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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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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...

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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 ...

引用

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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Modeling Bi-directional Wedge-type Segmented Soft Pneumatic Bending Actuator for Hand Rehabilitation using Finite Element Method 8

Modeling Bi-directional Wedge-type Segmented Soft Pneumatic ...

引用

8th international conference on Robotics and Automation engineering, ICRAE 2023

作者： Enojas, Mark Joseph B. Ramos, Manuel C. Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines

ISBN: (纸本)9798350327656

Models of different actuators for hand rehabilitation gloves have been sought by many developers. Hand rehabilitation exercises require flexion and extension of the fingers. However, existing actuators for this application do not conform to these requirements. In this paper, a bi-directional wedge-type segmented soft pneumatic bending actuator is proposed that could accomplish flexion and extension with positive and negative (vacuum) pressure, respectively. the model is simulated in finite element software and is characterized according to the varying pressure at each joint to its equivalent force depicted by each of the links of the finger. the results show that the model was able to bend in flexion -90at a pressure of 220 kPa and in extension at +15at -60 kPa pressure. An artificial neural network was set up with 2484 data sets as an inverse kinematic solution in deriving a pressure-to-bending angle function. An R of 0.99997 was obtained for both the training, validation, and test. the force and torque generated at the tip of each joint in an axial direction of the actuator have been evaluated to measure the actuator model's bending ability. this model can be used as an assistive device that can do hand rehabilitation exercises requiring flexion and extension such as tendon gliding exercises. © 2023 ieee.

关键词： Pneumatic actuators

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Identifying Underrepresented Driving Scenarios in Training Datasets with SafeML Robustness Monitoring for Autonomous Driving Vehicles 8

Identifying Underrepresented Driving Scenarios in Training D...

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8th international conference on System Reliability and Safety, ICSRS 2024

作者： Matthias, Bergler Ramin, Tavakoli-Kolagari Kristina, Lundqvist Nuremberg Institute of Technology Department for Computer Science Nuremberg Germany Mälardalens University Dependable Software Engineering Västerås Sweden

ISBN: (纸本)9798350354508

this study investigates the evaluation of the representativeness of datasets in autonomous driving systems using statistical distance metrics. Eight different datasets, created in the Carla simulation environment, cover various driving conditions, including normal and impaired lighting scenarios, alternative routes, and challenging conditions such as power outages. the datasets were designed to represent similar routes under different conditions. Various distance metrics- Wasserstein, Kuiper, Anderson-Darling, Chernoff, DTS, and CVM-were applied to measure pairwise dataset distances. We anticipated that the dataset for a given route under ideal conditions would exhibit a large distance measure (of any of the listed distance measures) compared to the same route under impaired conditions (e.g., a power failure at the streetlights). However, we were particularly interested in whether a measurable jump at a (potential threshold) value could be recognized even with a smaller drop in dataset condition quality. the results of the study show that a normalization of these distance measures enables precise divergence comparisons and the determination of meaningful threshold values. this in turn means that normalized deviation measures can effectively identify deviations in real time, hence contributing to the development and monitoring of more reliable autonomous driving models. © 2024 ieee.

关键词： Street lighting

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Time-frequency Analysis and Convolutional Neural Network based Radio Frequency Fingerprinting Identification 24

Time-frequency Analysis and Convolutional Neural Network bas...

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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： Chen, Songlin Xia, Weifeng Wu, Hao School of Aeronautics and Astronautics University of Electronic Science and Technology of China China Shool of Automation Shenyang Institute of Engineering China School of Information and Software Engineering University of Electronic Science and Technology of China China Cetc Rongwei Electronic Technology Co. Ltd. China

ISBN: (纸本)9798350319934

Radio frequency fingerprinting (RFF) identification, which is a physical layer based distinct native feature, can offer a trustworthy device identity authentication for device access, especially for the power and computing limited asym-metric loT settings scenario. However, constructing the best RFF features satisfying the high authentication is a challenge with the rapid development of integrated circuits. In this paper, we propose a novel approach where RFF features are learned from blind features via the time-frequency analysis- based convolutional neural network. Specifically, in order to overcome the complexity of manually selecting RFF features, we combine a time-frequency image with rich RFF information via time-frequency transform and convolution neural network to automatically extract RFF features algorithms. Finally, the effectiveness of the algorithm is verified by experiments and simulation analysis. © 2022 ieee.

关键词： Authentication

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