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Decoding Digital Dynamics: Attitude Algorithms in Online Gaming Redefining Communication Experiences

Decoding Digital Dynamics: Attitude Algorithms in Online Gam...

2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ghani, Miharaini Md Mustafa, Wan Azani Bin Hashim, Mohd Ekram Alhafis Hanafi, Hafizul Fahri Al-Farouni, Mohammed Universiti Sains Malaysia School of Communication Penang Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau Malaysia Universiti Pendidikan Sultan Idris Faculty of Art Sustainability and Creative Industry Department of Creative Multimedia Perak Tanjong Malim Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Tanjong Malim Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

The proliferation of online gaming has given rise to vast virtual worlds where communication and social interaction take on new dimensions. At the forefront of this digital frontier are "attitude algorithms"- advanced computational models designed to detect, interpret, and respond to the emotional states and behavioral patterns of players. These algorithms have the potential to fundamentally redefine the nature of communication experiences within online gaming environments. This study examines the impact of attitude algorithms on player interactions and social dynamics in popular multiplayer online games. Through a combination of data analysis, user surveys, and in-game observations, we explore how these algorithms shape how players express themselves, form connections, and navigate complex social landscapes within virtual gaming worlds. Our findings suggest that attitude algorithms introduce a new layer of emotional intelligence and responsiveness to online gaming communities. By tailoring in-game experiences based on players' attitudes and behaviors, these algorithms can foster more engaging and personalized forms of communication. However, we also uncover potential concerns regarding privacy, manipulation, and the erosion of authentic human-to-human interactions. Ultimately, this research sheds light on the profound impact of attitude algorithms, highlighting their capacity to redefine the boundaries of communication and social dynamics in the digital realm. As these technologies continue to evolve, it becomes increasingly important to understand their implications and guide their development in a responsible and ethical manner, ensuring that they enhance rather than diminish the richness of human connections in online gaming and beyond. © 2023 IEEE.

关键词： Dynamics

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Application of Evolutionary Computing for Optimal Design of Base Station Antenna 7

Application of Evolutionary Computing for Optimal Design of ...

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7th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2024

作者： Khan, Naveed Hamid Nawaz Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Chung, Kwok L. Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen518060 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou China Huizhou University School of Computer Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350384437

This study outlines the development and refinement of an innovative base station antenna for contemporary wireless communication systems, which operates within the frequency range of 1.9 to 4.9 GHz. The antenna is made of copper and has a radiating patch thickness of 0.2 mm. The ground patch measures 38×38 mm and the total height of the antenna is 16 mm. The performance of the system relies on four reflecting walls that have been carefully carved to reduce out-of-band radiations and minimize interference. The antenna attains a peak gain of 6 dBi throughout the operational range, while keeping the |S11| parameters below -10 dBi, ensuring effective signal transmission. The process of optimization involves the utilization of evolutionary computers, more specifically a Genetic Algorithm, to meticulously adjust the design parameters. This approach guarantees that the antenna fulfills the demanding criteria of high gain and wide bandwidth, offering a strong and reliable solution for modern communication requirements. © 2024 IEEE.

关键词： Antenna grounds

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Multi-View Human Activity Recognition in Ambient Assisted Living Using Lightweight Deep Learning Models

Multi-View Human Activity Recognition in Ambient Assisted Li...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Ahsanul Bari Hezerul Abdul Karim Fahmid Al Farid Mina Asaduzzaman Farshid Amirabdollahian Sarina Mansor Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Information Science and Technology Multimedia University Melaka Malaysia Faculty of Engineering and Computer Science the University of Hertfordshire Hatfield

ISBN: (数字)9798331529437

ISBN: (纸本)9798331529444

Human Activity Recognition (HAR) is crucial for the development of intelligent assistive technologies in Ambient Assisted Living (AAL) environments. This paper proposes an innovative method for Multi-View Human Activity Recognition (MV-HAR) using lightweight deep learning models, specifically MobileNet and Cyclone-CNN (CCNet), to achieve quick and precise activity detection. Utilizing the Robot House Multi-View Human Activity Recognition (RHM-HAR) dataset, which contains four different views-front, back, ceiling (omni), and mobile robot-our models effectively address challenges related to viewpoint variation and motion dynamics. The dataset includes 14 multi-view daily living action classes, providing a balanced set of synchronized human actions suitable for multi-domain neural network learning. MobileNet and CCNet are employed for their high recognition accuracy, computational efficiency, and real-time application capabilities in AAL scenarios. We propose a Mutual Information (MI)-based method to assess the redundancy and relevance of each viewpoint, ensuring the fusion of multi-view data with minimum redundancy and maximum relevance. Benchmarking results demonstrate that multi-view combinations significantly enhance recognition performance compared to single-view models, particularly in complex activities involving high levels of movement.

