OpenRaiser/Intern-Atlas huggingface dataset:
An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers
Venue: arXiv cs.CV 2020
Year: 2020

One important and particularly challenging step in the optical character recognition (OCR) of historical documents with complex layouts, such as newspapers, is the separation of text from non-text content (e.g. page borders or illustrations). This step is commonly referred to as page segmentation. While various rule-based algorithms have been proposed, the applicability of Deep Neural Networks (DNNs) for this task recently has gained a lot of attention. In this paper, we perform a systematic evaluation of 11 different published DNN backbone architectures and 9 different tiling and scaling configurations for separating text, tables or table column lines. We also show the influence of the number of labels and the number of training pages on the segmentation quality, which we measure using the Matthews Correlation Coefficient. Our results show that (depending on the task) Inception-ResNet-v2 and EfficientNet backbones work best, vertical tiling is generally preferable to other tiling approaches, and training data that comprises 30 to 40 pages will be sufficient most of the time.
Usable, Acceptable, Appropriable: Towards Practicable Privacy
Venue: arXiv cs.CY 2020
Year: 2020

A majority of the work on digital privacy and security has focused on users from developed countries who account for only around 20\% of the global population. Moreover, the privacy needs for population that is already marginalized and vulnerable differ from users who have privilege to access a greater social support system. We reflect on our experiences of introducing computers and the Internet to a group of sex-trafficking survivors in Nepal and highlight a few socio-political factors that have influenced the design space around digital privacy. These factors include the population's limited digital and text literacy skills and the fear of stigma against trafficked persons widely prevalent in Nepali society. We underscore the need to widen our perspective by focusing on practicable privacy, that is, privacy practices that are (1) usable, (2) acceptable, and (3) appropriable.
Trakhtenbrot's Theorem in Coq, A Constructive Approach to Finite Model Theory
Venue: arXiv cs.LO 2020
Year: 2020

We study finite first-order satisfiability (FSAT) in the constructive setting of dependent type theory. Employing synthetic accounts of enumerability and decidability, we give a full classification of FSAT depending on the first-order signature of non-logical symbols. On the one hand, our development focuses on Trakhtenbrot's theorem, stating that FSAT is undecidable as soon as the signature contains an at least binary relation symbol. Our proof proceeds by a many-one reduction chain starting from the Post correspondence problem. On the other hand, we establish the decidability of FSAT for monadic first-order logic, i.e. where the signature only contains at most unary function and relation symbols, as well as the enumerability of FSAT for arbitrary enumerable signatures. All our results are mechanised in the framework of a growing Coq library of synthetic undecidability proofs.
Neuromorphic Eye-in-Hand Visual Servoing
Venue: arXiv cs.RO 2020
Year: 2020

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based camera sets a limitation on continuous visual feedback due to their low sampling rate and redundant data in real-time image processing, especially in the case of high-speed tasks. Event cameras give human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution ($1μs$) with low latency and wide dynamic range. In this paper, we present a visual servoing method using an event camera and a switching control strategy to explore, reach and grasp to achieve a manipulation task. We devise three surface layers of active events to directly process stream of events from relative motion. A purely event based approach is adopted to extract corner features, localize them robustly using heat maps and generate virtual features for tracking and alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in spatio-temporal space. The controller switches its strategy based on the sequence of operation to establish a stable grasp. The event based visual servoing (EVBS) method is validated experimentally using a commercial robot manipulator in an eye-in-hand configuration. Experiments prove the effectiveness of the EBVS method to track and grasp objects of different shapes without the need for re-tuning.
Poisoning Attacks on Algorithmic Fairness
Venue: arXiv cs.LG 2020
Year: 2020

Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data. While the effects on model accuracy of such poisoning attacks have been widely studied, their potential effects on other model performance metrics remain to be evaluated. In this work, we introduce an optimization framework for poisoning attacks against algorithmic fairness, and develop a gradient-based poisoning attack aimed at introducing classification disparities among different groups in the data. We empirically show that our attack is effective not only in the white-box setting, in which the attacker has full access to the target model, but also in a more challenging black-box scenario in which the attacks are optimized against a substitute model and then transferred to the target model. We believe that our findings pave the way towards the definition of an entirely novel set of adversarial attacks targeting algorithmic fairness in different scenarios, and that investigating such vulnerabilities will help design more robust algorithms and countermeasures in the future.
Combinatorial 3D Shape Generation via Sequential Assembly
Venue: arXiv cs.CV 2020
Year: 2020

Sequential assembly with geometric primitives has drawn attention in robotics and 3D vision since it yields a practical blueprint to construct a target shape. However, due to its combinatorial property, a greedy method falls short of generating a sequence of volumetric primitives. To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework. The proposed framework reflects an important aspect of human generation processes in real life -- we often create a 3D shape by sequentially assembling unit primitives with geometric constraints. To find the desired combination regarding combination evaluations, we adopt Bayesian optimization, which is able to exploit and explore efficiently the feasible regions constrained by the current primitive placements. An evaluation function conveys global structure guidance for an assembly process and stability in terms of gravity and external forces simultaneously. Experimental results demonstrate that our method successfully generates combinatorial 3D shapes and simulates more realistic generation processes. We also introduce a new dataset for combinatorial 3D shape generation. All the codes are available at \url{https://github.com/POSTECH-CVLab/Combinatorial-3D-Shape-Generation}.
Fast exact computation of the $k$ most abundant isotope peaks with layer-ordered heaps
Venue: arXiv cs.CE 2020
Year: 2020

