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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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WRLqueue: A Lock-Free Queue For Embedded Real-Time System 24

WRLqueue: A Lock-Free Queue For Embedded Real-Time System

引用

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： Yang, Qidi Tang, Lei Guo, Yanrong Kuang, Nailiang Zhong, Sheng Luo, Hangzai Xi'an Microelectronics Technology Institute Department of Software Engineering Xian710065 China Northwest University Department of Software Engineering Xian710127 China

ISBN: (纸本)9798350319934

Data synchronization across tasks and devices are crucial for any system supporting multi-task. the standard lock-based blocking mechanism in the aforementioned process may have unpredictable latency overhead thus is not suitable for real time applications, and is prone to deadlock. As a result, lock-free and wait-free algorithms with worst-case latency and progress assurance are crucial for real time cases. Existing lock-free algorithms need to frequently allocate new storage buffers during execution, and design a series of complex memory recovery strategies and help mechanisms. It results in a high worst-case latency, complicated algorithm design, and poor embedded device adaptability. this work proposes a new lock-free method WRLqueue (write to read lock-free queue) for multi-producer and single consumer situation, which considerably enhances the real-time performance guarantee. Experimental results show that the cyclomatic complexity is reduced by 85% compared with existing algorithms. the worst-case latency of enqueue operation decreases by 90% in the case of no competition and 60% in the case of competition. © 2022 ieee.

关键词： Locks (fasteners)

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Development of Boost Converter System with Solar Charging Controller for Electrical Equipment 8

Development of Boost Converter System with Solar Charging Co...

引用

8th ieee international conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Ibrahim, Babul Salam Soomro, Dur Muhammad Sundarajoo, Sharman Shahirin, Mohd Fakhrul Gulf University for Science & Technology Electrical and Computer Engineering Department Kuwait Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia

ISBN: (纸本)9798350327090

there are numerous DC to DC energy alteration utilizations where the origin voltage needs to be transformed to a larger resulting voltage. Boost converters utilization includes energy equipment for semiconductor manufacturing supplies, ultrasound instruments, X-ray devices, solar systems, and mercury ion motors. Notably, this type of converter is used to step up the photovoltaic generator's voltage to the required load voltage in solar systems. Hence, this study's principal interest is to form and compose a boost converter and a solar charging controller circuit. this system aims to change the uncontrolled input of DC to a regulated output of DC with the wanted level of voltage. there are three critical procedures involved to produce a boost converter. the first process was to design. Next was to simulate, and the final process was to construct the boost converter and a solar charging controller circuit. the operational frequency was configured at 20 kHz, and a duty cycle of 50% was employed. In this study, the OrCAD PSpice, Multisim, and Proteus software environments were used to simulate the develop the circuit to obtain results. Every parameter value was obtained from the hardware. the values obtained were compared with the calculation, simulation, and hardware results from the boost converter circuit as a validation procedure. © 2023 ieee.

关键词： Boost converter

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Vision-based human pose estimation for learning behaviour analysis: A review 8

Vision-based human pose estimation for learning behaviour an...

引用

8th international conference on Information systems engineering, ICISE 2023

作者： Zhao, Li Shen, Dazheng Liu, Tingting Wang, Zhuowei Li, Weixin Liu, Hai Si, Junya Li, Jiawen Li, Zixin Wuhan Donghu University School of Computer Science Wuhan430212 China

ISBN: (纸本)9798350307009

In this article, we review the vision-based human pose estimation (HPE) methods in the past decades. Human pose estimation is the connection of all detected human body keypoints according to their interdependencies to form a structure similar to the skeleton. Furthermore, human posture is utilized in a wide variety of computer vision tasks, for instance, learning, sports, medical and human-computer interaction. Due to partial occlusion, noise problems, scale changes, and complex backgrounds, HPE remains an important issue for sustainable discussion and development. To obtain human posture from signals or perform behavioural analysis on the human body, HPE uses a variety of methods, including various transformer models and CNN models. Our methods are among them which in CNN module. Specifically, our EHPE and EDE achieve 79.1AP and 78.5AP on COCO dataset. © 2023 ieee.

关键词： Behavioral research

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Design and Comparative Analysis of a User-Friendly Telegram Bot for Image Steganography using F5 and LSB Algorithms 8

Design and Comparative Analysis of a User-Friendly Telegram ...

