OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Spatialtemporal Context Modeling for Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model.
Multi Camera Placement via Z-buffer Rendering for the Optimization of the Coverage and the Visual Hull
Venue: arXiv cs.RO 2021
Year: 2021

We can only allow human-robot-cooperation in a common work cell if the human integrity is guaranteed. A surveillance system with multiple cameras can detect collisions without contact to the human collaborator. A failure safe system needs to optimally cover the important areas of the robot work cell with safety overlap. We propose an efficient algorithm for optimally placing and orienting the cameras in a 3D CAD model of the work cell. In order to evaluate the quality of the camera constellation in each step, our method simulates the vision system using a z-buffer rendering technique for image acquisition, a voxel space for the overlap and a refined visual hull method for a conservative human reconstruction. The simulation allows to evaluate the quality with respect to the distortion of images and advanced image analysis in the presence of static and dynamic visual obstacles such as tables, racks, walls, robots and people. Our method is ideally suited for maximizing the coverage of multiple cameras or minimizing an error made by the visual hull and can be extended to probabilistic space carving.
Demystifying the Effects of Non-Independence in Federated Learning
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning (FL) enables statistical models to be built on user-generated data without compromising data security and user privacy. For this reason, FL is well suited for on-device learning from mobile devices where data is abundant and highly privatized. Constrained by the temporal availability of mobile devices, only a subset of devices is accessible to participate in the iterative protocol consisting of training and aggregation. In this study, we take a step toward better understanding the effect of non-independent data distributions arising from block-cyclic sampling. By conducting extensive experiments on visual classification, we measure the effects of block-cyclic sampling (both standalone and in combination with non-balanced block distributions). Specifically, we measure the alterations induced by block-cyclic sampling from the perspective of accuracy, fairness, and convergence rate. Experimental results indicate robustness to cycling over a two-block structure, e.g., due to time zones. In contrast, drawing data samples dependently from a multi-block structure significantly degrades the performance and rate of convergence by up to 26%. Moreover, we find that this performance degeneration is further aggravated by unbalanced block distributions to a point that can no longer be adequately compensated by higher communication and more frequent synchronization.
Spark Deficient Gabor Frame Provides a Novel Analysis Operator for Compressed Sensing
Venue: arXiv cs.IT 2021
Year: 2021

The analysis sparsity model is a very effective approach in modern Compressed Sensing applications. Specifically, redundant analysis operators can lead to fewer measurements needed for reconstruction when employing the analysis $l_1$-minimization in Compressed Sensing. In this paper, we pick an eigenvector of the Zauner unitary matrix and -- under certain assumptions on the ambient dimension -- we build a spark deficient Gabor frame. The analysis operator associated with such a spark deficient Gabor frame, is a new (highly) redundant Gabor transform, which we use as a sparsifying transform in Compressed Sensing. We conduct computational experiments -- on both synthetic and real-world data -- solving the analysis $l_1$-minimization problem of Compressed Sensing, with four different choices of analysis operators, including our Gabor analysis operator. The results show that our proposed redundant Gabor transform outperforms -- in all cases -- Gabor transforms generated by state-of-the-art window vectors of time-frequency analysis.
Structural Textile Pattern Recognition and Processing Based on Hypergraphs
Venue: arXiv cs.IR 2021
Year: 2021

The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing. To facilitate the clustering and search, we introduce an approach for recognising similar weaving patterns based on their structures for textile archives. We first represent textile structures using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs. Then, the resulting multisets are clustered using various distance measures and various clustering algorithms (K-Means for simplicity and hierarchical agglomerative algorithms for precision). We evaluate the different variants of our approach experimentally, showing that this can be implemented efficiently (meaning it has linear complexity), and demonstrate its quality to query and cluster datasets containing large textile samples. As, to the est of our knowledge, this is the first practical approach for explicitly modelling complex and irregular weaving patterns usable for retrieval, we aim at establishing a solid baseline.
ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs
Venue: arXiv cs.LG 2021
Year: 2021

Engineering simulations for analysis of structural and fluid systems require information of contacts between various 3-D surfaces of the geometry to accurately model the physics between them. In machine learning applications, 3-D surfaces are most suitably represented with point clouds or meshes and learning representations of interacting geometries form point-based representations is challenging. The objective of this work is to introduce a machine learning algorithm, ActivationNet, that can learn from point clouds or meshes of interacting 3-D surfaces and predict the quality of contact between these surfaces. The ActivationNet generates activation states from point-based representation of surfaces using a multi-dimensional binning approach. The activation states are further used to contact quality between surfaces using deep neural networks. The performance of our model is demonstrated using several experiments, including tests on interacting surfaces extracted from engineering geometries. In all the experiments presented in this paper, the contact quality predictions of ActivationNet agree well with the expectations.
A Modular and Unified Framework for Detecting and Localizing Video Anomalies
Venue: arXiv cs.CV 2021
Year: 2021

Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos. Extensive performance evaluations on benchmark datasets show that the proposed framework significantly outperforms the current state-of-the-art approaches.
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

