OpenRaiser/Intern-Atlas huggingface dataset:
Preprints as accelerator of scholarly communication: An empirical analysis in Mathematics
Venue: arXiv cs.DL 2020
Year: 2020

In this study we analyse the key driving factors of preprints in enhancing scholarly communication. To this end we use four groups of metrics, one referring to scholarly communication and based on bibliometric indicators (Web of Science and Scopus citations), while the others reflect usage (usage counts in Web of Science), capture (Mendeley readers) and social media attention (Tweets). Hereby we measure two effects associated with preprint publishing: publication delay and impact. We define and use several indicators to assess the impact of journal articles with previous preprint versions in arXiv. In particular, the indicators measure several times characterizing the process of arXiv preprints publishing and the reviewing process of the journal versions, and the ageing patterns of citations to preprints. In addition, we compare the observed patterns between preprints and non-OA articles without any previous preprint versions in arXiv. We could observe that the "early-view" and "open-access" effects of preprints contribute to a measurable citation and readership advantage of preprints. Articles with preprint versions are more likely to be mentioned in social media and have shorter Altmetric attention delay. Usage and capture prove to have only moderate but stronger correlation with citations than Tweets. The different slopes of the regression lines between the different indicators reflect different order of magnitude of usage, capture and citation data.
A Family of Experiments on Test-Driven Development
Venue: arXiv cs.SE 2020
Year: 2020

Context: Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable. Objectives: The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD. Method: We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD. Results: TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students. Conclusion: Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process...
Benchmarking Image Retrieval for Visual Localization
Venue: arXiv cs.CV 2020
Year: 2020

Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two tasks: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for these tasks. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes. However, robustness to viewpoint changes is not necessarily desirable in the context of visual localization. This paper focuses on understanding the role of image retrieval for multiple visual localization tasks. We introduce a benchmark setup and compare state-of-the-art retrieval representations on multiple datasets. We show that retrieval performance on classical landmark retrieval/recognition tasks correlates only for some but not all tasks to localization performance. This indicates a need for retrieval approaches specifically designed for localization tasks. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization.
WeiPS: a symmetric fusion model framework for large-scale online learning
Venue: arXiv cs.LG 2020
Year: 2020

The recommendation system is an important commercial application of machine learning, where billions of feed views in the information flow every day. In reality, the interaction between user and item usually makes user's interest changing over time, thus many companies (e.g. ByteDance, Baidu, Alibaba, and Weibo) employ online learning as an effective way to quickly capture user interests. However, hundreds of billions of model parameters present online learning with challenges for real-time model deployment. Besides, model stability is another key point for online learning. To this end, we design and implement a symmetric fusion online learning system framework called WeiPS, which integrates model training and model inference. Specifically, WeiPS carries out second level model deployment by streaming update mechanism to satisfy the consistency requirement. Moreover, it uses multi-level fault tolerance and real-time domino degradation to achieve high availability requirement.
The 2-Adic Complexity of Two Classes of Binary Sequences with Interleaved Structure
Venue: arXiv cs.IT 2020
Year: 2020

The autocorrelation values of two classes of binary sequences are shown to be good in [6]. We study the 2-adic complexity of these sequences. Our results show that the 2-adic complexity of such sequences is large enough to resist the attack of the rational approximation algorithm.
Gender bias in magazines oriented to men and women: a computational approach
Venue: arXiv cs.CL 2020
Year: 2020

Cultural products are a source to acquire individual values and behaviours. Therefore, the differences in the content of the magazines aimed specifically at women or men are a means to create and reproduce gender stereotypes. In this study, we compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group, over a decade (2008-2018). With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time. Then, we performed a word-frequency analysis to validate this methodology and extend the analysis to other subjects that did not emerge automatically. Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time. Conversely, in Fashion and Science topics, the initial differences between both magazines are maintained. Besides, we show that in 2012, the content associated with horoscope increased in the women-oriented magazine, generating a new gap that remained open over time. Also, we show a strong increase in the use of words associated with feminism since 2015 and specifically the word abortion in 2018. Overall, these computational tools allowed us to analyse more than 24,000 articles. Up to our knowledge, this is the first study to compare magazines in such a large dataset, a task that would have been prohibitive using manual content analysis methodologies.
Min-Sum Clustering (with Outliers)
Venue: arXiv cs.DS 2020
Year: 2020

