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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Helmuth Naumer Farzad Kamalabadi Department of Electrical and Computer Engineering and The Coordinated Science Laboratory University of Illinois Urbana–Champaign Urbana IL USA

We propose a method for the Bayesian prediction of shocks in scalar partial differential equations (PDEs) representing conservation equations from noisy observations of the boundary conditions. By considering the implicit transformation from boundary conditions to shocks, we construct an arrival process interpretation of shocks as well as an associated arrival rate function. We then introduce a Monte Carlo method to approximate the arrival rate of shocks based on the probability of a sufficiently large range of values in an epsilon ball conditioned on noisy boundary measurements. We illustrate the method with simulations of Burgers’ equation with initial conditions set by Brownian motion. Despite the non-smooth boundary, our proposed method constructs a sparse and readily interpretable probabilistic structure of shock arrival and propagation.

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A New Extension of the Topp–Leone-Family of Models with Applications to Real Data

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Annals of Data science 2023年第1期10卷 225-250页

作者： Muhammad, Mustapha Liu, Lixia Abba, Badamasi Muhammad, Isyaku Bouchane, Mouna Zhang, Hexin Musa, Sani Department of Mathematics Guangdong University of Petrochemical Technology Maoming525000 China School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China School of Mathematics and Statistics Central South University Changsha China Department of Mathematics Yusuf Maitama Sule University Kano Nigeria School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China Department of Mathematics and Computer Science Sule Lamido University Jigawa Kafin-Hausa Nigeria

In this article, we proposed a new extension of the Topp–Leone family of distributions. Some important properties of the model are developed, such as quantile function, stochastic ordering, model series representation, moments, stress–strength reliability parameter, Renyi entropy, order statistics, and moment of residual life. A particular member called new extended Topp–Leone exponential (NETLE) is discussed. Maximum likelihood estimation (MLE), least-square estimation (LSE), and percentile estimation (PE) are used for the model parameter estimation. Simulation studies were conducted using NETLE to assess the MLE, LSE, and PE performance by examining their bias and mean square error (MSE), and the result was satisfactory. Finally, the applications of the NETLE to two real data sets are provided to illustrate the importance of the NETLG families in practice;the data sets consist of daily new deaths due to COVID-19 in California and New Jersey, USA. The new model outperformed many other existing Topp–Leone’s and exponential related distributions based on the real data illustrations. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Maximum likelihood estimation

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Software Vulnerability Detection with GPT and In-Context Learning

Software Vulnerability Detection with GPT and In-Context Lea...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Zhihong Liu Qing Liao Wenchao Gu Cuiyun Gao School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

Code vulnerability detection is a software security analysis technique that focuses on recognizing and resolving possible code vulnerabilities and weaknesses. Its primary objective is to mitigate the chances of malicious attacks and system failures. Vulnerabilities encompass mistakes, defects, or insecure programming methodologies found within the code, which can lead to security risks, service denials, data leaks, and various other concerns. Previous research has predominantly focused on deep learning models such as VulDeePecker, Russell, and SySeVR. With the advent of large language models, impressive advancements have been made in various domains, including natural language generation, text classification, and sentiment analysis. However, there is currently no effective method for utilizing large language models in vulnerability detection. Therefore, this study explores and validates the application of such models for code vulnerability detection. In this paper we present a context-based learning approach to enhance the capability of code vulnerability detection named VUL-GPT. Our method combines code retrieval and code analysis, leveraging in-context learning to improve the performance of the GPT model in vulnerability detection. Specifically, we use GPT to generate analysis content for the test code and employ code retrieval methods such as BM-25 and TF-IDF to retrieve the most similar code snippet and its vulnerability information from the training set. Subsequently, we input them along with the test code and its analysis into the GPT model, leveraging the contextual learning ability of the large language model for vulnerability detection. Our experiments demonstrate that combining with code retrieval and code analysis, the GPT models can detect code vulnerability detection more effectively.

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Neuro-Haptic BCI Spark for TensorFlow Flying Avatars

Neuro-Haptic BCI Spark for TensorFlow Flying Avatars

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IEEE International Conference on Electro-Information Technology

作者： John Knudson Adriano Cavalcanti Maninder Singh Department of Computer Science and Information Technology Autonomous Systems Laboratory St. Cloud State University St. Cloud MN USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The article explores the bleeding-edge intersection of neuro-haptics, brain-computer interface (BCI) technology, and machine learning to enable the creation and control of flying avatars. Our work integrates neuroscience and haptic feedback to develop human computer system interfaces that allow users to control cyber physical systems using their brain signals. This innovative approach uses advanced machine learning to classify signal patterns and respond to brain thoughts in real-time, providing an immersive flying bird’s eyes feeling. The article highlights the challenges and opportunities associated with Neuro-Haptics BCI for flying avatars, paving the way for a new era in human-computer interaction.

