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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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GPU-INSCY: A GPU-parallel algorithm and tree structure for efficient density-based subspace clustering 24

GPU-INSCY: A GPU-parallel algorithm and tree structure for e...

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Advances in database Technology - 24th International Conference on Extending database Technology, EDBT 2021

作者： Jørgensen, Jakob Rødsgaard Scheel, Katrine Assent, Ira Department of Computer Science Aarhus University Denmark Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Aarhus University Denmark

ISBN: (纸本)9783893180844

Subspace clustering is the task of grouping objects based on mutual similarity in subspaces of the full-dimensional space. The INSCY algorithm extends the well-known density-based clustering algorithm DBSCAN. It finds dimensionality-unbiased non-redundant subspace clusters using a tree structure to speed up the processing of subspaces. Still, finding density-based clusters in all subspaces implies an exponential search space in the number of dimensions. Thus, the running time of INSCY is still measured in hours on even small datasets of 2000 points. For larger datasets, it becomes prohibitively expensive. To benefit from INSCY for real-world sized datasets, we propose a novel GPU-parallel approach that runs on standard graphics cards. To utilize the many cores of the GPU, we need new algorithmic strategies that fit the computational model of the GPU. While the GPU provides a large number of threads, traditional algorithms incur diverging threads and poor memory alignment, both of which lead to idle time and poor runtime performance. In INSCY, extracting subspace regions from the SCY-tree structure and the density-based clustering of regions itself are thus unfit for the GPU. Our novel GPU-friendly algorithm GPU-INSCY computes the same subspace clustering as INSCY at dramatically reduced runtimes. To achieve this, we devise a restructured SCY-tree index-structure and associated operations for the GPU, as well as a GPU-parallel density-based subspace clustering. We experimentally show that GPU-INSCY scales well with the size of the dataset and the number of dimensions, and improves the running time of INSCY by a factor of several thousand for large datasets of high dimensionality. © 2021 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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Moral Storytelling Model Using Artificial Intelligence-Driven Image-to-Text Synthesis 2

Moral Storytelling Model Using Artificial Intelligence-Drive...

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

作者： Rao, Abhishek S. Rakesh Kumar, B. Ranganatha, K. Poojary, Ramaprasad Karthik Pai, B.H. Lisa Bojamma, M.S. Jaleesh, Jeeva Nmam Institute of Technology Department of Information Science and Engineering Nitte India St. Aloysius Deemed to Be University Department of M.Sc Big Data Analytics Aimit Mangaluru India Canara Engineering College Department of Information Science & Engineering Benjanapadavu India School of Engineering & It Manipal Academy of Higher Education Dubai United Arab Emirates

ISBN: (纸本)9798350373110

Artificial intelligence in storytelling often struggles with conveying complex moral narratives due to its reliance on short and simplistic captions. This study introduces a novel approach to overcome this challenge by developing a model designed to craft more extensive and profound moral stories. Utilizing unsupervised deep learning techniques, the proposed model autonomously learns from a diverse corpus of short stories and images to generate extended narratives without requiring explicit human guidance. The study highlights three primary contributions: the creation of an autonomous model for generating moral stories with minimal human intervention, the development of a substantial reservoir of moral narratives for comprehensive training, and the empirical validation of the model's superior ability to produce innovative and captivating stories compared to existing methodologies. The proposed model exhibits remarkable accuracy, achieving 91.0% on the COCO dataset and 88% on the Flickr30K dataset, outperforming models such as Encoder-Decoder, Informative Cross-modal, LSTM, and AESOP. Additionally, the model's performance was evaluated using the BLEU and CIDEr scores, yielding 0.81 and 0.75, respectively. This model not only enhances the depth and quality of AI-generated moral stories but also has potential applications in education, entertainment, and virtual assistants, providing richer and more engaging content for users. © 2024 IEEE.

关键词： Deep learning

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SUBSPACE OPTIMIZATION FOR LARGE LANGUAGE MODELS WITH CONVERGENCE GUARANTEES

arXiv

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arXiv 2024年

作者： He, Yutong Li, Pengrui Hu, Yipeng Chen, Chuyan Yuan, Kun Peking University China Beihang University China AI for Science Institute Beijing China National Engineering Laboratory for Big Data Analytics and Applications China

Subspace optimization algorithms, with GaLore (Zhao et al., 2024) as a representative method, have gained popularity for pre-training or fine-tuning large language models (LLMs) due to their memory efficiency. However, their convergence guarantees remain unclear, particularly in stochastic settings. In this paper, we unexpectedly discover that GaLore does not always converge to the optimal solution and substantiate this finding with an explicit counterexample. We then investigate the conditions under which GaLore can achieve convergence, demonstrating that it does so either in deterministic scenarios or when using a sufficiently large mini-batch size. More significantly, we introduce GoLore (Gradient random Low-rank projection), a novel variant of GaLore that provably converges in stochastic settings, even with standard batch sizes. Our convergence analysis can be readily extended to other sparse subspace optimization algorithms. Finally, we conduct numerical experiments to validate our theoretical results and empirically explore the proposed mechanisms. Codes are available at https://***/pkumelon/Golore. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Multilingual Sentiment Analysis using Deep-Learning Architectures 5

Multilingual Sentiment Analysis using Deep-Learning Architec...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Dominic, Praveen Purushothaman, Niranjan Kumar, Anish Skanda Anil Prabagaran, A. Blessy, J. Angelin John, A. School of Graduate Studies MSc in Big Data Analytics Trent University Ontario Canada Visa Consolidated Support Services India Pvt. Ltd Bengaluru India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665474672

Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets shared by the people on social media can be mined to infer the valuable 'mindset' of the people which is much required for any ruling government in the world. The objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. The idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. The pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). The larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. The non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries' sentiment and their corresponding reliance on Russian oil is performed. © 2023 IEEE.

