OpenRaiser/Intern-Atlas huggingface dataset:
Variational Shape Approximation of Point Set Surfaces
Venue: arXiv cs.GR 2020
Year: 2020

In this work, we present a translation of the complete pipeline for variational shape approximation (VSA) to the setting of point sets. First, we describe an explicit example for the theoretically known non-convergence of the currently available VSA approaches. The example motivates us to introduce an alternate version of VSA based on a switch operation for which we prove convergence. Second, we discuss how two operations - split and merge - can be included in a fully automatic pipeline that is in turn independent of the placement and number of initial seeds. Third and finally, we present two approaches how to obtain a simplified mesh from the output of the VSA procedure. This simplification is either based on simple plane intersection or based on a variational optimization problem. Several qualitative and quantitative results prove the relevance of our approach.
Adoption of the open access business model in scientific journal publishing: A cross-disciplinary study
Venue: arXiv cs.DL 2020
Year: 2020

Scientific journal publishers have over the past twenty-five years rapidly converted to predominantly electronic dissemination, but the reader-pays business model continues to dominate the market. Open Access (OA) publishing, where the articles are freely readable on the net, has slowly increased its market share to near 20%, but has failed to fulfill the visions of rapid proliferation predicted by many early proponents. The growth of OA has also been very uneven across fields of science. We report market shares of open access in eighteen Scopus-indexed disciplines ranging from 27% (agriculture) to 7% (business). The differences become far more pronounced for journals published in the four countries, which dominate commercial scholarly publishing (US, UK, Germany and the Netherlands). We present contrasting developments within six academic disciplines. Availability of funding to pay publication charges, pressure from research funding agencies, and the diversity of discipline-specific research communication cultures arise as potential explanations for the observed differences.
SEPAR: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees
Venue: arXiv cs.DB 2020
Year: 2020

Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the "gig" economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms. Platforms within multi-platform crowdworking systems, therefore, need to collaborate to enforce cross-platform regulations. While collaborating to enforce global regulations requires the transparent sharing of information about tasks and their participants, the privacy of all participants needs to be preserved. In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. We then present SEPAR, a multi-platform crowdworking system that enforces a large sub-space of practical global regulations on a set of distributed independent platforms in a privacy-preserving manner. SEPAR, enforces privacy using lightweight and anonymous tokens, while transparency is achieved using fault-tolerant blockchains shared across multiple platforms. The privacy guarantees of SEPAR against covert adversaries are formalized and thoroughly demonstrated, while the experiments reveal the efficiency of SEPAR in terms of performance and scalability.
On Secure Coded Caching via Combinatorial Method
Venue: arXiv cs.IT 2020
Year: 2020

Coded caching is an efficient way to reduce network traffic congestion during peak hours by storing some content at the user's local cache memory without knowledge of later demands. The goal of coded caching design is to minimize the transmission rate and the subpacketization. In practice the demand for each user is sensitive since one can get the other users' preferences when it gets the other users' demands. The first coded caching scheme with private demands was proposed by Wan et al. However the transmission rate and the subpacketization of their scheme increase with the file number stored in the library. In this paper we consider the following secure coded caching: prevent the wiretappers from obtaining any information about the files in the server and protect the demands from all the users in the delivery phase. We firstly introduce a combinatorial structure called secure placement delivery array (SPDA in short) to realize a coded caching scheme for our security setting. Then we obtain three classes of secure schemes by constructing SPDAs, where one of them is optimal. It is worth noting that the transmission rates and the subpacketizations of our schemes are independent to the file number. Furthermore, comparing with the previously known schemes with the same security setting, our schemes have significantly advantages on the subpacketizations and for some parameters have the advantage on the transmission rates.
Locally testable codes via high-dimensional expanders
Venue: arXiv cs.CC 2020
Year: 2020