引用

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

作者： Gope, Bishakha Nawale, Sakshi Deo, Sakshi Chavan, Tanishka Kumbharkar, P.B. JSPM's Rajarshi Shahu College of Engineering Computer Engineering Pune India

ISBN: (纸本)9798350396638

Secure communication has become increasingly important with the rise of online communication and data privacy concerns. Image steganography is a popular method for secure communication, but existing systems face several challenges. One of the primary challenges is maintaining image quality while hiding a significant amount of data within the image. Additionally, many existing systems are not user-friendly or system-independent, making them less accessible to a wider range of users. Security and detection of hidden messages are also challenges that need to be addressed. To address these challenges, user-friendly Telegram bot for image steganography that uses the F5 algorithm for JPEG images and the LSB algorithm for PNG images is presented in the study. the objective is to provide a secure and user-friendly platform for image steganography that is easily accessible and system-independent. the use of Telegram as the platform for the bot provides a familiar and widely-used interface that many users are comfortable with. Additionally, the use of encryption ensures that hidden messages are transmitted securely and cannot be easily intercepted or detected. this survey demonstrates that the proposed system provides high-quality steganography images that are difficult to detect, addressing the challenge of detection. Overall, the proposed system aims to overcome the challenges faced by existing image steganography systems and provide a reliable and effective method for secure communication. © 2023 ieee.

关键词： Steganography

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Facial Expression Recognition Based on Time Series Self-Attention Feature Fusion 8

Facial Expression Recognition Based on Time Series Self-Atte...

引用

8th international conference on Information systems engineering, ICISE 2023

作者： Wu, Fei Li, Xinrui Wuhan Donghu University School of Computer Science Wuhan China

ISBN: (纸本)9798350307009

At present, most of the image-based video facial expression recognition methods only focus on the facial expression features of each frame of the image, and a small part of the work takes the time dimension information into account. It is noteworthy that the facial expression intensity of each frame in the video varies greatly from different angles. In order to make full use of the spatiotemporal dimension information in the video sequence and the force difference characteristics of different frame images on video expression recognition, a time series self-attention LSTM-Transformer network model facial expression recognition method is proposed. this method can effectively integrate the feature sub-modules of two different dimensions in time domain and space domain to ultimately improve the recognition performance of facial expressions. Finally, using CK+as the data source, the expression is recognized by the above model. the results show that the recognition effect of LSTM Transformer feature fusion is still higher than that of any single feature model. © 2023 ieee.

关键词： Time series

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FixGPT: A Novel three-Tier Deep Learning Model for Automated Program Repair 8

FixGPT: A Novel Three-Tier Deep Learning Model for Automated...

引用

8th international conference on Data Science in Cyberspace, DSC 2023

作者： Ye, Wenhao Xia, Jun Feng, Shuo Zhong, Xiangyu Yuan, Shuai Guan, Zhitao State Grid Hunan Electric Power Company Limited Changsha China School of Control and Computer Engineering North China Electric Power University Beijing China Hunan Sgit Technology CO. LTD. Changsha China Brock University Department of Finance Operations and Information Systems ON Canada

ISBN: (纸本)9798350331035

Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code representation, which hampers accurate patch generation. Furthermore, the training data used by these data-driven approaches may be limited, and they may not be able to capture the subtle differences between vulnerabilities and patches. To address these issues, FixGPT, we propose a three-tier deep learning model in the study. Specifically, a generative pre-trained transformer model is designed in the first tier to capture code characteristics and programming patterns. the second tier integrates a generation model based on the structure of neural machine translation, for the purpose of generating potential patches. Finally, a contrast model is introduced in the last tier to differentiate between the vulnerability and the patch. We also incorporate the Byte Pair Encoding approach to reduce the search space by converting identifiers into subwords. Detailed experimental studies have been carried out to evaluate the performance of FixGPT on two well-known benchmarking datasets: QuixBugs and Defects4J. the results demonstrated significant improvements in the effectiveness and accuracy in comparison with existing solutions. We complement these findings through the analysis of two case studies. © 2023 ieee.

关键词： deep learning program repair programming language model vulnerability

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A Github Actions Workflow for Continuous Delivery of software on Arduino-Based IoT Devices 8

A GitHub Actions Workflow for Continuous Delivery of Softwar...

引用

8th international conference on Computing in engineering and Technology, ICCET 2023

作者： Musitmani, Suthinan Chauhan, Naveen Department of Computer Science & Engineering National Institute of Technology Hamirpur Hamirpur India

ISBN: (纸本)9781839539237

the phrase "Internet of things"(loT) describes a wide range of actual physical things or gadgets that collect, measure, and wirelessly transmit data about physical properties over the Internet. loT systems have their own software development life cycle. Because the system involves human interaction, the testing and deployment requirements for these systems are slightly different than usual. therefore, a specialised continuous integration and delivery approach to releasing new software versions must be used. Github Actions help automate event-driven tasks and can be transferred between repositories, making it convenient for developers to simply build, test, and deploy any software project. the Github Actions workflow is suitable for loT software distribution because they can use it to automate the software loT system deployment process. this paper introduces over-the-air (OTA) software deployment algorithms, their implementation with a Github Actions workflow, and an example use case. the example use case demonstrates the ease of loT development using the proposed Github Actions workflow. the proposed algorithms therefore simplify the development of Arduino-based loT applications. © the Institution of engineering & Technology 2023.

