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Combining Deep Learning with Knowledge Graph for design Knowledge Acquisition in Conceptual Product design

computer Modeling in engineering & Sciences 2024年第1期138卷 167-200页

作者： Yuexin Huang Suihuai Yu Jianjie Chu Zhaojing Su Yangfan Cong Hanyu Wang Hao Fan Key Laboratory of Industrial Design and Ergonomics Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi’an710072China School of Industrial Design Engineering Delft University of TechnologyDelft2628 CEThe Netherlands Department of Industrial Design College of ArtsShandong University of Science and TechnologyTsingtao266590China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product *** study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge ***,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product ***,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction *** comparison verified the effectiveness and accuracy of the proposed knowledge extraction *** case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

关键词： Conceptual product design design knowledge acquisition knowledge graph entity extraction relation extraction

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Decomposition-Based Memetic Algorithm for Multi-Objective Fleet Size and Mix Vehicle Routing Problem 13

Decomposition-Based Memetic Algorithm for Multi-Objective Fl...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ba, Yunpeng Zheng, Ruihao Wang, Zhenkun School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

The heterogeneous fleet vehicle routing problem (HFVRP) is of great significance in logistics and transportation. This paper considers a crucial and challenging HFVRP variant, namely multi-objective fleet size and mix vehicle routing problem with unlimited fleet, fixed cost, and dependent cost (MO-FSMVRPFD). A decomposition-based memetic algorithm called MOEA/D-ALS is proposed to cope with MO-FSMVRPFD. MOEA/D-ALS uses the multi-objective evolutionary algorithm based on decomposition (MOEA/D) as the backbone. Beyond that, it adopts a replacement strategy based on maximal fitness improvement (MFI). In addition, the local search is conducted on difficult subproblems for further refinement. The experimental studies on 18 instances show that MOEA/D-ALS exhibits a significant performance superiority over two other representative algorithms. The effectiveness of both the MFI strategy and the adaptive local search procedure is also validated. © 2024 IEEE.

关键词： Vehicle routing

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I $^{2}$ RNN: An Incremental and Interpretable Recurrent Neural Network for Encrypted Traffic Classification

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IEEE Transactions on Dependable and Secure Computing 2023年 1-14页

作者： Song, Zhuoxue Zhao, Ziming Zhang, Fan Xiong, Gang Cheng, Guang Zhao, Xinjie Guo, Shize Chen, Binbin College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China Information Systems Technology and Design Singapore University of Technology and Design Singapore

Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE

关键词： Cryptography

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Synergizing Artistry and Technology by Unveiling the Integration of Matte Painting Techniques in Crafting Precise and Immersive Visual Effects Backgrounds 4th

Synergizing Artistry and Technology by Unveiling the Integra...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Raturi, Vinod Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Animation and Game Design Amity School of Engineering and Technology Amity University Punjab India

ISBN: (纸本)9789819752300

The film industry has evolved because of visual effects (VFX). Matte painting is a component of VFX and is important to VFX. Matte painting is a successful method for producing realistic visual effects and backgrounds for movies and television. To produce a scene or setting that can be utilized as a background in a movie or television show, this technique entails painting on a flat surface, such as a canvas or board. Filmmakers can depict a setting that is not actually there with the use of matte painting. The set will cost a lot of money to produce. This makes it possible to carry out actions that aren’t possible at the shooting site. This study paper’s aim is to investigate the background, methods, and applications of matte painting in the film and television industries. This essay also looks at the benefits and drawbacks of matte painting as well as its efficiency in producing realistic visual effects backdrops. A single work of art can now incorporate digital, photographic, and computer-generated imagery, providing artists with infinite options to create unique and cutting-edge pieces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Television applications

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Profanity Detection from Audio Recordings Using Natural Language Processing Techniques

Profanity Detection from Audio Recordings Using Natural Lang...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Patil, Swapnil Chandre, Pankaj R. Vanarote, Viresh Pathan, Shafi Nimbalkar, Madhukar Shinde, Gitanjali Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhwa India

ISBN: (纸本)9789819998104

Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natural language processing (NLP) techniques. We first convert the audio files into textual transcripts using automatic speech recognition (ASR) tools. We then apply pre-processing techniques such as tokenization, stemming, and stop-word removal to the transcripts. Finally, we train various machine learning models, such as Naive Bayes, Support Vector Machines (SVMs), and Random Forests, to classify the transcripts as either containing profane language or not. The proposed approach can be integrated into various applications, such as content moderation tools, online chat systems, and automatic transcription services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audio recordings

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Enhancing Heart Murmur Detection: A Comparative Study of Machine Learning Models Utilizing Digital Stethoscopes 3

Enhancing Heart Murmur Detection: A Comparative Study of Mac...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Mastracci, Nathaniel Derakhshan, Farnaz Sykes, Edward R. Abdullah, Saad Oakville Canada University of Guelph School of Computer Science Guelph Canada Mälardalen University School of Innovation Design and Engineering Västerås Sweden

