OpenRaiser/Intern-Atlas huggingface dataset:
Iterative Rule Extension for Logic Analysis of Data: an MILP-based heuristic to derive interpretable binary classification from large datasets
Venue: arXiv cs.LG 2021
Year: 2021

Data-driven decision making is rapidly gaining popularity, fueled by the ever-increasing amounts of available data and encouraged by the development of models that can identify beyond linear input-output relationships. Simultaneously the need for interpretable prediction- and classification methods is increasing, as this improves both our trust in these models and the amount of information we can abstract from data. An important aspect of this interpretability is to obtain insight in the sensitivity-specificity trade-off constituted by multiple plausible input-output relationships. These are often shown in a receiver operating characteristic (ROC) curve. These developments combined lead to the need for a method that can abstract complex yet interpretable input-output relationships from large data, i.e. data containing large numbers of samples and sample features. Boolean phrases in disjunctive normal form (DNF) are highly suitable for explaining non-linear input-output relationships in a comprehensible way. Mixed integer linear programming (MILP) can be used to abstract these Boolean phrases from binary data, though its computational complexity prohibits the analysis of large datasets. This work presents IRELAND, an algorithm that allows for abstracting Boolean phrases in DNF from data with up to 10,000 samples and sample characteristics. The results show that for large datasets IRELAND outperforms the current state-of-the-art and can find solutions for datasets where current models run out of memory or need excessive runtimes. Additionally, by construction IRELAND allows for an efficient computation of the sensitivity-specificity trade-off curve, allowing for further understanding of the underlying input-output relationship.
A proposed method using GPU based SDO to optimize retail warehouses
Venue: arXiv cs.DC 2021
Year: 2021

Research in warehouse optimization has gotten increased attention in the last few years due to e-commerce. The warehouse contains a waste range of different products. Due to the nature of the individual order, it is challenging to plan the picking list to optimize the material flow in the process. There are also challenges in minimizing costs and increasing production capacity, and this complexity can be defined as a multidisciplinary optimization problem with an IDF nature. In recent years the use of parallel computing using GPGPUs has become increasingly popular due to the introduction of CUDA C and accompanying applications in, e.g., Python. In the case study at the company in the field of retail, a case study including a system design optimization (SDO) resulted in an increase in throughput with well over 20% just by clustering different categories and suggesting in which sequence the orders should be picked during a given time frame. The options provided by implementing a distributed high-performance computing network based on GPUs for subsystem optimization have shown to be fruitful in developing a functioning SDO for warehouse optimization. The toolchain can be used for designing new warehouses or evaluating and tuning existing ones.
Pyramidal Blur Aware X-Corner Chessboard Detector
Venue: arXiv cs.CV 2021
Year: 2021

With camera resolution ever increasing and the need to rapidly recalibrate robotic platforms in less than ideal environments, there is a need for faster and more robust chessboard fiducial marker detectors. A new chessboard detector is proposed that is specifically designed for: high resolution images, focus/motion blur, harsh lighting conditions, and background clutter. This is accomplished using a new x-corner detector, where for the first time blur is estimated and used in a novel way to enhance corner localization, edge validation, and connectivity. Performance is measured and compared against other libraries using a diverse set of images created by combining multiple third party datasets and including new specially crafted scenarios designed to stress the state-of-the-art. The proposed detector has the best F1- Score of 0.97, runs 1.9x faster than next fastest, and is a top performer for corner accuracy, while being the only detector to have consistent good performance in all scenarios.
A deep learning based surrogate model for stochastic simulators
Venue: arXiv cs.LG 2021
Year: 2021

