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Public Outlook on E-sports in China: A Content and Sentiment analysis of Social Media 23

Public Outlook on E-sports in China: A Content and Sentiment...

引用

23rd IEEE International conference on Information Reuse and Integration for data Science (IEEE IRI)

作者： Wang, Yifan Zheng, Xiaochuan Fan, Tianda Wuhan Univ Sch Informat Management Wuhan Peoples R China Wuhan 39 Middle Sch Grade 11 Wuhan Peoples R China

ISBN: (数字)9781665466035

ISBN: (纸本)9781665466035

This paper aims to help people better understand the Chinese public outlook towards e-sports through the calculation and visual analysis of social media big data, so as to provide reference for the development of e-sports industry and relevant decisions. A mixed-method approach is employed to analyze social media texts by combining quantitative sentiment analysis with qualitative topic and keyword analysis. We have completed the topic coding table of public outlook to e-sports and analyzed the topic distribution and trend. We also have carried out keyword extraction and sentiment calculation for each topic to understand their main contents and sentiment changes. The results show that the Chinese public are paying more and more attention to e-sports and they care about E-sports Profession and E-sports Competition most. Especially, discussions on E-sports Culture show a trend of diversified, profound and normalized development. Combined with keyword analysis and sentiment analysis, it's indicated that people are trying to revise their past recognition of e-sports, but it is still closely related to games in the eyes of the public. Besides, people's sentiment towards the e-sports topics has changed from doubt to recognition, and tends to be stable, while they are more willing to exploration on the e-sports education system, e-sports professionalization and the combination between e-sports and traditional sports.

关键词： e-sports social media topic coding keyword analysis sentiment analysis

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Eurovis 2022 - 24th Eurographics conference on visualization 2022, Posters

EuroVis 2022 - 24th Eurographics Conference on Visualization...

引用

24th Eurographics conference on visualization, Eurovis 2022

ISBN: (纸本)9783038681854

The proceedings contain 34 papers. The topics discussed include: scientific convergence and divergence in visualization and visual analytics;digital twins of smart farms;automatic segmentation of tooth images: optimization of multi-parameter image processing workflow;explorative visual analysis of spatio-temporal regions to detect hemodynamic biomarker candidates;visually explaining publication ranks in citation-based literature search with PURE suggest;visualizing the evolution of multi-agent game-playing behaviors;visual exploration of genetic sequence variants in pangenomes;interactive visualization of machine learning model results predicting infection risk;a design space for explainable ranking and ranking models;visual queries on bipartite multivariate dynamic social networks;validating perception of hyperspectral textures in virtual reality systems;situated visualization in motion for video games;and using data comics to enhance visualization literacy.

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Larger Encoders, Smaller Regressors: Exploring Label Dimensionality Reduction and Multimodal Large Language Models as Feature Extractors for Predicting Social Perception 5

Larger Encoders, Smaller Regressors: Exploring Label Dimensi...

引用

5th Multimodal Sentiment analysis Challenge and Workshop: Social Perception and Humor (MuSe)

作者： Martin-Fernandez, Ivan Esteban-Romero, Sergio Bellver-Soler, Jaime Fernandez-Martinez, Fernando Gil-Martin, Manuel Univ Politecn Madrid IPTC THAU Grp Madrid Spain

ISBN: (纸本)9798400711992

Designing reliable automatic models for social perception can contribute to a better understanding of human behavior, enabling more trustworthy experiences in the multimedia on-line communication environment. However, predicting social attributes from video data remains challenging due to the complex interplay of visual, auditory, and linguistic cues. In this paper, we address this challenge by investigating the effectiveness of Multimodal Large Language Models (MM-LLMs) for feature extraction in the MuSe-Perception challenge. Firstly, our analysis of the novel LMU-ELP dataset has revealed high correlations between certain perceptual dimensions, motivating using a single regression model for all 16 social attributes to be predicted for a set of speakers appearing in recorded video clips. We demonstrate that dimensionality reduction through Principal Component analysis (PCA) can be applied to the label space without a relevant performance loss. Secondly, by employing frozen MM-LLMs as feature extractors, we explore their ability to capture perception-related information. We extract sequence embeddings from the Qwen-vL and Qwen-Audio models and train a Multi-Layer Perceptron over the attention-pooled vectors for each one of the encoders, obtaining a mean Pearson correlation of 0.22 using the average predictions for both models. Our best result of 0.31 is achieved by training the same architecture over the baseline vit-ver and w2v-msp features, which motivates further exploration on how to effectively leverage advanced MM-LLMs as feature extractors. Lastly, a post hoc analysis of our results highlights the limitations of Pearson correlation for evaluating regression performance in this context. In particular, a similar Pearson coefficient can be obtained with two very different prediction sets displaying different levels of variability. We take this result as a call to action in exploring alternative metrics to assess the regression performance for the task.

