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Computing Salient Feature Points of 3D Model Based on Geodesic Distance and Decision Graph Clustering

Recent Advances in Computer Science and Communications 2021年第8期14卷 2489-2494页

作者： Sun, Dechao Chen, Nenglun Wang, Renfang Liu, Bangquan Liang, Feng College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China Department of Computer Science Hong Kong University Hong Kong College of Digital Technology and Engineering Ningbo University of Finance & Economics Ningbo China

Introduction: Computing Salient Feature Points (SFP) of 3D models has important application value in the field of computer graphics. In order to extract the SFP more effectively, a novel SFP computing algorithm based on geodesic distance and decision graph clustering is proposed. Methods: Firstly, the geodesic distance of model vertices is calculated based on the heat conduction equation, and then the average geodesic distance and importance weight of vertices are calculated. Finally, the decision graph clustering method is used to calculate the decision graph of model vertices. Results and Discussion: 3D models in SHREC 2011 dataset are selected to test the proposed algorithm. Compared with the existing algorithms, this method calculates the SFP of the 3D model from a global perspective. Results show that it is not affected by model posture and noise. Conclusion: Our method maps the SFP of the 3D model to the 2D decision-making diagram, which simplifies the calculation process of SFP, improves the calculation accuracy and possesses strong robustness. © 2021 Bentham Science Publishers.

关键词： Clustering algorithms

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Integrating Branching and Pruning for Efficient Hyperdimensional Computing

Integrating Branching and Pruning for Efficient Hyperdimensi...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Jing Liu Zhiqian Guan Di Liu Shengfa Miao Fei Dai School of Software Yunnan University China Department of Computer Science Norwegian University of Science and Technology Norway School of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

关键词： Energy consumption Accuracy Computational modeling Memory management Predictive models Brain modeling Encoding

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Deep Wavelet Frequency Laplacian Pyramid Network for Vehicle Logo Super-Resolution 5

Deep Wavelet Frequency Laplacian Pyramid Network for Vehicle...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Yu, Yue She, Kun Kang, Jian Jackson, Jehoiada Shaukat, Hayat Li, Yang University of Electronic Science and Technology of China Information and Software Engineering Chengdu China Sichuan Provincial Big Data Technology Services Center Chengdu China Iqra National University Department of Computer Science Peshawar Pakistan

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In intelligent transportation systems, generated high-resolution images promote high-level version tasks compared to captured low-resolution vehicle logo images, which lack adequate details. However, the resolution of vehicle logo images is restricted by factors like hardware. Few attempts have been made with the Laplacian pyramid structure, while images can be represented on multi-scale in the wavelet domain. This paper proposes a progressive reconstruction network with a Laplacian pyramid (WLapsrn) structure to predict the sub-bands of wavelet transformed images. Firstly, we adopted a suitable network architecture for feature extraction using residual blocks for the advanced reconstruction network in the wavelet domain. Then, the Texture-Charbonnier loss function is used to achieve highfrequency sub-bands reconstruction. Ultimately, the inverse 2D discrete stationary wavelet transform is applied to generate predicting HR images from mapped coefficients. Extensive objective and subjective evaluations using benchmark datasets demonstrate that the proposed algorithm achieves superior visual effects compared with the state-of-the-art Laplacian-based algorithms. © 2022 IEEE.

关键词： Wavelet transforms

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Credit Risk Models using Rule-Based Methods and Machine-Learning Algorithms 22

Credit Risk Models using Rule-Based Methods and Machine-Lear...

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Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

作者： Maohong Tian Hualin Li Jing Huang Jian Liang Wenbin Bu Bingling Chen Department of Software Engineering and Artificial Intelligence ChongQing Institute of Engineering China Department of Software Engineering and Artificial Intelligence Shu Yi Xin Credit Management CoLTD China Department of Big Data Artificial Intelligence Nanfang College Guangzhou China

ISBN: (纸本)9781450397773

This study applies machine-learning techniques and rule-based methods to construct nonlinear nonparametric models to forecast retail consumer and medium-sized enterprises (SMEs) credit risk. By combining customer transactions and enterprise data from 2018 to 2020 sampled from a major business district in the People’s Republic of China, forecasts were constructed that significantly improved the classification rates of customer and enterprise delinquencies and defaults. Moreover, the time-series patterns of the estimated delinquency rates and credit scores over multiple dimensions produced by this model suggest that aggregated credit risk analytics may have important applications in forecasting systemic risk, which might shed some light on obtaining prospective insights regarding consumer credit that can be gleaned from historical data especially pandemic period.

关键词： Credit risk model 99-00 Credit Score 2021 MSC: 00-01 Rule-based Machine Learning

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Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

Deep Learning based Multilabel Hateful Speech Text Comments ...

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2022 IEEE International Conference on Current Development in engineering and Technology, CCET 2022

作者： Defersha, Naol Bakala Abawajy, Jemal Kekeba, Kula College of Electrical and Mechanical Engineering Centre of Excellence for Hpc & Big Data Analytics Addis Ababa Science and Technology Software Engineering Department 16417 Ethiopia School of Information Technology Deakin University Faculty of Science Engineering and Built Environment GeelongVIC3220 Australia Centre of Excellence College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Department of Software Engineering Technology 16417 Ethiopia

ISBN: (纸本)9781665454155

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%. © 2022 IEEE.

