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检索条件"任意字段=4th International Conference on Software and Data Technologies"

共 13474 条记录，以下是51-60 订阅

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Research on dynamic key generation and data encryption scheme based on chaotic mapping and SM4 24

Research on dynamic key generation and data encryption schem...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Feng, Dong Chang, Xiaorong Zhang, Xu Beijing Guodiantong Network Technology Co. Ltd. Beijing China

ISBN: (纸本)9798400709999

In the field of information security, the storage of private data is always one of the key security risk points. this study aims to design and implement a dynamic key generation and data encryption scheme based on chaotic mapping and SM4 algorithm. By selecting Logistic chaotic mapping to generate pseudo-random sequences, the unpredictability of chaotic systems is used for the dynamic update of SM4 keys, thereby enhancing the security and randomness of SM4 key updates. then, combining the encryption security of SM4 algorithm and the encryption speed advantage of chaotic sequence, data storage encryption is achieved through chaotic sequence mapping parameter initialization, chaotic sequence binary quantization, SM4 dynamic encryption and tail processing. Experimental analysis shows that the algorithm has the characteristics of large key space and good key sensitivity. While improving the encryption speed, it effectively reduces the memory usage and maintains high execution efficiency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Block encryption algorithm

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Optimizing Deep Learning Models to Address the Long Tail Problem and its Application in Semantic Segmentation 24

Optimizing Deep Learning Models to Address the Long Tail Pro...

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4th international conference on Signal Processing and Communication Technology, SPCT 2024

作者： Sun, Jiarui Zhao, Yong School of Software and Microelectronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Beijing China

ISBN: (纸本)9798400710636

In the semantic segmentation task, the long-tail problem causes the recognition performance of minority categories to be significantly lower than that of majority categories, affecting the overall effect of the model. this paper aims to optimize the FPN (Feature Pyramid Networks) model to better deal with the long-tail problem and improve the accuracy of semantic segmentation. First, by analyzing the category distribution of the dataset and identifying the severity of the long-tail phenomenon, a semantic segmentation dataset containing multiple categories is constructed. Secondly, for minority categories, data enhancement technology is used and resampling is performed to ensure that the model could be exposed to more minority category samples during training. A focus loss function is then integrated to prioritize hard-to-segment regions and underrepresented categories. Experimental results on standard benchmarks show that the proposed FPN model achieves a Dice coefficient of 88.1% and an F1-Score of 86.4%, significantly outperforming other models such as U-Net (Dice: 85.6%, F1-Score: 83.9%), DeepLab v3 (Dice: 87.4%, F1-Score: 85.6%), and DeepLab v1 (Dice: 84.3%, F1-Score: 82.8%). Notably, the minority class F1-Score for FPN reaches 72.1%, demonstrating its superior ability to tackle the long-tail problem. the results indicate that targeted methods, including dataset augmentation and loss function adjustment, can significantly enhance deep learning model performance, offering new insights for improving semantic segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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AI Ethics in Practice: Exploring Racial and Ethnic Stereotypes in Synthetic Resumes Written by ChatGPT 4th

AI Ethics in Practice: Exploring Racial and Ethnic Stereotyp...

引用

4th international conference on Artificial Intelligence and Knowledge Processing

作者： Benzel, Jessica Rege, Manjeet Univ St Thomas Dept Software Engn & Data Sci St Paul MN 55105 USA

ISBN: (纸本)9783031734762;9783031734779

Generative artificial intelligence (AI) and large language models (LLM) are gaining focus in a number of industries, including the field of human resources (HR), and are attracting new attention with the release of OpenAI's ChatGPT in late 2022. this study focuses on generative AI output in the context of ethical AI and algorithmic hiring practices by exploring differences in ChatGPT's responses to resume writing prompts for multiple race/ethnicity categories, using a variety of methods to identify and evaluate those differences and assess bias. We find that there are observable qualitative differences in some of the output produced by ChatGPT in response to prompts for different races and ethnicities. these differences reflect potentially harmful bias and may promote racial, ethnic, and cultural stereotypes. As such, it is critical for data scientists and HR practitioners to use a variety of methods to rigorously assess the presence of bias and the potential for harm when considering the use of synthetic data in algorithmic hiring practices and other applications. this study addresses ethical concerns regarding the use of ChatGPT to create synthetic text data to train other AI models due to its potential to perpetuate racial and ethnic bias.