Locally testable codes (LTC) are error-correcting codes that have a local tester which can distinguish valid codewords from words that are "far" from all codewords by probing a given word only at a very few (sublinear, typically constant) number of locations. Such codes form the combinatorial backbone of PCPs. A major open problem is whether there exist LTCs with positive rate, constant relative distance and testable with a constant number of queries. In this paper, we present a new approach towards constructing such LTCs using the machinery of high-dimensional expanders. To this end, we consider the Tanner representation of a code, which is specified by a graph and a base code. Informally, our result states that if this graph is part of a high-dimensional expander then the local testability of the code follows from the local testability of the base code. This work unifies and generalizes the known results on testability of the Hadamard, Reed-Muller and lifted codes on the Subspace Complex, all of which are proved via local self correction. However, unlike previous results, constant rounds of self correction do not suffice as the diameter of the underlying test graph can be logarithmically large in a high-dimensional expander and not constant as in all known earlier results. We overcome this technical hurdle by performing iterative self correction with logarithmically many rounds and tightly controlling the error in each iteration using properties of the high-dimensional expander. Given this result, the missing ingredient towards constructing a constant-query LTC with positive rate and constant relative distance is an instantiation of a base code that interacts well with a constant-degree high-dimensional expander.
Digital Sand: The Becoming of Digital Representations
Venue: arXiv cs.CY 2020
Year: 2020

The versatility of digital technologies relies on a capacity to represent and subsequently manipulate algorithmically selected physical processes, objects or qualities in a domain. Organizationally real digital representations are those that, beyond the mere capacity to, actually get woven into everyday work practices. Empirically, we draw on a four-year case study of offshore oil and gas production. Our case provides a vivid illustration of Internet of Things (IoT) based visualizations and data driven predictions characteristic for efforts of digitally transforming industrial process and manufacturing enterprises. We contribute by identifying and discussing three mechanisms through which digital representations become organizationally real: (i) noise reduction (the strategies and heuristics to filter out signal from noise), (ii) material tethering (grounding the digital representations to a corresponding physical measurement) and (iii) triangulating (in the absence of a direct correspondence, corroborating digital representations relative to other representations).
Off-Policy Adversarial Inverse Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Adversarial Imitation Learning (AIL) is a class of algorithms in Reinforcement learning (RL), which tries to imitate an expert without taking any reward from the environment and does not provide expert behavior directly to the policy training. Rather, an agent learns a policy distribution that minimizes the difference from expert behavior in an adversarial setting. Adversarial Inverse Reinforcement Learning (AIRL) leverages the idea of AIL, integrates a reward function approximation along with learning the policy, and shows the utility of IRL in the transfer learning setting. But the reward function approximator that enables transfer learning does not perform well in imitation tasks. We propose an Off-Policy Adversarial Inverse Reinforcement Learning (Off-policy-AIRL) algorithm which is sample efficient as well as gives good imitation performance compared to the state-of-the-art AIL algorithm in the continuous control tasks. For the same reward function approximator, we show the utility of learning our algorithm over AIL by using the learned reward function to retrain the policy over a task under significant variation where expert demonstrations are absent.
A Spectrum Aware Mobility Pattern Based Routing Protocol for CR-VANETs
Venue: arXiv cs.NI 2020
Year: 2020

Cognitive radio technology offers an important function in the efficient utilization of the radio spectrum. Besides, it is expected that CR-enabled vehicular ad-hoc networks (CR-VANETs) enrich the communication performance of the existing vehicular networks (VANETs). However, to ensure efficient performance in a multi-hop communication, the routing protocol in CR-VANETs needs to consider the autonomous mobility of the vehicles as well as the stochastic availability of the channels. Hence, this paper introduces a spectrum-aware mobility pattern based reactive routing protocol for CR-VANET. The proposed protocol accommodates the dynamic behavior and selects a stable transmission path from a source node to the destination. Therefore, the proposed protocol is outlined as a weighted graph problem where the weight for an edge is measured based on a parameter termed as NHDF (Next-hop Determination Factor). The NHDF implicitly considers mobility patterns of the nodes and channel availability to select the optimum path for communication. Therefore, in the proposed routing protocol, the mobility pattern of a node is defined from the viewpoint of distance, speed, direction, and node's reliability. Furthermore, the spectrum awareness in the proposed protocol is measured over the number of shared common channels and the channel quality. It is anticipated that the proposed protocol shows efficient routing performance by selecting stable and secured paths from source to destination. Simulation is carried out to assess the performance of the protocol where it is witnessed that the proposed routing protocol outperforms existing ones.
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
Venue: arXiv cs.CL 2020
Year: 2020

