OpenRaiser/Intern-Atlas huggingface dataset:
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets
Venue: arXiv cs.CL 2021
Year: 2021

Advancing the utility of social media data for research applications requires methods for automatically detecting demographic information about social media study populations, including users' age. The objective of this study was to develop and evaluate a method that automatically identifies the exact age of users based on self-reports in their tweets. Our end-to-end automatic natural language processing (NLP) pipeline, ReportAGE, includes query patterns to retrieve tweets that potentially mention an age, a classifier to distinguish retrieved tweets that self-report the user's exact age ("age" tweets) and those that do not ("no age" tweets), and rule-based extraction to identify the age. To develop and evaluate ReportAGE, we manually annotated 11,000 tweets that matched the query patterns. Based on 1000 tweets that were annotated by all five annotators, inter-annotator agreement (Fleiss' kappa) was 0.80 for distinguishing "age" and "no age" tweets, and 0.95 for identifying the exact age among the "age" tweets on which the annotators agreed. A deep neural network classifier, based on a RoBERTa-Large pretrained model, achieved the highest F1-score of 0.914 (precision = 0.905, recall = 0.942) for the "age" class. When the age extraction was evaluated using the classifier's predictions, it achieved an F1-score of 0.855 (precision = 0.805, recall = 0.914) for the "age" class. When it was evaluated directly on the held-out test set, it achieved an F1-score of 0.931 (precision = 0.873, recall = 0.998) for the "age" class. We deployed ReportAGE on more than 1.2 billion tweets posted by 245,927 users, and predicted ages for 132,637 (54%) of them. Scaling the detection of exact age to this large number of users can advance the utility of social media data for research applications that do not align with the predefined age groupings of extant binary or multi-class classification approaches.
Hiding Leader's Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning
Venue: arXiv cs.RO 2021
Year: 2021

Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission-critical information (e.g. goal location) that the followers lack, and this makes the leader crucial for the mission's success. However, this also makes the leader a vulnerability - an external adversary who wishes to sabotage the robot team's mission can simply harm the leader and the whole robot team's mission would be compromised. Since robot motion generated by traditional leader-follower navigation algorithms can reveal the identity of the leader, we propose a defense mechanism of hiding the leader's identity by ensuring the leader moves in a way that behaviorally camouflages it with the followers, making it difficult for an adversary to identify the leader. To achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural Networks and adversarial training. Our approach enables the multi-robot team to optimize the primary task performance with leader motion similar to follower motion, behaviorally camouflaging it with the followers. Our algorithm outperforms existing work that tries to hide the leader's identity in a multi-robot team by tuning traditional leader-follower control parameters with Classical Genetic Algorithms. We also evaluated human performance in inferring the leader's identity and found that humans had lower accuracy when the robot team used our proposed navigation algorithm.
Structure-From-Motion and RGBD Depth Fusion
Venue: arXiv cs.CV 2021
Year: 2021

This article describes a technique to augment a typical RGBD sensor by integrating depth estimates obtained via Structure-from-Motion (SfM) with sensor depth measurements. Limitations in the RGBD depth sensing technology prevent capturing depth measurements in four important contexts: (1) distant surfaces (>5m), (2) dark surfaces, (3) brightly lit indoor scenes and (4) sunlit outdoor scenes. SfM technology computes depth via multi-view reconstruction from the RGB image sequence alone. As such, SfM depth estimates do not suffer the same limitations and may be computed in all four of the previously listed circumstances. This work describes a novel fusion of RGBD depth data and SfM-estimated depths to generate an improved depth stream that may be processed by one of many important downstream applications such as robotic localization and mapping, as well as object recognition and tracking.
An electromechanically coupled beam model for dielectric elastomer actuators
Venue: arXiv cs.CE 2021
Year: 2021

In this work, the Cosserat formulation of geometrically exact beam dynamics is extended by adding the electric potential as an additional degree of freedom to account for the electromechanical coupling in the Dielectric Elastomer Actuators (DEAs). To be able to generate complex beam deformations via dielectric actuator, a linear distribution of electric potential on the beam cross section is proposed. Based on this electric potential, the electric field and the strain-like electrical variable are defined for the beam, where the strain-like electrical variable is work-conjugated to the electric displacement. The electromechanically coupled strain energy for the beam is derived consistently from continuum electromechanics, which leads to the direct application of the material models in the continuum to the beam model. The electromechanically coupled problem in beam dynamics is first spatially semidiscretized by 1D finite elements and then solved via variational time integration. By applying different electrical boundary conditions, different deformations of the beam are obtained in the numerical examples, including contraction, shear, bending and torsion. The damping effect induced by the viscosity as well as the total energy of the beam are evaluated. The deformations of the electromechanically coupled beam model are compared with the results of the 3D finite element model, where a good agreement of the deformations in the beam model and that in the 3D finite element model is observed. However, less degrees of freedom are required to resolve the complex deformations in the beam model.
Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education
Venue: arXiv cs.HC 2021
Year: 2021

