OpenRaiser/Intern-Atlas huggingface dataset:
Accessibility Across Borders
Venue: arXiv cs.HC 2021
Year: 2021

Since prior work has identified that cultural differences influence user design preferences and interaction methods, as well as emphasizing the need to reflect on the appropriateness of popular HCI principles, we believe that it is equally important to apply this inquiry to digital accessibility and how accessibility fits within the design process around the world. Our long-term plan is to build upon work in this area by investigating how digital designers in different parts of the world consider accessibility and whether current accessibility resources (often developed in the west) meet or conflict with their approach to design.
Intersection Patterns in Optimal Binary $(5,3)$ Doubling Subspace Codes
Venue: arXiv cs.IT 2021
Year: 2021

Subspace codes are collections of subspaces of a projective space such that any two subspaces satisfy a pairwise minimum distance criterion. Recent results have shown that it is possible to construct optimal $(5,3)$ subspace codes from pairs of partial spreads in the projective space $\mathrm{PG}(4,q)$ over the finite field $ \mathbb{F}_q $, termed doubling codes. We have utilized a complete classification of maximal partial line spreads in $\mathrm{PG}(4,2)$ in literature to establish the types of the spreads in the doubling code instances obtained from two recent constructions of optimum $(5,3)_q$ codes, restricted to $ \mathbb{F}_2 $. Further we present a new characterization of a subclass of binary doubling codes based on the intersection patterns of key subspaces in the pair of constituent spreads.
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments suggest that discourse context is unlikely the cause for the improvement of about +1 BLEU across four language pairs. Instead, we demonstrate that the improvement comes from three other factors unrelated to discourse: context diversity, length diversity, and (to a lesser extent) position shifting.
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
Venue: arXiv cs.RO 2021
Year: 2021

This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of projection-to-state safety paired with a machine learning framework in an attempt to learn the model uncertainty as it affects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem, which requires precise foot placement while walking dynamically.
How do developers discuss and support new programming languages in technical Q&A site? An empirical study of Go, Swift, and Rust in Stack Overflow
Venue: arXiv cs.SE 2021
Year: 2021

New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced to provide a better opportunity for the developers to make software development robust and easy. At the early stage, a programming language is likely to have resource constraints that encourage the developers to seek help frequently from experienced peers active in QA sites such as Stack Overflow (SO). In this study, we have formally studied the discussions on three popular new languages introduced after the inception of SO (2008) and match those with the relevant activities in GitHub whenever appropriate. For that purpose, we have mined 4,17,82,536 questions and answers from SO and 7,846 issue information along with 6,60,965 repository information from GitHub. Initially, the development of new languages is relatively slow compared to mature languages (e.g., C, C++, Java). The expected outcome of this study is to reveal the difficulties and challenges faced by the developers working with these languages so that appropriate measures can be taken to expedite the generation of relevant resources. We have used the LDA method on SO's questions and answers to identify different topics of new languages. We have extracted several features of the answer pattern of the new languages from SO to study their characteristics. These attributes were used to identify difficult topics. We explored the background of developers who are contributing to these languages. We have created a model by combining Stack Overflow data and issues, repository, user data of GitHub. Finally, we have used that model to identify factors that affect language evolution. We believe that the outcome of our study is likely to help the owner/sponsor of these languages to design better features and documentation. It will also help the software developers or students to prepare themselves to work on these languages in an informed way.
A Fast Partial Video Copy Detection Using KNN and Global Feature Database
Venue: arXiv cs.CV 2021
Year: 2021

We propose a fast partial video copy detection framework in this paper. In this framework all frame features of the reference videos are organized in a KNN searchable database. Instead of scanning all reference videos, the query video segment does a fast KNN search in the global feature database. The returned results are used to generate a short list of candidate videos. A modified temporal network is then used to localize the copy segment in the candidate videos. We evaluate different choice of CNN features on the VCDB dataset. Our benchmark F1 score exceeds the state of the art by a big margin.
When Can Accessibility Help?: An Exploration of Accessibility Feature Recommendation on Mobile Devices
Venue: arXiv cs.HC 2021
Year: 2021

