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Smart Fire Digital Twin Scene and Multi-Terminal Collaboration: A Systematic Design

Smart Fire Digital Twin Scene and Multi-Terminal Collaborati...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Minghong She Yongchi Zhao Key Laboratory of IOT Security Mianyang Teachers' College of Sichuan Province Mianyang Teachers' College Mianyang Sichuan China

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Digital Twin technology has emerged as a critical tool in various fields, integrating multidisciplinary data to construct virtual models that reflect physical entities throughout their life cycle. This study explores the application of digital twin technology in the context of intelligent fire protection systems, emphasizing the need for a systematic design approach to ensure compatibility, stability, and efficiency. Through a comprehensive examination of hardware-software integration, data processing, fault tolerance, and real-time monitoring, this study outlines a systematic approach to the design of intelligent fire protection systems. Furthermore, the implementation of a digital twin scene and multi-terminal collaboration is elucidated, highlighting the five-dimensional structural model and the role of virtual space. The proposed system is subjected to simulation and systematic verification, demonstrating its effectiveness in real-time data collection, modeling performance, and decision support. This research provides valuable insights into the systematic design and implementation of intelligent fire protection systems using digital twin technology.

关键词： Systematics Prevention and mitigation Collaboration data collection Real-time systems data models Stability analysis Digital twins Protection Monitoring

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PTT5-Paraphraser: Diversity and Meaning Fidelity in Automatic Portuguese Paraphrasing 15th

PTT5-Paraphraser: Diversity and Meaning Fidelity in Automati...

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15th international conference on the Computational processing of Portuguese (PROPOR)

作者： Amaral Orosco Pellicer, Lucas Francisco Pirozelli, Paulo Reali Costa, Anna Helena Inoue, Alexandre Univ Sao Paulo Escola Politecn Sao Paulo Brazil Univ Sao Paulo Inst Adv Studies Sao Paulo Brazil Univ Sao Paulo Oceanog Inst Sao Paulo Brazil

ISBN: (纸本)9783030983055;9783030983048

Paraphrasing is a fundamental technique for many text applications. Typically, this task is performed through models that perform lexical and translation operations, which tend to present a trade-off between meaning preservation and diversity. In this paper, we present a transformer-based approach called PTT5-Paraphraser, a PPT5 model fine-tuned on TaPaCo, a large corpus of paraphrases. PTT5-Paraphraser achieves good results according to a number of metrics, showing a good compromise between diversity and fidelity to the original meaning. Two human evaluations are made to explore the paraphrases produced by our model: the first analyzes their quality in terms of preserving the meaning and diversity of sentences, while the second compares automatically generated paraphrases with human-made ones. Finally, we perform a classification task, which shows that datasets augmented with paraphrases can substantially increase the performance of classifiers.

关键词： data augmentation PTT5 Paraphrases Text diversity

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Application of Improved Multi-class Active Learning Algorithm in data processing

Application of Improved Multi-class Active Learning Algorith...

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international conference on High Performance Big data and Intelligent Systems (HPBD and IS)

作者： Lin, Haiming Huang, Yao Yang, Dexiang Li, Ke Wei, Jun State Grid Chongqing South Power Supply Co Chongqing Peoples R China Southwest Petr Univ Chengdu Peoples R China

ISBN: (纸本)9781665413275

In the context of intelligent customer service in the electric power industry, in order to effectively classify customer data in a reasonable and correct manner, greatly improve the analysis effect of big data, and provide accurate customer service, a multi-category Active Learning algorithm based on multiple Clustering algorithms and multivariate Linear regression (ALCL) algorithm was proposed. Firstly, the category matrix corresponding to each algorithm was obtained through multiple heterogeneous clustering algorithms, the category matrix was labeled and pre-classified by querying common points. Secondly, the key examples used to train the weight coefficient model of the clustering algorithm were selected through the largest search strategy and the most confusing query strategy. Thirdly, the objective solving function was defined, the weight coefficients of each clustering algorithm were obtained by training key examples. Finally, samples with high confidence in the results were classified by the classification calculation combined with the weight coefficient. Six public customer data sets in Yuzhong District, Chongqing were used for experiments. The experimental results show that when the classification accuracy of ALCL is the highest, it is improved by 2.07%similar to 14.01% compared with traditional supervised learning algorithms and other active learning algorithms. The results of hypothesis and significance analysis prove that ALCL has better classification effect in customer data processing.

