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检索条件"丛书名=Communications in Computer and Information Science"

共 43057 条记录，以下是461-470 订阅

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Detecting Distributed Denial of Service (DDoS) Attacks in a Multi-controller SDN Environment Utilizing Machine Learning 5th

Detecting Distributed Denial of Service (DDoS) Attacks in a ...

引用

5th International Conference on Computing science, Communication and Security

作者： Sanghani, Nishant Vaghela, Gunjani Borisaniya, Bhavesh Gujarat Technol Univ Ahmadabad India Atmiya Univ Rajkot India Shantilal Shah Engn Coll Bhavnagar India

ISBN: (纸本)9783031751691;9783031751707

Software Define Network is a platform for network architecture used to create and design the virtual network hardware components, so it can be modified with dynamic connection settings with ease of implementation. The SDN controller is in charge of executing various network applications and preserving network services and functions. Due to the complication of the SDN architecture, the SDN faces numerous security challenges despite all of its incredible advantages. Because of its centralized and complex architecture, distributed denials of service (DDoS) attacks are frequent against SDN, particularly at the control layer where they can affect the entire network by using multiple resources at particular interval of time. Machine learning methods are utilized to quickly detect these types of threats. In current work, machine learning approach is proposed to detect the DDoS attack in multi-controller SDN environment of SDN. Experimental results shows that machine learning technique provides better accuracy and detection rate.

关键词： SDN DDoS SDN controller Multi-controller RSMQ Machine learning

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Psychological Consultation Dialogue Generation Based on Multi-label Classification Model and GPT 5th

Psychological Consultation Dialogue Generation Based on Mult...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Xu, Hongkui Zhao, Jingzheng Guo, Xubin Shandong Jianzhu Univ Jinan 250101 Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Psychological counseling chatbots have many advantages such as low cost and disregard for time and place. However, some technical requirements limit the use and effectiveness of counseling chatbots, such as how to make autoregressive models such as GPT able to understand complex counseling texts, how to be more attuned to the subject matter of the visitor's question to avoid meaningless answers, and how to generate gentle conversational language in the. To address the above problems this paper proposes a MMIAT-GPT (Maximum Mutual information ALBERT-TextCNN-GPT) model, which first uses ALBERT word vector model with strong text comprehension ability to extract complex semantic features in psychological counseling text. Then it is classified by TextCNN model to output the type of psychological problems for visitors or psychologists for auxiliary treatment, and spliced with the questions, adding transition statements to be inputted into the GPT model together to improve the ability of the GPT model to understand the psychological counseling questioning. Finally, the designed network structure is experimented on the extended PsyQA dataset, which outperforms the GPT2 network model in all results.

关键词： Psychological counseling Dialogue generation GPT ALBERT

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Safety Helmet-Wearing Detection Method Fusing Pose Estimation 5th

Safety Helmet-Wearing Detection Method Fusing Pose Estimatio...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhang, Xinyi Zhang, Yunchu Liu, Yiming Wang, Fei Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Wearing a helmet can protect the heads of production workers from falling objects. At present, there are many studies on safety helmet detection, but there are relatively few studies on the detection of the complex posture of workers, and it is not possible to determine whether the helmet-wearing position can protect the heads of workers. At the same time, there are problems in the rolling workshop such as a large space span, a wide range of operating equipment, a chaotic environment, a large difference between day and night light, dazzling light, and a large variation range of monitoring target scales. It increases the difficulty of helmet-wearing detection. To solve the above problems, a detection scheme for helmet-wearing in a steel rolling workshop was designed. Firstly, the improved YOLOv7 model was used for helmet and pedestrian detection, then the improved YOLOpose model was used for attitude estimation to detect the position of workers' heads, and finally, the Hungarian algorithm was used to match the position of helmets and heads to determine whether workers wore helmets correctly.

关键词： YOLOv7 YOLOpose Hungarian algorithm location matching

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Analysis of Different Gravity Models to Determine Key Nodes in Social Networks 4th

Analysis of Different Gravity Models to Determine Key Nodes ...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Singh, Hardeep Singh, Hardeep Guru Nanak Dev Univ Dept Comp Engn & Technol Amritsar 143005 Punjab India Guru Nanak Dev Univ Dept Comp Sci Amritsar 143005 Punjab India

ISBN: (纸本)9783031837890;9783031837906

Social networks represent real-world social structures and are essential for linking people across the globe. Determining key nodes in social networks is a challenging issue in the realm of network science. For solving the key node identification issue, numerous centrality measures have been developed, including degree, eigenvector, gravity index, etc. However, the gravity models relied on universal gravity law are unique measures that have grasped the attention of numerous researchers. In this study, we analyze eight previous and recently developed gravity models to identify the best gravity model with respect to three parameters: accuracy, distinguishing ability, and efficiency. Hence, distinct experiments are conducted on social networks utilizing the eight gravity models. The Kendall tau, SIR model, Jaccard similarity coefficient, and ranking monotonicity are utilized as performance metrics for assessing the efficacy of eight gravity models. The empirical findings indicate that the H-index-based gravity centrality has superior accuracy and distinguishing ability in contrast to other gravity models. Finally, we outline our plans for future gravity model-based research.

