Jiming Chen

Jiming Chen
University of Electronic Science and Technology of China | UESTC · Communication and information system

PhD

About

561
Publications
129,668
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
20,083
Citations
Introduction
Skills and Expertise

Publications

Publications (561)
Preprint
The Operating System (OS) kernel is foundational in modern computing, especially with the proliferation of diverse computing devices. However, its development also comes with vulnerabilities that can lead to severe security breaches. Kernel fuzzing, a technique used to uncover these vulnerabilities, poses distinct challenges when compared to usersp...
Preprint
Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination. Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills. In this paper, we introduce VTAO-BiM...
Article
Voltage collapse is a critical form of system instability in power systems, occurring when power generation is unable to meet power demand, resulting in considerable socio-economic impacts. Current methodologies for studying voltage collapse primarily utilize simulation-based approaches. While informative, they offer little theoretical insights int...
Article
3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementarily, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for weather-robust 3D human reconstruction is still an open cha...
Article
The widespread use of drones has provided numerous benefits, but it has also raised critical concerns regarding public safety and personal privacy due to many accidents stemming from their illegal use. Most radio frequency (RF) based methods for drone monitoring focus on the video-transmission signals (VTS), therefore bringing the potential risks t...
Article
Face authentication (FA) schemes are widely adopted in smart homes nowadays. However, existing FA systems for smart appliances are commonly camera-based and hence experience performance degradation in poor illumination conditions. Mainstream FA systems based on radio frequency require dedicated hardware that is inaccessible to many appliances. In t...
Preprint
Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly sharing the streaming graphs poses significant privacy risks. Differential privacy, offering strict theoretical guarantees, has emerged as a standard approach for privat...
Article
Global climate challenges, coupled with the rapid expansion of digital industries, such as cryptocurrency mining, necessitate a comprehensive understanding of their environmental impacts. This study presents a novel dynamic regional model to assess carbon emissions from Bitcoin (BTC) mining within China's coal-heavy, interconnected power system. Ou...
Article
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a large model into several sub-models, and deploying each of them to a different edge device to collabor...
Article
This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate, respiration rate, and blood pressure of users. MagWear's contributions are twofold. Firstly, we build a mathematical model that characterizes the magnetic coupling effect of blood f...
Article
The COVID-19 pandemic has challenged countries worldwide to strike a balance between implementing epidemic control measures and maintaining economic activity. In response, many countries have adopted sustainable, precise, region-specific, and multilevel prevention and control measures. To apply these measures more effectively and purposefully, it i...
Article
The Virtual Private Cloud (VPC) service enables users to configure shared resources within public clouds on demand, providing isolation between users. However, configuring the VPC network is a complex and error-prone task, and misconfiguration has been the leading cause of cloud network security issues. The large number of complex network component...
Preprint
Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in image recognition: ensuring that models trained on condensed datasets are unbiased towards protected attributes (PA), such as gender and race. Our investigation reveals t...
Article
Full-text available
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera...
Article
Tactile sensing, which serves as a modality parallel to vision and auditory, provides rich contact information that is irreplaceable by other modalities. Although tactile sensing technology has made great progress over past decades, existing sensors still lag far behind human skin in infinite-resolution sensing, large-area sensing, and thinness. In...
Article
The Metaverse refers to the integration of physical and virtual realities, offering new possibilities for enhancing operations and services across various industries. However, its application in the energy sector is still in its nascent stage. The energy industry, crucial for the global economy and society, faces significant challenges due to its c...
Article
Full-text available
Multi-object tracking (MOT) in the scenario of low-frame-rate videos is a promising solution to better meet the computing, storage, and transmitting bandwidth resource constraints of edge devices. Tracking with a low frame rate poses particular challenges in the association stage as objects in two successive frames typically exhibit much quicker va...
Article
Federated learning (FL) has emerged as a privacy-aware collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF) , i.e., a leaving participant has the right to request the deletion of its private data...
Article
Despite the substantial progress in predicting human mobility, most existing methods fail to reveal the spatiotemporal patterns under significant interventions such as COVID-19, which disrupt the routine of human mobility. To fill this gap, this paper presents a unified framework for learning human mobility in both regular and intervened scenarios...
Preprint
Full-text available
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera...
Preprint
Full-text available