We give a constant factor polynomial time pseudo-approximation algorithm for min-sum clustering with or without outliers. The algorithm is allowed to exclude an arbitrarily small constant fraction of the points. For instance, we show how to compute a solution that clusters 98\% of the input data points and pays no more than a constant factor times the optimal solution that clusters 99\% of the input data points. More generally, we give the following bicriteria approximation: For any $\eps > 0$, for any instance with $n$ input points and for any positive integer $n'\le n$, we compute in polynomial time a clustering of at least $(1-\eps) n'$ points of cost at most a constant factor greater than the optimal cost of clustering $n'$ points. The approximation guarantee grows with $\frac{1}{\eps}$. Our results apply to instances of points in real space endowed with squared Euclidean distance, as well as to points in a metric space, where the number of clusters, and also the dimension if relevant, is arbitrary (part of the input, not an absolute constant).
Domain-Transferable Method for Named Entity Recognition Task
Venue: arXiv cs.CL 2020
Year: 2020

Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.
Towards the sampling Lovász Local Lemma
Venue: arXiv cs.DS 2020
Year: 2020

Let $Φ= (V, \mathcal{C})$ be a constraint satisfaction problem on variables $v_1,\dots, v_n$ such that each constraint depends on at most $k$ variables and such that each variable assumes values in an alphabet of size at most $[q]$. Suppose that each constraint shares variables with at most $Δ$ constraints and that each constraint is violated with probability at most $p$ (under the product measure on its variables). We show that for $k, q = O(1)$, there is a deterministic, polynomial time algorithm to approximately count the number of satisfying assignments and a randomized, polynomial time algorithm to sample from approximately the uniform distribution on satisfying assignments, provided that \[C\cdot q^{3}\cdot k \cdot p \cdot Δ^{7} < 1, \quad \text{where }C \text{ is an absolute constant.}\] Previously, a result of this form was known essentially only in the special case when each constraint is violated by exactly one assignment to its variables. For the special case of $k$-CNF formulas, the term $Δ^{7}$ improves the previously best known $Δ^{60}$ for deterministic algorithms [Moitra, J.ACM, 2019] and $Δ^{13}$ for randomized algorithms [Feng et al., arXiv, 2020]. For the special case of properly $q$-coloring $k$-uniform hypergraphs, the term $Δ^{7}$ improves the previously best known $Δ^{14}$ for deterministic algorithms [Guo et al., SICOMP, 2019] and $Δ^{9}$ for randomized algorithms [Feng et al., arXiv, 2020].
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Graph neural networks have become very popular for machine learning on molecules due to the expressive power of their learnt representations. However, molecular machine learning is a classically low-data regime and it isn't clear that graph neural networks can avoid overfitting in low-resource settings. In contrast, fingerprint methods are the traditional standard for low-data environments due to their reduced number of parameters and manually engineered features. In this work, we investigate whether graph neural networks are competitive in small data settings compared to the parametrically 'cheaper' alternative of fingerprint methods. When we find that they are not, we explore pretraining and the meta-learning method MAML (and variants FO-MAML and ANIL) for improving graph neural network performance by transfer learning from related tasks. We find that MAML and FO-MAML do enable the graph neural network to outperform models based on fingerprints, providing a path to using graph neural networks even in settings with severely restricted data availability. In contrast to previous work, we find ANIL performs worse that other meta-learning approaches in this molecule setting. Our results suggest two reasons: molecular machine learning tasks may require significant task-specific adaptation, and distribution shifts in test tasks relative to train tasks may contribute to worse ANIL performance.
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification
Venue: arXiv cs.LG 2020
Year: 2020

