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arXiv 2024年

作者： Zhou, Qingyuan Yang, Weidong Fei, Ben Xu, Jingyi Zhang, Rui Liu, Keyi Luo, Yeqi He, Ying School of Computer Science Fudan University China Department of Information Engineering The Chinese University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on commonly used datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for dense and large-scale point clouds and their inclination to generate noisy outliers after denoising. To deal with this challenge, we introduce 3DMambaIPF, for the first time, exploiting Selective State Space Models (SSMs) architecture to handle highly-dense and large-scale point clouds, capitalizing on its strengths in selective input processing and large context modeling capabilities. Additionally, we present a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluations on commonly used datasets (typically with up to 50K points) demonstrate that 3DMambaIPF achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of 3DMam-baIPF on highly dense and large-scale point clouds with up to 500K points compared to off-the-shelf methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Deep Learning based Intelligent System for Skin Disease Detection

Deep Learning based Intelligent System for Skin Disease Dete...

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International Conference on Communications and Signal Processing

作者： K B Dhanush V M Arul Xavier S S Shyni Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamilnadu India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore Tamilnadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Malignant and benign skin diseases significantly impact public health and personal well-being. Traditional machine learning models encounter difficulties in processing image datasets, which is crucial for early and accurate diagnosis. This work employed two pre-trained deep learning models, ResNet50 and VGG19, known for their precise and swift image processing capabilities. These models underwent training utilizing an extensive dataset of images from patients battling benign keratosis, Melanoma, and Melanocytic nevus (Mole). The models were then tasked to distinguish between these three conditions. The results were remarkable, with the models achieving accuracies of $91.3 \%$ and $95.6 \%$, respectively. A comparative analysis revealed that the VGG19 model outperformed in accurately identifying these three diseases, surpassing traditional AI and machine learning methods. The trained VGG19 model was subsequently incorporated into an Android application to enhance user experience.

关键词： Deep learning Training Melanoma Skin Real-time systems User experience Public healthcare

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From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Pradosh Kumar Gantayat Tejinder Kaur Madhusmita Majhi Uttam Kumar Jena Swatismita Das Soni Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad Telangana INDIA Department of MMICTBM Maharishi Markandeshwar (Deemed To Be University) Mullana Ambala Haryana India Department of Computer Science and Engineering Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad Telangana India Department of Computer Science and Engineering Raajdhani Engineering College Bhubaneswar Biju Patnaik University of Technology Odisha Raajdhani Engineering College Bhubaneswar

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence.

关键词： Industries Technological innovation Service robots Human-machine systems Collaboration Fourth Industrial Revolution Internet Fifth Industrial Revolution Sustainable development Machine intelligence

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Population-Based Incremental Learning for Effective IoT Service Composition with Replication

Population-Based Incremental Learning for Effective IoT Serv...

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Software Services engineering (SSE) IEEE International Conference on

作者： Fengyang Sun Gang Chen Hui Ma Sven Hartmann Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-zellerfeld Germany

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

Internet of Things Service Composition (SCIoT) aims to find the best composite IoT service to fulfil users' requirements. Given the NP-hard complexity of SCIoT, Evolutionary Computation methods, especially Estimation of Distribution Algorithms (EDAs), have received increasing attention to solve SCIoT problems. As one of the most popular EDA methods, Population-Based Incremental Learning (PBIL) has demonstrated its strong competency in optimising composed services in SCIoT. However, conventional PBIL does not explicitly utilize problem knowledge such as QoS and service replication, limiting its effectiveness for IoT service composition. In this paper, we propose a new PBIL based approach, named Population-Based Incremental Learning to Improve Service Composition (PBILISC), to solve the SCIoT problem. Different from traditional PBIL, PBILISC seamlessly integrates PBIL with QoS-aware local search (QLS) to effectively handle replicated services in the SCIoT problem. Specifically, PBILISC evolves a series of populations of solutions jointly through PBIL and QLS. PBIL leverages a probability distribution for population updates, while QLS focuses on improving the best evolved solution by searching promising neighboring solutions under the guidance of QoS. Experimental results show that PBILISC can outperform PBIL and several state-of-the-art methods on multiple benchmark SCIoT problems.

关键词： Incremental learning Limiting Software algorithms Quality of service Evolutionary computation Benchmark testing Search problems

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Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection 34

Ethereum Smart Contract Representation Learning for Robust B...

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34th International Conference on Software engineering and Knowledge engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI

Detection of PCOS Leveraging Machine Learning and Interpreta...

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Women in Computing (InCoWoCo), International Conference for

作者： Bindu Madhavi V Asfiya Firdouse Ashwini Kodipalli Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability.