Numerous accessibility features have been developed and included in consumer operating systems to provide people with a variety of disabilities additional ways to access computing devices. Unfortunately, many users, especially older adults who are more likely to experience ability changes, are not aware of these features or do not know which combination to use. In this paper, we first quantify this problem via a survey with 100 participants, demonstrating that very few people are aware of built-in accessibility features on their phones. These observations led us to investigate accessibility recommendation as a way to increase awareness and adoption. We developed four prototype recommenders that span different accessibility categories, which we used to collect insights from 20 older adults. Our work demonstrates the need to increase awareness of existing accessibility features on mobile devices, and shows that automated recommendation could help people find beneficial accessibility features.
Texture for Colors: Natural Representations of Colors Using Variable Bit-Depth Textures
Venue: arXiv cs.CV 2021
Year: 2021

Numerous methods have been proposed to transform color and grayscale images to their single bit-per-pixel binary counterparts. Commonly, the goal is to enhance specific attributes of the original image to make it more amenable for analysis. However, when the resulting binarized image is intended for human viewing, aesthetics must also be considered. Binarization techniques, such as half-toning, stippling, and hatching, have been widely used for modeling the original image's intensity profile. We present an automated method to transform an image to a set of binary textures that represent not only the intensities, but also the colors of the original. The foundation of our method is information preservation: creating a set of textures that allows for the reconstruction of the original image's colors solely from the binarized representation. We present techniques to ensure that the textures created are not visually distracting, preserve the intensity profile of the images, and are natural in that they map sets of colors that are perceptually similar to patterns that are similar. The approach uses deep-neural networks and is entirely self-supervised; no examples of good vs. bad binarizations are required. The system yields aesthetically pleasing binary images when tested on a variety of image sources.
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
Venue: arXiv cs.RO 2021
Year: 2021

Model-based paradigms for decision-making and control are becoming ubiquitous in robotics. They rely on the ability to efficiently learn a model of the system from data. Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems, typically fit to data by minimizing the error between predicted and observed accelerations or next states. In this work, we propose a methodology for fitting SMMs to data by minimizing the discrete Euler-Lagrange residual. To study our methodology, we fit models to joint-angle time-series from undamped and damped double-pendulums, studying the quality of learned models fit to data with and without observation noise. Experiments show that our methodology learns models that are better in accuracy to those of the conventional schemes for fitting SMMs. We identify use cases in which our method is a more appropriate methodology. Source code for reproducing the experiments is available at https://github.com/sisl/delsmm.
Dynamic Enumeration of Similarity Joins
Venue: arXiv cs.DS 2021
Year: 2021

This paper considers enumerating answers to similarity-join queries under dynamic updates: Given two sets of $n$ points $A,B$ in $\mathbb{R}^d$, a metric $ϕ(\cdot)$, and a distance threshold $r > 0$, report all pairs of points $(a, b) \in A \times B$ with $ϕ(a,b) \le r$. Our goal is to store $A,B$ into a dynamic data structure that, whenever asked, can enumerate all result pairs with worst-case delay guarantee, i.e., the time between enumerating two consecutive pairs is bounded. Furthermore, the data structure can be efficiently updated when a point is inserted into or deleted from $A$ or $B$. We propose several efficient data structures for answering similarity-join queries in low dimension. For exact enumeration of similarity join, we present near-linear-size data structures for $\ell_1, \ell_\infty$ metrics with $\log^{O(1)} n$ update time and delay. We show that such a data structure is not feasible for the $\ell_2$ metric for $d \ge 4$. For approximate enumeration of similarity join, where the distance threshold is a soft constraint, we obtain a unified linear-size data structure for $\ell_p$ metric, with $\log^{O(1)} n$ delay and update time. In high dimensions, we present an efficient data structure with worst-case delay-guarantee using locality sensitive hashing (LSH).
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

Deep hamming hashing has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval. Until now, the deep hashing for the image retrieval community has been dominated by convolutional neural network architectures, e.g. \texttt{Resnet}\cite{he2016deep}. In this paper, inspired by the recent advancements of vision transformers, we present \textbf{Transhash}, a pure transformer-based framework for deep hashing learning. Concretely, our framework is composed of two major modules: (1) Based on \textit{Vision Transformer} (ViT), we design a siamese vision transformer backbone for image feature extraction. To learn fine-grained features, we innovate a dual-stream feature learning on top of the transformer to learn discriminative global and local features. (2) Besides, we adopt a Bayesian learning scheme with a dynamically constructed similarity matrix to learn compact binary hash codes. The entire framework is jointly trained in an end-to-end manner.~To the best of our knowledge, this is the first work to tackle deep hashing learning problems without convolutional neural networks (\textit{CNNs}). We perform comprehensive experiments on three widely-studied datasets: \textbf{CIFAR-10}, \textbf{NUSWIDE} and \textbf{IMAGENET}. The experiments have evidenced our superiority against the existing state-of-the-art deep hashing methods. Specifically, we achieve 8.2\%, 2.6\%, 12.7\% performance gains in terms of average \textit{mAP} for different hash bit lengths on three public datasets, respectively.
The Complexity of Symmetry Breaking in Massive Graphs
Venue: arXiv cs.DC 2021
Year: 2021

