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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

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Machine Learning Designed identification on cervical cancers in patient

Machine Learning Designed identification on cervical cancers...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Sangeetha, M. Sasikala, G. Anitha, K. Ragavendiran, S.D. Prabu Kumar, K.R. Senthil Deivakani, M. Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mathematics Srm Valliammai Engineering College Kattankulathur Tamil Nadu Chennai603202 India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Thandalam602105 India Department of Computer Science and Engineering Rvs Technical Campus - Coimbatore Tamil Nadu Coimbatore641402 India Department of Mechanical Engineering R. M. K. Engineering College Tamil Nadu Kavaraipettai601206 India Department of Electronics and Communication Engineering Psna College of Engineering and Technology Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350334357

In this paper, we develop an automatic diagnosis model that aims to screen and detect the presence of cervical cancer in women patients. The diagnosis model consists of a series of stages that involves pre-processing, feature extraction and classification of cancer using bees swarm optimization (BSO). The BSO helps to classify the instances from the extracted features in an effective way that does not fall into premature convergence. The fully grown solution provides effective classification of images from the pre-defined medical datasets. The simulation is conducted on python in a high-end computing system to test the efficacy of BSO in classifying the cervical cancer. The validation shows an increasing precision of classifying the instances than other state-of-art methods. © 2023 IEEE.

关键词： Python

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SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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computer, Communication and Control (IC4), International Conference on

作者： B. Jai Ganesh P. Vijayan V. Vaidehi S. Murugan R. Meenakshi M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety.

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Secure and Privacy-preserving data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing

LRScheduler: A Layer-aware and Resource-adaptive Container S...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Zhiqing Tang Wentao Peng Jianxiong Guo Jiong Lou Hanshuai Cui Tian Wang Yuan Wu Weijia Jia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China Guangdong Key Lab of AI & Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of Internet of Things for Smart City University of Macau SAR Macau China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

Lightweight containers provide an efficient approach for deploying computation-intensive applications in net-work edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computing. Specifically, we first utilize container image layer information to design and implement a node scoring and container scheduling mechanism. This mechanism can effectively reduce the download cost when deploying containers, which is very important in edge computing with limited bandwidth. Then, we design a dynamically weighted and resource-adaptive mechanism to enhance load balancing in edge clusters, increasing layer sharing scores when resource load is low to use idle resources effectively. Our scheduler is built on the scheduling framework of Kubernetes, enabling full process automation from task information acquisition to container deployment. Testing on a real system has shown that our design can effectively reduce the container deployment cost as compared with the default scheduler.

关键词： Costs Scheduling algorithms Image edge detection Reinforcement learning Containers Sensors Resource management Optimization Edge computing Testing

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VELO: A Vector database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework

arXiv

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arXiv 2024年

作者： Yao, Zhi Tang, Zhiqing Lou, Jiong Shen, Ping Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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Cloud-Based AI Solutions for Early Wound Infection Detection and Treatment Recommendations

Cloud-Based AI Solutions for Early Wound Infection Detection...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Mothiram Rajasekaran Chitra Sabapathy Ranganathan G Manikandan G Bhuvaneswari T. R. GaneshBabu M. Rajmohan Pine Candle Way Saint Augustine Florida USA Mphasis Corporation Chandler Arizona USA Department of Artificial Intelligence and Data Science RMK Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Wound infections are a major problem for healthcare systems worldwide. Infections cause patients to remain in the hospital longer., which increases healthcare expenses and negatively impacts their health outcomes. For patient care to be successful, it is crucial to identify these illnesses quickly and treat them appropriately. Recent developments in cloud computing and artificial intelligence (AI) have encouraged optimism for the potential of these technologies to improve wound infection diagnosis and treatment. Within healthcare systems, this research outlines a new method for early wound infection diagnosis and treatment recommendations generation using cloud-based AI algorithms. The proposed method employs machine learning to appropriately detect early indications of infection by analyzing a variety of patient data inputs, such as wound features, vital signs, laboratory findings, and medical history. Also, healthcare practitioners may benefit from real-time monitoring and decision assistance due to cloud-based architecture's easy interaction with existing electronic health record (HER) systems. The system is designed with a dynamic feedback loop that can be updated with fresh data inputs to make it more accurate and flexible. The proposed cloud-based AI system has been thoroughly tested and proven reliable in improving the early diagnosis and treatment of wound infections. This has led to better patient outcomes and more efficient use of healthcare resources. Key challenges include data privacy issues, the integration of diverse technologies, the reliability of precise forecasts, and maintaining system resiliency in changing environments.

