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arXiv 2022年

作者： Nguyen, Hoa X. Zhu, Shaoshu Liu, Mingming School of Electronic Engineering SFI Insight Centre for Data Analytics Dublin City University Ireland

Microservice-based architecture has become prevalent for cloud-native applications. With an increasing number of applications being deployed on cloud platforms every day leveraging this architecture, more research efforts are required to understand how different strategies can be applied to effectively manage various cloud resources at scale. A large body of research has deployed automatic resource allocation algorithms using reactive and proactive autoscaling policies. However, there is still a gap in the efficiency of current algorithms in capturing the important features of microservices from their architecture and deployment environment, for example, lack of consideration of graphical dependency. To address this challenge, we propose Graph-PHPA, a graph-based proactive horizontal pod autoscaling strategy for allocating cloud resources to microservices leveraging long short-term memory (LSTM) and graph neural network (GNN) based prediction methods. We evaluate the performance of Graph-PHPA using the Bookinfo microservices deployed in a dedicated testing environment with real-time workloads generated based on realistic datasets. We demonstrate the efficacy of Graph-PHPA by comparing it with the rule-based resource allocation scheme in Kubernetes as our baseline. Extensive experiments have been implemented and our results illustrate the superiority of our proposed approach in resource savings over the reactive rule-based baseline algorithm in different testing scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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A data-driven approach to predict hourly bill rates for US contingent workers

A data-driven approach to predict hourly bill rates for US c...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Giuliano Giuliani Fabrizio Giuliani Summer M. Husband Data Engineering & Analytics Randstad Sourceright Amsterdam Netherlands Data Engineering & Analytics Randstad Sourceright Houston TX

ISBN: (纸本)9781665424288

Contingent workers make up a large portion of the service industry workforce. A bill rate is the hourly fee a company pays to a staffing agency for the services of a temporary worker. In reality, the market for contract labor is fluid and bill rates sometimes fluctuate from week to week. Having a systematic approach that proactively aligns pay and bill rates with shifting market conditions can keep clients from underpaying or overpaying for contract talent and create a competitive advantage for recruiters and hiring managers. By using Randstad historical data, we build a machine learning model for the US labor market that predicts high confidence bill rates for a vast range of job titles at different geographical granularities (national, state and MSA) and that takes into account different experience levels.

关键词： Industries Systematics Fluids Conferences Machine learning Predictive models Prediction algorithms

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A Lightweight Blockchain-Based Cooperative Demand Response and Energy Trading Model for Smart Grid

A Lightweight Blockchain-Based Cooperative Demand Response a...

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2023 Power Electronics and Power System Conference, PEPSC 2023

作者： Ayenew, Melak Lei, Hang Li, Xiaoyu Kekeba, Kulla Abeje, Eyerusalem Tegene, Abebe Alemu, Habtamu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Addis Ababa Science and Technology University Center of Big Data Analytics and Hpc Addis Ababa Ethiopia Kotebe Education University Collage of Natural Department of Mathematic Science Shared Campus Addis Ababa Ethiopia

ISBN: (纸本)9798350341294

Public blockchain networks have been used for peer-to-peer energy trading;however, most of these networks are inefficient. The power-intensive consensus algorithm, Proof-of-Work (PoW), is one of the reasons for power inefficiency. This article proposes a lightweight consensus algorithm for demand response and energy trading models to improve efficiency in the smart grid. A cooperative approach encompassing all energy entities is also necessary for demand response management strategies to be more efficient. In the proposed model, we redesign the Proof-of-Fit (PoF) consensus algorithm for a smaller number of message exchanges and computations. We also propose a service-based use of grid infrastructure for energy trading and exchange by all energy trading entities in a cooperative approach while free energy price negotiation is exercised. The simulation results show that the redesigned consensus algorithm, Energy-Proof-Fit, reduces the average number of message broadcasts required to complete a consensus by 35 percent. Furthermore, the peak-to-average ratio of peak hour demand is reduced in the proposed model. © 2023 IEEE.