关键词： active learning multivariate linear regression sample selection strategy density clustering

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Development of an Application for data Structuring with the Possibility of Displaying 3D models 2nd

Development of an Application for Data Structuring with the ...

引用

2nd international conference on Information Systems and Design, ICID 2021

作者： Kurushkina, Alena Zein, Ali National Research University "Moscow Power Engineering Institute" Krasnokazarmennaya 17 Moscow111250 Russia

ISBN: (纸本)9783030954932

In the modern world in many industries, including energy, 3D modeling is the most visual means for demonstrating a part or assembly unit. Therefore, when creating a digital catalog for the presentation of new developments, along with the display of text information and images, it is important to provide the possibility of demonstrating 3D models. Two approaches are considered for displaying 3D objects: the first one - from the thread of the main application, we launch a 3D model as an external process;the second is the import of information about the 3D scene and its construction, then the configuration of the components required to display the scene is carried out. © 2022, Springer Nature Switzerland AG.

关键词： 3D modeling

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Transportation Mode Detection from GPS data: A data Science Benchmark study

Transportation Mode Detection from GPS data: A Data Science ...

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IEEE Intelligent Transportation Systems conference (ITSC)

作者： Muhammad, Akilu Rilwan Aguiar, Ana Mendes-Moreira, Joao Inst Telecomunicacoes Porto Portugal LIAAD INESC TEC Porto Portugal Univ Porto Porto Portugal

ISBN: (纸本)9781728191423

Understanding the distribution of people's transportation mode is a crucial facet of today's urban mobility for proper transportation planning. The penetration of smartphones combined with their sensing capability is an enabler for crowdsourcing large mobility data such as commuters' GPS records. In this paper, we leverage the GPS traces of commuters to infer five different transportation modes frequently used in urban areas including foot, bike, bus, car and metro. We compare three different approaches commonly reported in the literature for transportation mode detection from the family of machine learning algorithms (random forest -RF) and deep learning architectures (convolutional neural network -CNN and ensemble of autoencoders -EAE). By splitting the dataset into train-test by the period of data collection, as well as the conventional 80-20 split, we evaluate the impact of several data pre-processing decisions on overall classifiers' performance. Our results show RF and CNN performing better upon evaluation on classification metrics such as the f1 score and the area under the Receiver Operating Characteristics (ROC) curve.

关键词： Transportation mode detection GPS data urban mobility

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Securing Cloud Networks: A Hybrid Intrusion Detection Approach Using Fuzzy C-Means Clustering and Decision Tree Classification

Securing Cloud Networks: A Hybrid Intrusion Detection Approa...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Priyanka Verma R.K. Pateriya Savita Baghel Nakul Mehta Nisha Chaurasia Nitesh Bharot Dept. of ECE University of Limerick Ireland Dept. of CSE MANIT India Dept. of IT B R Ambedkar NIT Jalandhar India Insight SFI Research Centre for Data Analytics University of Galway Ireland

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Cloud computing has emerged as a pivotal technology for efficient data management and resource provisioning, leveraging internet connectivity to offer a broad range of services from basic storage and networking to advanced functionalities like natural language processing and artificial intelligence. However, this pervasive adoption also exposes cloud environments to significant cybersecurity threats. Denial of Service (DoS) attacks, data hijacking, information theft, and bandwidth consumption attacks pose serious risks, necessitating robust security functionalities. Intrusion Detection Systems (IDSs) help in mitigating these threats by tracking network behavior and system activities for suspicious behavior. Traditional IDS approaches often rely on machine learning algorithms such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RFs), and Naïve Bayes (NB), but these methods face challenges in effectively handling cloud-specific nuances. Therefore we present a hybrid IDS framework integrating the Fuzzy C-Means (FCM) clustering algorithm and decision tree classifier, called FCM-DT, to enhance detection accuracy while minimizing false alarms. FCM-DT is evaluated using the NSL-KDD dataset, indicating superior performance in comparison to traditional approaches. This study contributes to advancing intrusion detection capabil-ities in cloud environments, aiming to bolster security measures against evolving cyber threats.

关键词： Support vector machines Cloud computing Machine learning algorithms Intrusion detection Network security Nearest neighbor methods Natural language processing Probes Computer security Random forests

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Differentially Private data Synthesis: State of the Art and Challenges 22

Differentially Private Data Synthesis: State of the Art and ...

