OpenRaiser/Intern-Atlas huggingface dataset:
Python for Smarter Cities: Comparison of Python libraries for static and interactive visualisations of large vector data
Venue: arXiv cs.CY 2022
Year: 2022

Local governments, as part of 'smart city' initiatives and to promote interoperability, are increasingly incorporating open-source software into their data management, analysis, and visualisation workflows. Python, with its concise and natural syntax, presents a low barrier to entry for municipal staff without computer science backgrounds. However, with regard to geospatial visualisations in particular, the range of available Python libraries has diversified to such an extent that identifying candidate libraries for specific use cases is a challenging undertaking. This study therefore assesses prominent, actively-developed visualisation libraries in the Python ecosystem with respect to their suitability for producing visualisations of large vector datasets. A simple visualisation task common in urban development is used to produce near-identical thematic maps across static and an interactive 'tracks' of comparison. All short-listed libraries were able to generate the sample map products for both a small and larger dataset. Code complexity differed more strongly for interactive visualisations. Formal and informal documentation channels are highlighted to outline available resources for flattening learning curves. CPU runtimes for the Python-based portion of the process chain differed starkly for both tracks, pointing to avenues for further research. These results demonstrate that the Python ecosystem offers local governments powerful tools, free of vendor lock-in and licensing fees, to produce performant and consistently formatted visualisations for both internal and public distribution.
Analysis of Visual Reasoning on One-Stage Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

Current state-of-the-art one-stage object detectors are limited by treating each image region separately without considering possible relations of the objects. This causes dependency solely on high-quality convolutional feature representations for detecting objects successfully. However, this may not be possible sometimes due to some challenging conditions. In this paper, the usage of reasoning features on one-stage object detection is analyzed. We attempted different architectures that reason the relations of the image regions by using self-attention. YOLOv3-Reasoner2 model spatially and semantically enhances features in the reasoning layer and fuses them with the original convolutional features to improve performance. The YOLOv3-Reasoner2 model achieves around 2.5% absolute improvement with respect to baseline YOLOv3 on COCO in terms of mAP while still running in real-time.
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, the methods on matrix-based or bilinear discriminant analysis (BLDA) have received much attention. Despite their advantages, it has been reported that the traditional vector-based regularized LDA (RLDA) is still quite competitive and could outperform BLDA on some benchmark datasets. Nevertheless, it is also noted that this finding is mainly limited to image data. In this paper, we propose regularized BLDA (RBLDA) and further explore the comparison between RLDA and RBLDA on another type of matrix data, namely multivariate time series (MTS). Unlike image data, MTS typically consists of multiple variables measured at different time points. Although many methods for MTS data classification exist within the literature, there is relatively little work in exploring the matrix data structure of MTS data. Moreover, the existing BLDA can not be performed when one of its within-class matrices is singular. To address the two problems, we propose RBLDA for MTS data classification, where each of the two within-class matrices is regularized via one parameter. We develop an efficient implementation of RBLDA and an efficient model selection algorithm with which the cross validation procedure for RBLDA can be performed efficiently. Experiments on a number of real MTS data sets are conducted to evaluate the proposed algorithm and compare RBLDA with several closely related methods, including RLDA and BLDA. The results reveal that RBLDA achieves the best overall recognition performance and the proposed model selection algorithm is efficient; Moreover, RBLDA can produce better visualization of MTS data than RLDA.
Efficient Specialized Spreadsheet Parsing for Data Science
Venue: arXiv cs.DB 2022
Year: 2022

Spreadsheets are widely used for data exploration. Since spreadsheet systems have limited capabilities, users often need to load spreadsheets to other data science environments to perform advanced analytics. However, current approaches for spreadsheet loading suffer from either high runtime or memory usage, which hinders data exploration on commodity systems. To make spreasheet loading practical on commodity systems, we introduce a novel parser that minimizes memory usage by tightly coupling decompression and parsing. Furthermore, to reduce the runtime, we introduce optimized spreadsheet-specific parsing routines and employ parallelism. To evaluate our approach, we implement a prototype for loading Excel spreadsheets into R environments. Our evaluation shows that our novel approach is up to 3x faster while consuming up to 40x less memory than state-of-the-art approaches.
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
Venue: arXiv cs.LG 2022
Year: 2022

