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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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A Loop Closure Detection Method Based on LiDAR Scanning Range Encoding 8

A Loop Closure Detection Method Based on LiDAR Scanning Rang...

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8th international conference on Information systems engineering, ICISE 2023

作者： Jiang, Xudong Gao, Xinyu Wang, Gang Zhang, Tongzhou Changsha Automobile Innovation Research Institute Changsha410013 China Jilin University College of Computer Science and Technology Changchun130012 China Jilin University School of Artificial Intelligence Changchun130012 China

ISBN: (纸本)9798350307009

Loop closure detection (LCD) is a crucial component of simultaneous localization and mapping (SLAM), which enables compensation for accumulated errors caused by front-end odometry. However, existing LCD methods are vulnerable to changes in sensor position, and fail to account for LiDAR imaging property. Hence, a novel LCD method is proposed based on LiDAR scanning range encoding to handle the issues above. First, the origin of point clouds coordinates is redefined based on the centroid distribution of point clouds, achieving translation invariance at the descriptor level for LCD. then, an encoding method based on radial gradient segmentation of LiDAR scanning range is proposed, thereby enhancing the description of distant environmental features, as well as producing a discriminative descriptor. the proposed method achieves a maximum F1 score of 0.929 and an extension precision of 0.872 on KITTI benchmark dataset, which indicates the state-of-the-art performance. Finally, the proposed method is applied to the SLAM algorithm in a self-collected campus dataset, which enables accurately detecting loop closure during mapping process, thereby constructing a global-consistency map. © 2023 ieee.

关键词： Signal encoding

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Dynamic Energy Resource Allocation for Symbiotic Wireless Networks Based on DDQN 8

Dynamic Energy Resource Allocation for Symbiotic Wireless Ne...

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8th international conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Li, Xiang Cao, Kaitian Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai China

ISBN: (纸本)9798350369564

the combination of Symbiotic Radio (SR) technology and reinforcement learning is a very feasible solution to improve the performance of wireless communication systems. In this paper, we study the resource allocation problem in a symbiotic wireless network consisting of multiple Reconfigurable Intelligent Surfaces (RIS) and multiple receivers, and find that additional power is required during the backscattering of signals from the RIS, and this power requirement can be solved by energy harvesting. Aiming to address this problem, we propose a framework based on the DDQN (Double Deep Q-Network) algorithm for storing dynamic resource allocations in a network without the need to pretrain the network. Unlike the general DQN (Deep Q-Network) algorithm that overestimates the action values, we use the DDQN algorithm, which allows us to bring the Q values closer to the actual values, thus improving the decision accuracy. the framework makes full use of powerful deep reinforcement learning capabilities, solves the real-time control problem, and allows the system to implement energy harvesting. through simulation experiments, we verify the effectiveness and practicality of the framework. Simulation results show that our proposed framework improves the energy efficiency of wireless communication systems by ensuring good quality of service while ensuring energy efficiency. © 2023 ieee.

关键词： Reinforcement learning

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RLHavoc:Enhanced Historical Data Orientation Enhances Code Coverage 8

RLHavoc:Enhanced Historical Data Orientation Enhances Code C...

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8th international conference on Signal and Image Processing, ICSIP 2023

作者： Zhang, Shihe Liao, Chaohao Liu, Renhua University of Electronic Science and Technology of China School of Computer Science and Engineering ChengDu China

