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检索条件"任意字段=Knowledge-Based Software Engineering Conference"

共 17481 条记录，以下是851-860 订阅

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Comparative Study of Iteration and Coefficient Influence on Volume Preservation in Position-based Dynamics Simulation 6

Comparative Study of Iteration and Coefficient Influence on ...

引用

6th IEEE International conference on knowledge Innovation and Invention, ICKII 2023

作者： Va, Hongly Ma, Jun Hong, Min Soonchunhyang University Department of Software Convergence Asan-si Korea Republic of Soonchunhyang University Department of Computer Software Engineering Asan-si Korea Republic of

ISBN: (纸本)9798350323535

We proposed a practical approach with position-based dynamics (PBD) to simulate the volume of a deformable body. A variety of materials were simulated using the general constraints of PBD such as stretch, bending, and volume constraints. The method for the constraint system based on PBD was fast, unconditionally stable, and controllable. However, it also had limitations and challenges that need to be addressed. The animation of the simulator required high-performance speed, therefore we used the parallelization technique of PBD since the multi-core system and massively parallel GPUs were more efficient than the naïve approach (serialize method). Since we chose Unity3D and OpenGL as the main platforms, we used the compute shader as the kernel to execute the program to solve the system of constraints. Even though the PBD-based approach was fast, the coefficients were inaccurate to maintain the object's volume. Therefore, we demonstrated the complexity in the calculation of GPU-based PBD compared to CPU-based PBD in each different case of constraint type correlated with the coefficients. We also evaluated the effectiveness of various constraint methods within the PBD approach by comparing the error in the volume of the deformable object. © 2023 IEEE.

关键词： Graphics processing unit

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GAT-based Rating Model for Fair Recommendations on Online Health Communities 2

GAT-Based Rating Model for Fair Recommendations on Online He...

引用

2nd International conference on Cloud Computing, Big Data Application and software engineering, CBASE 2023

作者： Guo, Yufan Guo, Yushan Lin, Lin Tongji University College of Electronics and Information Engineering Shanghai China Tongji University School of Economics and Management Shanghai China

ISBN: (纸本)9798350331448

Online Health Community (OHC) is a platform that provides medical consultation and health knowledge-sharing services. For patients, OHC can recommend high-quality doctors according to their recommendation popularity (RP). Current OHC relies on factors such as visit frequency and consultation volume to rate RP primarily. However, this rating method will make doctors with high RP more likely to be discovered and selected by patients, further increasing their RP. This amplifies the popularity bias of doctors in each rating. In this paper, a rating model is proposed based on graph attention networks (GAT) to address this problem. This model makes full use of the characteristics and relationships of doctors to create similar trends in the RP of similar doctors by using graph data. Through data analysis, this method reduces the differences between high-RP and low-RP doctors, leading to a more balanced distribution of RP on OHCs and effectively limiting popularity bias. © 2023 IEEE.

关键词： graph attention networks Online Health Community popularity bias recommendation popularity

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Data Resilience Protection for Power Data Center based on Automatic Identification of Key Data and Incremental Backup

Data Resilience Protection for Power Data Center Based on Au...

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2023 International conference on Intelligent Management and software engineering, IMSE 2023

作者： Song, Yubo Gu, Yingcheng Sun, Wen Liu, Kai Jiang, Yu Fang, Lanting Purple Mountain Laboratories School of Cyber Science and Engineering Southeast University Nanjing China Information and Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd Nanjing China

ISBN: (纸本)9798350394146

In response to the challenges brought by the significant increase in data volume and the proprietary, this study proposes a technology for automatically quantifying attributes importance and mining association rules between attributes to ensure data resilience and secure data access. An attribute identification scheme for identifying key data in power data center is designed, incorporating both importance calculation and association rule building. The scheme proposes the concept of attribute importance by leveraging the knowledge the information entropy and the maximum discrete entropy. Then, K-means algorithm is employed to cluster attributes based on its importance and Apriori algorithm is employed to further mining association rules between data attributes. Once the key data is identified, the incremental backup algorithm is employed to avoid data corruption resulted from intrusion. Experimental results show that the identification accuracy of our scheme is better than that of other identification scheme, reaching 97.7%. The joint auto identification scheme has the advantages of low cost and low false alarm rate. ©2023 IEEE.

关键词： Association rules

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Analyzing Generative AI’s Impact on Graph Theory and software Testing: A Comparative Study

Analyzing Generative AI’s Impact on Graph Theory and Softwa...