关键词： Human computer interaction Training Neuroscience Avatars Machine learning Brain-computer interfaces Real-time systems

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Contrastive learning for body gesture detection during Adapted Physical Activity

Contrastive learning for body gesture detection during Adapt...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Juan Martinez Rocha Federico Pennino Cécile Dubois Eric Monacelli Maurizio Gabbrielli Versailles Engineering Systems Laboratory University of Paris-Saclay Vélizy France Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This paper presents a novel approach to body gesture recognition for powered wheelchair users, leveraging inertial data from wrist-mounted sensors to facilitate movement and enhance autonomy in Adapted Physical Activity (APA). Gesture recognition technology interprets human gestures to allow non-direct communication with devices, enhancing human-machine interaction across various fields. APA fosters inclusion and well-being through tailored physical engagement. Our model not only identifies known gestures with high accuracy, as indicated by a mean Average Precision (mAP) score of 0.92 and a Recall@1 score of 0.983, but also demonstrates the ability to recognize gestures not included in the training set. This research contributes to the field of human-robot interaction by offering a more dynamic and inclusive form of interaction for individuals reliant on powered mobility aids.

关键词： Training Adaptation models Wheelchairs Gesture recognition Contrastive learning Sensor systems Sensors Mobile robots Usability Anomaly detection

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Accelerating Persistent Hash Indexes via Reducing Negative Searches

Accelerating Persistent Hash Indexes via Reducing Negative S...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Renzhi Xiao Hong Jiang Dan Feng Yuchong Hu Wei Tong Kang Liu Yucheng Zhang Xueliang Wei Zhengtao Li Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering University of Texas at Arlington Arlington USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Hashing is a widely used and efficient indexing mechanism for key-value storage. Persistent memory (PM) has attracted extensive attention in research due to its non-volatility and DRAM-like performance. Intel DCPMM, as a PM, can provide large capacity and low total cost of ownership, further promoting the research of PM-based hash index. However, based on real-world workloads, we found that negative searches of existing PM-based hash indexes significantly degrade system performance. A direct method to solve this problem is to use a PM-based Bloom filter to reduce negative searches, but at the cost of the decreased lifespan of PM due to extra PM writes. An alternative method is to use a DRAM-based Bloom filter, but it still faces increased multi-threaded insertion/deletion/positive-search scalability overhead as well as increased data consistency and recovery *** this paper, we propose SmartHT, a small-size DRAM-based Bloom filter to accelerate hash table operations for PM while solving the aforementioned problems. SmartHT uses efficient merge write optimization with head insertion, lazy deletion, and shortened average chained length of head-bucket to provide high insertion/deletion/positive-search scalability, respectively. On the other hand, it utilizes a merged-flush mechanism based on an 8-byte failure-atomic write method to reduce flush instructions and extra PM writes to achieve low data consistency overhead. Experimental results on Intel Optane DCPMM show that, compared with the state-of-the-art persistent hash indexes, SmartHT improves multi-threaded negative queries under uniform and skewed distributions by 4.61x-13.86x and 2.76x-12.99x respectively, achieves high multi-threaded scalability and low data consistency overhead, at the modest cost of recovery time overhead.

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Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Network Environments with TCP-Aware Traffic Augmentation 23

Rosetta: Enabling Robust TLS Encrypted Traffic Classificatio...

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2023 ACM Turing Award Celebration Conference, CHINA 2023

作者： Xie, Renjie Wang, Yixiao Cao, Jiahao Dong, Enhuan Xu, Mingwei Sun, Kun Li, Qi Shen, Licheng Zhang, Menghao Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Technology Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Department of Information Sciences and Technology George Mason University United States Quan Cheng Laboratory China Kuaishou Technology China

ISBN: (纸本)9798400702334

As the majority of Internet traffic is encrypted by the Transport Layer Security (TLS) protocol, recent advances leverage Deep Learning (DL) models to conduct encrypted traffic classification. We propose Rosetta to enable robust TLS encrypted traffic classification for existing DL models. It leverages TCP-aware traffic augmentation mechanisms and self-supervised learning to understand implicit TCP semantics, and hence extracts robust features of TLS flows. Extensive experiments show that Rosetta can significantly improve the classification performance of existing DL models on TLS traffic in diverse network environments. © 2023 Owner/Author.

