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arXiv 2023年

作者： Feng, Yi Zhang, Ruge Du, Jiayuan Chen, Qijun Fan, Rui College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China High-Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

— Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detection. The concept of "U-V-Disparity" has been widely explored in the literature, while its counterpart in optical flow has received relatively little attention. Traditional motion analysis algorithms estimate optical flow by matching correspondences between two successive video frames, which limits the full utilization of environmental information and geometric constraints. Therefore, we propose a novel strategy to model optical flow in the collision-free space (also referred to as drivable area or simply freespace) for intelligent vehicles, with the full utilization of geometry information in a 3D driving environment. We provide explicit representations of optical flow and deduce the quadratic relationship between the optical flow component and the vertical coordinate. Through extensive experiments on several public datasets, we demonstrate the high accuracy and robustness of our model. Additionally, our proposed freespace optical flow model boasts a diverse array of applications within the realm of automated driving, providing a geometric constraint in freespace detection, vehicle localization, and more. We have made our source code publicly available at https://***/FSOF. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Correction to: Explicit Third-Order Unconditionally Structure-Preserving Schemes for Conservative Allen–Cahn Equations

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Journal of Scientific computing 2022年第1期91卷 1-2页

作者： Zhang, Hong Yan, Jingye Qian, Xu Chen, Xiaowei Song, Songhe Department of Mathematics College of Liberal Arts and Science National University of Defense Technology Changsha China Division of Mathematical Sciences Nanyang Technological University Singapore Singapore State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

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ISort: SSD Internal Sorting Algorithm for Big Data

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Mobile Information Systems 2022年第1期2022卷

作者： Liu, Yang Chen, Wenhan Ge, Xuran Chen, Zhiguang Ou, Yang Xiao, Nong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Hunan Province Changsha City410000 China Sun Yat-sen University Guangzhou Province Guangzhou City510000 China Institute of Computing Technology College of Computer National University of Defense Technology Hunan Province Changsha City410000 China

As a basic algorithm for big data processing, external sorting suffers from massive read and write operations in the external memory. Recent works separate part of the data processing work from the host side to the solid state drive (SSD) to reduce data transmission. However, the internal memory of the SSD is limited, and undesirable data retention could occur during the merge phase. Therefore, to improve the efficiency of memory, we propose an algorithm named ISort. Specifically, we build an index table between the memory and the address. The index table determines the order of pages being read in the merge phase according to their minimum values, which are read into memory sequentially to reduce the data residing in memory and improve memory efficiency. Since the merge phase is performed inside the SSD, ISort can take advantage of the high IO bandwidth within the SSD to speed up the execution of the merge phase. We search for the optimal ratio of read and write channels by comparing the "specialized channel"and the "hybrid channel"for data of read and write performance because the utilization of the channel will directly influence performance. Experimental results show that ISort can maintain better data processing speed when SSD memory is limited, outperforming other robust algorithms. In addition, the algorithm's performance using the crossover strategy is better than that using the specialization strategy. © 2022 Yang Liu et al.

关键词： Big data

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Symbolic verification of mpi programs with non-deterministic synchronizations 1

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6th International Symposium on Dependable Software Engineering, SETTA 2020

作者： Yu, Hengbiao Chen, Zhenbang Huang, Chun Wang, Ji College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (数字)9783030628222

ISBN: (纸本)9783030628215

Message Passing Interface (MPI) is the current de-facto standard for developing applications in high-performance computingMPI allows flexible implementations of message passing operations, which introduces non-deterministic synchronizations that challenge the correctness of MPI programsWe present in this paper a symbolic method for verifying the MPI programs with non-deterministic synchronizationsInsides the method, we propose a path-level modeling method that uses communicating sequential processes (CSP) to precisely encode the non-deterministic synchronizations of an execution pathFurthermore, for the execution paths without non-deterministic message receive operations, we propose an optimization method to reduce the complexity of the CSP modelsWe have implemented our technique on MPI-SV and evaluated it on 10 real-world MPI programs *** freedomThe experimental results demonstrate the effectiveness of our verification method. © 2020, Springer Nature Switzerland AG.

关键词： Message passing

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Polyp-PVT:Polyp Segmentation with Pyramid Vision Transformers

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CAAI Artificial Intelligence Research 2023年第1期2卷 1-15页

作者： Bo Dong Wenhai Wang Deng-Ping Fan Jinpeng Li Huazhu Fu Ling Shao College of Computer Science Nankai UniversityTianjin 300350China Shanghai Artificial Intelligence Laboratory Shanghai 200232China Computer Vision Lab Inception Institute of Artificial IntelligenceAbu DhabiUnited Arab Emirates Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore UCAS-Terminus AI Lab Terminus GroupChongqing 400042China

Most polyp segmentation methods use convolutional neural networks(CNNs)as their backbone,leading to two key issues when exchanging information between the encoder and decoder:(1)taking into account the differences in contribution between different-level features,and(2)designing an effective mechanism for fusing these *** existing CNN-based methods,we adopt a transformer encoder,which learns more powerful and robust *** addition,considering the image acquisition influence and elusive properties of polyps,we introduce three standard modules,including a cascaded fusion module(CFM),a camouflage identification module(CIM),and a similarity aggregation module(SAM).Among these,the CFM is used to collect the semantic and location information of polyps from high-level features;the CIM is applied to capture polyp information disguised in low-level features,and the SAM extends the pixel features of the polyp area with high-level semantic position information to the entire polyp area,thereby effectively fusing cross-level *** proposed model,named Polyp-PVT,effectively suppresses noises in the features and significantly improves their expressive *** experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations(e.g.,appearance changes,small objects,and rotation)than existing representative *** proposed model is available at https://***/DengPingFan/Polyp-PVT.