Trust in a recommendation system (RS) is often algorithmically incorporated using implicit or explicit feedback of user-perceived trustworthy social neighbors, and evaluated using user-reported trustworthiness of recommended items. However, real-life recommendation settings can feature group disparities in trust, power, and prerogatives. Our study examines a complementary view of trust which relies on the editorial power relationships and attitudes of all stakeholders in the RS application domain. We devise a simple, first-principles metric of editorial authority, i.e., user preferences for recommendation sourcing, veto power, and incorporating user feedback, such that one RS user group confers trust upon another by ceding or assigning editorial authority. In a mixed-methods study at Virginia Tech, we surveyed faculty, teaching assistants, and students about their preferences of editorial authority, and hypothesis-tested its relationship with trust in algorithms for a hypothetical `Suggested Readings' RS. We discover that higher RS editorial authority assigned to students is linked to the relative trust the course staff allocates to RS algorithm and students. We also observe that course staff favors higher control for the RS algorithm in sourcing and updating the recommendations long-term. Using content analysis, we discuss frequent staff-recommended student editorial roles and highlight their frequent rationales, such as perceived expertise, scaling the learning environment, professional curriculum needs, and learner disengagement. We argue that our analyses highlight critical user preferences to help detect editorial power asymmetry and identify RS use-cases for supporting teaching and research
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning
Venue: arXiv cs.LG 2021
Year: 2021

Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods. This is because meta-training needs to deal with more diverse distributions and train extra components such as context encoders. To address this, we propose a novel self-supervised learning task, which we named Trajectory Contrastive Learning (TCL), to improve meta-training. TCL adopts contrastive learning and trains a context encoder to predict whether two transition windows are sampled from the same trajectory. TCL leverages the natural hierarchical structure of context-based meta-RL and makes minimal assumptions, allowing it to be generally applicable to context-based meta-RL algorithms. It accelerates the training of context encoders and improves meta-training overall. Experiments show that TCL performs better or comparably than a strong meta-RL baseline in most of the environments on both meta-RL MuJoCo (5 of 6) and Meta-World benchmarks (44 out of 50).
LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation
Venue: arXiv cs.CL 2021
Year: 2021

The multilingual pre-trained language models (e.g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks. However, these models are computationally intensive and difficult to be deployed on resource-restricted devices. In this paper, we propose a simple yet effective distillation method (LightMBERT) for transferring the cross-lingual generalization ability of the multilingual BERT to a small student model. The experiment results empirically demonstrate the efficiency and effectiveness of LightMBERT, which is significantly better than the baselines and performs comparable to the teacher mBERT.
Robust 2D/3D Vehicle Parsing in CVIS
Venue: arXiv cs.CV 2021
Year: 2021

We present a novel approach to robustly detect and perceive vehicles in different camera views as part of a cooperative vehicle-infrastructure system (CVIS). Our formulation is designed for arbitrary camera views and makes no assumptions about intrinsic or extrinsic parameters. First, to deal with multi-view data scarcity, we propose a part-assisted novel view synthesis algorithm for data augmentation. We train a part-based texture inpainting network in a self-supervised manner. Then we render the textured model into the background image with the target 6-DoF pose. Second, to handle various camera parameters, we present a new method that produces dense mappings between image pixels and 3D points to perform robust 2D/3D vehicle parsing. Third, we build the first CVIS dataset for benchmarking, which annotates more than 1540 images (14017 instances) from real-world traffic scenarios. We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS. In practice, our approach outperforms SOTA methods on 2D detection, instance segmentation, and 6-DoF pose estimation, by 4.5%, 4.3%, and 2.9%, respectively. More details and results are included in the supplement. To facilitate future research, we will release the source code and the dataset on GitHub.
Optimizing Fund Allocation for Game-based Verifiable Computation Outsourcing
Venue: arXiv cs.GT 2021
Year: 2021

This paper considers the setting where a cloud server services a static set or a dynamic sequence of tasks submitted by multiple clients. Every client wishes to assure honest execution of tasks by additionally employing a trusted third party (TTP) to re-compute the tasks with a certain probability. The cloud server makes a deposit for each task it takes, each client allocates a budget (including the wage for the server and the cost for possibly hiring TTP) for each task submitted, and every party has its limited fund for either deposits or task budgets. We study how to allocate the funds optimally to achieve the three-fold goals: a rational cloud server honestly computes each task; the server's wage is maximized; the overall delay for task verification is minimized. We apply game theory to formulate the optimization problems, and develop the optimal or heuristic solutions for three application scenarios. For each of the solutions, we analyze it through either rigorous proofs or extensive simulations. To the best of our knowledge, this is the first work on optimizing fund allocation for verifiable outsourcing of computation in the setting of one server and multiple clients, based on game theory.
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT
Venue: arXiv cs.NI 2021
Year: 2021

Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streaming, VR is with ultra high-definition and dynamically changes with users' head and eye movements, which poses significant challenges for the realization of such potential. In this paper, we provide a detailed and systematic survey of enabling technologies of virtual reality and its applications in Internet of Things (IoT). We identify major challenges of virtual reality on system design, view prediction, computation, streaming, and quality of experience evaluation. We discuss each of them by extensively surveying and reviewing related papers in the recent years. We also introduce several use cases of VR for IoT. Last, issues and future research directions are also identified and discussed.
Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement
Venue: arXiv cs.CV 2021
Year: 2021

The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i.e.style feature, and the feature representing the invariant semantic content, i.e. content feature. Previous methods separate content feature apart by utilizing it to classify haze image during the training process. However, in this paper we recognize the incompleteness of the content-style disentanglement in such technical routine. The flawed style feature entangled with content information inevitably leads the ill-rendering of the haze images. To address, we propose a self-supervised style regression via stochastic linear interpolation to reduce the content information in style feature. The ablative experiments demonstrate the disentangling completeness and its superiority in level-aware haze image synthesis. Moreover, the generated haze data are applied in the testing generalization of vehicle detectors. Further study between haze-level and detection performance shows that haze has obvious impact on the generalization of the vehicle detectors and such performance degrading level is linearly correlated to the haze-level, which, in turn, validates the effectiveness of the proposed method.
Fast-Tracker 2.0: Improving Autonomy of Aerial Tracking with Active Vision and Human Location Regression
Venue: arXiv cs.RO 2021
Year: 2021

In recent years, several progressive works promote the development of aerial tracking. One of the representative works is our previous work Fast-tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: 1) the over simplification in target detection by using artificial markers and 2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this paper, we upgrade the target detection in Fast-tracker to detect and localize a human target based on deep learning and non-linear regression to solve the former problem. For the latter one, we equip the quadrotor system with 360 degree active vision on a customized gimbal camera. Furthermore, we improve the tracking trajectory planning in Fast-tracker by incorporating an occlusion-aware mechanism that generates observable tracking trajectories. Comprehensive real-world tests confirm the proposed system's robustness and real-time capability. Benchmark comparisons with Fast-tracker validate that the proposed system presents better tracking performance even when performing more difficult tracking tasks.
Hitting minors on bounded treewidth graphs. II. Single-exponential algorithms
Venue: arXiv cs.DS 2021
Year: 2021

For a finite collection of graphs ${\cal F}$, the ${\cal F}$-M-DELETION (resp. ${\cal F}$-TM-DELETION) problem consists in, given a graph $G$ and an integer $k$, decide whether there exists $S \subseteq V(G)$ with $|S| \leq k$ such that $G \setminus S$ does not contain any of the graphs in ${\cal F}$ as a minor (resp. topological minor). We are interested in the parameterized complexity of both problems when the parameter is the treewidth of $G$, denoted by $tw$, and specifically in the cases where ${\cal F}$ contains a single connected planar graph $H$. We present algorithms running in time $2^{O(tw)} \cdot n^{O(1)}$, called single-exponential, when $H$ is either $P_3$, $P_4$, $C_4$, the paw, the chair, and the banner for both $\{H\}$-M-DELETION and $\{H\}$-TM-DELETION, and when $H=K_{1,i}$, with $i \geq 1$, for $\{H\}$-TM-DELETION. Some of these algorithms use the rank-based approach introduced by Bodlaender et al. [Inform Comput, 2015]. This is the second of a series of articles on this topic, and the results given here together with other ones allow us, in particular, to provide a tight dichotomy on the complexity of $\{H\}$-M-DELETION in terms of $H$.
An Optimal-Transport-Based Reinforcement Learning Approach for Computation Offloading
Venue: arXiv cs.NI 2021
Year: 2021

With the mass deployment of computing-intensive applications and delay-sensitive applications on end devices, only adequate computing resources can meet differentiated services' delay requirements. By offloading tasks to cloud servers or edge servers, computation offloading can alleviate computing and storage limitations and reduce delay and energy consumption. However, few of the existing offloading schemes take into consideration the cloud-edge collaboration and the constraint of energy consumption and task dependency. This paper builds a collaborative computation offloading model in cloud and edge computing and formulates a multi-objective optimization problem. Constructed by fusing optimal transport and Policy-Based RL, we propose an Optimal-Transport-Based RL approach to resolve the offloading problem and make the optimal offloading decision for minimizing the overall cost of delay and energy consumption. Simulation results show that the proposed approach can effectively reduce the cost and significantly outperforms existing optimization solutions.
Hitting minors on bounded treewidth graphs. III. Lower bounds
Venue: arXiv cs.DS 2021
Year: 2021

