OpenRaiser/Intern-Atlas huggingface dataset:
Robotics Enabling the Workforce
Venue: arXiv cs.RO 2020
Year: 2020

Robotics has the potential to magnify the skilled workforce of the nation by complementing our workforce with automation: teams of people and robots will be able to do more than either could alone. The economic engine of the U.S. runs on the productivity of our people. The rise of automation offers new opportunities to enhance the work of our citizens and drive the innovation and prosperity of our industries. Most critically, we need research to understand how future robot technologies can best complement our workforce to get the best of both human and automated labor in a collaborative team. Investments made in robotics research and workforce development will lead to increased GDP, an increased export-import ratio, a growing middle class of skilled workers, and a U.S.-based supply chain that can withstand global pandemics and other disruptions. In order to make the United States a leader in robotics, we need to invest in basic research, technology development, K-16 education, and lifelong learning.
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems
Venue: arXiv cs.LG 2020
Year: 2020

Testing remains the primary method to evaluate the accuracy of neural network perception systems. Prior work on the formal verification of neural network perception models has been limited to notions of local adversarial robustness for classification with respect to individual image inputs. In this work, we propose a notion of global correctness for neural network perception models performing regression with respect to a generative neural network with a semantically meaningful latent space. That is, against an infinite set of images produced by a generative model over an interval of its latent space, we employ neural network verification to prove that the model will always produce estimates within some error bound of the ground truth. Where the perception model fails, we obtain semantically meaningful counter-examples which carry information on concrete states of the system of interest that can be used programmatically without human inspection of corresponding generated images. Our approach, Generate and Verify, provides a new technique to gather insight into the failure cases of neural network perception systems and provide meaningful guarantees of correct behavior in safety critical applications.
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Venue: arXiv cs.LG 2020
Year: 2020

Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models. Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider the interpretation in the feature dimension while ignoring the crucial temporal dimension. In this paper, we present the series saliency framework for temporal interpretation for multivariate time series forecasting, which considers the forecasting interpretation in both feature and temporal dimensions. By extracting the "series images" from the sliding windows of the time series, we apply the saliency map segmentation following the smallest destroying region principle. The series saliency framework can be employed to any well-defined deep learning models and works as a data augmentation to get more accurate forecasts. Experimental results on several real datasets demonstrate that our framework generates temporal interpretations for the time series forecasting task while produces accurate time series forecast.
Learning to Recover 3D Scene Shape from a Single Image
Venue: arXiv cs.CV 2020
Year: 2020

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. We investigate this problem in detail, and propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then use 3D point cloud encoders to predict the missing depth shift and focal length that allow us to recover a realistic 3D scene shape. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to enhance depth prediction models trained on mixed datasets. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot dataset generalization. Code is available at: https://git.io/Depth
The Quad Layout Immersion: A Mathematically Equivalent Representation of a Surface Quadrilateral Layout
Venue: arXiv cs.CG 2020
Year: 2020

Quadrilateral layouts on surfaces are valuable in texture mapping, and essential in generation of quadrilateral meshes and in fitting splines. Previous work has characterized such layouts as a special metric on a surface or as a meromorphic quartic differential with finite trajectories. In this work, a surface quadrilateral layout is alternatively characterized as a special immersion of a cut representation of the surface into the Euclidean plane. We call this a quad layout immersion. This characterization, while posed in smooth topology, naturally generalizes to piecewise-linear representations. As such, it mathematically describes and generalizes integer grid maps, which are common in computer graphics settings. Finally, the utility of the representation is demonstrated by computationally extracting quadrilateral layouts on surfaces of interest.
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Distant supervision makes it possible to automatically label bags of sentences for relation extraction by leveraging knowledge bases, but suffers from the sparse and noisy bag issues. Additional information sources are urgently needed to supplement the training data and overcome these issues. In this paper, we introduce two widely-existing sources in knowledge bases, namely entity descriptions, and multi-grained entity types to enrich the distantly supervised data. We see information sources as multiple views and fusing them to construct an intact space with sufficient information. An end-to-end multi-view learning framework is proposed for relation extraction via Intact Space Representation Learning (InSRL), and the representations of single views are jointly learned simultaneously. Moreover, inner-view and cross-view attention mechanisms are used to highlight important information on different levels on an entity-pair basis. The experimental results on a popular benchmark dataset demonstrate the necessity of additional information sources and the effectiveness of our framework. We will release the implementation of our model and dataset with multiple information sources after the anonymized review phase.
KHOVID: Interoperable Privacy Preserving Digital Contact Tracing
Venue: arXiv cs.CR 2020
Year: 2020