关键词： Social Perception Multimodal Sentiment analysis Affective Computing Multimodal Large Language Model

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High Precision Map Construction Based on Road Design data 3

High Precision Map Construction Based on Road Design Data

引用

3rd International conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Yu Peng, Yu Chongqing Municipal Research Institute of Design Co. Ltd. Chongqing400020 China Chongqing College of Electronic Engineering Chongqing401331 China

Compared to traditional electronic maps, high-precision maps are more about providing services for vehicles. Because vehicles lack the visual recognition and logic inherent in humans, high-precision maps can assist vehicles in identifying obstacles, vehicles, etc., and can also expand their environmental awareness capabilities. This can provide vehicles with a global perspective that other sensors cannot obtain. This article includes road, traffic, and facility information outside the sensor monitoring range. This paper analyzes and defines road design data and high-precision map construction in detail, and then verifies the progressiveness of high-precision map construction methods based on road design data compared with traditional high-precision map construction methods through experimental analysis. © 2023 The Authors. Published by Elsevier B.v.

关键词： vehicles

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, vaibhav P. Kumar, C.H.N. Santhosh Devi, v. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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analysis of Multilanguage Regional Music Tracks Using Representation Learning Techniques in Lower Dimensions 10th

Analysis of Multilanguage Regional Music Tracks Using Repres...

引用

10th International conference on Mathematics and Computing, ICMC 2024

作者： Pendyala, vishnu S. Konduri, Samhita Pendyala, Kriti v. Department of Applied Data Science San Jose State University 1 Washington Square San JoseCA95192 United States Palo Alto High School 50 Embarcadero Rd Palo AltoCA United States University Preparatory Academy 2315 Canoas Garden Ave San JoseCA United States

ISBN: (纸本)9789819720651

Machine understanding of music requires digital representation of the music using meaningful features and then analyzing the features. The work in this paper is unique in using representation learning techniques in lower dimensions for analyzing the effectiveness of mel-spectrogram features of assorted music tracks in multiple languages. The features are plotted in three different transformed feature spaces for visual inspection of the fine-grained attributes of the music rendition such as the vocal artist, their gender, language, and standing in the industry. The analysis of the music tracks in a chosen dataset using spectral and non-spectral algorithms such as Principal Component analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) provide valuable insights into the representation learning of the selected music tracks. UMAP performs better than the other two algorithms and is able to reasonably discern the various subtler aspects of a music rendition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Music

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Enhancement of v2X Technology with Artificial Intelligence

Enhancement of V2X Technology with Artificial Intelligence

引用

2022 International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Binisha, R. Rajasekaran, P. Kalpana, K. Chithrakumarthangaraj, T. Ramani, U. Irfanbasha, S. Sri Eshwar College of Engineering Coimbatore India Dr.N.G.P Institute of Technology Coimbatore India Lourdes Mount College of Engineering and Technology Kanyakumari India Sri Ramakrishna Engineering College Coimbatore India K.Ramakrishnan College of Enegineering Trichy India

ISBN: (纸本)9781665489621

Modern communications technology, Intelligent Transportation Systems, and a few possibilities for intelligent traffic safety, efficiency, and comfort solutions have been made possible by computational systems. Traditional data-driven methods have been extensively improved by Artificial Intelligence (AI) in various fields of scientific study. AI and the vehicle-to-Everything (v2X) technology can broaden the driver's perspective with information from other sources, anticipate accidents, and boost the driver's convenience, safety, and effectiveness. This work provides a thorough analysis of the exploration systems that have used AI to attack crucial exploration issues in v2Xsystems. The value of these exploration systems are outlined, and are divided into several operation fields. Eventually, the paper also discusses the unsolved issues and unborn exploration directions that must be pursued in order to completely use AI's pledge to develop v2X systems. © 2022 IEEE.

关键词： vehicular ad hoc networks

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Anomaly Detection in Blockchain Transactions: A Comparative Study of Isolation Forest, K-Means Clustering, and LSTM Models

Anomaly Detection in Blockchain Transactions: A Comparative ...