For a finite collection of graphs ${\cal F}$, the ${\cal F}$-M-DELETION problem consists in, given a graph $G$ and an integer $k$, decide whether there exists $S \subseteq V(G)$ with $|S| \leq k$ such that $G \setminus S$ does not contain any of the graphs in ${\cal F}$ as a minor. We are interested in the parameterized complexity of ${\cal F}$-M-DELETION when the parameter is the treewidth of $G$, denoted by $tw$. Our objective is to determine, for a fixed ${\cal F}$, the smallest function $f_{\cal F}$ such that ${\cal F}$-M-DELETION can be solved in time $f_{\cal F}(tw) \cdot n^{O(1)}$ on $n$-vertex graphs. We provide lower bounds under the ETH on $f_{\cal F}$ for several collections ${\cal F}$. We first prove that for any ${\cal F}$ containing connected graphs of size at least two, $f_{\cal F}(tw)= 2^{Ω(tw)}$, even if the input graph $G$ is planar. Our main contribution consists of superexponential lower bounds for a number of collections ${\cal F}$, inspired by a reduction of Bonnet et al.~[IPEC, 2017]. In particular, we prove that when ${\cal F}$ contains a single connected graph $H$ that is either $P_5$ or is not a minor of the banner (that is, the graph consisting of a $C_4$ plus a pendent edge), then $f_{\cal F}(tw)= 2^{Ω(tw \cdot \log tw)}$. This is the third of a series of articles on this topic, and the results given here together with other ones allow us, in particular, to provide a tight dichotomy on the complexity of $\{H\}$-M-DELETION, in terms of $H$, when $H$ is connected.
MPU: Towards Bandwidth-abundant SIMT Processor via Near-bank Computing
Venue: arXiv cs.AR 2021
Year: 2021

With the growing number of data-intensive workloads, GPU, which is the state-of-the-art single-instruction-multiple-thread (SIMT) processor, is hindered by the memory bandwidth wall. To alleviate this bottleneck, previously proposed 3D-stacking near-bank computing accelerators benefit from abundant bank-internal bandwidth by bringing computations closer to the DRAM banks. However, these accelerators are specialized for certain application domains with simple architecture data paths and customized software mapping schemes. For general purpose scenarios, lightweight hardware designs for diverse data paths, architectural supports for the SIMT programming model, and end-to-end software optimizations remain challenging. To address these issues, we propose MPU (Memory-centric Processing Unit), the first SIMT processor based on 3D-stacking near-bank computing architecture. First, to realize diverse data paths with small overheads while leveraging bank-level bandwidth, MPU adopts a hybrid pipeline with the capability of offloading instructions to near-bank compute-logic. Second, we explore two architectural supports for the SIMT programming model, including a near-bank shared memory design and a multiple activated row-buffers enhancement. Third, we present an end-to-end compilation flow for MPU to support CUDA programs. To fully utilize MPU's hybrid pipeline, we develop a backend optimization for the instruction offloading decision. The evaluation results of MPU demonstrate 3.46x speedup and 2.57x energy reduction compared with an NVIDIA Tesla V100 GPU on a set of representative data-intensive workloads.
Exploring the Mysteries of System-Level Test
Venue: arXiv cs.AR 2021
Year: 2021

System-level test, or SLT, is an increasingly important process step in today's integrated circuit testing flows. Broadly speaking, SLT aims at executing functional workloads in operational modes. In this paper, we consolidate available knowledge about what SLT is precisely and why it is used despite its considerable costs and complexities. We discuss the types or failures covered by SLT, and outline approaches to quality assessment, test generation and root-cause diagnosis in the context of SLT. Observing that the theoretical understanding for all these questions has not yet reached the level of maturity of the more conventional structural and functional test methods, we outline new and promising directions for methodical developments leveraging on recent findings from software engineering.
Arc-Completion of 2-Colored Best Match Graphs to Binary-Explainable Best Match Graphs
Venue: arXiv cs.DS 2021
Year: 2021

Best match graphs (BMGs) are vertex-colored digraphs that naturally arise in mathematical phylogenetics to formalize the notion of evolutionary closest genes w.r.t. an a priori unknown phylogenetic tree. BMGs are explained by unique least resolved trees. We prove that the property of a rooted, leaf-colored tree to be least resolved for some BMG is preserved by the contraction of inner edges. For the special case of two-colored BMGs, this leads to a characterization of the least resolved trees (LRTs) of binary-explainable trees and a simple, polynomial-time algorithm for the minimum cardinality completion of the arc set of a BMG to reach a BMG that can be explained by a binary tree.