We propose a deep learning-based surrogate model for stochastic simulators. The basic idea is to use generative neural network to approximate the stochastic response. The challenge with such a framework resides in designing the network architecture and selecting loss-function suitable for stochastic response. While we utilize a simple feed-forward neural network, we propose to use conditional maximum mean discrepancy (CMMD) as the loss-function. CMMD exploits the property of reproducing kernel Hilbert space and allows capturing discrepancy between the between the target and the neural network predicted distributions. The proposed approach is mathematically rigorous, in the sense that it makes no assumptions about the probability density function of the response. Performance of the proposed approach is illustrated using four benchmark problems selected from the literature. Results obtained indicate the excellent performance of the proposed approach.
Heterogeneous Temporal Graph Neural Network
Venue: arXiv cs.LG 2021
Year: 2021

Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their representation learning, majority of which focus on graphs with homogeneous structures in the spatial domain. However, many real-world graphs - i.e., heterogeneous temporal graphs (HTGs) - evolve dynamically in the context of heterogeneous graph structures. The dynamics associated with heterogeneity have posed new challenges for HTG representation learning. To solve this problem, in this paper, we propose heterogeneous temporal graph neural network (HTGNN) to integrate both spatial and temporal dependencies while preserving the heterogeneity to learn node representations over HTGs. Specifically, in each layer of HTGNN, we propose a hierarchical aggregation mechanism, including intra-relation, inter-relation, and across-time aggregations, to jointly model heterogeneous spatial dependencies and temporal dimensions. To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG. The proposed HTGNN is a holistic framework tailored heterogeneity with evolution in time and space for HTG representation learning. Extensive experiments are conducted on the HTGs built from different real-world datasets and promising results demonstrate the outstanding performance of HTGNN by comparison with state-of-the-art baselines. Our built HTGs and code have been made publicly accessible at: https://github.com/YesLab-Code/HTGNN.
HR-RCNN: Hierarchical Relational Reasoning for Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Incorporating relational reasoning in neural networks for object recognition remains an open problem. Although many attempts have been made for relational reasoning, they generally only consider a single type of relationship. For example, pixel relations through self-attention (e.g., non-local networks), scale relations through feature fusion (e.g., feature pyramid networks), or object relations through graph convolutions (e.g., reasoning-RCNN). Little attention has been given to more generalized frameworks that can reason across these relationships. In this paper, we propose a hierarchical relational reasoning framework (HR-RCNN) for object detection, which utilizes a novel graph attention module (GAM). This GAM is a concise module that enables reasoning across heterogeneous nodes by operating on the graph edges directly. Leveraging heterogeneous relationships, our HR-RCNN shows great improvement on COCO dataset, for both object detection and instance segmentation.
Rapid IoT Device Identification at the Edge
Venue: arXiv cs.LG 2021
Year: 2021

Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras. Along with their benefits come potential privacy and security threats. To limit these threats we must implement solutions to filter IoT traffic at the edge. To this end the identification of the IoT device is the first natural step. In this paper we demonstrate a novel method of rapid IoT device identification that uses neural networks trained on device DNS traffic that can be captured from a DNS server on the local network. The method identifies devices by fitting a model to the first seconds of DNS second-level-domain traffic following their first connection. Since security and privacy threat detection often operate at a device specific level, rapid identification allows these strategies to be implemented immediately. Through a total of 51,000 rigorous automated experiments, we classify 30 consumer IoT devices from 27 different manufacturers with 82% and 93% accuracy for product type and device manufacturers respectively.
Can't Fool Me: Adversarially Robust Transformer for Video Understanding
Venue: arXiv cs.CV 2021
Year: 2021

Deep neural networks have been shown to perform poorly on adversarial examples. To address this, several techniques have been proposed to increase robustness of a model for image classification tasks. However, in video understanding tasks, developing adversarially robust models is still unexplored. In this paper, we aim to bridge this gap. We first show that simple extensions of image based adversarially robust models slightly improve the worst-case performance. Further, we propose a temporal attention regularization scheme in Transformer to improve the robustness of attention modules to adversarial examples. We illustrate using a large-scale video data set YouTube-8M that the final model (A-ART) achieves close to non-adversarial performance on its adversarial example set. We achieve 91% GAP on adversarial examples, whereas baseline Transformer and simple adversarial extensions achieve 72.9% and 82% respectively, showing significant improvement in robustness over the state-of-the-art.
Controllable Data Augmentation Through Deep Relighting
Venue: arXiv cs.CV 2021
Year: 2021