关键词： Cloud computing data privacy Machine learning algorithms Medical services Wounds Real-time systems Reliability Artificial intelligence Usability Standards

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A bivariate two-state Markov modulated Poisson process for failure modelling

arXiv

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arXiv 2024年

作者： Yera, Yoel G. Lillo, Rosa E. Nielsen, Bo F. Ramírez-Cobo, Pepa Ruggeri, Fabrizio Department of Statistics Universidad Carlos III de Madrid Spain uc3m-Santander Big Data Institute Madrid Spain Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Statistics and Operations Research Universidad de Cádiz Spain IMATI-CNR Institute of Applied Mathematics and Information Technologies Milano Italy

Motivated by a real failure dataset in a two-dimensional context, this paper presents an extension of the Markov modulated Poisson process (MMPP) to two dimensions. The one-dimensional MMPP has been proposed for the modeling of dependent and non-exponential inter-failure times (in contexts as queuing, risk or reliability, among others). The novel two-dimensional MMPP allows for dependence between the two sequences of inter-failure times, while at the same time preserves the MMPP properties, marginally. The generalization is based on the Marshall-Olkin exponential distribution. Inference is undertaken for the new model through a method combining a matching moments approach with an Approximate Bayesian Computation (ABC) algorithm. The performance of the method is shown on simulated and real datasets representing times and distances covered between consecutive failures in a public transport company. For the real dataset, some quantities of importance associated with the reliability of the system are estimated as the probabilities and expected number of failures at different times and distances covered by trains until the occurrence of a failure. © 2024, CC BY-NC-ND.

关键词： Reliability

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AI-Driven Energy Forecasting for Electric Vehicle Charging Stations Powered by Solar and Wind Energy

AI-Driven Energy Forecasting for Electric Vehicle Charging S...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Nayana Shetty Polamarasetty P Kumar Ramakrishna S S Nuvvula Sanjeev Kumar Thalari Muhammad Waqas Arshad Raaid Alubady Baseem Khan Deparmtent of Electrical and Electronics Engineering NMAM Institute of Technology NITTE Deemed to be University) Karnataka India Department of Electrical and Electronics Engineering) GMR Institute of Technology Rajam India CMR Institute of Technology Computer Science and Engineering University of Bologna Technology Bologna Italy Department of Technical Engineering College Al-Ayen University Thi-Qar Iraq College of Information Networks University of Babylon Babylon Iraq Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia Department of Electrical and Electronic Engineering Technology University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

With the increasing demand for electric vehicles (EVs), accurate energy forecasting for charging stations powered by renewable sources is crucial. This study explores the implementation of an artificial intelligence (AI)-driven forecasting model for EV charging stations utilizing solar and wind energy. The model's precision is validated through comprehensive performance metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2), showcasing its exceptional ability to predict energy production. Comparisons with traditional forecasting methods, such as ARIMA and Naive forecasting, highlight the evident superiority of the AI-driven model. A significantly higher R-squared value of 0.92 underscores its advanced capacity to navigate intricate relationships within renewable energy sources. Operational optimization, depicted through aligned predicted and actual energy consumption, illustrates the model's effectiveness in reducing reliance on grid electricity during peak hours, contributing to enhanced grid stability. The model's integration with renewable energy aligns with sustainability objectives, mitigating the carbon footprint associated with EV charging. The model's scalability and adaptability render it suitable for diverse geographic locations and varying energy generation capacities. The implications for sustainable mobility are substantial, as the model aligns with broader environmental goals by minimizing emissions linked to EV usage. In conclusion, this research emphasizes the transformative potential of AI-driven energy forecasting for EV charging infrastructure. The demonstrated precision, superiority over traditional models, and positive operational impact position the model as a crucial tool in advancing the transition toward sustainable and efficient transportation systems. Future research avenues may explore real-time adaptation and deeper integration with smart grid technologies to enhance the model's applicability in r

关键词： Adaptation models Renewable energy sources Accuracy Wind energy Production Predictive models Electric vehicle charging

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