关键词： Internet of things

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Student Engagement Detection in Classroom using Deep CNN-based Learning Approach 8

Student Engagement Detection in Classroom using Deep CNN-bas...

引用

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

作者： Perumal, B. Nagaraj, P. Narsimha Charan, thulasi Sai Saideepak, Yellampalli Venkata Siva Vignesh Reddy, Chennuru Venkata Nagendra, Sanakam Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Krishnankoil India

ISBN: (纸本)9798350396638

In a large classroom, tutors or self-directed learning systems can more easily manage or summarize the situation by detecting and measuring students' attention levels. Today, many institutions are capable of developing effective systems for assisting teachers or tutoring systems. Deep learning is a sophisticated technology in machine learning. this article suggests a transfer learning technique that may be used to classify student activities in the classroom using a limited dataset. Proposed contribution includes creating a lightweight dataset with a small number of images for each category, which can be used for classification work. In comparison to other studies, the experiments yielded satisfactory results for action recognition with high accuracy. In this work, a system that can automatically detect threats. And also want to the classification of student engagement from images using create a CNN and Mobile Net with multiple layers namely convolution, pooling, flattening, and full connection. this model achieved an overall accuracy of 93% in detecting student engagement. Proposed model is also evaluated on a separate test set that included unseen images and achieved comparable results. © 2023 ieee.

关键词： Convolution

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Research on dynamic production plan models based on the ARMA model and Holt-Winters methods 8

Research on dynamic production plan models based on the ARMA...

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8th international conference on Information systems engineering, ICISE 2023

作者： Wang, Guixiang Huang, Yusen Li, Jiawen Wei, Shuaiyang Wuhan Donghu University School of Computer Science Hubei Wuhan430212 China

ISBN: (纸本)9798350307009

Nowadays, multi -type small batches have become a significant feature of electronics production. through accurate predictions on product production, it can effectively grasp the development trend of future production and investment, thereby expanding the market share of small -scale electronic products for small batches of products. ARMA models and three index smooth models are mostly applied to market forecastsss analysis. this article will first use the historical data from 2019 to 2022 as a sample. Based on the ARMA model and three-time quality smooth models, the historical data production of small-batch electronic products will be predicted afterwards, and the prediction results will be compared. then, the future long -term multi -type small - scale electronic products will be predicted and analyzed, and the average value of the prediction values of the two methods will be obtained to obtain a dynamic production plan. Finally, in order to improve the accuracy of forecasting, we will use other predictive models for postponing prediction. By comparing and analyzing the predictive accuracy of different models, the method of improving the accuracy of the production predictive accuracy of multiple types of small batches of electronic products is summarized. © 2023 ieee.

关键词： Forecasting

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Latent Transformer-based Non-Parallel Voice Conversion with Cycle Consistency and Variance Compensator 8

Latent Transformer-based Non-Parallel Voice Conversion with ...

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8th international conference on Information systems engineering, ICISE 2023

作者： Zhang, Zhiqin Zhang, Bo Wuhan Donghu University School of Computer Science Wuhan430000 China

ISBN: (纸本)9798350307009

Our proposed method presents a novel approach for training voice conversion models in a non-parallel setting, eliminating the need for a parallel corpus, called latent transformer-based non-parallel voice conversion (VC). Although GAN-based voice conversion methods are widely recognized as benchmark approaches, their capability to capture time-frequency patterns is limited, this limitation restricts their application to converting only the mel-cepstrum domain, rather than the mel-spectrogram domain. To overcome this limitation, previous approaches have incorporated additional modules such as time-frequency adaptive normalization (TFAN), but this results in an increase in the number of learned parameters. Our proposed method consists of a cycle consistency module (CCM), a latent transformer, and a variance compensator that enhances naturalness and expressiveness in generated speech. Specifically, the variance compensator module (VCM) incorporates an additional transformer-based latent improving module that provides more acoustic variance information, including pitch, duration, and energy. Our proposed method eliminates the need for an additional module like TFAN and achieves better results compared to CycleGAN-based voice conversion methods in terms of subjective evaluation of naturalness and speaker similarity. © 2023 ieee.

关键词： Benchmarking

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