ISBN: (纸本)9798350384598

Machine learning has proven to be a powerful tool across many domains including healthcare. Heart sound classification using machine learning can revolutionize cardiac care by improving diagnostic accuracy, enabling early intervention, and facilitating personalized treatment strategies. However, obtaining labeled data for classification model training can be difficult, especially for rare or complex conditions. Furthermore, classifying heart sounds accurately can be challenging due to the variability in sound patterns and the presence of noise which requires preprocessing. In this paper, two machine learning models were trained and evaluated using DenseNet architecture on the CirCor DigiScope Phonocardiagram dataset and an ensemble of Support Vector Machine (SVM) and Decision Tree (DT) algorithms on a custom dataset curated by our partner, Tech4Life. The F1 score of the CirCor trained model was 75%. This is our effort to advance the application of machine learning in heart sound classification. © 2024 IEEE.

关键词： Contrastive Learning

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Afra: An Eclipse-Based Tool with Extensible Architecture for Modeling and Model Checking of Rebeca Family Models 1

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10th IPM International Conference on Fundamentals of Software engineering, FSEN 2023

作者： Khamespanah, Ehsan Sirjani, Marjan Khosravi, Ramtin School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Computer Science Reykjavik University Reykjavik Iceland School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (数字)9783031424410

ISBN: (纸本)9783031424403

Afra is an Eclipse-based tool for the modeling and model checking of Rebeca family models. Together with the standard enriched editor, easy to trace counter-example viewer, modular temporal property definition, exporting a model and its transition system to some other formats facilities are features of Afra. Rebeca family provides actor-based modeling languages which are designed to bridge the gap between formal methods and software engineering. Faithfulness to the system being modeled, and the usability of Rebeca family languages help in ease of modeling and analysis of the model, together with the synthesis of the system based on the model. In this paper, architectural decisions and design strategies we made in the development of Afra are presented. This makes Afra an extensible and reusable application for the modeling and analysis of Rebeca family models. Here, we show how different compilers can be developed for the family of languages which are the same in general language constructs but have some minor differences. Then we show how the model checking engine for these different languages is designed. Despite the fact that Afra has a layered object-oriented design and is developed in Java technology, we use C++ codes for developing its model checking for the performance purposes. This decision made the design of the application even harder. © 2023, IFIP International Federation for Information Processing.

关键词： Model checking

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Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT

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Mathematical Biosciences and engineering 2024年第4期21卷 5735-5761页

作者： Zhang, Wencong Tao, Yuxi Huang, Zhanyao Li, Yue Chen, Yingjia Song, Tengfei Ma, Xiangyuan Zhang, Yaqin Department of Biomedical Engineering College of Engineering Shantou University Shantou China Department of Radiology The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bidirectional feature interaction among multiple phases, thus maximally exploiting the available multiphase information. To enhance the model’s capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multiphase CT data exploitation. The proposed MI-TransSeg network has the potential to assist rad

关键词： Tumors

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STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 224-238页

作者： Lujing Chen Rui Liu Xin Yang Dongsheng Zhou Qiang Zhang Xiaopeng Wei tional and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China

In recent years,human motion prediction has become an active research topic in computer ***,owing to the complexity and stochastic nature of human motion,it remains a challenging *** previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive ***,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in ***,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each *** overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous *** experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction *** effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

关键词： Human motion prediction Transformer Gragh convolutional network

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Quantum Secure Email Communication for Phishing Prevention 7

Quantum Secure Email Communication for Phishing Prevention

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Chandre, Pankaj Sontakke, Palash Patil, Rajkumar Shendkar, Bhagyashree D Dhotre, Dhanraj Vanarote, Viresh Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India Mit Art Design and Technology University Mit School of Computing Department of Information Technology Pune India

ISBN: (纸本)9798331527822

The rise of sophisticated phishing attacks has necessitated stronger security measures for email communication, especially in sensitive environments. This paper proposes a Quantum Secure Email Communication Framework designed to prevent phishing by integrating Quantum Key Distribution (QKD) with traditional email security mechanisms. The framework leverages quantum keys to encrypt email communication, ensuring that only legitimate users with the correct quantum key can send and receive secure emails. The architecture also incorporates a Phishing Detection Engine that validates email content and sender information using quantum-encrypted keys, alerting users to potential phishing threats. The system operates through the secure exchange of quantum keys between the sender's email server and the user via a QKD Channel, with quantum encryption applied to secure outgoing and incoming emails. In parallel, the phishing detection module analyses all email traffic for signs of malicious activity, using a QKD-based Email Validator to confirm the legitimacy of emails. This hybrid approach ensures both the privacy of communication and the proactive detection of phishing attempts, offering a robust defense mechanism in the increasingly hostile landscape of digital communication. © 2024 IEEE.

关键词： Phishing

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