We use large-scale corpora in six different gendered languages, along with tools from NLP and information theory, to test whether there is a relationship between the grammatical genders of inanimate nouns and the adjectives used to describe those nouns. For all six languages, we find that there is a statistically significant relationship. We also find that there are statistically significant relationships between the grammatical genders of inanimate nouns and the verbs that take those nouns as direct objects, as indirect objects, and as subjects. We defer a deeper investigation of these relationships for future work.
Obtaining Basic Algebra Formulas with Genetic Programming and Functional Rewriting
Venue: arXiv cs.NE 2020
Year: 2020

In this paper, we develop a set of genetic programming operators and an initialization population process based on concepts of functional programming rewriting for boosting inductive genetic programming. Such genetic operators are used within a hybrid adaptive evolutionary algorithm that evolves operator rates at the same time it evolves the solution. Solutions are represented using recursive functions where genome is encoded as an ordered list of trees and phenotype is written in a simple functional programming language that uses rewriting as operational semantic (computational model). The fitness is the number of examples successfully deduced over the cardinal of the set of examples. Parents are selected following a tournament selection mechanism and the next population is obtained following a steady-state strategy. The evolutionary process can use some previous functions (programs) induced as background knowledge. We compare the performance of our technique in a set of hard problems (for classical genetic programming). In particular, we take as test-bed the problem of obtaining equivalent algebraic expressions of some notable products (such as square of a binomial, and cube of a binomial), and the recursive formulas of sum of the first n and squares of the first n natural numbers.
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
Venue: arXiv cs.CV 2020
Year: 2020

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results. From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-framerate (60 fps) video sequences by estimating temporally intermediate frames. We employ the REDS VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. The competition had 62 registered participants, and a total of 8 teams competed in the final testing phase. The challenge winning methods achieve the state-of-the-art in video temporal superresolution.
Distributional Discrepancy: A Metric for Unconditional Text Generation
Venue: arXiv cs.CL 2020
Year: 2020

The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However, when different metrics are used for comparing the methods of unconditional text generation, contradictory conclusions are drawn. The difficulty is that both the diversity and quality of the sample should be considered simultaneously when the models are evaluated. To solve this problem, a novel metric of distributional discrepancy (DD) is designed to evaluate generators based on the discrepancy between the generated and real training sentences. However, it cannot compute the DD directly because the distribution of real sentences is unavailable. Thus, we propose a method for estimating the DD by training a neural-network-based text classifier. For comparison, three existing metrics, bi-lingual evaluation understudy (BLEU) versus self-BLEU, language model score versus reverse language model score, and Fréchet embedding distance, along with the proposed DD, are used to evaluate two popular generative models of long short-term memory and generative pretrained transformer 2 on both syntactic and real data. Experimental results show that DD is significantly better than the three existing metrics for ranking these generative models.
From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?
Venue: arXiv cs.CL 2020
Year: 2020

It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs).Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs. We then aim to offer a climax, suggesting that incorporating symbolic ideas proposed in SPMRL terms into nowadays neural architectures has the potential to push NLP for MRLs to a new level. We sketch strategies for designing Neural Models for MRLs (NMRL), and showcase preliminary support for these strategies via investigating the task of multi-tagging in Hebrew, a morphologically-rich, high-fusion, language
On Systematically Building a Controlled Natural Language for Functional Requirements
Venue: arXiv cs.SE 2020
Year: 2020

