arXiv subjects

Ruiqi Wang

Publications and source records attributed to Ruiqi Wang.

At least 91 records · Page 5Linked to original sources

arXiv2023

Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection

Traditional Time-series Anomaly Detection (TAD) methods often struggle with the composite nature of complex time-series data and a diverse array of anomalies. We introduce TADNet, an end-to-end TAD model that leverages Seasonal-Trend Decomposition to link various types of anomalies to specific decomposition components, thereby simplifying the analysis of complex time-series and enhancing detection performance. Our training methodology, which includes pre-training on a synthetic dataset followed by fine-tuning, strikes a balance between effective decomposition and precise anomaly detection. Experimental validation on real-world datasets confirms TADNet's state-of-the-art performance across a diverse range of anomalies.

cs.LG↗

arXiv2023

Initial Task Assignment in Multi-Human Multi-Robot Teams: An Attention-enhanced Hierarchical Reinforcement Learning Approach

Multi-human multi-robot teams (MH-MR) obtain tremendous potential in tackling intricate and massive missions by merging distinct strengths and expertise of individual members. The inherent heterogeneity of these teams necessitates advanced initial task assignment (ITA) methods that align tasks with the intrinsic capabilities of team members from the outset. While existing reinforcement learning approaches show encouraging results, they might fall short in addressing the nuances of long-horizon ITA problems, particularly in settings with large-scale MH-MR teams or multifaceted tasks. To bridge this gap, we propose an attention-enhanced hierarchical reinforcement learning approach that decomposes the complex ITA problem into structured sub-problems, facilitating more efficient allocations. To bolster sub-policy learning, we introduce a hierarchical cross-attribute attention (HCA) mechanism, encouraging each sub-policy within the hierarchy to discern and leverage the specific nuances in the state space that are crucial for its respective decision-making phase. Through an extensive environmental surveillance case study, we demonstrate the benefits of our model and the HCA inside.

cs.RO↗

arXiv2023

Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.

cs.DC↗

arXiv2023

NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning

Developing robotic technologies for use in human society requires ensuring the safety of robots' navigation behaviors while adhering to pedestrians' expectations and social norms. However, maintaining real-time communication between robots and pedestrians to avoid collisions can be challenging. To address these challenges, we propose a novel socially-aware navigation benchmark called NaviSTAR, which utilizes a hybrid Spatio-Temporal grAph tRansformer (STAR) to understand interactions in human-rich environments fusing potential crowd multi-modal information. We leverage off-policy reinforcement learning algorithm with preference learning to train a policy and a reward function network with supervisor guidance. Additionally, we design a social score function to evaluate the overall performance of social navigation. To compare, we train and test our algorithm and other state-of-the-art methods in both simulator and real-world scenarios independently. Our results show that NaviSTAR outperforms previous methods with outstanding performance\footnote{The source code and experiment videos of this work are available at: https://sites.google.com/view/san-navistar

cs.RO↗

arXiv2023

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise. To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multi-robot teams as contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.

cs.RO↗

arXiv2023

Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network

Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into pre-defined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a multi-scale dilated depthwise separable convolutional network (DSCN). The DSCN is a lightweight neural network with small size of parameters and thus suitable to be deployed in portable terminals with limited computing resources. To expand the receptive field with the same size of DSCN's parameters, dilated convolution, instead of normal convolution, is used in the DSCN for further improving the DSCN's performance. In addition, the embeddings of various scales learned by the dilated DSCN are concatenated as a multi-scale embedding for representing property differences among various classes of domestic activities. Evaluated on a public dataset of the Task 5 of the 2018 challenge on Detection and Classification of Acoustic Scenes and Events (DCASE-2018), the results show that: both dilated convolution and multi-scale embedding contribute to the performance improvement of the proposed method; and the proposed method outperforms the methods based on state-of-the-art lightweight network in terms of classification accuracy.

eess.AS↗

arXiv2023

Robot Patrol: Using Crowdsourcing and Robotic Systems to Provide Indoor Navigation Guidance to The Visually Impaired

Indoor navigation is a challenging activity for persons with disabilities, particularly, for those with low vision and visual impairment. Researchers have explored numerous solutions to resolve these challenges; however, several issues remain unsolved, particularly around providing dynamic and contextual information about potential obstacles in indoor environments. In this paper, we developed Robot Patrol, an integrated system that employs a combination of crowdsourcing, computer vision, and robotic frameworks to provide contextual information to the visually impaired to empower them to navigate indoor spaces safely. In particular, the system is designed to provide information to the visually impaired about 1) potential obstacles on the route to their indoor destination, 2) information about indoor events on their route which they may wish to avoid or attend, and 3) any other contextual information that might support them to navigate to their indoor destinations safely and effectively. Findings from the Wizard of Oz experiment of our demo system provide insights into the benefits and limitations of the system. We provide a concise discussion on the implications of our findings.

