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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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Computer Modeling in Engineering & sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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Optimization of Bias Mitigation in Word Embeddings: a Methodological Approach

Optimization of Bias Mitigation in Word Embeddings: a Method...

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IEEE WIC ACM International Conference on Web intelligence (WI)

作者： María José Zambrano Felipe Bravo-Marquez Department of Computer Science University of Chile Santiago Chile National Center for Artificial Intelligence (CENIA) Santiago Chile Millennium Institute for Foundational Research on Data (IMFD) Santiago Chile

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

Word embeddings (WEs) often reflect biases present in their training data, and various bias mitigation and evaluation techniques have been proposed to address this. Existing benchmarks for comparing different debiasing methods overlook two factors: the choice of training words and model hyper-parameters. We propose a robust comparison methodology that incorporates them using nested cross-validation, hyper-parameter optimization, and the corrected paired Student's t-test. Our results show that when using our evaluation approach many recent debiasing methods do not offer statistically significant improvements over the original hard debiasing model.

关键词： Training Statistical analysis Prevention and mitigation Supervised learning Training data Benchmark testing Natural language processing Intelligent agents Optimization

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A Novel Cascade Instruction Tuning Method for Biomedical NER

A Novel Cascade Instruction Tuning Method for Biomedical NER

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jin Zhao Chao Liu Jiaqing Liang Zhixu Li Yanghua Xiao Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Zhengzhou Zhongke Institute of Integrated Circuit and System Application School of Data Science Fudan University China Fudan-Aishu Cognitive Intelligence Joint Research Center Shanghai China

Large language models(LLMs) have achieved remarkable performance on various tasks. However, LLMs suffer from severe limitations in domain generalisation, primarily due to inherent limitations. Closed-source LLMs face constraints in fine-tuning, while open-source LLMs contend with the scarcity of domain-specific data. Moreover, LLMs often prioritize addressing standard patterns, inadvertently neglecting intricate and domain-specific patterns. This preference hampers effective domain generalization. In this paper, inspired by curriculum learning, we explore a cascade instruction tuning method to train a domain-specific LLMs that can excel in a broad application such as information extraction. Taking biomedical name entity recognition(BioNER) as a case study, we show how to cultivate general LLMs into domain-specific LLMs with limited domain data and address the complex pattern for BioNER. To validate our method, we construct NER INSTRUCTIONS, the largest and broadest benchmark sourced from 55 publicly available NER datasets across 17 domains. We conduct extensive experiments on the dataset, and the results demonstrate the effectiveness of our proposed framework in downstream task generalisation and its ability to tackle intricate patterns.

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GraphMorph: tubular structure extraction by morphing predicted graphs 24

GraphMorph: tubular structure extraction by morphing predict...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhao Zhang Ziwei Zhao Dong Wang Liwei Wang Center for Data Science Peking University and Pazhou Laboratory (Huangpu) Guangzhou Guangdong China Yizhun Medical AI Co. Ltd State Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Center for Machine Learning Research Peking University

ISBN: (纸本)9798331314385

Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon.

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DESIGN AND ANALYSIS OF WIND TURBINE FAULT DIAGNOSIS SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

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Diagnostyka 2025年第1期26卷

作者： Luo, Xiaoli Ning, Jinye Wu, Min Scientific Research Department Hunan Electrical College of Technology Xiangtan411101 China Institute of Big Data and Artificial Intelligence Application Technology Hunan Electrical College of Technology Xiangtan411101 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan City411201 China

Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation. © 202x by the Authors.

关键词： Convolutional neural networks

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OCRNet for Diabetic Foot Ulcer Segmentation Combined with Edge Loss 3rd

OCRNet for Diabetic Foot Ulcer Segmentation Combined with ...

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

作者： Yi, Huahui Xu, Wei Jiang, Zekun Gao, Jun Kang, Qingbo Lao, Qicheng Li, Kang West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu610041 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China West China Hospital-SenseTime Joint Lab Sichuan Chengdu610041 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

ISBN: (纸本)9783031263538

Diabetic foot ulcer is a serious manifestation of lesions on the diabetic foot that requires close monitoring and management. The research at hand investigates an approach on segmentation of diabetic foot ulcer area, conducted as part of the Diabetic Foot Ulcer Challenge (DFUC) 2022. We use OCRNet as the baseline for segmentation and a powerful ConvNeXt network was adopted as the backbone. To obtain better results, a boundary loss was introduced to further constrain the boundary of segmentation. In addition, gamma correction was used in the inference stage in order to reduce the difference in luminance between the training, validation and test sets. Our method won 2nd place in the DFUC2022 with a Dice score of 72.80%. Source code is available at: DFUC2022SegmentationOcrnet. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Proposing a Microservices Architecture for AMEL Information System Designed Using TOGAF-ADM

Proposing a Microservices Architecture for AMEL Information ...

