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Clustering Driven Multi-Hop Graph Attention Network for Speaker Diarization 4

Clustering Driven Multi-Hop Graph Attention Network for Spea...

引用

4th international conference on intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Mao, Qingqing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Jiangsu China

ISBN: (纸本)9798350343113

Recently, the segmented utterance-level modeling approach based on Graph Attention Network (GAT) has been proved to be effective in Clustering-based Speaker Diarization (CSD). However, these existing methods only rely on the message passing by a single neighbor per layer, ignoring the influence of sub-region and global information. In this paper, we propose clustering driven multi-hop Graph Attention Network (CD-MGAT) with the multi-hop neighbor module and the clustering-oriented prototype module, which effectively explores the sub-region and global information for each segmented utterance. Specifically, the developed modules can adaptively interact with each other by clustering-consistency loss, which ensures the consistency of learning between the prototype and speaker embedding. Extensive experiments demonstrate the effectiveness of our solution on the AMI datasets. © 2023 ieee.

关键词： Message passing

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Lattice-Based Decomposition of Transitive Homogeneous Class Hierarchies∗ 20

Lattice-Based Decomposition of Transitive Homogeneous Class ...

引用

20th ieee international conference on intelligent computer communication and processing conference, ICCP 2024

作者： Terletskyi, Dmytro O. V. M. Glushkov Institute of Cybernetics of Nas of Ukraine Department of Methods and Technological Tools for Creating Intelligent Software Systems Kyiv Ukraine

ISBN: (纸本)9798331539979

the decomposition of conceptual hierarchies is a significant task in knowledge engineering and software engineering, allowing the extraction of hidden or non-obvious knowledge pieces, or software subsystems, and the relations between them. During the last decades, many different approaches to the analysis and decomposition of conceptual hierarchies were developed, however, most of them do not pay enough attention to the semantic consistency of the decomposition, considering the internal semantic dependencies between the class attributes. Consequently, extracted knowledge items and software subsystems, can be semantically inconsistent due to the contradiction with such dependencies and model unrealistic or impossible entities or subsystems within a particular domain. therefore, we propose a new approach to conceptual hierarchy decomposition based on the internal semantic dependencies between the properties and methods of the hierarchy members. Implementing the approach, an appropriate algorithm was developed for the decomposition of class hierarchies within the object-oriented dynamic networks, that constructs only semantically consistent subclasses and sub hierarchies. To demonstrate the main scenarios of algorithm application, we provided a comprehensive example of the decomposition of conceptual hierarchy, consisting of homogeneous classes of objects. © 2024 ieee.

关键词： class decom-position Conceptual hierarchies decomposition decomposition consistency internal semantic dependencies

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Flood Discharge Cross-Domain Routing Selection Based on Resource Sharing Equilibrium for Electric Power communication Network 4

Flood Discharge Cross-Domain Routing Selection Based on Reso...

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4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Chen, Yanyu Tang, Jia Guo, Xiaoxi Guan, Zejing Zhang, Shuling Gao, Xiaofang Zhang, Xiangyi Xie, Yuhao Information and Communication Branch State Grid Corporation of China Beijing China College of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331531409

In order to address the issue of inter-domain blocking phenomena in cross-domain transmission, which results in low processing capacity for burst traffic and inadequate deployment capabilities for large traffic flows, this paper proposes a flood discharge cross-domain routing selection mechanism based on resource sharing balance, referred to as FR-CD. the proposed method is structured into two layers: inter-domain routing and intra-domain routing. the inter-domain routing primarily relies on service classification, which is informed by principles from water conservancy dam management. Intra-domain routing employs an improved shortest distance calculation method, supplemented with bandwidth utilization as a weighting factor to optimize the identification of intra-domain routes. this approach contributes significantly to establishing new pathways within power systems, enhancing both operational efficiency and security within communication backbone networks and access networks. Furthermore., it supports the construction and development of modern power systems. © 2024 ieee.

关键词： Resource allocation

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Context-Based Adaptation of Neural Network Compression for Unmanned Aerial Vehicle (UAV) Weed Detection 20

Context-Based Adaptation of Neural Network Compression for U...

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20th ieee international conference on intelligent computer communication and processing conference, ICCP 2024

作者： Igret, Ioana C. MacHidon, Alina L. MacHidon, Octavian M. Babes-Bolyai University Faculty of Mathematics and Computer Science Cluj-Napoca Romania University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia

ISBN: (纸本)9798331539979

UAV computer vision in precision agriculture can enhance efficiency and reduce environmental impact compared to traditional techniques. However, the computational limitations and power constraints of UAVs hinder their performance, especially for real-time deep learning tasks. Compressed neural network models offer a solution, yet fixed compression levels may result in unacceptable accuracy loss. this paper proposes a novel context-aware approach for energy-efficient, real-time on- UAV weed detection. By incorporating contextual factors such as brightness, saturation, contrast, and vegetation indices, our method estimates input image difficulty and dynamically selects the optimal neural network compression level. Leveraging slimmable neural networks, which enable training a single model with various widths during inference, our approach ensures the best accuracy-resource consumption trade-off. Experimental results with two different slimmable networks architectures showed that our approach can lead to up to 35 % less computations while also increasing the quality of the inference with up to 2 %. this significantly improves the practicality and performance of UAV-based weed detection systems. © 2024 ieee.