At the heart of the success of deep learning is the quality of the data. Through data augmentation, one can train models with better generalization capabilities and thus achieve greater results in their field of interest. In this work, we explore how to augment a varied set of image datasets through relighting so as to improve the ability of existing models to be invariant to illumination changes, namely for learned descriptors. We develop a tool, based on an encoder-decoder network, that is able to quickly generate multiple variations of the illumination of various input scenes whilst also allowing the user to define parameters such as the angle of incidence and intensity. We demonstrate that by training models on datasets that have been augmented with our pipeline, it is possible to achieve higher performance on localization benchmarks.
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these parameters to cluster the time series. These clusters can be viewed as multivariate time series, for which we then compute parameters. The forecasted values of a single time series can depend on the history of other time series in the same cluster, accounting for intra-cluster similarity while minimizing potential noise in predictions by ignoring inter-cluster effects. Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step. It is computationally efficient and embarrassingly parallel. We motivate our framework with a theoretical analysis in an idealized mixed linear regression setting, where we provide guarantees on the quality of the estimates. We accompany these guarantees with experimental results that demonstrate the advantages of our framework: when instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets, sometimes outperforming deep-learning-based approaches.
Finding the Best Partitioning Policy for Efficient Verification of Autonomous Systems at Runtime
Venue: arXiv cs.SE 2021
Year: 2021

The autonomous systems need to decide how to react to the changes at runtime efficiently. The ability to rigorously analyze the environment and the system together is theoretically possible by the model-driven approaches; however, the model size and timing limitations are two significant obstacles against such an autonomous decision-making process. To tackle this issue, the incremental approximation technique can be used to partition the model and only verify a partition if it is affected by the change. This paper proposes a policy-based analysis approach that finds the best partitioning policy among a set of available policies based on two proposed metrics, namely Balancing and Variation. The metrics quantitatively evaluate the generated components from the incremental approximation scheme according to their size and frequency. We investigate the validity of the approach both theoretically and experimentally via a case study on energy harvesting systems. The results confirm the effectiveness of the proposed approach.
Polynomial-Spline Neural Networks with Exact Integrals
Venue: arXiv cs.LG 2021
Year: 2021

Using neural networks to solve variational problems, and other scientific machine learning tasks, has been limited by a lack of consistency and an inability to exactly integrate expressions involving neural network architectures. We address these limitations by formulating a novel neural network architecture that combines a polynomial mixture-of-experts model with free knot B1-spline basis functions. Effectively, our architecture performs piecewise polynomial approximation on each cell of a trainable partition of unity. Our architecture exhibits both $h$- and $p$- refinement for regression problems at the convergence rates expected from approximation theory, allowing for consistency in solving variational problems. Moreover, this architecture, its moments, and its partial derivatives can all be integrated exactly, obviating a reliance on sampling or quadrature and enabling error-free computation of variational forms. We demonstrate the success of our network on a range of regression and variational problems that illustrate the consistency and exact integrability of our network architecture.
Argo Scholar: Interactive Visual Exploration of Literature in Browsers
Venue: arXiv cs.HC 2021
Year: 2021

Discovering and making sense of relevant research literature is fundamental to becoming knowledgeable in any scientific discipline. Visualization can aid this process; however, existing tools' adoption and impact have often been constrained, such as by their reliance on small curated paper datasets that quickly become outdated or a lack of support for personalized exploration. We introduce Argo Scholar, an open-source, web-based visualization tool for interactive exploration of literature and easy sharing of exploration results. Argo Scholar queries and visualizes Semantic Scholar's live data of almost 200 million papers, enabling users to generate personalized literature exploration results in real-time through flexible, incremental exploration, a common and effective method for researchers to discover relevant work. Our tool allows users to easily share their literature exploration results as a URL or web-embedded IFrame application. Argo Scholar is open-sourced and available at https://poloclub.github.io/argo-scholar/.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
Venue: arXiv cs.CV 2021
Year: 2021