cs.RO↗

arXiv2023

Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition

Human state recognition is a critical topic with pervasive and important applications in human-machine systems. Multi-modal fusion, the combination of metrics from multiple data sources, has been shown as a sound method for improving the recognition performance. However, while promising results have been reported by recent multi-modal-based models, they generally fail to leverage the sophisticated fusion strategies that would model sufficient cross-modal interactions when producing the fusion representation; instead, current methods rely on lengthy and inconsistent data preprocessing and feature crafting. To address this limitation, we propose an end-to-end multi-modal transformer framework for multi-modal human state recognition called Husformer. Specifically, we propose to use cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Using two such attention mechanisms enables effective and adaptive adjustments to noise and interruptions in multi-modal signals during the fusion process and in relation to high-level features. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive workload datasets (MOCAS and CogLoad) demonstrate that in the recognition of human state, our Husformer outperforms both state-of-the-art multi-modal baselines and the use of a single modality by a large margin, especially when dealing with raw multi-modal signals. We also conducted an ablation study to show the benefits of each component in Husformer.

cs.HC↗

arXiv2023

MECPformer: Multi-estimations Complementary Patch with CNN-Transformers for Weakly Supervised Semantic Segmentation

The initial seed based on the convolutional neural network (CNN) for weakly supervised semantic segmentation always highlights the most discriminative regions but fails to identify the global target information. Methods based on transformers have been proposed successively benefiting from the advantage of capturing long-range feature representations. However, we observe a flaw regardless of the gifts based on the transformer. Given a class, the initial seeds generated based on the transformer may invade regions belonging to other classes. Inspired by the mentioned issues, we devise a simple yet effective method with Multi-estimations Complementary Patch (MECP) strategy and Adaptive Conflict Module (ACM), dubbed MECPformer. Given an image, we manipulate it with the MECP strategy at different epochs, and the network mines and deeply fuses the semantic information at different levels. In addition, ACM adaptively removes conflicting pixels and exploits the network self-training capability to mine potential target information. Without bells and whistles, our MECPformer has reached new state-of-the-art 72.0% mIoU on the PASCAL VOC 2012 and 42.4% on MS COCO 2014 dataset. The code is available at https://github.com/ChunmengLiu1/MECPformer.

cs.CV↗

arXiv2023

A Via-less Fully Screen-Printed Reconfigurable Intelligent Surface for 5G Millimeter Wave Communication

In this paper, we propose a via-less fully screen-printed reconfigurable intelligent surface which can establish a second line-of-sight communication from 23.5GHz to 29.5GHz. By serially connecting the H shaped resonator along the H field of the incident wave, we minimize the effect of the biasing lines and make a via-less design, which reduces the fabrication difficulty and cost. The unit-cell simulation of the array with screen-printed VO2 switches shows a 215° to 160° phase shift difference between the ON and OFF states within bandwidth. During the field testing of the ideal arrays, we verify that the array can redirect the 45° incident wave to 0° reflection with a signal enhancement of at least 10 dB as compared to the array which has all unit cells in the OFF condition.

eess.SP↗

arXiv2022

Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization

With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semi-supervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. When accurate pseudo-labels are generated for unlabeled source domains, the domain mixup operation is applied to augment new domains between labeled and unlabeled domains, which is beneficial for boosting the generalization capability of the model. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method.

cs.CV↗

arXiv2022

Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation

Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching its goal without collisions, is a fundamental yet challenging task in the context of human-robot interaction. While existing learning-based methods have achieved better performance than the preceding model-based ones, they still have drawbacks: reinforcement learning depends on the handcrafted reward that is unlikely to effectively quantify broad social compliance, and can lead to reward exploitation problems; meanwhile, inverse reinforcement learning suffers from the need for expensive human demonstrations. In this paper, we propose a feedback-efficient active preference learning approach, FAPL, that distills human comfort and expectation into a reward model to guide the robot agent to explore latent aspects of social compliance. We further introduce hybrid experience learning to improve the efficiency of human feedback and samples, and evaluate benefits of robot behaviors learned from FAPL through extensive simulation experiments and a user study (N=10) employing a physical robot to navigate with human subjects in real-world scenarios. Source code and experiment videos for this work are available at:https://sites.google.com/view/san-fapl.

