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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Netthikumarage, Dilshika Liyanage, L.B. Samarakoon, S.M.H.N. Ramkumar, P.M. Harshanath, S.M.B. Rajapaksha, U.U.Samantha SriLanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering SriLanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

IoT is an important technology for Agriculture. When it comes to machine learning-based decisions related to agriculture the farmers can take precise decisions according to the sensor data collected from IoT. But most people who live in urban areas of Sri Lanka were adopted to agriculture because of COVID 19 pandemic situation. So most of the people have limited space for gardening, and newcomers haven't proper knowledge of what are the crops suitable for their area of garden space and relevant instructions to grow the crops which they like and environment factors needed. And they haven't understood the type of fertilizers that need for the crop. Almost nowadays people haven't much time for watering and care the plants that they like to grow in their garden. Garden Pro Android Application had introduced a feature Machine Learning based crop recommendation system for newcomers to home gardening. By using this feature of the app user can get an idea about crops that are suitable for their garden area, gardening instructions. And soil moisture also fertilizers and the application shows real-time temperature, soil moisture, and humidity, water level required for the crop that collected from IoT sensors and additionally the 3D View of the plants that predicted according to the soil conditions visualize to the user by Augmented reality technology. © 2022 IEEE.

关键词： Crops

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Enhancing Nurse Scheduling with User Preferences: Hybrid Genetic Algorithm and Variable Neighbourhood Search Approach

Enhancing Nurse Scheduling with User Preferences: Hybrid Gen...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Kehinde Segun Atoyebi Adebayo Abayomi-Alli Dauda Wisdom Daniel Morolake Oladayo Lawrence Orobosade Adewunmi Alabi Bayo Olushola Omoyiola Department of Software Engineering University of Bolton Bolton United Kingdom Department of Computer Science Federal University of Agriculture Abeokuta Nigeria Department of Computer Science Baze University Abuja Nigeria Department of Computer Science University of the People California United States

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The Nurse Scheduling Problem (NSP) assigns nurses to shifts while meeting constraints, making it an NP-hard problem. This study proposes GAV_NS 2 , a hybrid Genetic Algorithm (GA) and Variable Neighbourhood Search (VNS) model, to optimize scheduling while considering nurse preferences. Implemented in Java, GAV_NS 2 was evaluated using simulations and a dataset of 151 nurses from a Federal Medical Centre in Nigeria. Results showed allocation, duplication, clash, and multiple shift rates of 98.6%, 0.11%, 0.39%, and 0.2%, respectively. Simulations achieved 99.02%, 0%, 1.15%, and 0%, with computation times of 50.13ms−85.91ms. GAV_NS 2 outperforms manual and traditional GA-based scheduling. While it optimally distributes obligatory shifts, non-obligatory preferences like 3-day weekends were not fully met. The adoption of this system will enhance hospital efficiency, and nurse satisfaction, and provide historical data for future decision-making.

关键词： Schedules Processor scheduling Hospitals NP-hard problem Computational modeling Manuals Scheduling Resource management Standards Genetic algorithms

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Do people use social information to improve predictions about everyday events? 44

Do people use social information to improve predictions abou...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Payne, Stephen Jern, Alan Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology United States Department of Humanities Social Sciences and the Arts Rose-Hulman Institute of Technology United States

Following Griffiths and Tenenbaum (2006), we explore whether people use relevant social information to improve their already nearly optimal predictions about quantities in everyday events. We tested this question in two experiments involving quantities in three domains: cake baking times, movie runtimes, and podcast lengths. In Experiment 1, we found that participants were sensitive to the difference between relevant and irrelevant social information. In Experiment 2, we found that people consistently used relevant social information to adjust their predictions in the expected directions. We introduce an optimal social prediction model but find that it does not consistently perform better at accounting for our participants' social predictions than an optimal non-social prediction model. We conclude by discussing whether people use social information for prediction in an optimal way. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Forecasting

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Elevating Performance of LSM-Tree-Based Key-Value Stores with Gradient Data Hierarchy

Elevating Performance of LSM-Tree-Based Key-Value Stores wit...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Hui Sun Jinfeng Xu Xiao Qin School of Computer Science and Technology Anhui University Hefei China Department of Computer Science and Software Engineering Auburn University AL USA

Key-value stores are a key player of managing large-scale unstructured data in storage systems. Performance improvement of the LSM-tree structure has been extensively investigated, but current work primarily focuses on cache structural optimization rather than hot-and-cold data properties. Moreover, existing and external memory components of LSM-tree rarely have uniform hot and cold attributions. In this study, we make use of the gradient and hierarchy mechanism to optimize the components catering for cache data. We design an adaptive data migration method according to hot and cold data in the cache. We reform and expand a gradient cold-hot data hierarchy (GDH) mechanism that replaces the in-memory data structure to address the problem of missing hot and cold data attributes. The hot and cold data are placed in separate cache partitions to store hot data as far the high hierarchy as possible, reducing $\mathrm{I}/\mathrm{O}$ accesses. When it comes to frequently accessed hot data, we advocate for a hotness-aware technique for data stored on a disk, where read-write performance and the cache hit rate are revamped. The experiment results reveal that our proposed GDH achieves a high cache-hit ratio and low access latency under a wide range of workloads.