One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was developed for the OCC task. This paper introduces a novel extension of this classifier by embedding minimum variance information within its architecture and is referred to as VAAKELM. The minimum variance embedding forces the network output weights to focus in regions of low variance and reduces the intra-class variance. This leads to a better separation of target samples and outliers, resulting in an improvement in the generalization performance of the classifier. The proposed classifier follows a reconstruction-based approach to OCC and minimizes the reconstruction error by using the kernel extreme learning machine as the base classifier. It uses the deviation in reconstruction error to identify the outliers. We perform experiments on 15 small-size and 10 medium-size one-class benchmark datasets to demonstrate the efficiency of the proposed classifier. We compare the results with 13 existing one-class classifiers by considering the mean F1 score as the comparison metric. The experimental results show that VAAKELM consistently performs better than the existing classifiers, making it a viable alternative for the OCC task.
Revisiting graph neural networks and distance encoding from a practical view
Venue: arXiv cs.SI 2020
Year: 2020

Graph neural networks (GNNs) are widely used in the applications based on graph structured data, such as node classification and link prediction. However, GNNs are often used as a black-box tool and rarely get in-depth investigated regarding whether they fit certain applications that may have various properties. A recently proposed technique distance encoding (DE) (Li et al. 2020) magically makes GNNs work well in many applications, including node classification and link prediction. The theory provided in (Li et al. 2020) supports DE by proving that DE improves the representation power of GNNs. However, it is not obvious how the theory assists the applications accordingly. Here, we revisit GNNs and DE from a more practical point of view. We want to explain how DE makes GNNs fit for node classification and link prediction. Specifically, for link prediction, DE can be viewed as a way to establish correlations between a pair of node representations. For node classification, the problem becomes more complicated as different classification tasks may hold node labels that indicate different physical meanings. We focus on the most widely-considered node classification scenarios and categorize the node labels into two types, community type and structure type, and then analyze different mechanisms that GNNs adopt to predict these two types of labels. We also run extensive experiments to compare eight different configurations of GNNs paired with DE to predict node labels over eight real-world graphs. The results demonstrate the uniform effectiveness of DE to predict structure-type labels. Lastly, we reach three pieces of conclusions on how to use GNNs and DE properly in tasks of node classification.
Classification supporting COVID-19 diagnostics based on patient survey data
Venue: arXiv cs.LG 2020
Year: 2020

Distinguishing COVID-19 from other flu-like illnesses can be difficult due to ambiguous symptoms and still an initial experience of doctors. Whereas, it is crucial to filter out those sick patients who do not need to be tested for SARS-CoV-2 infection, especially in the event of the overwhelming increase in disease. As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated. Each of the methods was tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models was presented. The explanation enables the users to understand what was the basis of the decision made by the model. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set consisting of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
Multi-Stage CNN-Based Monocular 3D Vehicle Localization and Orientation Estimation
Venue: arXiv cs.CV 2020
Year: 2020