We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of the algorithm's execution, which leads to computing full gradients. In contrast, the proposed method, equipped with a stochastic average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques. In applications we put special emphasis on problems with a large number of separable constraints. Such problems are prevalent among semidefinite programming (SDP) formulations arising in machine learning and theoretical computer science. We provide numerical experiments on matrix completion, unsupervised clustering, and sparsest-cut SDPs.
The Quest for a Common Model of the Intelligent Decision Maker
Venue: arXiv cs.AI 2022
Year: 2022

The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is that multiple disciplines share an interest in goal-directed decision making over time. The idea of this paper is to sharpen and deepen this premise by proposing a perspective on the decision maker that is substantive and widely held across psychology, artificial intelligence, economics, control theory, and neuroscience, which I call the "common model of the intelligent agent". The common model does not include anything specific to any organism, world, or application domain. The common model does include aspects of the decision maker's interaction with its world (there must be input and output, and a goal) and internal components of the decision maker (for perception, decision-making, internal evaluation, and a world model). I identify these aspects and components, note that they are given different names in different disciplines but refer essentially to the same ideas, and discuss the challenges and benefits of devising a neutral terminology that can be used across disciplines. It is time to recognize and build on the convergence of multiple diverse disciplines on a substantive common model of the intelligent agent.
OCR Improves Machine Translation for Low-Resource Languages
Venue: arXiv cs.CL 2022
Year: 2022

We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts. We introduce and make publicly available a novel benchmark, OCR4MT, consisting of real and synthetic data, enriched with noise, for 60 low-resource languages in low resource scripts. We evaluate state-of-the-art OCR systems on our benchmark and analyse most common errors. We show that OCR monolingual data is a valuable resource that can increase performance of Machine Translation models, when used in backtranslation. We then perform an ablation study to investigate how OCR errors impact Machine Translation performance and determine what is the minimum level of OCR quality needed for the monolingual data to be useful for Machine Translation.
A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management
Venue: arXiv cs.CV 2022
Year: 2022

Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of government funding for infrastructure maintenance typically entails heightened risks of potentially fatal vehicular road accidents as a result of inadequate and infrequent manual inspection of road systems for road hazards. To remedy this, a recent research initiative organized by the Institute of Electrical and Electronics Engineers ("IEEE") as part of their 2020 Global Road Damage Detection ("GRDC") Challenge published in May 2020 a novel 21,041 annotated image dataset of various road distresses calling upon academic and other researchers to submit innovative deep learning-based solutions to these road hazard detection problems. Making use of this dataset, we propose a supervised object detection approach leveraging You Only Look Once ("YOLO") and the Faster R-CNN frameworks to detect and classify road distresses in real-time via a vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score experimental results ranking in the top 5 of 121 teams that entered this challenge as of December 2021.
Robust Continual Learning through a Comprehensively Progressive Bayesian Neural Network
Venue: arXiv cs.LG 2022
Year: 2022