During a pandemic, contact tracing is an essential tool to drive down the infection rate within a population. To accelerate the laborious manual contact tracing process, digital contact tracing (DCT) tools can track contact events transparently and privately by using the sensing and signaling capabilities of the ubiquitous cell phone. However, an effective DCT must not only preserve user privacy but also augment the existing manual contact tracing process. Indeed, not every member of a population may own a cell phone or have a DCT app installed and enabled. We present KHOVID to fulfill the combined goal of manual contact-tracing interoperability and DCT user privacy. At KHOVID's core is a privacy-friendly mechanism to encode user trajectories using geolocation data. Manual contact tracing data can be integrated through the same geolocation format. The accuracy of the geolocation data from DCT is improved using Bluetooth proximity detection, and we propose a novel method to encode Bluetooth ephemeral IDs. This contribution describes the detailed design of KHOVID; presents a prototype implementation including an app and server software; and presents a validation based on simulation and field experiments. We also compare the strengths of KHOVID with other, earlier proposals of DCT.
Node Failure Localization: Theorem Proof
Venue: arXiv cs.NI 2020
Year: 2020

This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7.3 Performance, 2015.
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.
LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos
Venue: arXiv cs.CV 2020
Year: 2020

Analyzing the interactions between humans and objects from a video includes identification of the relationships between humans and the objects present in the video. It can be thought of as a specialized version of Visual Relationship Detection, wherein one of the objects must be a human. While traditional methods formulate the problem as inference on a sequence of video segments, we present a hierarchical approach, LIGHTEN, to learn visual features to effectively capture spatio-temporal cues at multiple granularities in a video. Unlike current approaches, LIGHTEN avoids using ground truth data like depth maps or 3D human pose, thus increasing generalization across non-RGBD datasets as well. Furthermore, we achieve the same using only the visual features, instead of the commonly used hand-crafted spatial features. We achieve state-of-the-art results in human-object interaction detection (88.9% and 92.6%) and anticipation tasks of CAD-120 and competitive results on image based HOI detection in V-COCO dataset, setting a new benchmark for visual features based approaches. Code for LIGHTEN is available at https://github.com/praneeth11009/LIGHTEN-Learning-Interactions-with-Graphs-and-Hierarchical-TEmporal-Networks-for-HOI
Temporal LiDAR Frame Prediction for Autonomous Driving
Venue: arXiv cs.CV 2020
Year: 2020

Anticipating the future in a dynamic scene is critical for many fields such as autonomous driving and robotics. In this paper we propose a class of novel neural network architectures to predict future LiDAR frames given previous ones. Since the ground truth in this application is simply the next frame in the sequence, we can train our models in a self-supervised fashion. Our proposed architectures are based on FlowNet3D and Dynamic Graph CNN. We use Chamfer Distance (CD) and Earth Mover's Distance (EMD) as loss functions and evaluation metrics. We train and evaluate our models using the newly released nuScenes dataset, and characterize their performance and complexity with several baselines. Compared to directly using FlowNet3D, our proposed architectures achieve CD and EMD nearly an order of magnitude lower. In addition, we show that our predictions generate reasonable scene flow approximations without using any labelled supervision.
PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection
Venue: arXiv cs.CV 2020
Year: 2020

Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure. Semantics come from the appearance and context of objects to the sensor, while geometric structure is the actual 3D shape of point clouds. Most detectors on LiDAR point clouds focus only on analyzing the geometric structure of objects in real 3D space. Unlike previous works, we propose to learn both semantic feature and geometric structure via a unified multi-view framework. Our method exploits the nature of LiDAR scans -- 2D range images, and applies well-studied 2D convolutions to extract semantic features. By fusing semantic and geometric features, our method outperforms state-of-the-art approaches in all categories by a large margin. The methodology of combining semantic and geometric features provides a unique perspective of looking at the problems in real-world 3D point cloud detection.
Enhancing Balanced Graph Edge Partition with Effective Local Search
Venue: arXiv cs.DS 2020
Year: 2020

Graph partition is a key component to achieve workload balance and reduce job completion time in parallel graph processing systems. Among the various partition strategies, edge partition has demonstrated more promising performance in power-law graphs than vertex partition and thereby has been more widely adopted as the default partition strategy by existing graph systems. The graph edge partition problem, which is to split the edge set into multiple balanced parts to minimize the total number of copied vertices, has been widely studied from the view of optimization and algorithms. In this paper, we study local search algorithms for this problem to further improve the partition results from existing methods. More specifically, we propose two novel concepts, namely adjustable edges and blocks. Based on these, we develop a greedy heuristic as well as an improved search algorithm utilizing the property of the max-flow model. To evaluate the performance of our algorithms, we first provide adequate theoretical analysis in terms of the approximation quality. We significantly improve the previously known approximation ratio for this problem. Then we conduct extensive experiments on a large number of benchmark datasets and state-of-the-art edge partition strategies. The results show that our proposed local search framework can further improve the quality of graph partition by a wide margin.
Clique Is Hard on Average for Regular Resolution
Venue: arXiv cs.CC 2020
Year: 2020

We prove that for $k \ll \sqrt[4]{n}$ regular resolution requires length $n^{Ω(k)}$ to establish that an Erdős-Rényi graph with appropriately chosen edge density does not contain a $k$-clique. This lower bound is optimal up to the multiplicative constant in the exponent, and also implies unconditional $n^{Ω(k)}$ lower bounds on running time for several state-of-the-art algorithms for finding maximum cliques in graphs.
Embodied Visual Active Learning for Semantic Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding by actively selecting views for which to request annotation. While accurate on some benchmarks, today's deep visual recognition pipelines tend to not generalize well in certain real-world scenarios, or for unusual viewpoints. Robotic perception, in turn, requires the capability to refine the recognition capabilities for the conditions where the mobile system operates, including cluttered indoor environments or poor illumination. This motivates the proposed task, where an agent is placed in a novel environment with the objective of improving its visual recognition capability. To study embodied visual active learning, we develop a battery of agents - both learnt and pre-specified - and with different levels of knowledge of the environment. The agents are equipped with a semantic segmentation network and seek to acquire informative views, move and explore in order to propagate annotations in the neighbourhood of those views, then refine the underlying segmentation network by online retraining. The trainable method uses deep reinforcement learning with a reward function that balances two competing objectives: task performance, represented as visual recognition accuracy, which requires exploring the environment, and the necessary amount of annotated data requested during active exploration. We extensively evaluate the proposed models using the photorealistic Matterport3D simulator and show that a fully learnt method outperforms comparable pre-specified counterparts, even when requesting fewer annotations.
Digital Detox -- Mitigating Digital Overuse in Times of Remote Work and Social Isolation
Venue: arXiv cs.SI 2020
Year: 2020

Remote work arrangements and limited recreational options in times of social isolation increase the risk of digital overuse for individuals. Its consequences can range from impaired mental health to issues of technology addiction. A conformant countermovement has popularised digital detoxing, a practice that endorses to deliberately limit technology use to reduce digital involvement and physiological stress. In times of social isolation, however, digital networking may provide the principle access to social interactions. To provide empirical evidence about the sweet spot between mitigating digital overuse and perceived social connectedness, this paper proposes a mixed-methods design to scrutinise the impact of digital detox measures in a professional context. Possible results will help to better understand how digital overuse may effectively be mitigated by remote workers and what measures organisations can take to create a digital environment that supports employee satisfaction and mental health.
Conspiracy Machines -- The Role of Social Bots during the COVID-19 Infodemic
Venue: arXiv cs.SI 2020
Year: 2020