关键词： Image segmentation

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Location Protection Mechanism in Sparse Mobile CrowdSensing 4

Location Protection Mechanism in Sparse Mobile CrowdSensing

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4th international conference on Electronic Information Engineering and computer communication, EIECC 2024

作者： Li, Lanxin Wang, En Liu, Wenbin Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331534622

As Internet of things technology advances quickly, Sparse Mobile CrowdSensing has been used as a productive way to collect and process data. Leveraging the real time capabilities and wide coverage of Internet of things communication, Sparse Mobile CrowdSensing utilizes distributed sensing nodes to collect and process real-time data, thereby supporting diverse intelligent services. In practical scenarios, users are often required to upload data carrying location information to support subsequent data completion. However, such data can expose sensitive user information, posing significant privacy risks. To mitigate these risks, location obfuscation techniques are employed to reduce the likelihood of privacy leakage. Nevertheless, these privacy-preserving measures can significantly degrade the quality of data completion. To address this challenge, we propose a scheme that integrates data correction to balance privacy protection and data completion quality. Specifically, our approach obfuscates location data using geographic indistinguishability and leverages historical data to obtain the data regularity, enabling modifications to the obfuscated data to ensure its usability. Our mechanism performs better than other baseline methods in terms of ensuring data completion accuracy while preserving a certain degree of privacy, according to evaluation on the real datasets. © 2024 ieee.

关键词： Data obfuscation

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4th international conference on computer Science and Blockchain, CCSB 2024

作者： Huang, Guimin Shi, Canjun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350356502

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance. © 2024 ieee.

关键词： Convolutional neural networks

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Portable Electrochemical Impedance Analysis System 4

Portable Electrochemical Impedance Analysis System

引用

4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Wang, Ziqing Shu, Yongsheng Liao, Jianjun Lin, Shiwei School of Information and Communication Engineering Hainan University Haikou China School of Environmental Science and Engineering Hainan University Haikou China College of Materials Science and Engineering Hainan University Haikou China

ISBN: (纸本)9798331531409

the portable electrochemical analysis system has high integration and low cost, which solves the problem of large size and high price of traditional instruments on the market. Based on the existing research, this paper develops a multifunctional detector with STM32 processor as the core. the system realizes the data analysis through calibration and advanced processing technology. the hardware part adopts signal conditioning circuits such as controllable cross-group amplifier circuit to expand the measurement range and realize accurate measurement of data. through strict analysis of error factors, this design is optimized and the sensitivity is improved. the experimental results show that the average relative error is 0.56%, which can meet general measurement needs. the instrument is not only compact in structure, but also simple to operate and low in cost. © 2024 ieee.

关键词： Signal conditioning circuits

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Comparative Analysis of Machine Learning Algorithms for Traffic Flow Prediction in intelligent Transportation Systems 4

Comparative Analysis of Machine Learning Algorithms for Traf...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Yadav, Mahima Kumar, Bijendra Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350387490

As human society advances, the limitations of current transportation systems become more apparent, prompting a growing desire to leverage advanced technology for intelligent transportation solutions. However, low identification rates and complicated processing plague many of the vehicle detection techniques used in video analytics today. this paper gives a comparative analysis of multiple machine learning algorithms for traffic flow prediction in order to improve intelligent Transportation Systems (ITS). Using an extensive dataset of historical traffic flow and environmental variables, we evaluate the performance of several methods: Linear Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests (RF) and deep Neural Networks (DNN). Numerous Variables are taken into account in the evaluation, including scalability, computing efficiency, and forecast accuracy. the findings show that while algorithms like RF and SVM give good performance with less computational overhead, DNN obtain the maximum accuracy. the work offers valuable information on the disadvantages of various machine learning (ML) techniques, which may be used to select suitable models for the prediction of traffic movement in real-world ITS. © 2024 ieee.

关键词： Random forests

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Extracting Appliance Behavior from Heterogeneous Data 19

Extracting Appliance Behavior from Heterogeneous Data

引用

19th ieee international conference on intelligent computer communication and processing conference, ICCP 2023

作者： Rus, Iulia-Maria Portase, Raluca Tolas, Ramona Lemnaru, Camelia Dinsoreanu, Mihaela Potolea, Rodica Computer Science Department Technical University Cluj-Napoca Romania

ISBN: (纸本)9798350370355

Machine learning has triggered a paradigm shift, facilitating the gathering of relevant information from a large amount of seemingly diverse data. the primary goal of this research paper is to emphasize the effectiveness of a varied set of data analysis methods used to extract meaningful intelligence from a complex data set gathered by experimenting with house-hold appliances in a controlled environment. the methods differ depending on the goal of the analysis and the characteristics of the data: machine learning, statistical modeling, and natural language processing. In this paper, we demonstrate how visual representations of the information can be a deciding factor when analyzing experimental data. the impact of computations on the semantic and structural form of the data, as a pre-processing step, is also detailed. © 2023 ieee.

关键词： Machine learning

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Using Artificial Intelligence to Fight Clickbait in Romanian News Articles 19

Using Artificial Intelligence to Fight Clickbait in Romanian...

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19th ieee international conference on intelligent computer communication and processing conference, ICCP 2023

作者： Pacurar, Aralda Oprisa, Ciprian Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

On-line press is infested with poor quality content that uses misleading headlines in order to increase web traffic and gain popularity and profit. this phenomenon is called clickbait and impacts the usability and, in some cases, even the security by exposing the user to malicious advertisement. this paper proposes an automated solution for detecting clickbait by using Natural Language processing techniques to extract features from both the article title and the article content. the extracted features are used with a set of Machine Learning classifiers which are fine-tuned to improve the performance metrics. the resulting model shows good results and can be integrated in a news aggregator that selects only quality content for the user to read. © 2023 ieee.

关键词： Natural language processing systems

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