引用

17th ACM ASIA conference on Computer and Communications Security 2022 (ACM ASIACCS)

作者： Li, Ninghui Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450391405

Differential privacy has been accepted as the de facto notion for protecting privacy. Companies and government agencies use differential privacy for privacy-preserving data analysis. For example, the US census bureau applied differential privacy in the 2020 census. One important approach to use a private dataset is to generate a synthetic dataset that is similar to the private dataset in a way that satisfies differential privacy. This enables data analysts to directly apply existing algorithms for performing data analysis. Furthermore, as additional data analysis tasks performed on the published dataset are post-processing, they do not incur additional privacy cost. In recent years, US National Institutes of Standards and Technology ran two competitions on differentially private data synthesis, which drove the development of practically effective data synthesis *** this talk, I will discuss the current state of the art for private data synthesis, with a focus on those approaches that have performed well in the NIST competitions. One family of approaches uses probabilistic graphical models. My group's approach uses private marginals and a procedure that is similar to Iterative Proportional Fitting, which has been studied in many fields. We also discuss the remaining challenge and open questions.

关键词： differential privacy synthetic data microdata publishing

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms

Bitcoin price prediction based on financial data, technical ...

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Distributed Computing and High Performance Computing (DCHPC), international conference on

作者： Masoud Arjmand Saman Kazeminia Hedieh Sajedi Department of Computer Engineering Kish International Campus University of Tehran Kish Iran Department of Computer Vision and Data Science NHL Stenden University of Applied Science Leeuwarden Netherlands Department of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model’s efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results.

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Lightweight High-Resolution Remote Sensing Scene Classification via Adaptive Enhanced Knowledge Distillation 5

Lightweight High-Resolution Remote Sensing Scene Classificat...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Huang, Zhiming Weng, Qian Lin, Jiawen Jian, Cairen Fuzhou University College of Computer and Data Science Fuzhou China Xiamen University Tan Kah Kee College School of Information Science and Technology Zhangzhou China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Scene classification is a key step in intelligent information processing of high-resolution remote sensing, which aims to identify the land-use types of remote sensing image blocks. In recent years, the models based on deep convolution neural networks (CNN) have made outstanding achievements in the field of remote sensing, but these models are computationally expensive and time-consuming, while the lightweight shallow network models have few parameters, fast speed, but low accuracy. Both the deep CNN models and the lightweight shallow network models could not be directly applied to embedded devices. Therefore, we propose an Adaptive Enhanced Knowledge Distillation (AE-KD) to deeply mine the output and feature information from teacher model and transfer them to student model, so as to improve the performance of lightweight model. Firstly, aiming at the uneven degree of difference among remote sensing image categories, an adaptive temperature mechanism is proposed by improving the temperature mechanism in the traditional knowledge distillation, which promotes the student model to better learn the probability distribution knowledge from the output layer of the large and deep teacher model. And then, the spatial attention and inter-channel correlation of features are added as constraints in order to make the student model learn the multi-level knowledge from teacher model. The experimental results on UC Merced Land-Use and AID public datasets show that the proposed method reduces 91 % parameters of the teacher model and improves the prediction speed by 22 times, where it has only a small loss of classification accuracy, which is effective for the lightweight model. ablation study also further analyzes and discusses the performance improvement of the student model under different levels of knowledge distillation. © 2022 IEEE.

关键词： Image enhancement

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Boosting Machine Learning algorithms with Grid-Search for Transport IoT data Prediction 12th

Boosting Machine Learning Algorithms with Grid-Search for ...

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12th international conference on Computer Engineering and Networks, CENet 2022

作者： Zhang, Lu Sun, Bin Geng, Renkang Li, Shuai Shen, Tao School of EE University of Jinan Jinan250022 China Blekinge Institute of Technology Karlskrona37179 Sweden HIT Robot Group Co. Ltd Harbin150060 China

ISBN: (纸本)9789811969003

IoT (internet of things) data is a topic we have discussed a lot in recent years. Traffic data is an important part of IoT data. Traffic flow prediction not only facilitates people’s travel and saves our time, but also provides effective technical support for highway traffic control and scheduling. To achieve accurate traffic flow prediction, this study aims to build a machine learning-based traffic flow prediction model. We first screen out the features that have a greater impact on traffic flow. On this basis, this work establishes a traffic flow prediction model based on CatBoost. By comparing with other machine learning models, conclusions can be drawn: CatBoost model can accurately predict traffic flow;CatBoost outperforms traditional machine learning models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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