The theoretical computation of isotopic distribution of compounds is crucial in many important applications of mass spectrometry, especially as machine precision grows. A considerable amount of good tools have been created in the last decade for doing so. In this paper we present a novel algorithm for calculating the top $k$ peaks of a given compound. The algorithm takes advantage of layer-ordered heaps used in an optimal method of selection on $X+Y$ and is able to efficiently calculate the top $k$ peaks on very large molecules. Among its peers, this algorithm shows a significant speedup on molecules whose elements have many isotopes. The algorithm obtains a speedup of more than 31x when compared to $\textsc{IsoSpec}$ on \ch{Au2Ca10Ga10Pd76} when computing 47409787 peaks, which covers 0.999 of the total abundance.
Sustainable Recipes. A Food Recipe Sourcing and Recommendation System to Minimize Food Miles
Venue: arXiv cs.SI 2020
Year: 2020

Sustainable Recipes is a tool that (1) connects food recipes ingredient lists with the closest organic providers to minimize the distance that food travels from farm to food preparation site and (2) recommends recipes given a GPS coordinate to minimize food miles. Sustainable Recipes provides consumers, entrepreneurs, cooking enthusiasts, and restauranteurs in the United States and elsewhere with an easy to use interface to help them (1) connect with organic ingredient producers to source ingredients to produce food recipes minimizing food miles and (2) recommend recipes using locally grown food. The main academic contribution of Sustainable Recipes is to bridge the gap between two streams of literature in data science of food recipes: studies of food recipes and studies of food supply chains. The outcomes of the interphase are (1) a map visualization that highlights the location of the producers that can supply the ingredients for a food recipe along with a ticket consisting of their contact addresses and the food miles used to produce a recipe and (2) a list of recipes that minimize food miles for a given GPS coordinate in which the recipe is going to be produced.
ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking
Venue: arXiv cs.CV 2020
Year: 2020

One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets, typically done via a scoring function. Despite the great advances in MOT, designing a reliable scoring function remains a challenge. In this paper, we introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion. One key property of our model is its ability to generate multiple likely futures of a tracklet given partial observations. This allows us to not only score tracklets but also effectively maintain existing tracklets when the detector fails to detect some objects even for a long time, e.g., due to occlusion, by sampling trajectories so as to inpaint the gaps caused by misdetection. Our experiments demonstrate the effectiveness of our approach to scoring and inpainting tracklets on several MOT benchmark datasets. We additionally show the generality of our generative model by using it to produce future representations in the challenging task of human motion prediction.
Pulsar: Efficient Sphere-based Neural Rendering
Venue: arXiv cs.GR 2020
Year: 2020

We propose Pulsar, an efficient sphere-based differentiable renderer that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) an efficient differentiable rendering engine, and 3) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to general neural rendering.
Results from a replicated experiment on the affective reactions of novice developers when applying test-driven development
Venue: arXiv cs.SE 2020
Year: 2020

Test-driven Development (TDD) is an incremental approach to software development. Despite it is claimed to improve both quality of software and developers' productivity, the research on the claimed effects of TDD has so far shown inconclusive results. Some researchers have ascribed these inconclusive results to the negative affective states that TDD would provoke. A previous (baseline) experiment has, therefore, studied the affective reactions of (novice) developers---i.e., 29 third-year undergraduates in Computer Science (CS)---when practicing TDD to implement software. To validate the results of the baseline experiment, we conducted a replicated experiment that studies the affective reactions of novice developers when applying TDD to develop software. Developers in the treatment group carried out a development task using TDD, while those in the control group used a non-TDD approach. To measure the affective reactions of developers, we used the Self-Assessment Manikin instrument complemented with a liking dimension. The most important differences between the baseline and replicated experiments are: (i) the kind of novice developers involved in the experiments---third-year vs. second-year undergraduates in CS from two different universities; and (ii) their number---29 vs. 59. The results of the replicated experiment do not show any difference in the affective reactions of novice developers. Instead, the results of the baseline experiment suggest that developers seem to like TDD less as compared to a non-TDD approach and that developers following TDD seem to like implementing code less than the other developers, while testing code seems to make them less happy.
DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance
Venue: arXiv cs.CV 2020
Year: 2020

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1st generation such as UADFV and FaceForensics++ up to the latest databases of the 2nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1st and 2nd DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2nd generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors.
Approximating Independent Set and Dominating Set on VPG graphs
Venue: arXiv cs.DS 2020
Year: 2020