ISBN: (纸本)9798350397932

Fuzzing is a common method in software test engineering, which tries to trigger potential vulnerabilities in the target program by continuously providing modified input to the test target. the traditional fuzzy test tools are white box test, gray box test, black box test. the mutation strategies used in these traditional testing tools have high randomness. As a result, the traditional test methods have high randomness in the selection of strategic actions, so effective strategic actions maybe be ignored. Aiming at improving the coverage rate of fuzzy test code based on variation, we implement RLHavoc to improve the efficiency of American fuzzy lop(AFL) based on Deep Q Network(DQN) algorithm by using DQN to prioritize the generation of test cases in AFL. RLHavoc combines AFL's Havoc phase mutation strategy with DQN algorithm. DQN algorithm takes advantage of AFL's Havoc strategy to conduct grey box testing for different binaries and use comprehensive historical data to guide the action selection of variation strategies in the fuzzy test vulnerability detection process, taking seeds as state and new the number of unique execution paths as reward, which enhances the action space orientation of the secondary fuzz target binaries, thus improving the code coverage. Compared to the traditional AFL fuzz effect, our experimental results show that the code coverage rate is improved by about 3%. © 2023 ieee.

关键词： Random processes

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A Blockchain-Based Decentralized Privacy-Preserving Mobile Payment Scheme Using Anonymous Credentials 24

A Blockchain-Based Decentralized Privacy-Preserving Mobile P...

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24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yu, Linsheng He, Mingxing Xiong, Ling Luo, Qian Niu, Xianhua School of Computer and Software Engineering Xihua University Chengdu China The Second Research Institute Civil Aviation Administration of China Chengdu China

ISBN: (纸本)9798350319934

Mobile communication technology has facilitated the rapid spread of convenient mobile payments. Leading third-party payment platforms (TPPs) such as Alipay, Paypal, and Wexpay, have launched popular mobile payment services. However, TPPs-led centralized mobile payments increase the risk of single-point failure and the leakage of user privacy. In this paper, we propose a blockchain-based decentralized privacy-preserving mobile payment scheme by utilizing threshold anonymous credentials. the integration of the blockchain and threshold cryptography technology enables mobile payment to possess fault tolerance for malicious attacks. Additionally, we leverage Pedersen commitments and zero-knowledge proofs to prevent users' sensitive transaction information from being leaked to curious entities on the transparent blockchain. through performance analysis, we make clear that users in the proposed scheme have low computation overhead and storage costs in the payment process, and can adopt flexible payment strategies to execute micro-payments and large payments. © 2022 ieee.

关键词： Blockchain

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An Efficient Design and Performance Analysis of Novel 8 Bit Modified Wallace Multiplier Using Sklansky Adder in Comparison with Kogge-Stone Adder (KSA) 8

An Efficient Design and Performance Analysis of Novel 8 Bit ...

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8th ieee international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Sathish, K. Jagadeesh, P. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350347791

Aim: this study aims to compare the delay performance of an 8-bit modified Wallace multiplier utilising the proposed Sklansky Adder and the Kogg-Stone Adder. Materials and Methods: the ModelSim simulation software was used to simulate the design. this software allowed the researcher to visualize the output waveforms and evaluate the performance of the multiplier. VHDL was used to implement the design of an 8-bit modified Wallace multiplier utilising suggested Sklansky Adder and the Kogge-Stone Adder. During the simulation, input data was collected and used to test the multiplier. Different-sized input data were used to evaluate the multiplier's performance under diverse settings. the performance of the multiplier was evaluated in terms of speed and power consumption. the research involved 20 sample data sets, with 10 from each of two groups. the Sklansky Adder and Kogge-Stone Adder were applied to the modified Wallace multiplier, with an equal number of samples for each adder. the performance evaluation was carried out using the G-power software with alpha and beta values of 0.05 and 0.20, respectively. the results were based on a 95% confidence interval. Result: the propagation delay of the novel modified Wallace multiplier utilising the Sklansky adder is 55 ns, which is significantly less than the propagation delay of the Wallace multiplier utilising the Kogge-Stone adder, which is 85 ns. the significance value that was obtained from the analysis was 0.0436 (P less than 0.05), which indicates that there is a statistically significant difference between the two multipliers in terms of the amount of time it takes for propagation. Conclusion: In conclusion, this study demonstrated the effectiveness of the Sklansky Adder in improving the performance of the modified Wallace multiplier. the results of this research could be helpful to designers who are developing applications for digital signal processing and computer arithmetic that demand high-speed operations that co

关键词： Adders

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Algorithm based personalized push Research on "information Cocoon Room" 8

Algorithm based personalized push Research on "information C...