Semantic segmentation is an important task for scene understanding in self-driving cars and robotics, which aims to assign dense labels for all pixels in the image. Existing work typically improves semantic segmentation performance by exploring different network architectures on a target dataset. Little attention has been paid to build a unified system by simultaneously learning from multiple datasets due to the inherent distribution shift across different datasets. In this paper, we propose a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Learning (CDCL). Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets. Specifically, we first introduce a family of Dataset-Aware Blocks (DAB) as the fundamental computing units of the network, which help capture homogeneous convolutional representations and heterogeneous statistics across different datasets. Second, we present a Dataset Alternation Training (DAT) mechanism to facilitate the collaborative optimization procedure. We conduct extensive evaluations on diverse semantic segmentation datasets for autonomous driving. Experiments demonstrate that our method consistently achieves notable improvements over prior single-dataset and cross-dataset training methods without introducing extra FLOPs. Particularly, with the same architecture of PSPNet (ResNet-18), our method outperforms the single-dataset baseline by 5.65\%, 6.57\%, and 5.79\% mIoU on the validation sets of Cityscapes, BDD100K, CamVid, respectively. We also apply CDCL for point cloud 3D semantic segmentation and achieve improved performance, which further validates the superiority and generality of our method. Code and models will be released.
Online Strongly Convex Optimization with Unknown Delays
Venue: arXiv cs.LG 2021
Year: 2021

We investigate the problem of online convex optimization with unknown delays, in which the feedback of a decision arrives with an arbitrary delay. Previous studies have presented a delayed variant of online gradient descent (OGD), and achieved the regret bound of $O(\sqrt{T+D})$ by only utilizing the convexity condition, where $D$ is the sum of delays over $T$ rounds. In this paper, we further exploit the strong convexity to improve the regret bound. Specifically, we first extend the delayed variant of OGD for strongly convex functions, and establish a better regret bound of $O(d\log T)$, where $d$ is the maximum delay. The essential idea is to let the learning rate decay with the total number of received feedback linearly. Furthermore, we consider the more challenging bandit setting, and obtain similar theoretical guarantees by incorporating the classical multi-point gradient estimator into our extended method. To the best of our knowledge, this is the first work that solves online strongly convex optimization under the general delayed setting.
Natural Perturbed Training for General Robustness of Neural Network Classifiers
Venue: arXiv cs.CV 2021
Year: 2021

We focus on the robustness of neural networks for classification. To permit a fair comparison between methods to achieve robustness, we first introduce a standard based on the mensuration of a classifier's degradation. Then, we propose natural perturbed training to robustify the network. Natural perturbations will be encountered in practice: the difference of two images of the same object may be approximated by an elastic deformation (when they have slightly different viewing angles), by occlusions (when they hide differently behind objects), or by saturation, Gaussian noise etc. Training some fraction of the epochs on random versions of such variations will help the classifier to learn better. We conduct extensive experiments on six datasets of varying sizes and granularity. Natural perturbed learning show better and much faster performance than adversarial training on clean, adversarial as well as natural perturbed images. It even improves general robustness on perturbations not seen during the training. For Cifar-10 and STL-10 natural perturbed training even improves the accuracy for clean data and reaches the state of the art performance. Ablation studies verify the effectiveness of natural perturbed training.
Characterization and Prediction of Questions without Accepted Answers on Stack Overflow
Venue: arXiv cs.SE 2021
Year: 2021

A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is the user's acceptance. In this paper, we investigate unresolved questions, without accepted answers, on Stack Overflow. The number of unresolved questions is increasing. As of August 2019, 47% of Stack Overflow questions were unresolved. In this study, we analyze the effectiveness of various features, including some novel features, to resolve a question. We do not use the features that contain information not present at the time of asking a question, such as answers. To evaluate our features, we deploy several predictive models trained on the features of 18 million questions to predict whether a question will get an accepted answer or not. The results of this study show a significant relationship between our proposed features and getting accepted answers. Finally, we introduce an online tool that predicts whether a question will get an accepted answer or not. Currently, Stack Overflow's users do not receive any feedback on their questions before asking them, so they could carelessly ask unclear, unreadable, or inappropriately tagged questions. By using this tool, they can modify their questions and tags to check the different results of the tool and deliberately improve their questions to get accepted answers.
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus
Venue: arXiv cs.CL 2021
Year: 2021

Word embeddings are a basic building block of modern NLP pipelines. Efforts have been made to learn rich, efficient, and interpretable embeddings for large generic datasets available in the public domain. However, these embeddings have limited applicability for small corpora from specific domains such as automotive, manufacturing, maintenance and support, etc. In this work, we present a comprehensive notion of interpretability for word embeddings and propose a novel method to generate highly interpretable and efficient embeddings for a domain-specific small corpus. We report the evaluation results of our resulting word embeddings and demonstrate their novel features for enhanced interpretability.
Potential Gap: Using Reactive Policies to Guarantee Safe Navigation
Venue: arXiv cs.RO 2021
Year: 2021