The goal of this paper is to understand the complexity of symmetry breaking problems, specifically maximal independent set (MIS) and the closely related $β$-ruling set problem, in two computational models suited for large-scale graph processing, namely the $k$-machine model and the graph streaming model. We present a number of results. For MIS in the $k$-machine model, we improve the $\tilde{O}(m/k^2 + Δ/k)$-round upper bound of Klauck et al. (SODA 2015) by presenting an $\tilde{O}(m/k^2)$-round algorithm. We also present an $\tildeΩ(n/k^2)$ round lower bound for MIS, the first lower bound for a symmetry breaking problem in the $k$-machine model. For $β$-ruling sets, we use hierarchical sampling to obtain more efficient algorithms in the $k$-machine model and also in the graph streaming model. More specifically, we obtain a $k$-machine algorithm that runs in $\tilde{O}(βnΔ^{1/β}/k^2)$ rounds and, by using a similar hierarchical sampling technique, we obtain one-pass algorithms for both insertion-only and insertion-deletion streams that use $O(β\cdot n^{1+1/2^{β-1}})$ space. The latter result establishes a clear separation between MIS, which is known to require $Ω(n^2)$ space (Cormode et al., ICALP 2019), and $β$-ruling sets, even for $β= 2$. Finally, we present an even faster 2-ruling set algorithm in the $k$-machine model, one that runs in $\tilde{O}(n/k^{2-ε} + k^{1-ε})$ rounds for any $ε$, $0 \le ε\le 1$.
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. This competition has two sub-tasks: Table Structure Reconstruction (TSR) and Table Content Reconstruction (TCR). We treat both sub-tasks as two individual image-to-sequence recognition problems. We leverage our previously proposed algorithm MASTER \cite{lu2019master}, which is originally proposed for scene text recognition. We optimize the MASTER model from several perspectives: network structure, optimizer, normalization method, pre-trained model, resolution of input image, data augmentation, and model ensemble. Our method achieves 0.7444 Exact Match and 0.8765 Exact Match @95\% on the TSR task, and obtains 0.5586 Exact Match and 0.7386 Exact Match 95\% on the TCR task.
Tree Path Minimum Query Oracle via Boruvka Trees
Venue: arXiv cs.DS 2021
Year: 2021

Tree path minimum query problem is a fundamental problem while processing trees, and is used widely in minimum spanning tree verification and randomized minimum spanning tree algorithms. In this paper, we study the possibility of building an oracle in advance, which is able to answer the queries efficiently. We present an algorithm based on Boruvka trees. Our algorithm is the first to achieve a near-optimal bound on query time, while matching the currently optimal trade-off between construction time and the number of comparisons required at query. Particularly, in order to answer each query within $2k$ comparisons, our algorithm requires $O(n \log λ_k(n))$ time and space to construct the oracle, and the oracle can answer queries in $O(k + \log λ_k(n))$ time. Here $λ_k(n)$ is the inverse of the Ackermann function along the $k$-th column. This algorithm not only is simpler than the previous ones, but also gives a completely different method of solving this problem.
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
Venue: arXiv cs.LG 2021
Year: 2021

This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical learning require computation of capacity measures, such as VC dimension, to provably bound this error. It is however unclear how to extend these measures to DNNs and therefore the existing analyses are applicable to simple neural networks, which are not used in practice, e.g., linear or shallow ones or otherwise multi-layer perceptrons. Moreover, many theoretical error bounds are not empirically verifiable. We derive estimates of the generalization error that hold for deep networks and do not rely on unattainable capacity measures. The enabling technique in our approach hinges on two major assumptions: i) the network achieves zero training error, ii) the probability of making an error on a test point is proportional to the distance between this point and its nearest training point in the feature space and at a certain maximal distance (that we call radius) it saturates. Based on these assumptions we estimate the generalization error of DNNs. The obtained estimate scales as O(1/(δN^{1/d})), where N is the size of the training data and is parameterized by two quantities, the effective dimensionality of the data as perceived by the network (d) and the aforementioned radius (δ), both of which we find empirically. We show that our estimates match with the experimentally obtained behavior of the error on multiple learning tasks using benchmark data-sets and realistic models. Estimating training data requirements is essential for deployment of safety critical applications such as autonomous driving etc. Furthermore, collecting and annotating training data requires a huge amount of financial, computational and human resources. Our empirical estimates will help to efficiently allocate resources.
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization
Venue: arXiv cs.LG 2021
Year: 2021