关键词： Sentiment analysis

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Parallel Implementation of Random Forest using MPI

Parallel Implementation of Random Forest using MPI

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Zyad Atef Allam Fatehan Tael Shajera Reem Jamal Majed Abdulla Alqaddoumi MSc. in Big Data Science and Analytics University of Bahrain Sakhir Bahrain Department of Computer Science University of Bahrain Sakhir Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Computer vision is a quickly rising field in machine learning. It has many use cases and implementations across various industries. Powerful classifiers can be trained to recognize visual patterns and detect people and objects. However, since these models use images as inputs, they often end up being trained with very complex architectures, with a high number of epochs and very large data sizes. This often results in very long training times, especially for weaker machines. This study investigated the use of parallel processing to speed up machine learning model training. Message-passing Interface (MPI) is used to parallelize the training of Random Forest, achieving speed-ups of up to 5 and an efficiency of up to 1.1. The study concludes that when dealing with big and complex data and building complex models, parallel processing can significantly reduce the training time of the model with minimal loss in accuracy.

关键词： Training Industries Radio frequency Visualization Parallel processing data models Hardware

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Multimodal Biological Knowledge Graph Completion via Triple Co-Attention Mechanism 39

Multimodal Biological Knowledge Graph Completion via Triple ...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Derong Zhou, Jingbo Xu, Tong Xia, Yuan Liu, Ji Chen, Enhong Dou, Dejing University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Baidu Research Business Intelligence Lab Baidu Intelligent Healthcare Unit Baidu Research Big Data Lab BCG X

ISBN: (纸本)9798350322279

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset. © 2023 IEEE.

关键词： Knowledge graph

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Deep reinforcement learning for maintenance planning of offshore vessel transfer 4

Deep reinforcement learning for maintenance planning of offs...

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4th International Conference on Renewable Energies Offshore, RENEW 2020

作者： Chatterjee, Joyjit Dethlefs, Nina Department of Computer Science and Technology Big Data Analytics Research Group University of Hull Hull United Kingdom

ISBN: (纸本)9780367681319

Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and cost-effective manner. The present optimization of offshore vessel transfer uses mathematical models rather than learning decisions from historical data. In this paper, we design a simulated environment for an offshore wind farm based on Supervisory Control & Acquisition (SCADA) data and alarm logs of historical faults in an operational turbine. Firstly, we utilise a state-of-art decision tree model to predict fault types using SCADA features, and provide explainable decisions. Next, we apply deep reinforcement learning to automatically learn maintenance priorities corresponding to different fault types for ensuring prioritized vessel transfers for critical conditions, and deciding on optimal vessel fleet size. This can lead to significant savings in maintenance costs for the offshore wind industry. © 2021 Taylor & Francis Group, London.

关键词： Offshore wind farms

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RainAI - Precipitation Nowcasting from Satellite data

arXiv

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arXiv 2023年

作者： Sarabia, Rafael Pablos Nyborg, Joachim Birk, Morten Assent, Ira Aarhus University Department of Computer Science Cordulus Denmark Cordulus Denmark Aarhus University Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark

This paper presents a solution to the Weather4Cast 2023 competition, where the goal is to forecast high-resolution precipitation with an 8-hour lead time using lower-resolution satellite radiance images. We propose a simple, yet effective method for spatiotemporal feature learning using a 2D U-Net model, that outperforms the official 3D U-Net baseline in both performance and efficiency. We place emphasis on refining the dataset, through importance sampling and dataset preparation, and show that such techniques have a significant impact on performance. We further study an alternative cross-entropy loss function that improves performance over the standard mean squared error loss, while also enabling models to produce probabilistic outputs. Additional techniques are explored regarding the generation of predictions at different lead times, specifically through Conditioning Lead Time. Lastly, to generate high-resolution forecasts, we evaluate standard and learned upsampling methods. The code and trained parameters are available at https://***/rafapablos/w4c23-rainai. © 2023, CC BY.

关键词： Importance sampling

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Trend Analysis and Intervention Effect Starting Point Detection of COVID-19 Epidemics Using Recalibrated Time Series Models—Worldwide,2020

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China CDC weekly 2021年第20期3卷 417-422页

作者： Shu Li Chen Chen Shuyin Cao Kejia Hu Hao Lei Xiaolin Xu Qinchuan Wang Changzheng Yuan Sicong Wang Sisi Wang Junlin Jia Yuanqing Ye Xifeng Wu Center of Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data Health Science School of Public HealthZhejiang University School of MedicineHangzhouZhejiangChina National Institute for Data Science in Health and Medicine HangzhouZhejiang UniversityZhejiangChina Department of Surgical Oncology Affiliated Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina.

Objective:This study aimed to identify a model for short-term coronavirus disease 2019(COVID-19)trend prediction and intervention ***:We compared the autoregressive integrated moving average(ARIMA)model and Holt exponential smoothing(Holt)model on predicting the number of cumulative COVID-19 cases in *** on the mean absolute percentage error(MAPE)value,the optimal model was selected and further tested using data from the united States,Italy and Republic of *** intervention effect starting time points and abnormal trend changes were detected by observing the pattern of differences between the predicted and real ***:The recalibrated ARIMA model with a 5-day prediction time span has the best model performance with MAPEs ranged between 2%and 5%.The intervention effects started to show on February 7 in the mainland of China,March 5 in Republic of Korea and April 27 in Italy,but have not been detected in the US as of May *** abnormal trends were detected in Korea and Italy,but the overall epidemic trends were stable since the effect starting ***:The recalibrated ARIMA model can detect the intervention effects starting points and abnormal trend changes;thus to provide valuable information support for epidemic trend analysis and intervention evaluation.

关键词： starting absolute smoothing

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