The omnipresent COVID-19 pandemic gave rise to a parallel spreading of misinformation, also referred to as an Infodemic. Consequently, social media have become targets for the application of social bots, that is, algorithms that mimic human behaviour. Their ability to exert influence on social media can be exploited by amplifying misinformation, rumours, or conspiracy theories which might be harmful to society and the mastery of the pandemic. By applying social bot detection and content analysis techniques, this study aims to determine the extent to which social bots interfere with COVID- 19 discussions on Twitter. A total of 78 presumptive bots were detected within a sample of 542,345 users. The analysis revealed that bot-like users who disseminate misinformation, at the same time, intersperse news from renowned sources. The findings of this research provide implications for improved bot detection and managing potential threats through social bots during ongoing and future crises.
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets
Venue: arXiv cs.DC 2020
Year: 2020

e-Infrastructures have powered the successful penetration of e-services across domains, and form the backbone of the modern computing landscape. e-Infrastructure is a broad term used for large, medium and small scale computing environments. The increasing sophistication and complexity of applications have led to even small-scale data centers consisting of thousands of interconnects. However, efficient utilization of resources in data centers remains a challenging task, mainly due to the complexity of managing physical nodes, network equipment, cooling systems, electricity, etc. This results in a very strong carbon footprint of this industry. In recent years, efforts based on machine learning approaches have shown promising results towards reducing energy consumption of data centers. Yet, practical solutions that can help data center operators in offering energy efficient services are lacking. This problem is more visible in the context of medium and small scale data center operators (the long tail of e-infrastructure providers). Additionally, a disconnect between solution providers (machine learning experts) and data center operators has been observed. This article presents a community-driven open source software framework that allows community members to develop better understanding of various aspects of resource utilization. The framework leverages machine learning models for forecasting and optimizing various parameters of data center operations, enabling improved efficiency, quality of service and lower energy consumption. Also, the proposed framework does not require datasets to be shared, which alleviates the extra effort of organizing, describing and anonymizing data in an appropriate format.
Validate and Enable Machine Learning in Industrial AI
Venue: arXiv cs.LG 2020
Year: 2020

Industrial Artificial Intelligence (Industrial AI) is an emerging concept which refers to the application of artificial intelligence to industry. Industrial AI promises more efficient future industrial control systems. However, manufacturers and solution partners need to understand how to implement and integrate an AI model into the existing industrial control system. A well-trained machine learning (ML) model provides many benefits and opportunities for industrial control optimization; however, an inferior Industrial AI design and integration limits the capability of ML models. To better understand how to develop and integrate trained ML models into the traditional industrial control system, test the deployed AI control system, and ultimately outperform traditional systems, manufacturers and their AI solution partners need to address a number of challenges. Six top challenges, which were real problems we ran into when deploying Industrial AI, are explored in the paper. The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models, and more importantly, how to address such challenges in an Industrial AI system.
Who is more ready to get back in shape?
Venue: arXiv cs.LG 2020
Year: 2020

This empirical study estimates resilience (adaptive capacity) around the periods of the 2013 heavy flood in Cambodia. We use nearly 1.2 million microfinance institution (MFI) customer data and implement the unsupervised learning method. Our results highlight the opportunity to develop resilience by having a better understanding of which areas are likely to be more or less resilient based on the characteristics of the MFI customers, and the individual choices or situations that support stronger adaptiveness. We also discuss the limitation of this approach.
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory
Venue: arXiv cs.AI 2020
Year: 2020

In this work, we report the practical and theoretical aspects of Explainable AI (XAI) identified in some fundamental literature. Although there is a vast body of work on representing the XAI backgrounds, most of the corpuses pinpoint a discrete direction of thoughts. Providing insights into literature in practice and theory concurrently is still a gap in this field. This is important as such connection facilitates a learning process for the early stage XAI researchers and give a bright stand for the experienced XAI scholars. Respectively, we first focus on the categories of black-box explanation and give a practical example. Later, we discuss how theoretically explanation has been grounded in the body of multidisciplinary fields. Finally, some directions of future works are presented.