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A Travelling Recommendation System based on Knowledge Graph 3

A Travelling Recommendation System based on Knowledge Graph

引用

3rd International Academic Exchange Conference on Science and technology Innovation, IAECST 2021

作者： Dai, G.R. Xu, J.Y. Zhou, H. Zhou, L. Cao, Z.H. Zhu, M.D. Huaiyin Institute of Technology Faculty of Computer and Software Engineering China

ISBN: (纸本)9781665402675

In recent years, the improvement of people's live standard lead to an increasing demand for travelling, but the information on scenic spots on the Internet is ponderous and the accuracy of scenic spot recommendations is not high. This study aims to solve these problems to realize a more accurate scenic spot recommendation for self-drivings and provide a better visualization of recommendation results. A recommendation model based on the Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (MKR) algorithm is designed in this study. Then, based on this model, an intelligent knowledge graph based recommendation system for scenic spots is constructed. Rich unstructured text data of scenic spots in major scenic spot websites are crawled using Selenium and are stored and managed by the neo4j graph database. Through experimental simulation, the recommendation accuracy can reach over 84%. Compared to the Propagating User Preferences on the Knowledge Graph for Recommender Systems (RippleNet), the accuracy rate is enhanced by 6.2%. © 2021 IEEE.

关键词： Knowledge graph

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Application of Lightweight DSC-NET Network Model in Histopathological Images of Lung Cancer and Colon Cancer

Application of Lightweight DSC-NET Network Model in Histopat...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Wenlong An Hui Zong Heng Zhou Jian Wang Faculty of Computer and Software Engineering Huaiyin Institute Of Technology Huaian China

Classification of histopathological images is a fundamental task in the workflow of pathological diagnosis. Due to the complexity of pathological images, it is particularly important to use deep learning to improve diagnostic efficiency. This paper designs a lightweight network model DSC-NET, which consists of multi-scale feature stitching Multi-Conv, coordinate attention CA. The improved selfcalibrated convolution MSC-Conv is composed of selfcalibrated convolution SC-Conv, coordinate attention CA and depthwise DW. In addition, the DSC-NET network model converts the 1×1 convolutional layer (Conv) in the Block module into a linear layer (Linear), which reduces the computational complexity of the model while maintaining the ability of the convolution operation to capture local features. The research in this paper adopts the lung cancer and colon cancer datasets and adds Gaussian noise to these datasets to simulate the equipment shooting situation and evaluate the lightweight DSC-NET network model. Through quantitative comparisons with previous state-of-the-art methods, our experimental results demonstrate that the proposed method achieves superior accuracy. Furthermore, our method stands out with a smaller parameter count and significantly lower FLOPs, highlighting its efficiency and computational advantages. It has important potential to assist pathologists in pathological diagnosis.

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Reliability of Multipath Networks with Optimization of the Location of Inter-Path Communication Nodes

Reliability of Multipath Networks with Optimization of the L...

引用

2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Bogatyrev, Vladimir A. Tu Le, Anh Abramova, Evgenia A. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia University ITMO Faculty of Software Engineering and Computer Technology Saint Peterburg Russia

ISBN: (纸本)9781665464291

The article explores the possibility of improving the reliability of a network with multipath routing. A feature of the proposed study is the analysis of the influence of the placement of switching nodes that switch path segments on the probability of network connectivity with servers of the same type and heterogeneous in functionality. The purpose of the article is to increase the reliability of the network, taking into account the influence of the placement of communication nodes that implement the switching of route segments during reconfiguration, on the connectivity of the network with servers that are homogeneous and heterogeneous in functionality. The research involves the construction of a structural reliability model that takes into account the possibility of failures of both communication nodes and the links between them. The reliability model is built taking into account the availability of request sources with full and non-full access connections to the set of network paths. © 2023 IEEE.

关键词： Reliability

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Reliability of a Reconfigurable Network with Segment Switching

Reliability of a Reconfigurable Network with Segment Switchi...

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2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

作者： Tu, Le Anh Bogatyrev, V.A. Abramova, E.A. ITMO University Faculty of Software Engineering and Computer Technology Saint Peterburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia

ISBN: (纸本)9798350348293

The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of segment switching nodes, aimed at achieving maximum network performance with the option of connecting request sources to the available communication paths with servers. The study considers the options for connecting each source of requests with one of the paths or with all paths for connecting to servers. The paper shows the existence of an optimal placement of path switching nodes, in which the maximum reliability of the system is achieved. © 2023 IEEE.