Various post-training uniform quantization methods have usually been studied based on convex optimization. As a result, most previous ones rely on the quantization error minimization and/or quadratic approximations. Such approaches are computationally efficient and reasonable when a large number of quantization bits are employed. When the number of quantization bits is relatively low, however, non-convex optimization is unavoidable to improve model accuracy. In this paper, we propose a new post-training uniform quantization technique considering non-convexity. We empirically show that hyper-parameters for clipping and rounding of weights and activations can be explored by monitoring task loss. Then, an optimally searched set of hyper-parameters is frozen to proceed to the next layer such that an incremental non-convex optimization is enabled for post-training quantization. Throughout extensive experimental results using various models, our proposed technique presents higher model accuracy, especially for a low-bit quantization.
Modulating Regularization Frequency for Efficient Compression-Aware Model Training
Venue: arXiv cs.LG 2021
Year: 2021

While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time.In this paper, we introduce regularization frequency (i.e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method. For various regularization techniques, such as weight decay and dropout, optimizing the regularization strength is crucial to improve generalization in Deep Neural Networks (DNNs). While model compression also demands the right amount of regularization, the regularization strength incurred by model compression has been controlled only by compression ratio. Throughout various experiments, we show that regularization frequency critically affects the regularization strength of model compression. Combining regularization frequency and compression ratio, the amount of weight updates by model compression per mini-batch can be optimized to achieve the best model accuracy. Modulating regularization frequency is implemented by occasional model compression while conventional compression-aware training is usually performed for every mini-batch.
The EMPATHIC Project: Mid-term Achievements
Venue: arXiv cs.HC 2021
Year: 2021

The goal of active aging is to promote changes in the elderly community so as to maintain an active, independent and socially-engaged lifestyle. Technological advancements currently provide the necessary tools to foster and monitor such processes. This paper reports on mid-term achievements of the European H2020 EMPATHIC project, which aims to research, innovate, explore and validate new interaction paradigms and platforms for future generations of personalized virtual coaches to assist the elderly and their carers to reach the active aging goal, in the vicinity of their home. The project focuses on evidence-based, user-validated research and integration of intelligent technology, and context sensing methods through automatic voice, eye and facial analysis, integrated with visual and spoken dialogue system capabilities. In this paper, we describe the current status of the system, with a special emphasis on its components and their integration, the creation of a Wizard of Oz platform, and findings gained from user interaction studies conducted throughout the first 18 months of the project.
Formally Justifying MDL-based Inference of Cause and Effect
Venue: arXiv cs.IT 2021
Year: 2021

The algorithmic independence of conditionals, which postulates that the causal mechanism is algorithmically independent of the cause, has recently inspired many highly successful approaches to distinguish cause from effect given only observational data. Most popular among these is the idea to approximate algorithmic independence via two-part Minimum Description Length (MDL). Although intuitively sensible, the link between the original postulate and practical two-part MDL encodings is left vague. In this work, we close this gap by deriving a two-part formulation of this postulate, in terms of Kolmogorov complexity, which directly links to practical MDL encodings. To close the cycle, we prove that this formulation leads on expectation to the same inference result as the original postulate.
Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Competitive point cloud semantic segmentation results usually rely on a large amount of labeled data. However, data annotation is a time-consuming and labor-intensive task, particularly for three-dimensional point cloud data. Thus, obtaining accurate results with limited ground truth as training data is considerably important. As a simple and effective method, pseudo labels can use information from unlabeled data for training neural networks. In this study, we propose a pseudo-label-assisted point cloud segmentation method with very few sparsely sampled labels that are normally randomly selected for each class. An adaptive thresholding strategy was proposed to generate a pseudo-label based on the prediction probability. Pseudo-label learning is an iterative process, and pseudo labels were updated solely on ground-truth weak labels as the model converged to improve the training efficiency. Experiments using the ISPRS 3D sematic labeling benchmark dataset indicated that our proposed method achieved an equally competitive result compared to that using a full supervision scheme with only up to 2$\unicode{x2030}$ of labeled points from the original training set, with an overall accuracy of 83.7% and an average F1 score of 70.2%.