This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the provable properties are lost when applied to more realistic models. We describe a set of algorithm modifications that correct for these errors and enhance robustness to non-ideal models. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect gaps and use them for the synthesis of the APF. Modifications are given for a nonholonomic robot model. Integration of the local planner, called potential gap, into a hierarchical navigation system provides the local goals and trajectories needed for collision-free navigation through unknown environments. Monte Carlo experiments in benchmark worlds confirm the asserted safety and robustness properties by testing under various robot models.
Dual Monte Carlo Tree Search
Venue: arXiv cs.AI 2021
Year: 2021

AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural MCTS algorithm has been successful in finding near-optimal strategies for games through self-play. However, the AlphaZero algorithm has a significant drawback; it takes a long time to converge and requires high computational power due to complex neural networks for solving games like Chess, Go, Shogi, etc. Owing to this, it is very difficult to pursue neural MCTS research without cutting-edge hardware, which is a roadblock for many aspiring neural MCTS researchers. In this paper, we propose a new neural MCTS algorithm, called Dual MCTS, which helps overcome these drawbacks. Dual MCTS uses two different search trees, a single deep neural network, and a new update technique for the search trees using a combination of the PUCB, a sliding-window, and the epsilon-greedy algorithm. This technique is applicable to any MCTS based algorithm to reduce the number of updates to the tree. We show that Dual MCTS performs better than one of the most widely used neural MCTS algorithms, AlphaZero, for various symmetric and asymmetric games.
Multi-directional Bicycle Robot for Steel Structure Inspection
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents a novel design of a multi-directional bicycle robot, which targets inspecting general ferromagnetic structures including complex-shaped structures. The locomotion concept is based on arranging two magnetic wheels in a bicycle-like configuration with two independent steering actuators. This configuration allows the robot to possess multi-directional mobility. An additional free joint helps the robot naturally adapt to non-flat and complex surfaces of steel structures. The robot has the biggest advantage to be mechanically simple with high mobility. Besides, the robot is equipped with sensing tools for structure health monitoring. We demonstrate the deployment of our robot to perform steel rust detection on steel bridges. The final inspection results are visualized as 3D models of the bridges together with marked locations of detected rusty areas.
ConfInLog: Leveraging Software Logs to Infer Configuration Constraints
Venue: arXiv cs.SE 2021
Year: 2021

Misconfigurations have become the dominant causes of software failures in recent years, drawing tremendous attention for their increasing prevalence and severity. Configuration constraints can preemptively avoid misconfiguration by defining the conditions that configuration options should satisfy. Documentation is the main source of configuration constraints, but it might be incomplete or inconsistent with the source code. In this regard, prior researches have focused on obtaining configuration constraints from software source code through static analysis. However, the difficulty in pointer analysis and context comprehension prevents them from collecting accurate and comprehensive constraints. In this paper, we observed that software logs often contain configuration constraints. We conducted an empirical study and summarized patterns of configuration-related log messages. Guided by the study, we designed and implemented ConfInLog, a static tool to infer configuration constraints from log messages. ConfInLog first selects configuration-related log messages from source code by using the summarized patterns, then infers constraints from log messages based on the summarized natural language patterns. To evaluate the effectiveness of ConfInLog, we applied our tool on seven popular open-source software systems. ConfInLog successfully inferred 22 to 163 constraints, in which 59.5% to 61.6% could not be inferred by the state-of-the-art work. Finally, we submitted 67 documentation patches regarding the constraints inferred by ConfInLog. The constraints in 29 patches have been confirmed by the developers, among which 10 patches have been accepted.
From Coupling to Spectral Independence and Blackbox Comparison with the Down-Up Walk
Venue: arXiv cs.DM 2021
Year: 2021

We show that the existence of a "good"' coupling w.r.t. Hamming distance for any local Markov chain on a discrete product space implies rapid mixing of the Glauber dynamics in a blackbox fashion. More specifically, we only require the expected distance between successive iterates under the coupling to be summable, as opposed to being one-step contractive in the worst case. Combined with recent local-to-global arguments \cite{CLV21}, we establish asymptotically optimal lower bounds on the standard and modified log-Sobolev constants for the Glauber dynamics for sampling from spin systems on bounded-degree graphs when a curvature condition \cite{Oll09} is satisfied. To achieve this, we use Stein's method for Markov chains \cite{BN19, RR19} to show that a "good" coupling for a local Markov chain yields strong bounds on the spectral independence of the distribution in the sense of \cite{ALO20}. Our primary application is to sampling proper list-colorings on bounded-degree graphs. In particular, combining the coupling for the flip dynamics given by \cite{Vig00, CDMPP19} with our techniques, we show optimal $O(n\log n)$ mixing for the Glauber dynamics for sampling proper list-colorings on any bounded-degree graph with maximum degree $Δ$ whenever the size of the color lists are at least $\left(\frac{11}{6} - ε\right)Δ$, where $ε\approx 10^{-5}$ is small constant. While $O(n^{2})$ mixing was already known before, our approach additionally yields Chernoff-type concentration bounds for Hamming Lipschitz functions in this regime, which was not known before. Our approach is markedly different from prior works establishing spectral independence for spin systems using spatial mixing \cite{ALO20, CLV20, CGSV20, FGYZ20}, which crucially is still open in this regime for proper list-colorings.