[Context] Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. [Objective] In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. [Method] We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. [Results] Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is general and applicable to information systems beyond the financial domain, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. [Conclusion] Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.
On the Benefits of Models with Perceptually-Aligned Gradients
Venue: arXiv cs.CV 2020
Year: 2020

Adversarial robust models have been shown to learn more robust and interpretable features than standard trained models. As shown in [\cite{tsipras2018robustness}], such robust models inherit useful interpretable properties where the gradient aligns perceptually well with images, and adding a large targeted adversarial perturbation leads to an image resembling the target class. We perform experiments to show that interpretable and perceptually aligned gradients are present even in models that do not show high robustness to adversarial attacks. Specifically, we perform adversarial training with attack for different max-perturbation bound. Adversarial training with low max-perturbation bound results in models that have interpretable features with only slight drop in performance over clean samples. In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
An Integrated Framework for Sensing Radio Frequency Spectrum Attacks on Medical Delivery Drones
Venue: arXiv cs.CY 2020
Year: 2020

Drone susceptibility to jamming or spoofing attacks of GPS, RF, Wi-Fi, and operator signals presents a danger to future medical delivery systems. A detection framework capable of sensing attacks on drones could provide the capability for active responses. The identification of interference attacks has applicability in medical delivery, disaster zone relief, and FAA enforcement against illegal jamming activities. A gap exists in the literature for solo or swarm-based drones to identify radio frequency spectrum attacks. Any non-delivery specific function, such as attack sensing, added to a drone involves a weight increase and additional complexity; therefore, the value must exceed the disadvantages. Medical delivery, high-value cargo, and disaster zone applications could present a value proposition which overcomes the additional costs. The paper examines types of attacks against drones and describes a framework for designing an attack detection system with active response capabilities for improving the reliability of delivery and other medical applications.
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
Venue: arXiv cs.LG 2020
Year: 2020

It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model. Users currently rely on empirical trial-and-error to obtain their own set of battle-tested guidelines to inform their modeling decisions. In this study, we aim to demystify this dark art by understanding how people iterate on ML workflows in practice. We analyze over 475k user-generated workflows on OpenML, an open-source platform for tracking and sharing ML workflows. We find that users often adopt a manual, automated, or mixed approach when iterating on their workflows. We observe that manual approaches result in fewer wasted iterations compared to automated approaches. Yet, automated approaches often involve more preprocessing and hyperparameter options explored, resulting in higher performance overall--suggesting potential benefits for a human-in-the-loop ML system that appropriately recommends a clever combination of the two strategies.
Ten Questions in Lifelog Mining and Information Recall
Venue: arXiv cs.IR 2020
Year: 2020

With the advance of science and technology, people are used to record their daily life events via writing blogs, uploading social media posts, taking photos, or filming videos. Such rich repository personal information is useful for supporting human living assistance. The main challenge is how to store and manage personal knowledge from various sources. In this position paper, we propose a research agenda on mining personal knowledge from various sources of lifelogs, personal knowledge base construction, and information recall for assisting people to recall their experiences.
Intelligent Reflecting Surface Enhanced Millimeter-Wave NOMA Systems
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, a downlink intelligent reflecting surface (IRS) enhanced millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) system is considered. A joint optimization problem over active beamforming, passive beamforming and power allocation is formulated. Due to the highly coupled variables, the formulated optimization problem is non-convex. To solve this problem, an alternative optimization and successive convex approximation based iterative algorithm is proposed. Numerical results illustrate that: 1) the proposed scheme offers significant sum-rate gains, which confirms the effectiveness of introducing IRS for mmWave-NOMA systems; 2) the proposed algorithm with discrete phase shifts can achieve close performance to that of continuous phase shifts.