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检索条件"主题词=detection algorithms"

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Learning Communities from Equilibria of Nonlinear Opinion Dynamics

Learning Communities from Equilibria of Nonlinear Opinion Dy...

引用

IEEE Conference on Decision and Control

作者： Yu Xing Anastasia Bizyaeva Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and with Digital Futures Stockholm Sweden Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the intercommunity connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms.

关键词： Accuracy Heuristic algorithms Image edge detection Stochastic processes Clustering algorithms Mathematical models Numerical models detection algorithms

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Context-Aware YOLOv7 Algorithm for Classroom Seating Distribution Recognition

Context-Aware YOLOv7 Algorithm for Classroom Seating Distrib...

引用

Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： Yifeng Wen Xinyao Yu Department of Computer Science and Engineering Xihua University Sichuan China Department of Electronic and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

Evaluating the effectiveness of classroom teaching is challenging, and one potential solution is the implementation of intelligent classrooms. A key difficulty in intelligent classrooms lies in accurately identifying student seating positions. To address this issue, this paper focuses on improving the YOLOv7 detection algorithm for classroom seat detection. The main contributions of this study are as follows: (1) To improve YOLOv7's detection efficiency in classroom environments, we propose an enhancement that incorporates local context and prior information to increase seat detection accuracy; (2) We introduce the concept of using local context based on classroom seating and YOLO's grid system to refine the algorithm's post-processing stage. Testing results show that the improved algorithm detects more seats in a classroom environment, with higher confidence in recognized seats. This approach enhances both recall rate and average recognition rate to a certain extent.

关键词： Accuracy Education Object detection Approximation algorithms detection algorithms Standards Testing

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A Method to Detect Vulnerability of Java Source Code based on AST and Graph Attention Networks

A Method to Detect Vulnerability of Java Source Code based o...

引用

International Conference on Software Technology and Engineering (ICSTE)

作者： Jin Zhu Hui Ge Xiao Jin Rui Luo China Aerospace Academy of Systems Science and Engineering Beijing China

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

It is crucial to Detecting vulnerabilities in source code at the beginning of projects. Traditional source code vulnerability detection algorithms are hard to represent the feature information and the relationships between code blocks. To address these issues, this paper uses abstract syntax tree to represent the feature of source code. This paper adds data dependency edges and control dependency edges to the original AST, transforming the tree-structured AST into a graph-structured Enhanced-AST in order to solve the limited ability of the original AST to represent data flow and control flow. Word2Vec algorithm is used for embedding Enhanced-AST nodes, and Graph Attention Networks (GAT), which introduces an attention mechanism to better capture the dependencies and important information between nodes, are used to learn the feature information of Enhanced-AST. This paper collects Java CWE vulnerability datasets from the SARD dataset, implements a function-level vulnerability detection method based on the dataset, and compares it with BiLSTM and method using AST to extract code features. Experimental results show that the proposed method achieves optimal F1 values in most cases.

关键词： Java Codes Source coding Image edge detection Neural networks Bidirectional long short term memory Syntactics Feature extraction Software detection algorithms

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Real-Time Path Generation and Alignment Control for Autonomous Curb Following

Real-Time Path Generation and Alignment Control for Autonomo...

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

作者： Yuanzhe Wang Yunxiang Dai Danwei Wang School of Control Science and Engineering Shandong University Jinan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Curb following is a key technology for autonomous road sweeping vehicles. Currently, existing implementations primarily involve pre-recording waypoints during human driving and subsequently retracing them autonomously. Moreover, existing research related to this topic predominately focuses on curb detection for driver assistance, yet the resultant curb detection outcomes remain underutilized in the development of autonomous curb following systems. To fill this gap, this paper proposes a real-time path generation and alignment control approach to facilitate autonomous curb following. Firstly, a segmented path generation algorithm is introduced that progressively generates reference path segments while ensuring the overall continuity of the reference path. Secondly, a parameterized alignment control algorithm is developed to accurately navigate the vehicle along the planned reference path with proved stability. Real public road experiments have been conducted to validate the proposed approach. The experimental results demonstrate the efficacy of the proposed methodologies across various curb following scenarios, including common concave, convex, and straight-concave curbs, thereby showcasing the practical viability of our methods in real-world applications.