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International Conference on Computer, Control, Informatics and Its applications (IC3INA)

作者： Gita Citra Puspita Hayuning Titi Karsanti Arief Sartono Adam Firman Denna Arfiani Research Center for Data and Information Science National Research and Innovation Agency Jakarta Indonesia Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia

The government is accelerating digital transformation in several fields, including procuring government goods and services. The government needs a system or application to monitor and evaluate government procurement of goods/services to facilitate this acceleration. The National Public Procurement Agency has developed software to monitor and evaluate the government procurement of goods/services at the ministries, agencies, and local government levels. The name of the application is the Local Monitoring-Evaluation application. It was developed using a monolithic design and then integrated into the e-procurement system. This application manages the collection, processing, aggregation, and display of data in the form of a dashboard. The planning, implementation, and payment phases of a transactional procurement system are all connected with this application. However, because AMEL is a decentralized system, the central government needs help when trying to display a similar dashboard at the national level. Considering that the current AMEL performance monitoring and evaluation dashboard requirements must comply with the requirements in the Decree of the Minister of Administrative Reform and Bureaucratic Reform Number 1148 of 2021 concerning the Implementation of General applications in the Sector of Government Procurement of Goods/Services, we propose to change the information system architecture from the Local Monitoring-Evaluation uses Microservices Architectures (MSA). The proposed architectural design should improve data capture, processing, aggregation, sharing, and visualization of monitoring-evaluation of data. We will use the Open Group Architecture Framework-Architecture Development Method (TOGAF-ADM) as an enterprise architecture framework. Artifacts produced from the preliminary phase, vision architecture phase, and business architecture TOGAF-ADM can help determine which business processes require IT support so that the resulting information system architectu

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Smart Contract Vulnerability Detection Model Based on Siamese Network 7th

Smart Contract Vulnerability Detection Model Based on Siames...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Chen, Weijie Guo, Ran Wang, Guopeng Zhang, Lejun Qiu, Jing Su, Shen Liu, Yuan Xu, Guangxia Chen, Huiling College of Information Engineering Yangzhou University Yangzhou225127 China Cyberspace Institute Advanced Technology Guangzhou University Guangzhou510006 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China

ISBN: (纸本)9783031281235

Blockchain is experiencing the transition from the first generation to the second generation, and smart contract is the symbol of the second generation blockchain. Under the background of the explosive growth of the second-generation blockchain platform and applications represented by smart contracts, frequent smart contract vulnerability events seriously threaten the ecological security of the blockchain, reflecting the importance and urgency of smart contract vulnerability detection. In this paper, we proposed a smart contract vulnerability detection method based on a Siamese network. We combined the Siamese network with Long Short-Term Memory (LSTM) Network neural network to complete the task of smart contract vulnerability detection. The Siamese network used in this paper consists of two subnetworks that share the same parameters onto a low dimension and easily separable feature space. Siamese network is now widely used in the field of image similarity and target tracking. In this paper, we improve the Siamese network so that it can be used for smart contract vulnerability detection. By comparing with previous research results, the model has better vulnerability detection performance and a lower false-positive rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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An Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities 7th

An Efficient Detection Model for Smart Contract Reentrancy V...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Yuan Guo, Ran Wang, Guopeng Zhang, Lejun Qiu, Jing Su, Shen Liu, Yuan Xu, Guangxia Chen, Huiling College of Information Engineering Yangzhou University Yangzhou225127 China Cyberspace Institute Advanced Technology Guangzhou University Guangzhou510006 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China

ISBN: (纸本)9783031281235

We propose a novel smart contract re-entry vulnerability detection model based on BiGAS. The model combines a BiGRU neural network that introduces an attention mechanism with an SVM. We start from the data features of smart contracts, learn the model layer by layer to achieve feature extraction and vulnerability identification, introduce batch normalization, Dropout processing and use improved model classifiers to improve the vulnerability identification accuracy, model convergence speed and generalization capability of smart contracts. We had conducted numerous experiments, and the experimental results showed that BiGAS Detection Model has a strong vulnerability detection ability. The accuracy of vulnerability detection reached 93.24%, and the F1-score was 93.17%. We compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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CLOUDY WITH A CHANCE OF ANOMALIES: DYNAMIC GRAPH NEURAL NETWORK FOR EARLY DETECTION OF CLOUD SERVICES’ USER ANOMALIES

arXiv

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arXiv 2024年

作者： Marbel, Revital Cohen, Yanir Dubin, Ran Dvir, Amit Hajaj, Chen Israel Department of Computer Science Ariel Cyber Innovation Center Ariel University Kiryat Hamada 3 Ariel Israel Department of Industrial Engineering and Management Data Science and Artificial Intelligence Research Center Ariel University Israel

Ensuring the security of cloud environments is imperative for sustaining organizational growth and operational efficiency. As the ubiquity of cloud services continues to rise, the inevitability of cyber threats underscores the importance of preemptive detection. This paper introduces a pioneering time-based embedding approach for Cloud Services Graph-based Anomaly Detection (CS-GAD), utilizing a Graph Neural Network (GNN) to discern anomalous user behavior during interactions with cloud services. Our method employs a dynamic tripartite graph representation to encapsulate the evolving interactions among cloud services, users, and their activities over time. Leveraging GNN models in each time frame, our approach generates a graph embedding wherein each user is assigned a score based on their historical activity, facilitating the identification of unusual behavior. Results demonstrate a notable reduction in false positive rates (2-9%) compared to prevailing methods, coupled with a commendable true positive rate (100%). The contributions of this work encompass early detection capabilities, a low false positive rate, an innovative tripartite graph representation incorporating action types, the introduction of a new cloud services dataset featuring various user attacks, and an open-source implementation for community collaboration in advancing cloud service security. © 2024, CC BY-NC-ND.

关键词： Cloud security

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