关键词： Key Nodes Gravity Models Social Networks

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Advancing Food Sustainability: A Prototype Platform for Informed Decision-Making 4th

Advancing Food Sustainability: A Prototype Platform for Info...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Espinoza Perez, Lorena Mauricio, Cristobal Araya, Catalina Contreras, Rodrigo Suazo, Fernanda Velastin, Natalia Bianchi, Javiera Espinoza Perez, Andrea Vasquez, Oscar C. Univ Santiago Chile USACH Dept Ind Engn Fac Engn Estacion Cent Chile Univ Santiago Chile USACH Program Dev Sustainable Prod Syst PDSPS Fac Engn Estacion Cent Chile

ISBN: (纸本)9783031832093;9783031832109

Transforming our food system into one that is more sustainable and equitable emerges as a critical requirement for a future in which humanity thrives in harmony with the environment. In this context, massive food services emerge as pivotal actors where small changes can lead to significant impacts. Furthermore, considering that nutrition labels and eco-labels can support consumers in making healthier and more sustainable choices, tools are needed to access this information framed in their territorial reality. In this context, our study introduces the development of a prototype platform customized for a specific massive food service in Chile. This platform facilitates menu assessment and design by providing insights into various environmental impacts and nutritional aspects, empowering informed decision-making.

关键词： Massive food services Sustainable menus Environmental impacts

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Towards Comparison of Various Variants of CKKS 2nd

Towards Comparison of Various Variants of CKKS

引用

2nd International Conference on information Technologies and Their Applications

作者： Hati, Nargiz Khankishiyeva Ostim Tech Univ Ankara Turkiye

ISBN: (纸本)9783031734168;9783031734175

Homomorphic encryption is the type of encryption scheme which does not require decryption for encrypted data in order to do arithmetic computations on this encrypted data. Homomorphic encryption methods have been developing using several approaches. CKKS is the HE scheme which supports an approximate addition and multiplication of encrypted messages, together with a new rescaling procedure for managing the magnitude of plaintext. After CKKS is revealed, it is widely used in privacy preserving in data analyses. However, as the computations are performed, the ciphertext modulus in CKKS decreases and eventually, more computations cannot be done and therefore CKKS needs bootstrapping procedures. By far, the Residue Number System (RNS) variants of CKKS and new approximation methods for CKKS and RNS-CKKS are proposed. In this paper, the new approximation methods for CKKS are analyzed and advantages and disadvantages of latest variants are discussed. The aim of this work to compile various papers about bootstrapping of HEAAN and give brief survey of those papers.

关键词： Homomorphic Encryption CKKS RNS Approximate Number

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Contribution of Fuzzy-Possibility Approach to Assessing the Complexity Level of IT-Systems Design 5th

Contribution of Fuzzy-Possibility Approach to Assessing the ...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Kimyaev, Igor Spesivtsev, Alexander Spesivtsev, Vasily Shilov, Nikolay LLC Nornickel Sputn Moscow Russia Russian Acad Sci SPIIRAS St Petersburg Inst Informat & Automat St Petersburg Russia

ISBN: (纸本)9783031807749;9783031807756

The problem of assessing the level of the design work complexity in the IT industry is of vital importance. This is due to the fact that design in this industry involves the consolidation of a wide variety of knowledge sectors: programming, network technologies, engineering infrastructure, etc. From such diversity follows the absence of generally accepted methods for quantitative assessment of the complexity and volume of design work based on the most general requirements for the designed IT system. In this research, a method for quantitative indicative assessment of the complexity of one of the main phases of IT system design is proposed: architectural planning. Using a seven-factor structure of fuzzy linguistic variables, a fuzzy possibility model was developed based on expert knowledge and experience. This model allows for quantitative assessment and forecasting of the complexity of tasks in the architecture of information technology systems.

关键词： Technology digitalization Fuzzy possibility approach Business entity Operational / informational technology Functional specification Expert knowledge Enterprise architect

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Enhancing Soil-Transmitted Helminths Diagnosis Through AI: A Self-supervised Learning Approach with Smartphone-Based Digital Microscopy 1st

Enhancing Soil-Transmitted Helminths Diagnosis Through AI: A...