关键词： Accuracy Machine learning algorithms Ultrasonic imaging Computational modeling Medical services Transforms Boosting Prediction algorithms Bagging Tuning

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CBR - Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution

arXiv

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arXiv 2024年

作者： Lukach, Amir Dubin, Ran Dvir, Amit Hajaj, Chen Department of Computer Science Ariel Cyber Innovation Center Ariel University Israel Department of Industrial Engineering and Management Data Science and Artificial Intelligence Research Center Ariel University Israel

Encrypted network traffic Classification tackles the problem from different approaches and with different goals. One of the common approaches is using Machine learning or Deep Learning-based solutions on a fixed number of classes, leading to misclassification when an unknown class is given as input. One of the solutions for handling unknown classes is to retrain the model, however, retraining models every time they become obsolete is both resource and time-consuming. Therefore, there is a growing need to allow classification models to detect and adapt to new classes dynamically, without retraining, but instead able to detect new classes using few shots learning [1]. In this paper, we introduce Adaptive Classification By Retrieval CBR, a novel approach for encrypted network traffic classification. Our new approach is based on an ANN-based method, which allows us to effectively identify new and existing classes without retraining the model. The novel approach is simple, yet effective and achieved similar results to RF with up to 5% difference (usually less than that) in the classification tasks while having a slight decrease in the case of new samples (from new classes) without retraining. To summarize, the new method is a real-time classification, which can classify new classes without retraining. Furthermore, our solution can be used as a complementary solution alongside RF or any other machine/deep learning classification method, as an aggregated *** Codes ACM-class: F.2.2, I.2.7 ACM-class: F.2.2, I.2.7 ACM-class: F.2.2, I.2.7 ACM-class: F.2.2, I.2.7 ACM-class: I.2.6 © 2024, CC BY-NC-ND.

关键词： Nearest neighbor search

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS

Symptoms-based Classification of Disease Using Various ML Al...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Soujanya KJ Ashwini Kodipalli Sanjeev Gosai Sneha BJ Trupthi Rao Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare.

关键词： Technological innovation Machine learning algorithms Accuracy Machine learning Predictive models Prediction algorithms Vectors Classification algorithms Kernel Diseases

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Digital Innovation in Water Governance: Exploring the Synergy of Digital Twins and Blockchain

Digital Innovation in Water Governance: Exploring the Synerg...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Prakash Agarwal Rajendra Hegadi Prathamesh P Pai Sudeepto Chatterjee Siddhant Dixit Computer Science and Engineering Indian Institute of Information Technology Dharwad Dharwad Karnataka India Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad Karnataka India

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Water supply management is a critical aspect of ensuring sustainable access to clean water resources. However, challenges such as water scarcity, aging infrastructure, and inefficient resource allocation persists. This research explores the potential of utilizing a digital twin framework combined with blockchain for effective water supply management. The proposed framework offers numerous benefits, including optimized water allocation, early detection of leaks and anomalies, predictive maintenance, and efficient decision-making. Incorporation of smart contracts in the blockchain infrastructure enables automated governance mechanisms, ensuring compliance with predefined rules and agreements. By demonstrating the feasibility and advantages of combining digital twin technology with blockchain in water supply management systems, this research contributes to the existing body of knowledge. The findings can inform policymakers, water utility operators, and researchers on the practical implementation and potential impact of this innovative approach in addressing water management challenges and fostering sustainable water resource utilization.

关键词： Technological innovation Smart contracts Decision making Aging Blockchains Digital twins Resource management

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Multilingual Lexical Simplification via Zero-Shot Paraphrasing

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2200-2214页

作者： Kang Liu Jipeng Qiang Yun Li Yi Zhu Yunhao Yuan Kaixun Hua Xindong Wu Department of Computer Science Yangzhou University Yangzhou China Department of Industrial and Management Systems Engineering University of South Florida Tampa USA Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei China

Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings. Nonetheless, these methods necessitate distinct pretrained models tailored to diverse languages, often overlooking the imperative task of preserving a sentence’s meaning. In this paper, we propose a novel multilingual LS method via zero-shot paraphrasing (LSPG), as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. Once the input sentence is channeled into the paraphrasing, we embark on the generation of the substitutes. This endeavor is underpinned by a pioneering decoding strategy that concentrates exclusively on the lexical modifications of the complex word. To utilize the strong capabilities of large language models (LLM), we further introduce a novel approach PromLS that incorporates the results of LSPG to generate heuristic-enhanced context, enabling the LLM to generate diverse candidate substitutions. Experimental results demonstrate that LSPG surpasses BERT-based methods and zero-shot GPT3-based methods significantly in English, Spanish, and Portuguese. We also demonstrate a substantial improvement achieved by PromLS compared to the previous state-of-the-art LLM approach. LS approaches usually assume that complex words and their replacements are individual terms, concentrating on word-for-word substitutions. To tackle the more challenging task of multi-word lexical simplification, including phrase-to-phrase replacements, we extend LSPG and PromLS into MultiLSPG and MultiPromLS. MultiLSPG identifies multi-word expressions matched with their corresponding word counts in specific positions, while MultiPromLS, akin to PromLS, utilizes these candidates to generate a heuristi

关键词： Multilingual Decoding Translation Rhetoric Large language models Training Neural machine translation Encoding Speech processing Probability distribution

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