关键词： AI Ethics Generative AI Applications Digital Humanities

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A distributed machine learning dynamic remote proof scheme that resists collusion attacks 4

A distributed machine learning dynamic remote proof scheme t...

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4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Wang, Ke Wu, Qiuxin Qin, Yu School of Applied Science Beijing Information Science and Technology University Beijing100192 China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Science Beijing100190 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Based on the security of distributed machine learning data and model based on remote proof, a cross-domain dynamic remote proof scheme based on ring signature improves the efficiency of computer cluster proof, the application of dynamic signature, realizes the real-time measurement of the confidence of computer cluster, and strengthens the real-time protection of data and model. Computers with different security domain may adopt different remote proof mechanism, and convert the proof information of different proof methods into unified proof information, thus realizes the cross-domain dynamic remote proof between computer clusters. Noise is added when the cloud server distributes data to a distributed computer cluster. Due to the particularity of distributed architecture and the unsolution of indefinite equations, the enemy cannot conspire with one or more computers to steal data or the model parameters trained by endpoints. Finally, the safety of this method is verified through safety analysis and experimental analysis, and the computational overhead required in the proof process is reduced compared with other methods. © 2025 SPIE.

关键词： Authentication

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OrcaFlex model verification: Assessing mooring effects on the M4 wave energy converter 6th

OrcaFlex model verification: Assessing mooring effects on th...

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6th international conference on Renewable Energies Offshore, RENEW 2024

作者： Zhao, C. Johanning, L. Stansby, P.K. University of Plymouth United Kingdom University of Manchester United Kingdom

ISBN: (纸本)9781032905570

this study evaluates the mooring systems of wave energy converters (WECs), particularly focusing on the tensile loads of a single buoy and a novel hinged multi-buoy system, the M4, developed by the University of Manchester. Using OrcaFlex software, the research validates a numerical model through a comparison with experimental results from the University of Plymouth’s COAST Laboratory. this process assesses OrcaFlex’s ability to simulate complex floating structures under various conditions. Key findings reveal that discrepancies between numerical and experimental data increase under severe wave conditions and when multiple constrained floating bodies are involved. the study identifies critical factors contributing to these discrepancies, enhancing both experimental and numerical methodologies. this research substantiates the numerical model’s accuracy for the M4 WEC, contributing significantly to advancing efficient and reliable wave energy technologies. © 2025 the Author(s).

关键词： Mooring

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Multi-label text classification method based on ReBERTa-TextCNN 4

Multi-label text classification method based on ReBERTa-Text...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Zhang, Shengnan Xu, Huiyang Shenyang University of Technology School of Software Liaoning Shenyang China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Multi Label Text Classification (MLTC) is an important research problem widely used in the field of natural language processing (NLP) text classification. Aiming at the problem of small sample data sets and unbalanced labels, this paper proposes a multi label text classification method based on prompt learning and RoBERTA Te People's Republic of China NN model. First, use the RoBERta model to obtain the label word vector in the text, and obtain the global features containing the text context information. the dynamic mask can improve the efficiency of the work. then use the Te People's Republic of China NN model to extract the local features of the text, and use the feature fusion method to fuse the global features and local features, so as to improve the classification accuracy of the model. Among them, prompt learning is used to process small sample data, and loss function is used to solve the problem of label imbalance and long tail distribution. the experiment on the actual dataset shows that the accuracy rate of multi label text classification reaches 96%, which reflects the advantage of this model in effectively improving the effect of multi label text classification. © 2025 SPIE.

关键词： Contrastive Learning

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Mobile-Aware Placement: Optimizing Energy Consumption and Delay of data Stream Applications in Edge Computing 4

Mobile-Aware Placement: Optimizing Energy Consumption and De...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Pengcheng Wu, Yi Huang, Jingyu Ji, Ying Software Technology Co. Ltd. Jiangsu Suzhou China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

With the rapid development of 5G and IoT, traditional cloud computing faces limitations due to increasing demands. Edge computing addresses these issues by offloading tasks to nearby nodes, thereby improving efficiency and user experience for latency-sensitive applications such as live video and autonomous driving. However, balancing energy consumption and latency in heterogeneous edge devices remains a challenge, particularly as previous studies often overlook user mobility. In this work, we tackle the problem of data stream processing (DSP) task placement within dynamic user mobility scenarios, addressing the challenges posed by frequent movement. Additionally, we introduce a reinforcement learning approach to enhance the adaptability of DSP tasks, allowing the system to effectively adjust to changing user mobility patterns. through simulation experiments, it is verified that compared with the traditional method, the algorithm proposed in this paper effectively optimizes energy consumption and delay while considering the mobility attributes, and improves the utilization rate of each edge node. © 2025 SPIE.