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International conference on Smart Business and technology, ICSBT 2024

作者： Elasri, Chaimae Kharmoum, Nassim Saoiabi, Fadwa Lagmiri, Souad Najoua Ziti, Soumia IPSS Research Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

Generative AI has been considered to fit Science and Technology especially in Graph Theory and software Testing and as such the subject has attracted some attention. based on the literature, this paper reviews previous works concerning the effect of Generative AI in these fields owing to the call to build more knowledge, address challenges, and improve practices. Graph theory which is a branch of discrete mathematics is vital in computer science and operation research. For this field, generative AI would help in the discovery of graphs, algorithms as well as solutions. With this proposal, this study examines how the existing GANs and VAEs are applied to graph generation, node prediction, and anomaly detection before pointing out the existing research gaps and future research avenues. Likewise, software Testing essential in system development is evolving with Generative AI. The conventional ways are thus more cumbersome and very often inaccurate. In terms of testing, generative AI can come up with test cases, predict failures, and generate smart data therefore changing the test techniques. This paper discusses and analyze models of AI used for test automation and identification of defects for test automation. Consequently, comparing the literature, the paper elucidates that Generative AI transforms Graph Theory and software Testing while promoting interdisciplinary cooperation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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A Novel Kick Detection Tool based on knowledge-Data-Dual-Driven Approaches

A Novel Kick Detection Tool Based on Knowledge-Data-Dual-Dri...

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2023 SPE Offshore Europe conference and Exhibition, OE 2023

作者： Shi, Xiaoyan Cui, Meng Ji, Yong Wu, Dong Guo, Weihong Jing, Lingzhi Tian, Yumeng Yang, Xinyi CNPC Engineering Technology R&D Company Limited Beijing China

ISBN: (纸本)9781613999936

Recognizing abnormal fluid influx is the first and most important step for well control safety. However, due to the complexity of the wellbore fluid mechanism, uncertainty of the formation pressure and noises in the real-time measurement data, it is difficult to detect kicks by conventional approaches. The purpose of this paper is to propose a knowledge-data hybrid approach and build a reliable and easy-to-use tool to automatically detect kicks. based on the knowledge of the kick main indicators, this work utilizes Long-Short moving window average algorithm combining advanced filtration technology to capture subtle change trends precisely and timely in noisy real-time measured surface data during drilling. Self-adaptive kick threshold value adopting approaches are proposed to improve the accuracy of kick detection for different data measuring technologies. The knowledge and inference logic of specific drilling operations and phenomena, such as wellbore ballooning effects and drilling fluid poring, are embedded in the data analysis technologies to reduce the false alarms of the kick detection system. A reliable and practical intelligent kick detection tool is built based on this knowledge-data-dual-driven method. The must-have input set of this tool only contains 8 channels, including pit volumes, outlet flowrate, stand pipe pressure, and etc, which makes the tool flexible and widely applicable. The tool consumes both real-time and archived drilling data, and provide audible and easy-to-understand visual alarms when kick is detected. Moreover, the tool supports different outlet flowrate measurement technologies, including Paddle meter, Radar meter and Coriolis meter. Extensive testing process using historical data manifests that the kick detection rate is 100% (correctly detected 16 kicks out of total 16 kicks), and the overall detection time is 6.1 mins earlier comparing to that of conventional methods. The tool was also deployed in a real-time operation center for

关键词： Infill drilling

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Enhanced Multi-Modal knowledge Graphs Representation Learning via Self-Attention Fusion 6

Enhanced Multi-Modal Knowledge Graphs Representation Learnin...

引用

6th International conference on software engineering and Computer Science, CSECS 2023

作者： Jiahong, Tian Yongchuan, Zhang Yong, He School of Information Science and Engineering Chongqing Jiaotong University Chongqing China School of Smart City Chongqing Jiaotong University Chongqing China

ISBN: (纸本)9798350306378

The complexity and diversity of multi-modal knowledge graphs arise from their ability to fuse information from multiple sources. Traditional knowledge graph representation learning faces challenges in effectively capturing and efficiently processing the complexity of multi-modal knowledge graphs. Moreover, conventional methods primarily rely on embeddings, overlooking the richness of multi-modal information, further diminishing the effectiveness of representation learning. To address these issues, this study proposes an Enhanced Multi-modal knowledge Graph Representation Learning model (MATUCKER) based on the core of TuckER, which adaptively fuses multi-modal information using multi-head self-attention. In this approach, we employ the ResNet model to encode image features of multi-modal entities. These encoded features are then concatenated with the semantic embeddings of multi-modal entities and fed into a multi-head self-attention mechanism for fusion. The resulting multi-modal entity feature encodings are subsequently input into the TuckER model for representation learning. The model's performance is evaluated on the publicly available OpenBG-IMG dataset for link prediction tasks. The experimental results clearly demonstrate that our proposed model significantly outperforms both traditional methods and the multi-modal representation learning model, TransAE, in terms of multi-modal representation learning performance. We achieved significant improvements in key performance metrics compared to the TuckER backbone: the Mean Reciprocal Rank (MRR) increased by 3%, the HITS@10 metric improved by 5%, and the Mean Rank (MR) decreased by approximately 1,000. © 2023 IEEE.