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with the data, such as using unauthorized online artworks or face images to train ML models. To address thi...
Preprint
Full-text available
Deep learning-based feature matching has shown great superiority for point cloud registration in the absence of pose priors. Although coarse-to-fine matching approaches are prevalent, the coarse matching of existing methods is typically sparse and loose without consideration of geometric consistency, which makes the subsequent fine matching rely on...
Article
Covert channel, which can break the logical protections of the computer system and leak confidential or sensitive information, has long been considered a security issue in the network research community. However, recent research has shown that cooperative agents can use the “covert” channel to augment the communication of legitimate applications, r...
Preprint
Zero-shot (ZS) 3D anomaly detection is a crucial yet unexplored field that addresses scenarios where target 3D training samples are unavailable due to practical concerns like privacy protection. This paper introduces PointAD, a novel approach that transfers the strong generalization capabilities of CLIP for recognizing 3D anomalies on unseen object...
Preprint
Full-text available
Large language models (LLMs) have revolutionized natural language processing with their exceptional capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory constraints, and edge hardware heterogeneity. This survey summarizes recent developments in edge LLMs...
Preprint
Full-text available
Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioritization techniques. However, the fact that DNNs can deliver high-confidence predictions for incorrectly predicted examples, known as the over-confidence problem, causes th...
Preprint
Recent advancements in sensor technology and deep learning have led to significant progress in 3D human body reconstruction. However, most existing approaches rely on data from a specific sensor, which can be unreliable due to the inherent limitations of individual sensing modalities. On the other hand, existing multi-modal fusion methods generally...
Article
High-resolution large-scale urban traffic speed estimation is vital for intelligent traffic management and urban planning. However, single-source data from commonly used sources like cameras, loop detectors, or onboard devices exhibit limitations due to uneven distribution and significant noise, especially in large-scale urban areas. Consequently,...
Conference Paper
Full-text available
Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robust-ness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either independent or loosely coupled, so they cannot fully adapt to each other. In this work, we propose a tightly coupled keypoint detector...
Article
Full-text available
Superalloy materials exhibit susceptibility to fracture failures stemming from the influence of thermomechanical factors. To comprehensively understand the fracture mechanisms, material properties, root causes of failure, and the subsequent optimization of alloys, a detailed analysis of the internal fracture process and the morphological traits of...
Preprint
The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is important for ubiquitous gesture interaction in many applications. In this paper, we propose GesturePrint, which is the first to achieve gesture recognition and gesture-b...
Conference Paper
Full-text available
Telerobotics, which can be traced back to 1940s and 1950s, is perhaps one of the earliest research areas in robotics. Over past decades, many outcomes have been achieved in tackling the unavoidable time delay caused by long-distance communications, to guarantee stability or improve transparency. These outcomes enable the telerobotic systems to be a...
Preprint
Full-text available
Tactile sensing, which serves as a modality parallel to vision and auditory, provides rich contact information that is irreplaceable by other modalities. Although tactile sensing technology has made great progress over past decades, existing sensors still lag far behind human-skins in infinite-resolution sensing, large-area sensing, and thinness. I...
Conference Paper
Full-text available
It is essentially required to predict the ball's flight trajectory accurately and timely for a robotic table tennis ball bouncing task. Existing solutions, which can be categorized into model-based and learning-based groups, both exhibits unpleasant disadvantages. For example, they often require to identify many dynamic parameters accurately or to...
Article
Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the i...
Article
Anomaly detection in multivariate time series has been widely studied in one-class classification (OCC) setting. The training samples in this setting are assumed to be normal. In more practical situations, it is difficult to guarantee that all samples are normal. Meanwhile, preparing a completely clean training dataset is costly and laborious. Such...
Article
Driven by the continuous growth of network demands, Large-scale Deterministic Network (LDN) has been proposed to deliver deterministic services with improved latency and jitter, zero packet loss, configurable bandwidth, and high reliability across a broad geographical area. LDN serves as a bridge for businesses to connect and share information acro...
Article
Full-text available
Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the sys...
Article
Wind speed forecasting provides the upcoming wind information and is important to the safe operation of High-Speed Railway (HSR). However, it remains a challenge due to the stochastic and highly varying characteristics of wind. In this paper, we propose a novel Transformer-based method for short-term wind speed forecasting, named WindTrans. Two maj...
Article