关键词： Mobile edge computing

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Exploring the Challenges and software Risks of Agile Methodologies in software Development 4th

Exploring the Challenges and Software Risks of Agile Methodo...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Choudhary, Neha Sain, Mahaveer Chopra, Sumitra Kota India

ISBN: (纸本)9789819751990

software development model is the way that software development is organized and managed, which has a direct effect on the quality of software development. It is also one of the most significant topics of research in software engineering. It is necessary to take into account the software development process as well as its various phases in order to assure that the objectives are met effectively. software engineering is concerned with the application of software systems to various system processes. Agile is a method of software development that emphasizes flexibility before its needed and pragmatic methods for delivering a final product. Agile software development requires a change in the way many organizations do things because it focuses on clean delivery of individual software parts instead of whole solution. Agile is an iterative approach to the management of projects as well as development of software. It makes it possible for teams to give value to customers more rapidly and with fewer complications than is possible with old methods. A "big bang" release is something an agile team strives to steer clear of in favor of smaller, more manageable components of work. First, deliver a quick introduction to the agile software development concepts. In this problem, lack of communication and collaboration among team members: Agile methodology relies heavily on communication and collaboration among team members. However, this can be challenging for teams that are geographically dispersed or have members working in different time zones. Afterward discussed different software development techniques including two techniques waterfall model and spiral model, agile software development, agile methodologies. After that, challenges in agile risk management and software risk have been discussed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agile methodologies Risk factor Risk management software development

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Application of LSTM emotion recognition based on deep learning and attention mechanism in patient relationship mediation 4

Application of LSTM emotion recognition based on deep learni...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Ma, Yuxuan School of Software Engineering Lappeenrannan University of Technology Lahti Finland

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Medical disputes are a kind of disharmonious interpersonal relationship caused by different positions, views, and ways of thinking between doctors and patients. In recent years, it has shown an upward trend and has become a major issue affecting social stability and hospital development. this paper refers to the research process and results of Henkel et al. (2020) in the service industry and uses the CREMA-D dataset to train the model to obtain the accuracy of the algorithm and design an experimental process for using AI to solve doctor-patient relationships. this paper uses the CREMA-D dataset to divide emotions into six types: anger, disgust, fear, happy, neutral, and sad. Using natural language processing and LSTM to process emotions in speech data provides help and guidance for doctors to regulate patients' emotions, and helps doctors use AI integrated tools to regulate their own emotions and improve the happiness index of both parties. © 2025 SPIE.

关键词： Emotion Recognition

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Noise Prediction: A Case Study of Gjilan City 4th

Noise Prediction: A Case Study of Gjilan City

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Bajrami, Buen Veljanovska, Kostandina Faculty of Information and Communication Technologies University St. Kliment Ohridski - Bitola 1 Maj bb. Bitola7000 Macedonia

ISBN: (纸本)9789819751990

Recently, we have started to hear more often about the smart city. this is a new term, but recently there is a growing trend, which uses computer sciences to create an integrated, intelligent system, which is easily used and managed by people. Such smart grid technologies can directly impact everything from operations to infrastructure maintenance and planning. A key part of this research will be noise monitoring in specific areas of the city depending on population, traffic density, number of cars, and wind speed. We will make real-time measurements in four areas per day and hourly. the data will be placed in a database, where its data will be processed through two algorithms on our platform designed in PHP. the two forecasting algorithms we used are Linear Regression and the ARIMA Forecasting Model. We have also included the correlation coefficient to see the report of the parameters. At the end, we have presented the factors that influence the increase in noise as well as the recommendations that we believe will help prevent noise in the city. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： ARIMA model algorithm Correlation coefficient Linear regression algorithm Noise prediction

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