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Systematic Review and Propose an Investment Type Recommender System Using Investor’s Demographic Using ANFIS 8th

Systematic Review and Propose an Investment Type Recommender...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Asemi, Asefeh Asemi, Adeleh Ko, Andrea Doctoral School of Economics Business and Informatics Corvinus University of Budapest Budapest Hungary Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Corvinus University of Budapest Budapest Hungary

ISBN: (纸本)9789819932429

The development of investment recommender systems (IRSs) has increased due to advancements in technology. This study aims to present a new model for IRSs based on potential investor’s demographic data and feedback, using fuzzy neural inference solutions. Both qualitative and quantitative methods were used in this research, including a review of past studies and analysis of data through Scopus analyze tool, Voyant, and VosViewer. The proposed model combines expert’s knowledge with demographic data to present the most suitable type of investment through an adaptive neuro-fuzzy inference recommender system. The model is processed in several steps, including clustering investment data types in JMP and proposing the results through MATLAB. This study provides a framework for IRSs that can give relevant and accurate recommendations for potential and actual investors, thus enhancing their investment experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Investments

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Study of HVDC-based Offshore Wind Farm Performance under New Zealand Context

Study of HVDC-based Offshore Wind Farm Performance under New...

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Annual Conference of Industrial Electronics Society

作者： Priya Singh Ramesh Rayudu Praveen Kumar Abhisek Ukil Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Research and Development Oak Ridge National Laboratory Tennessee United States Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The imperative to transition towards renewable energy sources has catalyzed global interest in harnessing offshore wind energy, owing to its immense potential to boost sustainable electricity generation. However, integrating offshore wind farms into existing power grids poses formidable challenges, encompassing grid stability, power quality, and fault management. Nonetheless, the integration of offshore wind farms (OWF) with the power grid presents a significant opportunity for advancing renewable energy generation on a large scale. This study embarks on an exploration of the intricate dynamics and performance metrics inherent in the integration of OWF with onshore grids (OnG), particularly focusing on the interaction between a 480 MW Offshore high-voltage direct current (HVDC) system and the modified IEEE 39-bus network. The study incorporates a comprehensive wind generation profile from a 10-minute to 24-hourly timescale synthetic wind speed dataset for actual or proposed New Zealand wind farm sites, as well as load profiles, to provide a robust basis for analysis. Utilizing detailed modeling and analysis, this research delves into the nuanced aspects of offshore wind farm integration, shedding light on key factors such as grid stability, power flow dynamics, and system reliability.

关键词： Measurement Renewable energy sources Wind energy Power system dynamics Power quality Wind farms Power system stability Stability analysis Power grids Reliability

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Crime Based Evaluation of GPS Network Using Machine Learning Techniques 4

Crime Based Evaluation of GPS Network Using Machine Learning...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Rahul Monika Durgapal, Vartika Hari Department Of Software Engineering Delhi Technological University Delhi India Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi110096 India

ISBN: (纸本)9781665401180

With the increased usage of data transmission, data leakage and privacy protection are becoming increasingly critical. Data comes in a variety of forms, and the amount of protection required for each one differs. With the rapid development and demand of Global Positioning S ystem (GPS) networks in every of big data technology, the vulnerability of the transportation system to unintentional and intentional GPS disruption is also increasing. The GPS signal is degraded and lost as a result of opposing interests' attacks. Potential assaults vary from GPS signal jamming and spoofing to GPS ground stations and satellite disruption. S ecurity issues with respect to data increases during data transmission. Data travelling across an organization's network is frequently sensitive and internet being the storehouse of every kind of data with methods for accessing the precise information serves as an auspicious medium for the hackers [19]. Network security solutions must deal with complicated data in this setting in order to detect and minimize any threats in real time. There are several techniques to the problem of data security such as cryptographic algorithms, classification-based models, and block-chain methods [1]-[6]. Through this research, crime rates are evaluated in a GPS network data with different other parameters using machine learning (ML) algorithms. Moreover, the inferred features effecting the crimes during data transmission are extracted. S ome of the techniques used includes techniques such as 'Decision Tree (DT)', 'K Nearest Neighbors (KNN)', 'Random Forest (RF)', 'Multivariable Regression (MR)' and 'Naïve Bayes (NB)'. It was seen that the best accuracy was given by RF - of approximately 84.25%. © 2022 IEEE

关键词： Crime

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