关键词： Blockchain

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A IoT Edge Advanced VANET Technique for Vehicle Communication and Improve Safety in Hill Station Critical Scenario

A IoT Edge Advanced VANET Technique for Vehicle Communicatio...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： Murali N D. Beulah David Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

The safety of the vehicles is given great importance by advanced intelligent transportation systems. We are here to make sure that swift transit vehicles and infrastructure communicate with one other quickly. The majority of the vehicles need regular monitoring for appropriate warnings and road maps to achieve their ultimate objective in a timely manner in the hill station bad environment, and travel in hairpin bends and risky turning in particular is a duty that is vital for ITS. Here, my study will provide a strategy for preventing accidents by applying a sophisticated early warning system before notifying the car in a certain circumstance. In order to prevent accidents, machine learning is a technique where the system automatically learns and enhances the rapid object detection stage in bobby pin road turning scenarios. Here, my study offers effective unsupervised learning of these characteristics based on the acquired data, which in turn serves as the foundation for the clustering I performed, which successfully builds a mobile ad hoc network. In this work, we suggested an innovative automated discovering vehicle prier that uses the UNetXST technique in real-time to notify warring for vehicle in three-way shining LED light with blinking mode, slowing warring message in display unit, and beep alarm sound. By combining V2I and V2V technologies and sending messages to the central traffic light management system, we highlighted safety and communication. In worst-case scenarios, we employ a v2v technology sensor to detect the vehicle that will cross the junction road, and we investigate two-way merging technology's potential to implement and resolve these problems in transparent object tracking in the turning system. We looked at future direction and hurdles at this time in addition to a case study illustrating a VANET-based scenario of a critical urban road turning, receiving the earliest notice to flee from a traffic collision, and experiencing the perfect EV momen

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Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer's Disease

arXiv

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arXiv 2024年

作者： Chiumento, Francesco Liu, Mingming School of Electronic Engineering Dublin City University Glasnevin Dublin 9 Ireland Insight Research Ireland Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

The rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have shown great potential in medical diagnostics, particularly in radiology, where datasets such as X-rays are paired with human-generated diagnostic reports. However, a significant research gap exists in the neuroimaging field, especially for conditions such as Alzheimer's disease, due to the lack of comprehensive diagnostic reports that can be utilized for model fine-tuning. This paper addresses this gap by generating synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset, which comprises 663 patients. Using the synthetic reports as ground truth for training and validation, we then generated neurological reports directly from the images in the dataset leveraging the pre-trained BiomedCLIP and T5 models. Our proposed method achieved a BLEU-4 score of 0.1827, ROUGE-L score of 0.3719, and METEOR score of 0.4163, revealing its potential in generating clinically relevant and accurate diagnostic reports. © 2024, CC BY.

关键词： Neuroimaging

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Capturing Soldier Fitness Levels in Combat Simulations

Capturing Soldier Fitness Levels in Combat Simulations

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Simulation Winter Conference

作者： Vikram Mittal Paul Evangelista Dept. of Systems Engineering United States Military Academy West Point NY USA Office of Data and Analytics United States Military Academy West Point NY USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Combat simulations typically depict individual soldiers as identical entities, neglecting differences in their ability to execute warfighting tasks. An essential aspect of a soldier's effectiveness is their physical fitness, which impacts their performance on the battlefield, including their mobility, survivability, and lethality. This paper develops two models to correlate soldier physical fitness to movement speeds and shooting accuracy. The first model, derived from literature, links soldier movement speeds and durations to their physical fitness levels, characterized by their $VO_{2.\max}$ , which is correlated with their two-mile run time. The second model used data collected from soldiers (n=60) to correlate shooting accuracy, fitness, and marksmanship scores when firing from a standing position. These models are integrated into an agent-based combat simulation to assess the impact of physical fitness on soldier survivability and lethality in urban operations. The results show that increased soldier physical fitness significantly improves soldier survivability.