关键词： Accuracy Navigation Roads Feature extraction Real-time systems Robustness Complexity theory detection algorithms Intelligent robots Vehicles

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Pedestrian Indoor Positioning Algorithm Suitable for Unknown Environments

Pedestrian Indoor Positioning Algorithm Suitable for Unknown...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Xueting Zhang Lingke Zhou Sheng Li School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

IMU plays an important role in pedestrian positioning due to its small size and light weight, but it also has the problem of accumulated errors. This article improves the traditional fixed threshold zero speed detection algorithm and proposes a valley based zero speed correction algorithm that can dynamically adjust with changes in pedestrian step frequency and correct speed errors during the positioning process. For heading drift, a real-time heuristic drift elimination algorithm is adopted to complete heading correction. Finally, we use landmark points with angular features observed during pedestrian walking to complete position error correction. The results of the two-dimensional indoor positioning experiment show that the endpoint positioning error is only 1.28 meters.

关键词： Legged locomotion Meters Pedestrians Heuristic algorithms Real-time systems Error correction Sensors Indoor environment Personnel detection algorithms

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A Taxonomy and Survey on Sensor Blockage-Induced Degradation and its Impact on Advanced Driver Assistance Systems

A Taxonomy and Survey on Sensor Blockage-Induced Degradation...

引用

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Tim Dieter Eberhardt Tim Brühl Robin Schwager Tin Stribor Sohn Wilhelm Stork Dept. of Highly Automated Driving Systems Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute for Information Processing Technologies Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350387278

ISBN: (纸本)9798350387285

Advanced Driver Assistance Systems (ADAS) rely extensively on sensors to perceive and interpret their environment, enabling safe and efficient navigation, object detection or other functionalities. However, sensor blockage due to environmental contaminants such as dirt, rain, or snow poses a significant challenge, impacting sensor performance and consequently ADAS reliability. This paper presents a comprehensive taxonomy and survey on sensor blockage in ADAS, focusing on cameras, radars, and lidar sensors. The survey begins with an taxonomy overview of sensor principles used in ADAS and their vulnerabilities to blockage. Cameras, based on geometric optics, are susceptible to opaque contaminants like mud and transparent ones like raindrops. Advanced studies in computer vision and deep learning techniques are discussed for detecting and mitigating these contaminants. Radar systems, operating on electromagnetic waves, face blockage challenges from snow and water, affecting their range and accuracy. detection algorithms and impact analyses highlight methods to mitigate these effects, crucial for maintaining radar reliability in adverse weather conditions. Lidar sensors, using laser pulses for precise distance measurement, are vulnerable to dust and moisture accumulation. detection strategies such as total internal reflection and deep neural networks are reviewed, emphasizing advancements in contamination identification and cleaning mechanisms. The paper concludes with insights into the overall impact of sensor blockage on ADAS, stressing the need for robust detection algorithms and standardized testing protocols.

关键词： Surveys Laser radar Taxonomy Radar Benchmark testing Cameras Sensor systems Sensors detection algorithms Contamination

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Machine Learning-Based Navigation of Cosmos for Habitable Planets

Machine Learning-Based Navigation of Cosmos for Habitable Pl...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Garima Chandel Nakka Rajagopala Rao Warda Jannat Kripa Kattel Nakka Suvarna Raju Saba E Jannat Department of Electronics and Communication Engineering Chandigarh University Mohali India Department of Aerospace Engineering Chandigarh University Punjab India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

For years, the hunt for liveable planets has been going on and the idea itself has captivated scientists. [1] Planetary-scale disasters like globalization and climate change might only be avoidable if human habitation is expanded to space and for that the potentiality of another planet being able to sustain life should not be thrown out the window so fast. [1] [2] This paper looks into such potential planets, especially exoplanets. This paper incorporates machine learning to identify exoplanets by drawing upon a range of strategies including exoplanet detection, habitable zone analysis, and robotic exploration. The dataset based on which the model in this study has been created was obtained from “NASA Exoplanet Archive”. This dataset predicts the existence of exoplanet candidates as a three-categorical classification task, using a decision tree, KNN, and Gradient boosting. This study included the following essential analysis: (1) radial velocity analysis (2) atmospheric composition (3) estimating surface temperatures along with mass (4) size of exoplanets and classifying the magnetic fields (5) radiation levels (infrared, X-ray, gamma rays). This paper presents some obtained results by using different classifiers like decision tree which had an accuracy of 90%, KNN had an accuracy of 98% & gradient boosting had an accuracy of 98.5 %.