引用

2023 IEEE Technology and Engineering Management conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Bharathi Mohan, G. Sharon, K.v. Reddy, P. Praneeth Sree Kailash Hemanth, Y. Akshay, S. Sai Bhavana, B. Amrita School of Computing Amrita Vishwa Vidyapeetam Department of Cse Chennai India

ISBN: (纸本)9798350384659

Blockchain technology has gained widespread adoption across various industries due to its potential for secure and transparent data management. However, ensuring the integrity and security of transactions within blockchain networks is a critical challenge. Anomaly detection plays a pivotal role in identifying irregularities and potential fraudulent activities within these networks. This research paper delves into the intricacies of anomaly detection in blockchain data, employing a multifaceted approach that encompasses data preprocessing, machine learning models, and feature selection. The study employs the Isolation Forest and K-Means clustering models, offering robust solutions for identifying anomalies in blockchain transactions. Additionally, Long Short-Term Memory (LSTM) networks are leveraged for time series analysis. The results reveal insights into the strengths and limitations of these models, enhancing security measures and data integrity within the blockchain landscape. This comprehensive exploration aids in fortifying the trustworthiness of blockchain networks by detecting unusual and potentially fraudulent activities, contributing to the broader field of anomaly detection in digital transactions. © 2023 IEEE.

关键词： Anomaly detection

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Artificial intelligence and machine learning for defect detection in castings. 6

Artificial intelligence and machine learning for defect dete...

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6th International conference on Advancements in Materials and Manufacturing, ICAAMM 2024

作者： Alamuru, Sunanda Reddy, G.S. Jagannadha Raju, M.v. Department of Mechanical Engineering Sreenidhi Institute of Science and Technology Yamnampet Ghatkesar Telangana Hyderabad501301 India Department of Mechanical Engineering A.U. College of Engineering AP Visakhapatnam India

This research delves into the intersection of artificial intelligence (AI) and quality control processes in the context of casting operations, presenting a multifaceted exploration of methodologies aimed at enhancing efficiency, accuracy, and overall quality in manufacturing. The study encompasses diverse approaches, ranging from defect prediction utilizing machine learning models to applying AI techniques in various casting methods. One pivotal contribution is the introduction of Smart Quality Inspection (SQI), an innovative AI-based approach. The study details the design and implementation of a custom Convolutional Neural Network (CNN) model for SQI, achieving an exceptional accuracy rate of 99.86% in inspecting casting products. The success of SQI not only transforms traditional inspection practices but also minimizes the impact of operator, social, organizational, task, and environmental factors. The research highlights the versatility of AI in defect identification, addressing challenges in visual inspection through proposed methods such as second-order derivative and morphology operations, row-by-row adaptive thresholding, and 2-D wavelet transform. Notably, the wavelet transform emerges as a versatile technique, effectively addressing various casting defects, marking a promising avenue for automatic defect detection in castings based on X-ray inspection. While celebrating accomplishments, the study recognizes ongoing challenges, emphasizing the need for continuous refinement and innovation. Future directions include formally classifying defects, localized defect detection, and exploring controlled environmental conditions for data collection, ensuring the adaptability and reliability of AI-based approaches. Looking ahead, the vision involves seamlessly integrating AI systems into assembly lines, automating inspections, and leveraging real-time accessibility for continual improvements. Furthermore, the potential synergy with Natural Language Processing (NLP) t

关键词： Wavelet transforms

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The use of image graph in the medical data analyzing

The use of image graph in the medical data analyzing

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Advances in data-Driven Analytics And Intelligent Systems (ADACIS), IEEE International conference on

作者： Charifa Laghridat Mohamed Essalih Ilham Mounir LAPSSII The Safi’s Graduate School of Technology Cadi Ayyad University Marrakesh Morocco LRIT - CNRST URAC 29 Rabat IT Center - Faculty of Sciences Mohammed V University in Rabat Morocco

Medical data analysis is a critical process aimed at extracting valuable insights and knowledge from complex healthcare information. It plays a vital role in enhancing diagnostics, treatment planning, and medical research. In this context, medical images serve as a fundamental source of information, providing visual representations of anatomical structures and pathological conditions. Image processing techniques, utilizing mesh-based representations, offer unique opportunities for advancing the analysis of medical *** article presents a new method for analyzing medical data based on the concept of meshing. By representing medical images as undirected graphs, the proposed approach enables efficient exploration and analysis of spatial relationships.

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