cs.RO↗

arXiv2022

Adaptive Edge Offloading for Image Classification Under Rate Limit

This paper considers a setting where embedded devices are used to acquire and classify images. Because of limited computing capacity, embedded devices rely on a parsimonious classification model with uneven accuracy. When local classification is deemed inaccurate, devices can decide to offload the image to an edge server with a more accurate but resource-intensive model. Resource constraints, e.g., network bandwidth, however, require regulating such transmissions to avoid congestion and high latency. The paper investigates this offloading problem when transmissions regulation is through a token bucket, a mechanism commonly used for such purposes. The goal is to devise a lightweight, online offloading policy that optimizes an application-specific metric (e.g., classification accuracy) under the constraints of the token bucket. The paper develops a policy based on a Deep Q-Network (DQN), and demonstrates both its efficacy and the feasibility of its deployment on embedded devices. Of note is the fact that the policy can handle complex input patterns, including correlation in image arrivals and classification accuracy. The evaluation is carried out by performing image classification over a local testbed using synthetic traces generated from the ImageNet image classification benchmark. Implementation of this work is available at https://github.com/qiujiaming315/edgeml-dqn.

cs.DC↗

arXiv2022

SMARTmBOT: A ROS2-based Low-cost and Open-source Mobile Robot Platform

This paper introduces SMARTmBOT, an open-source mobile robot platform based on Robot Operating System 2 (ROS2). The characteristics of the SMARTmBOT, including low-cost, modular-typed, customizable and expandable design, make it an easily achievable and effective robot platform to support broad robotics research and education involving either single-robot or multi-robot systems. The total cost per robot is approximately $210, and most hardware components can be fabricated by a generic 3D printer, hence allowing users to build the robots or replace any broken parts conveniently. The SMARTmBot is also equipped with a rich range of sensors, making it competent for general task scenarios, such as point-to-point navigation and obstacle avoidance. We validated the mobility and function of SMARTmBOT through various robot navigation experiments and applications with tasks including go-to-goal, pure-pursuit, line following, and swarming. All source code necessary for reading sensors, streaming from an embedded camera, and controlling the robot including robot navigation controllers is available through an online repository that can be found at https://github.com/SMARTlab-Purdue/SMARTmBOT.

cs.RO↗

arXiv2021

Towards Interpretable Attention Networks for Cervical Cancer Analysis

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals, including analysis of cervical cancer. Many previous works focus on the analysis of isolated cervical cells, or do not offer sufficient methods to explain and understand how the proposed models reach their classification decisions on multi-cell images. Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells. As we aim to provide interpretable deep learning models to address this task, we also compare their explainability through the visualization of their gradients. We demonstrate the importance of using images that contain multiple cells over using isolated single-cell images. We show the effectiveness of the residual channel attention model for extracting important features from a group of cells, and demonstrate this model's efficiency for this classification task. This work highlights the benefits of channel attention mechanisms in analyzing multiple-cell images for potential relations and distributions within a group of cells. It also provides interpretable models to address the classification of cervical cells.

cs.CV↗

arXiv2021

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems

Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders' nuanced viewpoints in real-world contexts. To address this gap, we propose a framework for eliciting stakeholders' subjective fairness notions. Combining a user interface that allows stakeholders to examine the data and the algorithm's predictions with an interview protocol to probe stakeholders' thoughts while they are interacting with the interface, we can identify stakeholders' fairness beliefs and principles. We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system. Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints. We also discuss how our results can inform the design of predictive systems.

cs.HC↗

arXiv2020

Time-constrained Adaptive Influence Maximization

The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process. In its adaptive version, additional seed users can be selected after observing certain diffusion results. On the other hand, social computing tasks are often time-critical, and therefore only the influence resulted in the early period is worthwhile, which can be naturally modeled by enforcing a time constraint. In this paper, we present an analysis of the time-constrained adaptive influence maximization problem. We show that the new problem is combinatorially different from the existing problems, and the current techniques such as submodular maximization and adaptive submodularity are unfortunately inapplicable. On the theory side, we provide the hardness results of computing the optimal policy and a lower bound on the adaptive gap. For practical solutions, from basic to advanced, we design a series of seeding policies for achieving high efficacy and scalability. Finally, we investigate the proposed solutions through extensive simulations based on real-world datasets.

cs.SI↗

arXiv2020

SocialGrid: A TCN-enhanced Method for Online Discussion Forecasting

As a means of modern communication tools, online discussion forums have become an increasingly popular platform that allows asynchronous online interactions. People share thoughts and opinions through posting threads and replies, which form a unique communication structure between main threads and associated replies. It is significant to understand the information diffusion pattern under such a communication structure, where an essential task is to predict the arrival time of future events. In this work, we proposed a novel yet simple framework, called SocialGrid, for modeling events in online discussing forms. Our framework first transforms the entire event space into a grid representation by grouping successive evens in one time interval of a particular length. Based on the nature of the grid, we leverage the Temporal Convolution Network to learn the dynamics at the grid level. Varying the temporal scope of an individual grid, the learned grid model can be used to predict the arrival time of future events at different granularities. Leveraging the Reddit data, we validate the proposed method through experiments on a series of applications. Extensive experiments and a real-world application. Results have shown that our framework excels at various cascade prediction tasks comparing with other approaches.

cs.SI↗