This work proposes a comprehensively progressive Bayesian neural network for robust continual learning of a sequence of tasks. A Bayesian neural network is progressively pruned and grown such that there are sufficient network resources to represent a sequence of tasks, while the network does not explode. It starts with the contention that similar tasks should have the same number of total network resources, to ensure fair representation of all tasks in a continual learning scenario. Thus, as the data for new task streams in, sufficient neurons are added to the network such that the total number of neurons in each layer of the network, including the shared representations with previous tasks and individual task related representation, are equal for all tasks. The weights that are redundant at the end of training each task are also pruned through re-initialization, in order to be efficiently utilized in the subsequent task. Thus, the network grows progressively, but ensures effective utilization of network resources. We refer to our proposed method as 'Robust Continual Learning through a Comprehensively Progressive Bayesian Neural Network (RCL-CPB)' and evaluate the proposed approach on the MNIST data set, under three different continual learning scenarios. Further to this, we evaluate the performance of RCL-CPB on a homogeneous sequence of tasks using split CIFAR100 (20 tasks of 5 classes each), and a heterogeneous sequence of tasks using MNIST, SVHN and CIFAR10 data sets. The demonstrations and the performance results show that the proposed strategies for progressive BNN enable robust continual learning.
On Intercultural Transferability and Calibration of Heterogeneous Shared Space Motion Models
Venue: arXiv cs.MA 2022
Year: 2022

Modelling and simulation of mixed-traffic zones is an important tool for transportation planners to assess safety, efficiency, and human-friendliness of future urban areas. This paper addresses problems of calibration and transferability of existing shared space models when applied to scenarios that differ in terms of cultural aspects, traffic conditions, and spatial layout. In particular, the first contribution of this work is an enhancement of the Game-Theoretic Social Force Model (GSFM) by a generic methodology for largely automated model calibration; we illustrate the use of the calibration method for a shared space environment in Germany. The second contribution is an investigation into transferability of shared space models. We define criteria for model transferability and present a case study, in which we analyse and evaluate transferability of the model we constructed based on the ``German dataset'' to a different shared space environment from China. Our results indicate that although -- as to be expected -- the model faces difficulties to replicate the movement behaviours of road users from a new environment, by adding social norms (derived through analysis) of that environment to our model, satisfactory improvement of model accuracy can be obtained with limited effort.
Byzantine Agreement in Polynomial Time with Near-Optimal Resilience
Venue: arXiv cs.DC 2022
Year: 2022

It has been known since the early 1980s that Byzantine Agreement in the full information, asynchronous model is impossible to solve deterministically against even one crash fault [FLP85], but that it can be solved with probability 1 [Ben83], even against an adversary that controls the scheduling of all messages and corrupts up to $f<n/3$ players [Bra87]. The main downside of [Ben83, Bra87] is that they terminate in $2^{Θ(n)}$ rounds in expectation whenever $f=Θ(n)$. King and Saia [KS16, KS18(arXiv:1812.10169)] developed a polynomial protocol (polynomial rounds, polynomial computation) that is resilient to $f < (1.14\times 10^{-9})n$ Byzantine faults. The new idea in their protocol is to detect -- and blacklist -- coalitions of likely-bad players by analyzing the deviations of random variables generated by those players over many rounds. In this work we design a simple collective coin-flipping protocol such that if any coalition of faulty players repeatedly does not follow protocol, then they will eventually be detected by one of two simple statistical tests. Using this coin-flipping protocol, we solve Byzantine Agreement in a polynomial number of rounds, even in the presence of up to $f<n/4$ Byzantine faults. This comes close to the $f<n/3$ upper bound on the maximum number of faults [BT85,FLM86,LSP82].
Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks
Venue: arXiv cs.CL 2022
Year: 2022

The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of complex annotation work in many interdisciplinary research areas, for example, legal argument mining in digital humanities. Argument mining aims to develop text analysis tools that can automatically retrieve arguments and identify relationships between argumentation clauses. Since argumentation is one of the key aspects of case law, argument mining tools for legal texts are applicable to both academic and non-academic legal research. Domain-specific BERT variants (pre-trained with corpora from a particular background) have also achieved strong performance in many tasks. To our knowledge, previous machine learning studies of argument mining on judicial case law still heavily rely on statistical models. In this paper, we provide a broad study of both classic and contextual embedding models and their performance on practical case law from the European Court of Human Rights (ECHR). During our study, we also explore a number of neural networks when being combined with different embeddings. Our experiments provide a comprehensive overview of a variety of approaches to the legal argument mining task. We conclude that domain pre-trained transformer models have great potential in this area, although traditional embeddings can also achieve strong performance when combined with additional neural network layers.
Roadway Design Matters: Variation in Bicyclists' Psycho-Physiological Responses in Different Urban Roadway Designs
Venue: arXiv cs.HC 2022
Year: 2022