关键词： Climate change Habitats Machine learning Classification algorithms Measurement Planets Human factors Sustainable development NASA Extrasolar planets Space exploration Mobile robots Surface topography Decision trees Atmospheric measurements Gradient methods detection algorithms

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Monitoring High-Dimensional Streaming Data via Fusing Nonparametric Shiryaev-Roberts Statistics

Monitoring High-Dimensional Streaming Data via Fusing Nonpar...

引用

IEEE International Symposium on Information Theory

作者： Xinyuan Zhang Yajun Mei School of Industrial and Systems Engineering Georgia Institute of Technology Department of Biostatistics School of Global Public Health New York University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Monitoring high-dimensional streaming data has a wide range of applications in science, engineering, and industry. In this work, we propose an efficient and robust sequential change-point detection algorithm for monitoring high-dimensional streaming data. It has two components. At the local level, we adopt a window-limited nonparametric Shiryaev-Roberts (WL-NPSR) statistic for detecting potential distribution changes at each dimension of the streaming data. At the global level, we fuse local WL-NPSR statistics together to construct a global monitoring statistic via quantile filtering and sum-shrinkage functions. Theoretical analysis and extensive numerical experiments demonstrate the efficiency and robustness of our proposed algorithm.

关键词： Industries Fuses Filtering Robustness detection algorithms Monitoring Information theory

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Ultra-Low Complexity Communication algorithms Based on (n, n(n-l), n-l) PGCs

Ultra-Low Complexity Communication Algorithms Based on (n, n...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Li Peng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

This paper first defined the new concept of ultralow complexity algorithm (ULCA) which helps to find and design 1 millisecond algorithms so as to support the future applications of ultra-reliability low-latency communications (URLLC) promoted by 5G. Then it demonstrates the existence of the communication ULCA by introducing an original modulation scheme. This scheme is a new family of ndimensional high-order dual-domain modulation signal permutation array constellations (PAC). The PAC can generate the ULCA, including mapping encoding, decoding and detection algorithms. The time complexity of these algorithms can be counted by the number of clock periods of basic unit circuits instead of the number of additions and multiplications. The best simulation results of the P ACs are 2.0 dB better than the BPSK modulation at the WER of 10 −9 .

关键词： Simulation Conferences Modulation Ultra reliable low latency communication Picture archiving and communication systems Encoding Complexity theory Decoding Time complexity detection algorithms

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Detecting VPN Traffic in Real-Time with Active Probing

Detecting VPN Traffic in Real-Time with Active Probing

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IEEE International Symposium on Network Computing and Applications

作者： Yuan Tian Zechun Cao Shou-Hsuan Stephen Huang Department of Computer Science University of Houston Houston Texas USA Dept of Computational Engineering and Mathematical Sciences Texas A&M University-San Antonio San Antonio Texas USA

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

Malicious attackers often exploit anonymity networks like VPNs to conceal their identities. This paper introduces a novel real-time detection algorithm based on discrepancies in packet round-trip times (RTTs) and active probing of VPN servers. When a client accesses a target server via a VPN, two distinct RTTs emerge anchored on the target: target-VPN and target-VPN-client. The proposed detection algorithm determines whether one or two significantly different RTT clusters are present, with two clusters indicating the use of a VPN. The focus of this study is the detection of Secure Shell (SSH) connections through VPNs. While measuring the RTT between the target and the client is relatively straightforward due to the packet exchange of the TCP/SSH protocol, estimating the RTT between the target and the VPN presents challenges. An active probing method is proposed to calculate the RTT, accounting for the potential involvement of a malicious or compromised VPN. By employing machine learning to analyze these RTTs, the system can effectively detect intruders utilizing VPNs. This research serves as a foundation for developing secure and efficient VPN detection systems, which are critical for preserving the integrity and privacy of digital resources.

关键词： Privacy Intrusion detection Machine learning Real-time systems HTTP Virtual private networks Servers Security detection algorithms

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