关键词： Redundancy

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A Surface Defect Detection Method Based on Attention Mechanism for Workpieces

A Surface Defect Detection Method Based on Attention Mechani...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Junlin Qiu Yongshan Shen Wanli Feng Haodong Zhang Keke Sun Yuxin Qin Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an China

With the rapid development of image processing technology in recent years, faced with the issues of low detection accuracy and missed detections in the process of surface defect detection on workpieces, we propose a workpiece surface defect detection method based on attention mechanism. The model uses EfficientDet-d0 as the baseline network, mixes the Fused-MBConv structure and MBConv structure in EfficientNetv2 network as part of the feature extraction network, and uses the convolutional attention module CBAM to focus on the information of space and channel direction at the same time, and the Hardmish activation function is used in the structure. Introducing a fast spatial pyramid pooling module (SPPF) at the top of the feature extraction network increases the network’s depth and enhances its expressive power. The extracted features are fed into the Improved-BiFPN (Bidirectional Feature Pyramid Network) for feature enhancement, improving the model’s ability to detect defects of different sizes. Experimental results demonstrate that our proposed surface defect detection method for workpieces outperforms other advanced detectors.

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A Multi-constraint 3D Mesh Models Deformation Method

A Multi-constraint 3D Mesh Models Deformation Method

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Chuangchuang Zhang Xiaobing Chen Jianchu Lin Shuxin Guo Rongrong Wang Yinxi Gu Faculty of computer and software engineering Huaiyin insititute of technology Huai’an China

Aiming at the problems of too many control vertices and difficult operation of the traditional free deformation technique, a multi-constraint 3D mesh models deformation method is proposed. Firstly, the input model is embedded into a sparse tetrahedral control mesh, and the positional relationship between the model and the control mesh is established using tetrahedral coordinates. Secondly, skeleton nodes are set on the model to generate skeleton segments, control mesh vertices are bound to the skeleton segments, the control mesh is deformed by manipulating the rotation and translation of the skeleton nodes, and finally the new positions of the model vertices are computed based on the control mesh. Since the skeleton drives a sparse tetrahedral control mesh, the accuracy of model deformation will be reduced, resulting in a series of problems in the deformed model. In order to optimize the model deformation, this paper sets the influence range of the control mesh on the model deformation by the weight of the constraint region, adds point constraints to the control mesh vertices to set the change range of this vertex, and finally calculates the correction factor according to the stretch constraints and volume constraints, and optimally adjusts the position of the control meshes vertices. The experimental results show that this method can make up for the problems of the traditional free deformation technique, which is complicated and unintuitive to operate, and improve the accuracy of deforming the model.

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Cluster Availability Model with Information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

引用

2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

作者： Bogatyrev, V.A. Bogatyrev, S.V. Sivov, V.V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer Technology Saint-Petersburg Russia

ISBN: (纸本)9798350348293

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. Information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. Information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node. © 2023 IEEE.

关键词： Fault tolerance

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Safety Helmet Detection Algorithm for Complex Scenarios Based on PConv-YOLOv8

Safety Helmet Detection Algorithm for Complex Scenarios Base...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yuanyuan Wang Feilong Jiang Yazhou Li Haiyan Zhang Meifeng Wang Shaofeng Yan Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

Detecting safety helmets in complex environments is challenging due to issues like occlusion and lighting variations. Addressing the issues of slow detection speed and low object detection accuracy in complex environments with the YOLOv8 model, this paper introduces a lightweight safety helmet detection model, called PConv-YOLOv8, that is suitable for real-time applications in complex environments. Our method incorporates the PConv (Partial Convolution) module into the YOLOv8 model, reducing the complexity of the feature extraction network while enhancing feature representation accuracy. It also incorporates SimAM attention to extract and enhance the most relevant features by evaluating their similarity. Additionally, it considers category imbalance and positional regression in the target detection task, enhancing the model’s performance in target category identification and positional localization. Moreover, we propose the Wise-Distribution Focal Loss function to improve bounding box selection accuracy and enhance the model’s robustness. This paper introduces the Wise-Distribution Focal Loss method, which enhances the performance of target category recognition and location localization by improving the accuracy of bounding box selection and increasing the robustness of the overall model. The experimental results demonstrate that the method proposed in this paper achieves a 125% improvement in detection speed and a 1.8% increase in mAP0.5 compared to the YOLOv8 model.

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Nik Ahmad, Nik Azlina Lokman, Anitawati Mohd Abdullah, Munaisyah Hakim Suhaimi, Ahmad Iqbal Universiti Kuala Lumpur Malaysian Institute of Information Technology Software Engineering Section Kuala Lumpur Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Shah Alam Malaysia

ISBN: (纸本)9798350302684

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs. © 2023 IEEE.

关键词： Product design

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