We consider Independent Set and Dominating Set restricted to VPG graphs (or, equivalently, string graphs). We show that they both remain $\mathsf{NP}$-hard on $B_0$-VPG graphs admitting a representation such that each grid-edge belongs to at most one path and each horizontal path has length at most two. On the other hand, combining the well-known Baker's shifting technique with bounded mim-width arguments, we provide simple PTASes on VPG graphs admitting a representation such that each grid-edge belongs to at most $t$ paths and the length of the horizontal part of each path is at most $c$, for some $c \geq 1$.
Short Paper: Design and Evaluation of Privacy-preserved Supply Chain System based on Public Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

Securing the traceability of products in the supply chain is an urgent issue. Recently, supply chain systems that use public blockchain (PBC) have been proposed. In these systems, PBC is used as a common database shared between supply chain parties to secure the integrity and reliability of distribution information such as ownership transfer records. Thus, these systems secure a high level of traceability in the supply chain. However, the distribution information, which can be private information, is made public since the information recorded in PBC can be read by anyone. In this paper, we propose a method for preserving privacy while securing traceability in a supply chain system using PBC. The proposed method preserves privacy by concealing the distribution information via encryption. In addition, the proposed method ensures distribution among legitimate supply chain parties while concealing their blockchain address by using a zero-knowledge proof to prove their authenticity. We implement the proposed method on Ethereum smart contracts and evaluate cost performance based on transaction fees. The results show that the fee per party is at most 2.6 USD.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising approach for simplifying registration problems, and has been gradually applied to medical image registration. However, most existing deep learning registrations do not consider the problem that when the receptive field cannot cover the corresponding features in the moving image and the fixed image, it cannot output accurate displacement values. In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution. Multi-resolution and multi-convolution techniques can improve but fail to avoid this problem. In this study, we constructed pyramidal feature sets on moving and fixed images and used the warped moving and fixed features to estimate their "residual" deformation field at each scale, called the Pyramidal Residual Deformation Field Estimation module (PRDFE-Module). The "total" deformation field at each scale was computed by upsampling and weighted summing all the "residual" deformation fields at all its previous scales, which can effectively and accurately transfer the deformation fields from low resolution to high resolution and is used for warping the moving features at each scale. Simulation and real brain data results show that our method improves the accuracy of the registration and the rationality of the deformation field.
Should I tear down this wall? Optimizing social metrics by evaluating novel actions
Venue: arXiv cs.MA 2020
Year: 2020

One of the fundamental challenges of governance is deciding when and how to intervene in multi-agent systems in order to impact group-wide metrics of success. This is particularly challenging when proposed interventions are novel and expensive. For example, one may wish to modify a building's layout to improve the efficiency of its escape route. Evaluating such interventions would generally require access to an elaborate simulator, which must be constructed ad-hoc for each environment, and can be prohibitively costly or inaccurate. Here we examine a simple alternative: Optimize By Observational Extrapolation (OBOE). The idea is to use observed behavioural trajectories, without any interventions, to learn predictive models mapping environment states to individual agent outcomes, and then use these to evaluate and select changes. We evaluate OBOE in socially complex gridworld environments and consider novel physical interventions that our models were not trained on. We show that neural network models trained to predict agent returns on baseline environments are effective at selecting among the interventions. Thus, OBOE can provide guidance for challenging questions like: "which wall should I tear down in order to minimize the Gini index of this group?"
Algorithmic Foundations for the Diffraction Limit
Venue: arXiv cs.DS 2020
Year: 2020

For more than a century and a half it has been widely-believed (but was never rigorously shown) that the physics of diffraction imposes certain fundamental limits on the resolution of an optical system. However our understanding of what exactly can and cannot be resolved has never risen above heuristic arguments which, even worse, appear contradictory. In this work we remedy this gap by studying the diffraction limit as a statistical inverse problem and, based on connections to provable algorithms for learning mixture models, we rigorously prove upper and lower bounds on the statistical and algorithmic complexity needed to resolve closely spaced point sources. In particular we show that there is a phase transition where the sample complexity goes from polynomial to exponential. Surprisingly, we show that this does not occur at the Abbe limit, which has long been presumed to be the true diffraction limit.
Optimising the Fit of Stack Overflow Code Snippets into Existing Code
Venue: arXiv cs.SE 2020
Year: 2020

Software developers often reuse code from online sources such as Stack Overflow within their projects. However, the process of searching for code snippets and integrating them within existing source code can be tedious. In order to improve efficiency and reduce time spent on code reuse, we present an automated code reuse tool for the Eclipse IDE (Integrated Developer Environment), NLP2TestableCode. NLP2TestableCode can not only search for Java code snippets using natural language tasks, but also evaluate code snippets based on a user's existing code, modify snippets to improve fit and correct errors, before presenting the user with the best snippet, all without leaving the editor. NLP2TestableCode also includes functionality to automatically generate customisable test cases and suggest argument and return types, in order to further evaluate code snippets. In evaluation, NLP2TestableCode was capable of finding compilable code snippets for 82.9% of tasks, and testable code snippets for 42.9%.
Continuous Health Interface Event Retrieval
Venue: arXiv cs.HC 2020
Year: 2020

Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources to retrieve complex biological events, such as cardiovascular volume overload. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on relevant biological systems. They are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using an event operator language.