This paper aims to design a 3D object detection model from 2D images taken by monocular cameras by combining the estimated bird's-eye view elevation map and the deep representation of object features. The proposed model has a pre-trained ResNet-50 network as its backend network and three more branches. The model first builds a bird's-eye view elevation map to estimate the depth of the object in the scene and by using that estimates the object's 3D bounding boxes. We have trained and evaluate it on two major datasets: a syntactic dataset and the KIITI dataset.
Performance Evaluation and Diversity Analysis of RIS-Assisted Communications Over Generalized Fading Channels in the Presence of Phase Noise
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we develop a comprehensive theoretical framework for analyzing the performance of reconfigurable intelligent surfaces (RISs)-assisted communication systems over generalized fading channels and in the presence of phase noise. To this end, we propose the Fox's H model as a unified fading distribution for a large number of widely used generalized fading channels. In particular, we derive a unified analytical framework for computing the outage probability and for estimating the achievable diversity order of RIS-aided systems in the presence of phase shifts that either are optimally configured or are impaired by phase noise. The resulting expressions are general, as they hold for an arbitrary number of reflecting elements, and various channel fading and phase noise distributions. As far as the diversity order is concerned, notably, we introduce an asymptotic analytical framework for determining the diversity order in the absence of phase noise, as well as sufficient conditions based on upper bounds and lower bounds for ensuring that RIS-assisted systems achieve the full diversity order in the presence of phase noise. More specifically, if the absolute difference between pairs of phase errors is less than $π/2$, RIS-assisted communications achieve the full diversity order over independent fading channels, even in the presence of phase noise. The theoretical frameworks and findings are validated with the aid of Monte Carlo simulations.
A Pattern-mining Driven Study on Differences of Newspapers in Expressing Temporal Information
Venue: arXiv cs.CL 2020
Year: 2020

This paper studies the differences between different types of newspapers in expressing temporal information, which is a topic that has not received much attention. Techniques from the fields of temporal processing and pattern mining are employed to investigate this topic. First, a corpus annotated with temporal information is created by the author. Then, sequences of temporal information tags mixed with part-of-speech tags are extracted from the corpus. The TKS algorithm is used to mine skip-gram patterns from the sequences. With these patterns, the signatures of the four newspapers are obtained. In order to make the signatures uniquely characterize the newspapers, we revise the signatures by removing reference patterns. Through examining the number of patterns in the signatures and revised signatures, the proportion of patterns containing temporal information tags and the specific patterns containing temporal information tags, it is found that newspapers differ in ways of expressing temporal information.
Multiscale Modeling of Elasto-Plasticity in Heterogeneous Geomaterials Based on Continuum Micromechanics
Venue: arXiv cs.CE 2020
Year: 2020

In this paper, we investigate some micromechanical aspects of elasto-plasticity in heterogeneous geomaterials. The aim is to upscale the elasto-plastic behavior for a representative volume of the material which is indeed a very challenging task due to the irreversible deformations involved. Considering the plastic strains as eigen-strains allows us to employ the powerful tools offered by Continuum Micromechanics which are mainly developed for upscaling of eigen-stressed elastic media. The validity of such eigen-strain based formulation of multiscale elasto-plasticity is herein examined by comparing its predictions against Finite Element (FE) simulations.
Towards Mass Adoption of Contact Tracing Apps -- Learning from Users' Preferences to Improve App Design
Venue: arXiv cs.CR 2020
Year: 2020

Contact tracing apps have become one of the main approaches to control and slow down the spread of COVID-19 and ease up lockdown measures. While these apps can be very effective in stopping the transmission chain and saving lives, their adoption remains under the expected critical mass. The public debate about contact tracing apps emphasizes general privacy reservations and is conducted at an expert level, but lacks the user perspective related to actual designs. To address this gap, we explore user preferences for contact tracing apps using market research techniques, and specifically conjoint analysis. Our main contributions are empirical insights into individual and group preferences, as well as insights for prescriptive design. While our results confirm the privacy-preserving design of most European contact tracing apps, they also provide a more nuanced understanding of acceptable features. Based on market simulation and variation analysis, we conclude that adding goal-congruent features will play an important role in fostering mass adoption.
Hindsight Network Credit Assignment
Venue: arXiv cs.LG 2020
Year: 2020

We present Hindsight Network Credit Assignment (HNCA), a novel learning method for stochastic neural networks, which works by assigning credit to each neuron's stochastic output based on how it influences the output of its immediate children in the network. We prove that HNCA provides unbiased gradient estimates while reducing variance compared to the REINFORCE estimator. We also experimentally demonstrate the advantage of HNCA over REINFORCE in a contextual bandit version of MNIST. The computational complexity of HNCA is similar to that of backpropagation. We believe that HNCA can help stimulate new ways of thinking about credit assignment in stochastic compute graphs.