关键词： Training Military standards Accuracy Loading Mathematical models data models Health and safety Standards Robots Load modeling

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Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： T Philip Paul Arunodhayam Akhil Raj Vadde Thimmampali Prasanna Kumar Salaja Silas Shalem Preetham Gandu Chamarti Harshith Kumar Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Analytics Engineering College of Engineering and Computing George Mason University Fairfax VA United States

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks.

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Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3265-3275页

作者： Kaur, Inderjeet Nassa, Vinay Kumar Kavitha, T. Mohan, Prakash Velmurugan, S. Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Department of Artificial Intelligence & Data Science Sharad Institute of Technology College of Engineering Maharashtra Yadrav-Kohiapur India Department Computer Applications Kongu Engineering College Perundurai Tamil Nadu Erode India Data Science and Analytics Center Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Vel Tech Multi Tech Dr. RR & Dr.SR Engineering College Tamil Nadu Chennai India

In recent times, speech enhancement (SE) becomes a significant process in the field of speech signal processing. Since the speech signal in real time gets affected by background noise, the efficacy of the speech-based applications gets severely affected. The SE techniques have gained considerable attention in the usage of speaker recognition, video conferences, speech broadcast, speech-enabled biometric systems, smartphones, hearing aid, and so on. Numerous conventional SE and machine learning (ML) models have been employed for processing and removing the additive noise from the speech signal. This paper presents a novel Maximum Likelihood Based Estimation with Quasi Oppositional Chemical Reaction Optimization (MLE-QOCRO) algorithm for speech signal enhancement. The presented MLE-QOCRO algorithm follows three-stage processes namely preprocessing, mask generation, and spectral filtering. Besides, the mask generation process involves QOCRO algorithm to classify the speech signal into noise or speech frames. In addition, MLE based spectral filtering technique is employed at the final stage to get enhanced speech signals. A set of experiments were performed to highlight the improved performance of the proposed MLE-QOCRO algorithm, and the results are investigated in terms of several performance measures. Speech signal augmentation using the unique MLE-QOCRO algorithm, which combines maximum likelihood estimation with quasi-opposing chemical reaction optimization, is the subject of this paper. The QOCRO algorithm is used in the mask generation process in the model to distinguish between noise and speech frames in the input speech signal. A spectral filtering method based on MLE is also utilized to improve the voice signal at the very end. The resultant experimental values pointed out the enhanced outcome of the proposed MLE-QOCRO algorithm over the other compared techniques. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Appl

关键词： Chemical reaction optimization Maximum likelihood Spectral filtering Speech signal enhancement

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A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Murali N D. Beulah David Department of Computer Science and Engineering SIMATS Saveetha School of Engineering Chennai Tamilnadu India Department of Data Analytics SIMATS Institute of Information Technology Saveetha School of Engineering Chennai Tamilnadu India

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design.

关键词： Machine learning algorithms Urban areas Sociology Neural networks Machine learning Traffic control Internet of Things

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Almost minimax optimal best arm identification in piecewise stationary linear bandits 24

Almost minimax optimal best arm identification in piecewise ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunlong Hou Vincent Y. F. Tan Zixin Zhong Department of Mathematics National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Data Science and Analytics Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (纸本)9798331314385

We propose a novel piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent. We design Piecewise-Stationary ε-Best Arm Identification+ (PSεBAI+), an algorithm that is guaranteed to identify an ε-optimal arm with probability ≥ 1 - δ and with a minimal number of samples. PSεBAI+ consists of two subroutines, PSεBAI and NAÏVE ε-BAI(NεBAI), which are executed in parallel. PSεBAI actively detects changepoints and aligns contexts to facilitate the arm identification process. When PSεBAI and NεBAI are utilized judiciously in parallel, PSεBAI+ is shown to have a finite expected sample complexity. By proving a lower bound, we show the expected sample complexity of PSεBAI+ is optimal up to a logarithmic factor. We compare PSεBAI+ to baseline algorithms using numerical experiments which demonstrate its efficiency. Both our analytical and numerical results corroborate that the efficacy of PSεBAI+ is due to the delicate change detection and context alignment procedures embedded in PSεBAI.

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