关键词： Deep learning Ambient assisted living Accuracy Computational modeling Termination of employment Assistive technologies Transformers Computational efficiency Human activity recognition Robots

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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2025 IEEE International Conference on Cybernetics and Innovations, ICCI 2025

作者： Thaiparnit, Sattarpoom Janchidfah, Thanakrit Farooq, Adil Rajamangala University of Technology Suvarnabhumi Faculty of Business Administration Dep. Of Information System and Computer Business Suphan Buri72130 Thailand Muban Chombueng Rajabhat University dep. Of Digital Media Technology Ratchaburi70150 Thailand Maynooth University Department of Electronic Engineering Ireland

ISBN: (纸本)9798331533267

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors. © 2025 IEEE.

关键词： Random forests

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Towards Real Time Traffic Analysis with Reduced Image Quality 21

Towards Real Time Traffic Analysis with Reduced Image Qualit...

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21st IEEE India Council International Conference, INDICON 2024

作者： Mohanraj, Rohit Dayaratna, Erunika Oshani National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal India University of peradeniya Department of Statistics and Computer Science Faculty or science Sri Lanka

ISBN: (纸本)9798350391282

This paper presents a detailed study of two computationally efficient object recognition models-YOLOv5 and Fast R-CNN with a MobileNet backbone-focusing on their performance under different degrees of image / video qualities. Blurring is a common problem in autonomous driving due to motion, weather conditions, and other factors, which can greatly reduce picture quality and, consequently, the accuracy of object recognition. Also, the low-quality images produced by on-car devices suffer when trying to utilize them for advanced detection applications. Utilizing the Zenseact Open Dataset (ZOD), images are generated by varying kernel sizes of Gaussian blur to examine how each model's recognition accuracy is affected by picture degradation. Our study shows key differences in the stability of these models, showing the trade-offs between processing speed and recognition confidence in poor visual settings. The results provide helpful guidance for choosing object recognition systems that keep performance in difficult situations, crucial for the safety and efficiency of driverless cars. © 2024 IEEE.

关键词： Fast R-CNN Gaussian Blur YOLOv5 ZOD Dataset

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Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Architecture for Image Manipulation Localization 39

Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Arch...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Xuekang Ma, Xiaochen Su, Lei Jiang, Zhuohang Du, Bo Wang, Xiwen Lei, Zeyu Feng, Wentao Pun, Chi-Man Zhou, Ji-Zhe College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education of China China The Hong Kong Polytechnic University Hong Kong Faculty of Science and Technology University of Macau China

ISBN: (纸本)157735897X

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Deep Learning Scheme for Modulation Classification under Fading Channels 7

Deep Learning Scheme for Modulation Classification under Fad...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Ghauri, Sajjad A. Sarfraz, Mubashar Chhabra, Gunjan Kaushik, Keshav Zafar, Asim Khan, Inam Ullah Isra University School of Engineering & Applied Sciences Islamabad Pakistan Numl University Faculty of Engineering and Computing Islamabad Pakistan Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Amity University Amity School of Engineering and Technology Punjab India National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350350067

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture's performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats. © 2024 IEEE.

关键词： Signal modulation

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Combining Heuristic and Reinforcement Learning to Achieve the Low-latency and High-throughput Receiver-side Congestion Control

arXiv

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arXiv 2025年

作者： Jiang, Xianliang Gong, Guanghui Jin, Guang Faculty of Electrical Engineering and Computer Science of Ningbo University China Eastern Institute of Technology Ningbo China

Traditional congestion control algorithms struggle to maintain the consistent and satisfactory data transmission performance over time-varying networking condition. Simultaneously, as video traffic becomes dominant, the loose coupling between the DASH framework and TCP congestion control results in the un-matched bandwidth usage, thereby limiting video streaming performance. To address these issues, this paper proposes a receiver-driven congestion control framework named Nuwa. Nuwa deploys the congestion avoidance phase at the receiver-side, utilizing one-way queueing delay detection to monitor network congestion and setting specific target delays for different applications. Experimental results demonstrate that, in most cases, with appropriate parameter configuration, Nuwa can improve the throughput of TCP flows 4% to 15.4% and reduce average queueing delay by 6.9% to 29.4%. Furthermore, we also introduce the use of reinforcement learning to dynamically adjust Nuwa’s key parameter k, enhancing Nuwa’s adaptability to the unpredictable environment. © 2025, CC BY-NC-ND.

关键词： Video streaming

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