As a healthier and more sustainable way of mobility, cycling has been advocated by literature and policy. However, current trends in bicyclist crash fatalities suggest deficiencies in current roadway design in protecting these vulnerable road users. The lack of cycling data is a common challenge for studying bicyclists' safety, behavior, and comfort levels under different design contexts. To understand bicyclists' behavioral and physiological responses in an efficient and safe way, this study uses a bicycle simulator within an immersive virtual environment (IVE). Off-the-shelf sensors are utilized to evaluate bicyclists' cycling performance (speed and lane position) and physiological responses (eye tracking and heart rate (HR)). Participants bike in a simulated virtual environment modeled to scale from a real-world street with a shared bike lane (sharrow) to evaluate how introduction of a bike lane and a protected bike lane with pylons may impact perceptions of safety, as well as behavioral and psycho-physiological responses. Results from 50 participants show that the protected bike lane design received the highest perceived safety rating and exhibited the lowest average cycling speed. Furthermore, both the bike lane and the protected bike lane scenarios show a less dispersed gaze distribution than the as-built sharrow scenario, reflecting a higher gaze focus among bicyclists on the biking task in the bike lane and protected bike lane scenarios, compared to when bicyclists share right of way with vehicles. Additionally, heart rate change point results from the study suggest that creating dedicated zones for bicyclists (bike lanes or protected bike lanes) has the potential to reduce bicyclists' stress levels.
Sparsity-aware neural user behavior modeling in online interaction platforms
Venue: arXiv cs.LG 2022
Year: 2022

Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups). In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms.
Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds
Venue: arXiv cs.CV 2022
Year: 2022

Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, the existing methods based on deep neural networks focus mainly on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes unseen during the training phase. In this work, we investigate a more challenging task in the LiDAR point clouds, class-agnostic tracking, where a general model is supposed to be learned for any specified targets of both observed and unseen categories. In particular, we first investigate the class-agnostic performances of the state-of-the-art trackers via exposing the unseen categories to them during testing, finding that a key factor for class-agnostic tracking is how to constrain fused features between the template and search region to maintain generalization when the distribution is shifted from observed to unseen classes. Therefore, we propose a feature decorrelation method to address this problem, which eliminates the spurious correlations of the fused features through a set of learned weights and further makes the search region consistent among foreground points and distinctive between foreground and background points. Experiments on the KITTI and NuScenes demonstrate that the proposed method can achieve considerable improvements by benchmarking against the advanced trackers P2B and BAT, especially when tracking unseen objects.
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks
Venue: arXiv cs.LG 2022
Year: 2022

A graph convolutional network (GCN) employs a graph filtering kernel tailored for data with irregular structures. However, simply stacking more GCN layers does not improve performance; instead, the output converges to an uninformative low-dimensional subspace, where the convergence rate is characterized by the graph spectrum -- this is the known over-smoothing problem in GCN. In this paper, we propose a sparse graph learning algorithm incorporating a new spectrum prior to compute a graph topology that circumvents over-smoothing while preserving pairwise correlations inherent in data. Specifically, based on a spectral analysis of multilayer GCN output, we derive a spectrum prior for the graph Laplacian matrix $\mathbf{L}$ to robustify the model expressiveness against over-smoothing. Then, we formulate a sparse graph learning problem with the spectrum prior, solved efficiently via block coordinate descent (BCD). Moreover, we optimize the weight parameter trading off the fidelity term with the spectrum prior, based on data smoothness on the original graph learned without spectrum manipulation. The output $\mathbf{L}$ is then normalized for supervised GCN training. Experiments show that our proposal produced deeper GCNs and higher prediction accuracy for regression and classification tasks compared to competing schemes.