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8th international conference on Information systems engineering, ICISE 2023

作者： Li, Ting Dong, Yun Zhang, Bo Wuhan Donghu University Literature and Law Institute Wuhan China Wuhan Donghu University School of Computer Science Wuhan430100 China

ISBN: (纸本)9798350307009

With the continuous development of information technology, our society has entered the Internet era. Faced with massive amounts of online information, audiences are often overwhelmed by inefficient information filtering, which greatly reduces their media experience. With the rapid progress of intelligent media technology, algorithms have been introduced into the media industry as an efficient information filtering technology. Algorithm push helps users quickly find the content they need in massive data, saving them time and reducing the trouble caused by information overload. But it also brings the drawbacks of an "information cocoon". As a new technology that promotes the development of the media industry, it is also accompanied by many problems. the "information cocoon room"triggered by the algorithm based personalized push technology in the media industry has attracted extensive attention from all parties. How to better leverage the advantages of algorithmic push and avoid the drawbacks of the "information cocoon"in the future, better help the healthy development of the media industry, and meet the needs and experiences of users are all worthy of in-depth exploration. © 2023 ieee.

关键词： Information filtering

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Education Application Usability Assessments: Comparison Between Blackboard and Canvas Based on App Reviews 8

Education Application Usability Assessments: Comparison Betw...

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8th international conference On Mobile And Secure Services (MobiSecServ)

作者： Algamdi, Shabbab Albanyan, Abdullah Ludi, Stephanie Univ North Texas Comp Sci Dept Denton TX 76203 USA Prince Sattam Bin Abdulaziz Univ Comp Sci Dept Riyadh Saudi Arabia

ISBN: (纸本)9798350316490

A Learning Management System, or LMS, is a software that helps educators and students increase and improve their educational involvement and learning. Two excellent examples are Canvas and Blackboard. Different course materials are organized and readily accessible by LMS, which incorporates social learning engagement. this research aims to assess the usability of the two learning management systems, compare the usability of Blackboard and Canvas based on student satisfaction, and calculate the usability scores of the two leaning management tools. Reviews of apps are crucial for understanding user input. To evaluate the usability of software, we analyze app reviews to categorize the usability issues based on prior research in usability engineering. Apps stakeholders are students, teachers, and system administrators. In this study, we investigate the issues with the Black-board and Canvas mobile apps for education. We group them according to Jacob Neilson's heuristics [14];ten general guidelines for creating user interfaces. Because they are more comparable to general rules of thumb than precise usability requirements, they are known as "heuristics." Breaking down app reviews using Natural Language Processing (NLP), also sometimes referred to as text preparation, is an essential process that involves getting text data ready for analysis. Text cleaning is the process of removing noise and unimportant material from text data so that it can be more effectively examined. In fact, the annotator would find it easier to connect the review with the right heuristic [13].

关键词： Usability Human-computer Interaction Learning Management systems User Reviews Mobile Application Platform

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Optimal Allocation of DG Units in Radial Distribution systems 8

Optimal Allocation of DG Units in Radial Distribution System...

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8th international conference on Mathematics and computers in Sciences and Industry, MCSI 2023

作者： Bobric, Elena Crenguta Irimia, Daniela Ştefan Cel Mare University Electrical Engineering and Computer Science Faculty Suceava Romania

