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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer Science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset

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IEEE Transactions on Visualization and computer Graphics 2025年第6期31卷 3706-3718页

作者： Wang, Fen Wang, Bomiao Shu, Xueli Liu, Zhen Shao, Zekai Liu, Chao Chen, Siming Zhengzhou University Zhengzhou Zhongke Institute of Integrated Circuit and System Application Henan Institute of Advanced Technology China Fudan University Shanghai China Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application School of Computer Science China

Effective chart summary can significantly reduce the time and effort decision makers spend interpreting charts, enabling precise and efficient communication of data insights. Previous studies have faced challenges in generating accurate and semantically rich summaries of time-series data charts. In this paper, we identify summary elements and common hallucination types in the generation of time-series chart summaries, which serve as our guidelines for automatic generation. We introduce ChartInsighter, which automatically generates chart summaries of time-series data, effectively reducing hallucinations in chart summary generation. Specifically, we assign multiple agents to generate the initial chart summary and collaborate iteratively, during which they invoke external data analysis modules to extract insights and compile them into a coherent summary. Additionally, we implement a self-consistency test method to validate and correct our summary. We create a high-quality benchmark of charts and summaries, with hallucination types annotated on a sentence-by-sentence basis, facilitating the evaluation of the effectiveness of reducing hallucinations. Our evaluations using our benchmark show that our method surpasses state-of-the-art models, and that our summary hallucination rate is the lowest, which effectively reduces various hallucinations and improves summary quality. © 1995-2012 IEEE.

关键词： Data visualization

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Improved Speaker-Emotion Voice Conversion with Limited Corpus based on Large Language Model and Pre-trained Model 6

Improved Speaker-Emotion Voice Conversion with Limited Corpu...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

Existing research on speaker and emotional voice conversion often focuses on separate tasks, neglecting their joint exploration. Furthermore, the limited availability of emotional corpora for target speakers poses a significant challenge for training robustness and generalized models. This paper proposes an improved scheme for speaker-emotion voice conversion with limited target speaker's emotional corpus, integrating a large language model and a pre-trained emotional speech synthesis model. It introduces several enhancements to enhance the quality of converted speech in terms of speaker similarity and emotional expressiveness. First, emotionally tagged text is generated using a large language model and emotional speech is synthesized from this text using a fine-tuned pre-trained emotional speech synthesis model. Then, a speaker-emotion voice conversion model is co-trained with both synthesized and real target emotional speech. Finally, the model is fine-tuned with the real target emotional speech to further boost the speaker and emotional similarity. © 2025 IEEE.

关键词： Speech enhancement

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Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention 6

Sentiment Analysis Scheme for Image-Text Comments based on M...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

ISBN: (纸本)9798331522667

A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes. © 2025 IEEE.

关键词： Contrastive Learning

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Internet of Things: A Survey on Air Pollution Monitoring, Challenges and applications 7

Internet of Things: A Survey on Air Pollution Monitoring, Ch...

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7th International Conference on Computing, Communication and Security, ICCCS 2022 and 2022 4th International Conference on Big Data and Computational Intelligence, ICBDCI 2022

作者： Jiyal, Sarita Sheetlani, Jitendra Saini, Rakesh Kumar Sri Satya Sai University of Technology and Medical Sciences School of Computer Application M.P Bhopal India DIT University School of Computing Uttarakhand Dehradun India

ISBN: (纸本)9798350309904

Due to urbanization and the development of cities, many forests have been cut down for the accommodation and conveyance of human beings. Demand for vehicles is growing exponentially due to the excessive use of resources. Due to how air pollution occurs from vehicles, we are using resources excessively, which is not good for us and our future generations. We are burning fossil fuel and coal for factories, which causes air pollution due to many diseases that occur in human beings, such as respiratory diseases and cardiovascular diseases. Smart sustainable cities are a good concept for every country using the internet of things. In this paper, we discussed air pollution and how it can be monitored using the internet of things. We use gas sensors and dust sensors, which are placed in street lights and signals along city roads, to sense the data. The Arduino is used for analysing the data;the data is displayed on an LCD display and sent to the cloud. People can know the status of air pollution using an Android application. © 2022 IEEE.