We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoints detected from dense points but usually struggle to guarantee their repeatability. To address this issue, we present CoFiNet - Coarse-to-Fine Network which extracts hierarchical correspondences from coarse to fine without keypoint detection. On a coarse scale and guided by a weighting scheme, our model firstly learns to match down-sampled nodes whose vicinity points share more overlap, which significantly shrinks the search space of a consecutive stage. On a finer scale, node proposals are consecutively expanded to patches that consist of groups of points together with associated descriptors. Point correspondences are then refined from the overlap areas of corresponding patches, by a density-adaptive matching module capable to deal with varying point density. Extensive evaluation of CoFiNet on both indoor and outdoor standard benchmarks shows our superiority over existing methods. Especially on 3DLoMatch where point clouds share less overlap, CoFiNet significantly outperforms state-of-the-art approaches by at least 5% on Registration Recall, with at most two-third of their parameters.
A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum $2$ dB and $19$ times for various M-MIMO system configurations.
Stubbifier: Debloating Dynamic Server-Side JavaScript Applications
Venue: arXiv cs.SE 2021
Year: 2021

JavaScript is an increasingly popular language for server-side development, thanks in part to the Node.js runtime environment and its vast ecosystem of modules. With the Node.js package manager npm, users are able to easily include external modules as dependencies in their projects. However, npm installs modules with all of their functionality, even if only a fraction is needed, which causes an undue increase in code size. Eliminating this unused functionality from distributions is desirable, but the sound analysis required to find unused code is difficult due to JavaScript's extreme dynamicity. We present a fully automatic technique that identifies unused code by constructing static or dynamic call graphs from the application's tests, and replacing code deemed unreachable with either file- or function-level stubs. If a stub is called, it will fetch and execute the original code on-demand, thus relaxing the requirement that the call graph be sound. The technique also provides an optional guarded execution mode to guard application against injection vulnerabilities in untested code that resulted from stub expansion. This technique is implemented in an open source tool called Stubbifier, which supports the ECMAScript 2019 standard. In an empirical evaluation on 15 Node.js applications and 75 clients of these applications, Stubbifier reduced application size by 56% on average while incurring only minor performance overhead. The evaluation also shows that Stubbifier's guarded execution mode is capable of preventing several known injection vulnerabilities that are manifested in stubbed-out code. Finally, Stubbifier can work alongside bundlers, popular JavaScript tools for bundling an application with its dependencies. For the considered subject applications, we measured an average size reduction of 37% in bundled distributions.
Smooth head tracking for virtual reality applications
Venue: arXiv cs.CV 2021
Year: 2021

In this work, we propose a new head-tracking solution for human-machine real-time interaction with virtual 3D environments. This solution leverages RGBD data to compute virtual camera pose according to the movements of the user's head. The process starts with the extraction of a set of facial features from the images delivered by the sensor. Such features are matched against their respective counterparts in a reference image for the computation of the current head pose. Afterwards, a prediction approach is used to guess the most likely next head move (final pose). Pythagorean Hodograph interpolation is then adapted to determine the path and local frames taken between the two poses. The result is a smooth head trajectory that serves as an input to set the camera in virtual scenes according to the user's gaze. The resulting motion model has the advantage of being: continuous in time, it adapts to any frame rate of rendering; it is ergonomic, as it frees the user from wearing tracking markers; it is smooth and free from rendering jerks; and it is also torsion and curvature minimizing as it produces a path with minimum bending energy.
Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques
Venue: arXiv cs.SI 2021
Year: 2021

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (\textit{e.g.}, public opinion monitoring, rumor source identification, and viral marketing.) Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (\textit{i.e.,} granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.