关键词： Semantics

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Reducing Network Distance Measurement Overhead: A Tensor Completion Solution With a New Minimum Sampling Bound

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IEEE/ACM Transactions on Networking 2024年

作者： Tian, Jiazheng Xie, Kun Wang, Xin Wen, Jigang Xie, Gaogang Cao, Jiannong Liang, Wei Li, Kenli Hunan University College of Computer Science and Electronics Engineering Changsha410082 China Ministry of Education Key Laboratory of 'Fusion Computing of Supercomputing and Artificial Intelligence' Changsha410082 China State of New York University at Stony Brook Department of Electrical and Computer Engineering Stony BrookNY11794 United States Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411199 China Chinese Academy of Sciences Computer Network Information Center Beijing100080 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consuming, making the reduction of network distance measurement costs a top priority. The tensor completion method efficiently reduces measurement costs by utilizing a small amount of measured data to estimate the entire network's distance data. Unfortunately, current tensor completion methods still suffer from issues such as complex sample selection, high measurement overhead, slow recovery, and low inference accuracy. To address the aforementioned challenges, we present an online network-wide distance measurement scheme. In this approach, continuous distance data are structured into sliding-window-based tensors. Our method incorporates a lightweight sample selection algorithm with a lowest sampling bound and a rapid, accurate unmeasured data inference algorithm. We have conducted extensive experiments using four real network distance datasets and two citywide crowd flow datasets. The empirical evaluations demonstrate the effectiveness of our approach, particularly in reducing measurement costs and enhancing data recovery accuracy. © 1993-2012 IEEE.

关键词： Distance measurement

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Edge-Assisted Accelerated Cooperative Sensing for CAVs: Task Placement and Resource Allocation

arXiv

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arXiv 2024年

作者： Wang, Yuxuan Qu, Kaige Wu, Wen Shen, Xuemin Frontier Research Center Pengcheng Laboratory China School of Electronic and Computer Engineering Peking University China School of Transportation Science and Engineering Beihang University China Department of Electrical and Computer Engineering University of Waterloo Canada

In this paper, we propose a novel road side unit (RSU)-assisted cooperative sensing scheme for connected autonomous vehicles (CAVs), with the objective to reduce completion time of sensing tasks. Specifically, LiDAR sensing data of both RSU and CAVs are selectively fused to improve sensing accuracy, and computing resources therein are cooperatively utilized to process tasks in real time. To this end, for each task, we decide whether to compute it at the CAV or at the RSU and allocate resources accordingly. We first formulate a joint task placement and resource allocation problem for minimizing the total task completion time while satisfying sensing accuracy constraint. We then decouple the problem into two subproblems and propose a two-layer algorithm to solve them. The outer layer first makes task placement decision based on the Gibbs sampling theory, while the inner layer makes spectrum and computing resource allocation decisions via greedy-based and convex optimization subroutines, respectively. Simulation results based on the autonomous driving simulator CARLA demonstrate the effectiveness of the proposed scheme in reducing total task completion time, comparing to benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Multiplanar Self-Calibration for Mobile Cobot 3D Object Manipulation Using 2D Detectors and Depth Estimation

Multiplanar Self-Calibration for Mobile Cobot 3D Object Mani...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Dang Khang Nguyen Manfred Huber Department of Computer Science and Engineering Learning and Adaptive Robotics Laboratory University of Texas at Arlington Arlington TX USA

Calibration is the first and foremost step in dealing with sensor displacement errors that can appear during extended operation and off-time periods to enable robot object manipulation with precision. In this paper, we present a novel multiplanar self-calibration between the camera system and the robot's end-effector for 3D object manipulation. Our approach first takes the robot end-effector as ground truth to calibrate the camera's position and orientation while the robot arm moves the object in multiple planes in 3D space, and a 2D state-of-the-art vision detector identifies the object's center in the image coordinates system. The transformation between world coordinates and image coordinates is then computed using 2D pixels from the detector and 3D known points obtained by robot kinematics. Next, an integrated stereo-vision system estimates the distance between the camera and the object, resulting in 3D object localization. We test our proposed method on the Baxter robot with two 7-DOF arms and a 2D detector that can run in real time on an onboard GPU. After self-calibrating, our robot can localize objects in 3D using an RGB camera and depth image. The source code is available at https://***/tuantdang/calib_cobot.

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