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1st MICCAI Meets Africa Workshop

作者： Lin, Lin Cuadrado, Daniel Mancebo-Martin, Roberto Kepha, Stella Gichuki, Paul Mwandawiro, Charles Ledesma-Carbayo, Maria Jesus Luengo-Oroz, Miguel Dacal, Elena Bermejo-Pelaez, David Spotlab R&D Madrid Spain Univ Politecn Madrid ETSI Telecomunicac Madrid Spain CIBER BBN Madrid Spain Kenya Med Res Inst KEMRI ESACIPAC Nairobi Kenya

ISBN: (纸本)9783031791024;9783031791031

Soil-transmitted helminths (STH), including hookworm, Ascaris lumbricoides, and Trichuris trichiura, impose significant health burdens in low- and middle-income tropical and subtropical regions, infecting over 1.5 billion people globally. Traditional diagnostic methods like the Kato-Katz technique are time consuming. This study introduces an innovative AI-driven system utilizing affordable 3D-printed adapters and smartphones to digitize Kato-Katz microscopy samples, capturing high-resolution images for subsequent analysis. These digitized images can be uploaded to a telemedicine platform for remote diagnosis and expert consultation. Central to our system is the development of a foundational AI model for parasite detection and classification. The model operates in two stages: First, an object detection algorithm identifies all parasites in the image, achieving a mean average precision (mAP) of 97.90% on the validation set using the YOLOv8 architecture. Second, a classification algorithm categorizes each detected parasite by species. The classification model is initially trained on a large, unannotated dataset of parasite images using a self-supervised learning (SSL) approach to learn domain-specific visual features, which are often missed while using generic pre-training datasets. Subsequently, it is fine-tuned on a labeled dataset, significantly improving performance. The model initialized with SSL on STH images achieved an F1 score of 91.70%, outperforming those initialized with random weights (F1 score of 55%) and those trained on DINO-Imagenet weights (F1 score of 53%). By integrating AI with low-cost digital imaging, our approach aims to revolutionize STH diagnosis in resource-constrained settings, aligning with the WHO's 2030 Roadmap for the elimination of neglected tropical diseases.

关键词： STH NTD AI foundation model object detection self-supervised learning

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Multi-user VR Content Wireless Delivery Using Motion Prediction and Adaptive Multicasting 7th

Multi-user VR Content Wireless Delivery Using Motion Predict...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Wang, Ke Li, Yuqi Chi, Kaikai Huang, Liang Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Efficient multi-user wireless collaborative Virtual Reality (VR) systems are becoming more and more widely used in the field of education, especially during the epidemic period, teachers and students can teach and watch movies together through VR devices. However, the following challenges need to be addressed: 1. How to achieve efficient VR content delivery and high-quality user experience under limited wireless network resources? 2. How can we efficiently realize the scenario of multiple users watching the same VR video? To address these challenges, this paper introduces a novel wireless collaborative VR system. It proposes a 360-degree video transmission algorithm based on motion prediction, and an adaptive multicast algorithm using a hybrid clustering method to improve transmission efficiency and user experience. Specifically, we apply a Long Short-Term Memory Network (LSTM) model using the real-time position of the primary user's head to achieve efficient motion prediction, and only transmit the visual image data to the other following users, thus improving the utilization efficiency of network resources. Additionally, in the adaptive multicast algorithm, based on real-time network information, a clustering approach using the Self-Organizing Map (SOM) network and the k-means method realizes the effective partition of multicast groups. Then, VR content quality selection is carried out according to the status of the multicast group network, further reducing the transmission content within the system. We experimentally verified the accuracy of the LSTM model in achieving motion prediction, and verified our algorithm by deploying it to commodity mobile devices. Our system improves the quality of experience (QoE) index by nearly 74% and the frame rate index by more than 30% compared to the state-ofthe-art (SOTA) technology.

关键词： Virtual Reality in Education Motion Prediction Video Streaming QoE Optimization Multicast

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Learning Insertion Patterns to Enhance Operational Efficiency in Large-Scale Dial-a-Ride Systems 7th

Learning Insertion Patterns to Enhance Operational Efficienc...

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7th International Conference on Optimization and Learning

作者： Liu, Chijia Quilliot, Alain Toussaint, Helene Feillet, Dominique Univ Clermont Auvergne CNRS UMR 6158 LIMOS Aubiere France Univ Clermont Auvergne INP Clermont Auvergne CNRS F-42023 St Etienne France

ISBN: (纸本)9783031779404;9783031779411

We study a large-scale dial-a-ride system considering around 300, 000 dynamic requests. An efficient routing algorithm is crucial to guaranteeing the viability of the system. Large-scale requests are assumed to be dominated by daily commuting needs and thus should exhibit similar mobility patterns from one day to another. Consequently, daily vehicle trajectories should also be recurring if similar requests can be served in the same manner. We introduce a greedy insertion algorithm integrating a Guided Insertion Mechanism that learns insertion patterns from reference resolution to enhance the operational efficiency of the underlined systems while maintaining high-quality solutions.

关键词： Large-Scale Dial-A-Ride On-Demand Transportation System Ride-Sharing

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