a good idea? Are too many heroes bad for software quality? Is it better to have more/less heroes for different kinds of projects? To answer these questions, we studied 661 open source projects from Public open source software (OSS) Github and 171 projects from an Enterprise Github. We find that hero projects are very common. In fact, as projects grow in size, nearly all project become hero projects. These findings motivated us to look more closely at the effects of heroes on software development. Analysis shows that the frequency to close issues and bugs are not significantly affected by the presence of project type (Public or Enterprise). Similarly, the time needed to resolve an issue/bug/enhancement is not affected by heroes or project type. This is a surprising result since, before looking at the data, we expected that increasing heroes on a project will slow down howfast that project reacts to change. However, we do find a statistically significant association between heroes, project types, and enhancement resolution rates. Heroes do not affect enhancement resolution rates in Public projects. However, in Enterprise projects, the more heroes increase the rate at which project complete enhancements. In summary, our empirical results call for a revision of a long-held truism in software engineering. Software heroes are far more common and valuable than suggested by the literature, particularly for medium to large Enterprise developments. Organizations should reflect on better ways to find and retain more of these heroes
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
Venue: arXiv cs.CV 2018
Year: 2018

This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans. We combine both local and global context to obtain a representation of the ROI. We then use Relation Networks (RNs) to predict the corresponding anatomy of the ROI based on its relationship score for each class. Further, we propose a novel strategy employing nearest neighbors approach for training RNs. We train RNs to learn the relationship of the target ROI with the joint representation of its nearest neighbors in each class instead of all data-points in each class. The proposed strategy leads to better training of RNs along with increased performance as compared to training baseline RN network.
Minimal Synthesis of String To String Functions From Examples
Venue: arXiv cs.FL 2018
Year: 2018

We study the problem of synthesizing string to string transformations from a set of input/output examples. The transformations we consider are expressed using deterministic finite automata (DFA) that read pairs of letters, one letter from the input and one from the output. The DFA corresponding to these transformations have additional constraints, ensuring that each input string is mapped to exactly one output string. We suggest that, given a set of input/output examples, the smallest DFA consistent with the examples is a good candidate for the transformation the user was expecting. We therefore study the problem of, given a set of examples, finding a minimal DFA consistent with the examples and satisfying the functionality and totality constraints mentioned above. We prove that, in general, this problem (the corresponding decision problem) is NP-complete. This is unlike the standard DFA minimization problem which can be solved in polynomial time. We provide several NP-hardness proofs that show the hardness of multiple (independent) variants of the problem. Finally, we propose an algorithm for finding the minimal DFA consistent with input/output examples, that uses a reduction to SMT solvers. We implemented the algorithm, and used it to evaluate the likelihood that the minimal DFA indeed corresponds to the DFA expected by the user.
Cross-identification of stellar catalogs with multiple stars: Complexity and Resolution
Venue: arXiv cs.DM 2018
Year: 2018

In this work, I present an optimization problem which consists of assigning entries of a stellar catalog to multiple entries of another stellar catalog such that the probability of such assignment is maximum. I show a way of modeling it as a Maximum Weighted Stable Set Problem which is further used to solve a real astronomical instance and I partially characterize the forbidden subgraphs of the resulting family of graphs given by that reduction. Finally, I prove that the problem is NP-Hard.
Runtime Fault Detection in Programmed Molecular Systems
Venue: arXiv cs.ET 2018
Year: 2018

Watchdog timers are devices that are commonly used to monitor the health of safety-critical hardware and software systems. Their primary function is to raise an alarm if the monitored systems fail to emit periodic "heartbeats" that signal their well-being. In this paper we design and verify a molecular watchdog timer for monitoring the health of programmed molecular nanosystems. This raises new challenges because our molecular watchdog timer and the system that it monitors both operate in the probabilistic environment of chemical kinetics, where many failures are certain to occur and it is especially hard to detect the absence of a signal. Our molecular watchdog timer is the result of an incremental design process that uses goal-oriented requirements engineering, simulation, stochastic analysis, and software verification tools. We demonstrate the molecular watchdog's functionality by having it monitor a molecular oscillator. Both the molecular watchdog timer and the oscillator are implemented as chemical reaction networks, which are the current programming language of choice for many molecular programming applications.
Maximum Principle Based Algorithms for Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate per-iteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.
Exhaustive Exploration of the Failure-oblivious Computing Search Space
Venue: arXiv cs.SE 2018
Year: 2018

High-availability of software systems requires automated handling of crashes in presence of errors. Failure-oblivious computing is one technique that aims to achieve high availability. We note that failure-obliviousness has not been studied in depth yet, and there is very few study that helps understand why failure-oblivious techniques work. In order to make failure-oblivious computing to have an impact in practice, we need to deeply understand failure-oblivious behaviors in software. In this paper, we study, design and perform an experiment that analyzes the size and the diversity of the failure-oblivious behaviors. Our experiment consists of exhaustively computing the search space of 16 field failures of large-scale open-source Java software. The outcome of this experiment is a much better understanding of what really happens when failure-oblivious computing is used, and this opens new promising research directions.
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.