关键词： knowledge graph

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Dual-encoder-based image-text fusion algorithm

Dual-encoder-based image-text fusion algorithm

引用

2024 International conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Xia, Min Wu, Zhonghai School of Software and Microelectronics Peking University Beijing China

ISBN: (纸本)9781510681514

Many sectors are challenged by how to effectively represent knowledge in files that contain multiple images closely related to text, and how to make models understand the relationship between images and text. Contrastive Language-Image Pre-training (CLIP) and Bootstrapping Language-Image Pre-training (BLIP) acquire the capability of understanding the image-text relationship through large-scale model pre-training. CLIP not only considers images and their related text but also contrasts images with massive irrelevant text, to improve its capability of generalizing the relationship between images and related text. BLIP enhances its understanding of complex image-text relationships by pre-training and fine-tuning matched image-text pairs. This paper presents an image-text fusion algorithm based on CLIP and BLIP, which gives an accurate and consistent picture of the image-text relevance by fully using CLIP’s image-text generalization capacity and BLIP’s capacity of understanding the complex image-text relationship. © 2024 SPIE.

关键词： Signal encoding

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Energy Consumption Prediction Method for Refrigeration Systems based on Adversarial Networks and Transformer Networks 1

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17th International conference on knowledge Science, engineering and Management (KSEM)

作者： Zhang, Hu Liu, Huifeng Zhang, Youli Guo, Ying Dai, Hongjun Shao, Minghao Xu, Hongyu Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minis Shandong Comp Sci CtrNatl Supercomp Ctr Jinan Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China Shandong Univ Sch Software Jinan Peoples R China

ISBN: (数字)9789819754892

ISBN: (纸本)9789819754885;9789819754892

Given the significant practical implications of energy consumption prediction for the design and energy conservation control of refrigeration systems, numerous methods have been proposed in this field. However, existing approaches face two primary challenges. Firstly, refrigeration system energy consumption is heavily influenced by external factors, making it difficult for current methods or models to capture the randomness of energy consumption data. Secondly, prediction models struggle to control error accumulation during inference, leading to difficulties in forecasting energy consumption data over extended time ranges. To address these issues, this paper proposes an energy consumption prediction model based on adversarial networks and Transformer networks-ANFormer. The ANFormer model, leveraging self-attention mechanisms and feedforward neural networks, encodes and models input sequences to forecast future energy consumption of refrigeration systems. Ultimately, validation experiments conducted on datasets from two real refrigeration systems demonstrate the accuracy and efficiency of *** experimental results show that the ANFormer model proposed in this paper has optimized the values of MSE and RMSE by approximately 3%-15% compared to FreTS, DLinear, and NLinear models. Compared to Linear, Transformer, AutoFormer, and Informer models, the results in terms of MSE, RMSE, and MAE have all been optimized by more than 79%.

关键词： Refrigeration system energy consumption prediction generative adversarial networks Transformer network

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Improving multi-class opinion classification in social networks by combining Fuzzy SVM and CNN 28th

Improving multi-class opinion classification in social netwo...

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28th International conference on knowledge based and Intelligent information and engineering Systems, KES 2024

作者： Ghezaiel Hammouda, Nadia Toujani, Radhia College of Computer Science and Software Engineering University of Hail Hail Saudi Arabia ESTMOD Department Higher Institute of management University of Tunis Tunisia

Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social networks content and the necessity to handle diverse opinions prompt ongoing advancements in sentiment analysis methodologies. In this study, we propose an innovative approach by integrating a Convolutional Neural Network (CNN) model with fuzzy SVM. Our key contribution lies in the fusion of these two methodologies, capitalizing on the strengths of fuzzy SVM for opinion mining and CNN for extracting intricate features. Additionally, we extend this method to enable multi-class opinion classification, allowing for a finer understanding of nuances in expressed sentiments. Through evaluation on benchmark datasets, we highlight a notable improvement in performance compared to baseline methods. The results underscore the enhanced generalization capabilities and increased accuracy in opinion classification offered by our hybrid model, rendering it a promising asset for decision-making applications based on multi-opinion analysis. © 2024 The Authors.

关键词： Convolutional neural networks

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Semantic Approach for the classification of knowledge representation and reasoning analysis 4

Semantic Approach for the classification of Knowledge repres...

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4th International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Akhil, Karnam Redddy, A.Brahmananda Reddy, Ragireddy Rahul Borad, Abhinav VnrVignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India La Trobe University Department of Engineering Manufacturing Australia Malla Reddy Engineering College Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665474368

A form of artificial intelligence (AI) that is advanced and involves rules that are applied to data to simulate a person's thought process in a particular area, knowledge engineering can be described as an advanced form of artificial intelligence (AI). knowledge engineering had evolved from its original form when it focused on transferring and analyzing knowledge from human problem-solvers into computer programs that could do the same..It is essential to realize that transfer processing has limitations because humans make decisions differently than machines do. Non-linear thinking and analogous reasoning, often not logical, have not been considered in this *** is becoming increasingly common today to use a modeling process for knowledge engineering, which creates a system that can reach the same conclusions as experts without following the same path or acquiring the same information from the same sources. In this paper, we aim to represent knowledge engineering to transform knowledge into software that makes decisions similar to those made by human experts, such as financial advisors, whose decisions are based on their ***, it is expected that human experts will be replaced by knowledge engineering in decision support software © 2022 IEEE.

关键词： Semantics

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