关键词： polyp segmentation pyramid vision transformer colonoscopy computer vision

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EASE: Energy-efficient task scheduling for edge computing under uncertain runtime and unstable communication conditions

EASE: Energy-efficient task scheduling for edge computing un...

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作者： Yan, Hui Li, Ya Zhu, Xiaomin Zhang, Dayu Wang, Ji Chen, Huangke Bao, Weidong College of Systems Engineering National University of Defense Technology Changsha China Frontier Medical Training Brigade of Army Medical University Shanghai China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

Continuously growing network traffic has become a major technical bottleneck of the cloud service to develop the Internet of Things (IoTs) and mobile applications. Edge computing as a promising computing pattern deployed close to service users is expected to improve the quality of service (QoS). To fully utilize the capabilities of edge devices, a Device-to-Device (D2D)–based computing resource sharing and aggregation framework is proposed. Under this framework, this paper exploits the Beta distributions to characterize the uncertain communication rate and processing capability of the edge environment. The reliability and energy consumption of local computing and shared computing under uncertain conditions are, respectively, studied. We model the task scheduling as an Integer Programming problem, whose objective is to minimize the energy consumption while ensuring the reliability. Based on that, a heuristic task scheduling algorithm named EASE is proposed. Through a lot of simulation experiments, the performance of EASE is effectively evaluated under the static and dynamic environments. Compared with three comparison algorithms, EASE shows many advantages in terms of reliability, adaptability, and energy saving. © 2019 John Wiley & Sons, Ltd.

关键词： Reliability

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MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation 39

MTVHunter: Smart Contracts Vulnerability Detection Based on ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Guokai Zhuang, Yuan Zhang, Shuo Feng, Xiaoyu Liu, Zhenguang Zhang, Liguo College of Computer Science and Technology Harbin Engineering University Heilongjiang China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions;(ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Teaching

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Qian Chen, Panfeng Li, Jiali Feng, Linkun Liu, Shuyu Zhao, Heng Chen, Mei Li, Hui Wang, Yanhao State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guizhou Engineering Laboratory for Advanced Computing and Medical Information Services Guiyang China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Data Science and Engineering East China Normal University Shanghai China

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published anonymously at https://***/ACMISLab/PediaBench. © 2024, CC BY-SA.

关键词： Question answering

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PolarBEVU: Multi-Camera 3D Object Detection in Polar Bird's-Eye View via Unprojection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Hou, Minghui Lyu, Chuanhao Wang, Gang Ma, Baorui Xu, Rongtao Hu, Jue Fan, Xiaopeng Jilin University College of Computer Science and Technology Changchun China Tsinghua University School of Software Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Harbin Institute of Technology National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin150080 China Harbin Institute of Technology Centre for Composite Materials and Structures Harbin150001 China Harbin Institute of Technology Faculty of Computing Harbin150001 China

3D object detection from a Bird's Eye View (BEV) has emerged as a novel perception paradigm for autonomous driving scenarios. While most current 3D object detection methods still rely on the conventional Cartesian coordinates, they fail to align with the non-aligned coordinate system inherent in image geometry. The Polar coordinates, on the other hand, better fit with the geometric shape corresponding to the perception of cameras. However, transforming between coordinate systems introduces distortions in the perception information, resulting in issues such as "Weak Adaptability to Heatmap Distribution"and "Offset in the Center Point of the Bounding Box."To address these challenges, this paper proposes a cutting-edge 3D object detection model named PolarBEVU, which leverages the bird's-eye view under the Polar coordinates along with multi-camera unprojection. The model introduces an innovative "Deformable Uniform Heatmap Distribution"method that adjusts heatmap computations based on box shapes, generating high-quality heatmaps and effectively resolving the issue of "Weak Adaptability to Heatmap Distribution."Moreover, the model incorporates the concept of "Dynamic high-risk Regression Region"to enhance the accuracy and robustness of the center point regression at the bounding box, thus mitigating the issue of "Offset in the Center Point of the Bounding Box."In extensive experiments on the nuScenes dataset, PolarBEVU achieves impressive results with 49.9% mAP and 57.4% NDS on the test set, surpassing other comparative approaches and reaching the state-of-the-art (SOTA) performance among methods utilizing Polar coordinates. This clearly demonstrates the efficacy and superiority of PolarBEVU. In addition, the model is successfully deployed on Nvidia Jetson AGX Orin, showcasing real-time inference speeds of 31.42ms. These findings affirm PolarBEVU's potential for practical applications. Code is available at https: //***/JLUrob/PolarBEVU. © 2025 IEEE.

关键词： Object tracking

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