ISBN: (纸本)9798350341652

In the current context of increasing electricity consumption and at the same time reducing CO2 emissions, distributed renewable energy sources have gradually become a new trend in distribution network development. their widespread use can have a negative impact on the operation of the network. Arbitrary placement of these sources can cause both an increase in active power losses and a change in the voltage level in the nodes, with effects on the system's stability. To address this issue, the paper uses the Grey Wolf Optimizer (GWO) algorithm to determine the optimal location and value of the DG that leads to minimal losses of active power, under the conditions of compliance with the restrictions regarding the voltage level in the nodes. the work takes into account DG that produces only active power (for example photovoltaic panels). ieee 33-bus and ieee 69-bus test distribution networks were used for the case study. the determinations were performed in MATLAB, by associating the GWO algorithm to calculating power losses in the analyzed network. the results demonstrate the importance of the optimal choice of both the DG location and their sizes for the efficient network operation. through the optimal placement of the distributed energy source according to the results of the applied algorithm, the benefit of using a green energy source is accumulated with the reduction of active power losses and the improvement of the bus voltage level. © 2023 ieee.

关键词： Distributed power generation

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Cross-Mapping Net: Unsupervised Change Detection from Heterogeneous Remote Sensing Images Using a Transformer Network 8

Cross-Mapping Net: Unsupervised Change Detection from Hetero...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Wei, Lifeng Chen, Gao Zhou, Qingfeng Liu, Chanzi Cai, Chuanbao School of Computer Science and Technology Dongguan University of Technology Dongguan China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China Shen Zhen Everwin Precision Technology Co. LTD. Shenzhen China

ISBN: (纸本)9781665456128

Change detection from comparing SAR and optical images is a challenging problem. the popular solution is to map the two images into a common domain, and then do comparing. In general, the mapping algorithm used convolution as the backbone. However, the convolution operation has difficulties in extracting global information in space-time. this paper proposes to replace convolution by transformer. In specific, we first use a stacked convolution denoise autoencoder (SCDAE) to extract deep semantic features of each image. then we design a transformer-based cross-mapping autoencoder (TCMA) to transform the features into a common representation space. Finally, we compare the two common-domain feature maps to obtain the difference map, and refine the difference map by similarity measurement and clustering algorithm. the parameters of the two steps above are learned by commonalities between feature maps instead of labels, i.e. unsupervised learning. the proposed algorithm is named as cross-mapping network method (CM-Net). © 2023 ieee.

关键词： Convolution

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An Enhanced Deep Learning Model for Tomato Leaf Disease Prediction 8

An Enhanced Deep Learning Model for Tomato Leaf Disease Pred...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Dhivyaa, C.R. Nithya, K. Vignesh, T. Sudhakar, R. Kumar, K. Sathis Janani, T. Kongu Engineering College Computer Science and Engineering Erode India Kongu Engineering College Computer Technology-PG Erode India Nandha College of Technology Computer Science and Engineering Erode India Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Information Technology Sathyamangalam India

ISBN: (纸本)9798350396638

the rising need for tomato cultivation has raised concerns about detecting and diagnosing diseases affecting tomato plants. Tomato leaf disease prediction faces several challenges, including the variability of symptoms, limited availability of high-quality data, lack of standardization in disease identification and classification, and difficulty in accounting for environmental factors such as temperature and humidity. these challenges can make it challenging to develop accurate and reliable predictive models, particularly in developing countries. Detecting these diseases in the earlier stage is essential to prevent losses in yield and ensure food security. In current years, deep learning approaches have been increasingly utilized to tackle this issue. In this work, an improved deep learning model is introduced for predicting leaf diseases of tomato plants. the proposed model is based on a visual geometric group VGG-16 architecture, which has been proven effective in image classification tasks. the paper presents a Bayesian optimization method that involves fine-tuning a pre-trained VGG-16 model with a collection of images. To further increase the accuracy of the proposed work, data augmentation techniques are incorporated, such as random rotations, flips, and brightness adjustments, which increase the diversity of the training data. the diseases of tomato leaf prediction model are tested on both a publicly accessible dataset and custom dataset of tomato plant's leaf images. the experimental results demonstrate that the VGG-16 model with Bayesian optimization approach performs well than other methods. © 2023 ieee.

关键词： Forecasting

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