关键词： Speech recognition

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Personalized Audiobook Generation Using Voice Cloning and Text-to-Speech Synthesis

Personalized Audiobook Generation Using Voice Cloning and Te...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Chun-Cheng Wei Long-Chen Hung Si-Yu Gao Yi-Chang Chen Shih-Pang Tseng School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electrical Engineering Cheng Shiu University Kaohsiung Taiwan Department of Multimedia Design Tajen University Pingtung Taiwan

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

This project leverages advancements in voice cloning and text-to-speech (TTS) to develop a novel audio converter for personalized audiobook creation. Users upload a brief voice sample, allowing the system to learn and replicate unique vocal characteristics, such as tone and pitch, producing customized audio outputs. Integrating voice cloning with natural language processing (NLP) and TTS, the system first analyzes text to identify characters, dialogues, and emotional cues. This enables realistic, expressive narration tailored to each character and context. Unlike traditional TTS, this approach reduces monotony, allowing users to "narrate" stories in their own voice, creating an immersive and individualized listening experience.

关键词： Sentiment analysis Scalability Cloning Production Speech enhancement User experience Real-time systems Text to speech Social implications of technology Usability

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Research on HIV/AIDS Epidemic Trend Prediction Model Based on ARIMA-LSTM 10

Research on HIV/AIDS Epidemic Trend Prediction Model Based o...

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10th International Conference on Orange Technology, ICOT 2022

作者： Guo, Xin Yang, Yichen Tseng, Shih-Pang Yin, Zilong Wang, Cheng School of Information Science and Technology Sanda University Shanghai201209 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China Shanghai University of Engineering Science Department of Electronic and Electrical Engineering Shanghai201620 China

ISBN: (纸本)9781665454193

In China, the number of persons who have infected Acquired Immune Deficiency Syndrome (AIDS) is rising quickly every year. Various public health and social issues have been brought on by the Human Immunodeficiency Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS) epidemic. Understanding the epidemic's development trend is crucial for controlling and preventing HIV/AIDS. Long Short-Term Memory (LSTM) is a nonlinear model, whereas Autoregressive Integrated Moving Average model (ARIMA) is a linear model. Both linear and nonlinear tendencies can be seen in China's HIV/AIDS prevalence rates. In order to forecast the HIV/AIDS pandemic trajectory, this research investigates the combined ARIMA-LSTM model. The increasing number of HIV/AIDS patients was predicted using the ARIMA model, the LSTM model, and the ARIMA-LSTM combination model, and their fitting effects were compared. The findings demonstrate that all four models can be used to predict the rising number of HIV/AIDS patients, however the parallel ARIMA-LSTM combination model has a better fitting prediction effect than the other three models. It works better as a forecasting model for the AIDS pandemic trend. © 2022 IEEE.

关键词： Long short-term memory

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A Lightweight Blockchain-Based Cooperative Demand Response and Energy Trading Model for Smart Grid

A Lightweight Blockchain-Based Cooperative Demand Response a...

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2023 Power Electronics and Power System Conference, PEPSC 2023

作者： Ayenew, Melak Lei, Hang Li, Xiaoyu Kekeba, Kulla Abeje, Eyerusalem Tegene, Abebe Alemu, Habtamu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Addis Ababa Science and Technology University Center of Big Data Analytics and Hpc Addis Ababa Ethiopia Kotebe Education University Collage of Natural Department of Mathematic Science Shared Campus Addis Ababa Ethiopia

ISBN: (纸本)9798350341294

Public blockchain networks have been used for peer-to-peer energy trading;however, most of these networks are inefficient. The power-intensive consensus algorithm, Proof-of-Work (PoW), is one of the reasons for power inefficiency. This article proposes a lightweight consensus algorithm for demand response and energy trading models to improve efficiency in the smart grid. A cooperative approach encompassing all energy entities is also necessary for demand response management strategies to be more efficient. In the proposed model, we redesign the Proof-of-Fit (PoF) consensus algorithm for a smaller number of message exchanges and computations. We also propose a service-based use of grid infrastructure for energy trading and exchange by all energy trading entities in a cooperative approach while free energy price negotiation is exercised. The simulation results show that the redesigned consensus algorithm, Energy-Proof-Fit, reduces the average number of message broadcasts required to complete a consensus by 35 percent. Furthermore, the peak-to-average ratio of peak hour demand is reduced in the proposed model. © 2023 IEEE.

关键词： Blockchain

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Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

arXiv

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arXiv 2024年

作者： Ruan, Weilin Wang, Wenzhuo Zhong, Siru Chen, Wei Liu, Li Liang, Yuxuan The Hong Kong University of Science and Technology Guangzhou China The Computer Science Department Jinan University Guangzhou China The School of Big Data and Software Engineering Chongqing University Chongqing China

Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture both spatial and temporal dependencies while addressing spatio-temporal heterogeneity through techniques such as distribution alignment and feature fusion. It also ensures both predictive accuracy and computational efficiency. Central to STUM is the Adaptive Spatio-temporal Unitized Cell (ASTUC), which utilizes low-rank matrices to seamlessly store, update, and interact with space, time, as well as their correlations. Our framework is also modular, allowing it to integrate with various spatio-temporal graph neural networks through components such as backbone models, feature extractors, residual fusion blocks, and predictive modules to collectively enhance forecasting outcomes. Experimental results across multiple real-world datasets demonstrate that STUM consistently improves prediction performance with minimal computational cost. These findings are further supported by hyperparameter optimization, pre-training analysis, and result visualization. We provide our source code for reproducibility at https://***/r/STUM-E4F0. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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