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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text 24

GPT Assisted Annotation of Rhetorical and Linguistic Feature...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Hamilton, Kyle Longo, Luca Božić, Bojan School of Computer Science and SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland Artificial Intelligence and Cognitive Load Research Lab School of Computer Science SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland

ISBN: (纸本)9798400701726

While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box" solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set labeled with propaganda techniques. To help human experts annotate natural language sentences with these features, RhetAnn, a web application, was specifically designed to minimize an otherwise considerable mental effort. Finally, a small set of annotated data was used to fine-tune GPT-3.5, a generative large language model (LLM), to annotate the remaining data while optimizing for financial cost and classification accuracy. This study demonstrates how combining a small number of human annotated examples with GPT can be an effective strategy for scaling the annotation process at a fraction of the cost of traditional annotation relying solely on human experts. The results are on par with the best performing model at the time of writing, namely GPT-4, at 10x less the cost. Our contribution is a set of features, their properties, definitions, and examples in a machine-readable format, along with the code for RhetAnn and the GPT prompts and fine-tuning procedures for advancing state-of-the-art interpretable propaganda technique detection. © 2024 Copyright held by the owner/author(s).

关键词： machine learning

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

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Optimizing AUV Positioning and Data Transmission with Actor Critic (A3C) Algorithm in Underwater Wireless Sensor Networks 4

Optimizing AUV Positioning and Data Transmission with Actor ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kodukulla, Aruna Gayatri Amanullah, M. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method. © 2024 IEEE.

关键词： Sensor nodes

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Factorised Neural Relational Inference for Particle Decay Reconstruction 5

Factorised Neural Relational Inference for Particle Decay Re...

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5th International Seminar on artificial intelligence, Networking and Information Technology, AINIT 2024

作者： Lu, Junjian Liu, Siwei Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning United Arab Emirates

ISBN: (纸本)9798350385557

Investigating the interactions among particles in high-energy physics is essential for various tasks, such as reconstructing particle decays. The neural relational inference encoder model, capable of capturing inter-entity dependencies and system dynamics, has been recently introduced for this purpose. However, it faces limitations in accurately capturing diverse relations between complex particles. To address this, we propose the factorised neural relational inference (fNRI) encoder model, which factorises the inferred interaction graph into a multigraph, with each layer encoding a distinct relation. Additionally, we present a simplified version of our model that offers improved efficiency without compromising performance. Moreover, we explore three different inference strategies to leverage the learned relation representations effectively. We demonstrate on a simulated particle physics decay dataset that fNRI improves the reconstruction of the decay tree structure. © 2024 IEEE.

关键词： Graph neural networks

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs machine learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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StaR Maps: Unveiling Uncertainty in Geospatial Relations 27

StaR Maps: Unveiling Uncertainty in Geospatial Relations

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Flade, Benedict Kohaut, Simon Eggert, Julian Dhami, Devendra Singh Kersting, Kristian Honda Research Institute Europe GmbH Offenbach63073 Germany Artificial Intelligence and Machine Learning Lab Department of Computer Science Tu Darmstadt Darmstadt64283 Germany Tu Eindhoven Uncertainty in Artificial Intelligence Group Department of Mathematics and Computer Science MB Eindhoven5600 Netherlands Hessian Ai Centre for Cognitive Science Germany

ISBN: (纸本)9798331505929

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information. © 2024 IEEE.

关键词： Knowledge representation

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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Node Coverage and Connectivity Optimization in Heterogeneous Wireless Sensor Networks Using Improved Flower Pollination Algorithm 4

Node Coverage and Connectivity Optimization in Heterogeneous...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mahankali, Saritha Kesavan, R. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Vardhaman College of Engineering Department of Computer Science & Engineering Telangana India Rajalakshmi Engineering Department of Artificial Intelligence & Machine Learning Chennai India

ISBN: (纸本)9798331529635

Wireless sensor networks (WSNs) rely on energy-conserving routing protocols due to the limited power and communication capabilities. The LEACH protocol has seen extensive usage despite its homogeneous network foundation, leads inadequate signal coverage. To solve the issue, this research proposes an improved flower pollination algorithm (IFPA) improves flower pollination algorithm (FPA) addresses issues with slow convergence and local optima. This research presents an enhanced clustering technique that may be utilized in heterogeneous sensor networks. Its main feature is the ability to adjust the status of data transmission. Once the observed information intensity above the predefined threshold, the data transmission connection is initiated and data is sent to the sink by the cluster heads, which filter the perceived information. Otherwise, cluster nodes keep track of the data and wait for the next batch of nodes to provide data. The good news is that the simulation results show a network lifespan that is several times longer than the LEACH protocol. The IFPA method stands out because it increases coverage rates while drastically decreasing the cost of deploying networks. As a result, the suggested method might find many uses, helping to lower costs and improve the performance of wireless sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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Performance Analysis of Acoustic Scene Classification Using ANN and CNN Techniques

Performance Analysis of Acoustic Scene Classification Using ...

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2023 International Conference on Integrated intelligence and Communication Systems, ICIICS 2023

作者： Rakshitha Karthik, K. Shetty, Aishwarya D Sowmya, P. Shettigar, Rashmitha Department of Artificial Intelligence & Machine Learning Nitte India School of Computer Science and Engineering Vellore India Department of Computer Science and Engineering Nitte India /CS Dhaisar Mumbai India

ISBN: (纸本)9798350315455

artificial neural networks have achieved notable improvements in various learning tasks and have also been used to classify environmental sounds. The technique for allowing devices to recognize and interpret the surroundings from a recording is called Acoustic Scene Classification (ASC). Computational Auditory/Scene analysis is analyzing scenes using computational techniques. This research paper's primary goal is to categorize audio recordings based on the scenarios or environments in which they were captured. In most applications, deep learning is one of the most recent trends. In this work, we have concentrated on two deep learning techniques employed to classify audio scenes: Convolution Neural Networks (CNN) and artificial Neural Networks (ANN) to develop the model. ReLU and LeakyReLU activation functions are used to train and validate the model. For evaluating the model performance, K-fold cross-validation was used with K=10 and obtained an accuracy of 75.8% using an artificial Neural Network and a proposed model with 90.6% accuracy using a Convolutional Neural Network. Comparing the results with other baseline models that achieved a performance of 73.3% for MCLNN with Mel-spectrogram, 78.2% for Pyramid-Combined CNN, 78.3% for Envnet-v2, and 81.9% for D-CNN. © 2023 IEEE.

关键词： Acoustic Scene Classification ANN CNN Environmental Sound Recognition

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