Traffic incident detection is a critical task within traffic monitoring systems, enabling on-the-fly alerts for emergency actions. Numerous efforts have been made to detect and localize traffic incidents using data recorded by inductive loop detectors. However, they only focus on the node-level incidents that happen within the surveillance areas an...
Article
Vertical federated learning (VFL) revolutionizes privacy-preserved collaboration for small businesses, that have distinct but complementary feature sets. However, as the scope of VFL expands, the constant entering and leaving of participants, as well as the subsequent exercise of the “right to be forgotten” pose a great challenge in practice. The q...
Article
Our work aims to reconstruct a 3D object that is held and rotated by a hand in front of a static RGB camera. Previous methods that use implicit neural representations to recover the geometry of a generic hand-held object from multi-view images achieved compelling results in the visible part of the object. However, these methods falter in accurately...
Preprint
Full-text available
The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including ha...
Conference Paper
Full-text available
Recently, pre-trained vision models have gained significant attention in motor control, showcasing impressive performance across diverse robotic learning tasks. While previous work predominantly concentrates on the significance of the pre-training phase, the equally important task of extracting more effective representations based on existing pre-t...
Conference Paper
Recent works on the pretraining for robot manipulation have demonstrated that representations learning from large human manipulation data can generalize well to new manipulation tasks and environments. However, these approaches mainly focus on human vision or natural language, neglecting tactile feedback. In this article, we make an attempt to expl...
Article
In multi-source two-dimensional (2D) direction-of-arrival (DOA) estimation, the essential matching process between the estimated and the true DOAs in the mean square error (MSE) calculation is often based on minimum Euclidean distance criterion, which is substantially different from 1D DOA estimation that is based on simple ordering process. Hence,...
Article
Stealthy attacks manipulate the operation of Industrial Control Systems (ICSs) without being undetected, allowing persistent manipulation of system operation and thus the potential to cause destructive damage. This paper introduces a new vulnerability of ICS that can be exploited to mount stealthy attacks without requiring any domain knowledge. Thi...
Article
The security risk of semantic attacks to Industrial Control Systems (ICSs) is increasing. Semantic attacks manipulate targeted system modules by identifying the physical semantics of variables in Programmable Logic Controllers (PLCs) programs, i.e., the sensing/actuating modules represented by the variables, which is usually and inefficiently achie...
Article
Differentiable ARchitecture Search (DARTS) is a prevailing direction in automatic machine learning, but it may suffer from performance collapse and generalization issues. Recent efforts mitigate them by integrating regularization into architectural parameters or rule-based operations selection. These efforts primarily emphasize learning the global...
Article
Modeling and solving the Flexible Job Shop Scheduling Problem (FJSP) is critical for modern manufacturing. However, existing works primarily focus on the time-related makespan target, often neglecting other practical factors such as transportation. To address this, we formulate a more comprehensive multi-target FJSP that integrates makespan with va...
Article
While small drone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present Mighty, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video c...
Article
Learning from the lessons of the COVID-19 pandemic, nations are increasingly recognizing the imperative to develop sustainable mobility interventions that effectively balance epidemic control and economic stability. In response, we study a novel network immunity problem: the formulation of precise capacity limitation measures for each point of inte...
Conference Paper
Recommender systems predict and suggest relevant options to users in various domains, such as e-commerce, streaming services, and social media. Recently, deep reinforcement learning (DRL)-based recommendation systems have become increasingly popular in academics and industry since DRL can characterize the long-term interaction between the system an...
Article
In recent years, multi-target localization has been identified as an essential technology for delivering on the promise of the Internet of Things (IoT). Multi-target localization has been well-studied and widely deployed in conventional communications systems, but the techniques used in such systems are largely unable to meet the power, cost, and a...
Article
This paper is concerned with periodic event-triggering laws in robust model predictive control (MPC) for continuous-time constrained nonlinear systems. The online optimal control problem solved at triggering times is introduced. A periodic static event-triggering condition, in which a fixed sampling time interval plays an important role in avoiding...
Article
Full-text available
Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application n...
Preprint
Full-text available
Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robust-ness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either independent or loosely coupled, so they cannot fully adapt to each other. In this work, we propose a tightly coupled keypoint detector...
Preprint
Full-text available
Data is a critical asset in AI, as high-quality datasets can significantly improve the performance of machine learning models. In safety-critical domains such as autonomous vehicles, offline deep reinforcement learning (offline DRL) is frequently used to train models on pre-collected datasets, as opposed to training these models by interacting with...