关键词： Air quality

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Texture-Based Garments Defect Detection Method Using Machine Learning 2nd

Texture-Based Garments Defect Detection Method Using Machine...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Rahul, Mayur Verma, Shekhar Singh, Akhilesh Verma, Sarvachan Yadav, Vikash Department of Computer Application School of Engineering & Technology CSJM University Uttar Pradesh Kanpur India Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Department of Technical Education Government Polytechnic Bighapur Unnao Uttar Pradesh Manpur India

ISBN: (纸本)9789819735938

Fabric defects identification is significantly important in textile industry. Various activities are involved in detecting fabric quality. The total profits of the industries depend on the defects on the quality of fabrics that produces loss. Existing defect identification methods are conducting in different organizations with the help of human defect inspectors who draw manually defect pattern. However, these detection techniques have few drawbacks like complication, inaccuracy, negligence, tediousness, and exhaustion in addition to time-consuming which causes decrease performance in failure finding. For the sake of overcoming these problems, a framework that depends on processing of image has been introduced to efficiently and effectively identify the defects of fabrics. The given introduced work consists of image segmentation, morphological methods, feature extraction, and classification. We have used edge detection for image segmentation, ICA for feature extraction, and SVM for image classification. The results achieved from the given framework are superior than existing framework. The experiments have been conducted in Python environment using TILDA datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textures

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Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

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computer Modeling in Engineering & Sciences 2024年第11期141卷 1305-1325页

作者： Yongsheng Zhu Chong Liu Chunlei Chen Xiaoting Lyu Zheng Chen Bin Wang Fuqiang Hu Hanxi Li Jiao Dai Baigen Cai Wei Wang School of Automation and Intelligence Beijing Jiaotong UniversityBeijing100044China Institute of Computing Technologies China Academy of Railway Sciences Corporation LimitedBeijing100081China School of Computer Science and Technology Beijing Jiaotong UniversityBeijing100044China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing100044China Institute of Infrastructure Inspection China Academy of Railway Sciences Corporation LimitedBeijing100081China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and CybersecurityHangzhou310053China

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system *** learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private ***,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s *** this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated *** experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s ***,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

关键词： Privacy-preserving intelligent railway transportation system federated learning poisoning attacks defenses

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Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Databases 39

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Qiu, Rui Ming, Yi Hong, Yisen Li, Haoyu Yang, Tong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peking University Academy for Advanced Interdisciplinary Studies China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases. © 2023 IEEE.

关键词： Graph Databases

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Named Entity Recognition of Chinese Legal Text Based on BERT 4

Named Entity Recognition of Chinese Legal Text Based on BERT

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4th International Conference on Applied Machine Learning, ICAML 2022

作者： Lu, Huawei Peng, Yinan Unicom Digital Technology Co. Ltd Internet Application Security Department Beijing China Central South University The School of Computer Changsha China

ISBN: (纸本)9781665462655

With the deepening of national judicial reform, how to combine artificial intelligence with judicial work has become a research emphasis in judicial intelligence research. Aiming at polysemy problem in Chinese, and characteristics of Chinese legal text that complicated context, professional, and diverse types of entities, we design a named entity recognition method for Chinese legal text based on BERT. Firstly, we build a Chinese legal text corpus, and utilize the corpus to domain pre-train the pre-training model BERT to make it perform better for named entity recognition task in Chinese legal text. Secondly, we design the method of adding clue words and the method of replacing synonyms for training data augmentation to increase the diversity of training data. Finally, we combine the BERT model after domain pre-training with a CRF layer, and utilize the datasets after data augmentation to train it. Meanwhile, we utilize adversarial training in the training process to improve the generalization ability of the model. To verify the performance of our model, we conduct experiments on the competition data set of the information extraction track of the China Legal Intelligence technology Evaluation Competition, which proves the feasibility and effectiveness of the method. © 2022 IEEE.

关键词： Character recognition

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The IoT Ecosystem in Ningbo, China: Current Status, Challenges, and Future Directions

The IoT Ecosystem in Ningbo, China: Current Status, Challeng...

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International Conference on Internet of Things 2024, ICIoT 2024

作者： Wang, Steven Peng Yu, Heng Ningbo IoT Intelligent Technology Application Association No. 1277 Zhongguanxi Road Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China Next-Generation Internet of Everything Lab University of Nottingham Ningbo China Ningbo China

This paper briefly summarizes the current development status of the IoT ecosystem in Ningbo, China, overviews the challenges and difficulties faced by the IoT industry in the city, and predicts its future development directions. This paper articulates Ningbo's efforts and dedication in forming industrial clusters, aligning with its "361"trillion-level industrial cluster development strategy, to create a major centre for intelligent manufacturing innovation. Future mainstream technologies such as edge computing, artificial intelligence, and blockchain are expected to thrive in the city, and IoT applications in fields like digital twins, the metaverse, and healthcare will also flourish. © The Institution of Engineering & technology 2024.

关键词： Smart manufacturing

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