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Wind Vector Inversion of ADS-B data Based on Improved PSO-PF Algorithm

Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF...

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international conference on Guidance, Navigation and Control, ICGNC 2022

作者： Chen, Wantong Wu, Duo Ren, Shiyu Diao, Tianru Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300300 China School of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789811966125

At present, inversing wind vector from aircraft trajectory captured by automatic dependent surveillance-broadcast (ADS-B) is one of the main solutions to obtain wind field in aviation meteorology. However, some algorithms in this scheme still have some shortcomings, especially the poor performance when the aircraft turns at a small angle. In this paper, the geometric structure of ADS-B data is used to improve particle swarm optimized particle filter algorithm (PSO-PF) to inverse wind vectors in small area of single aircraft data and large area of multi-aircraft data respectively. The mean absolute error of wind estimation under different turning angles is evaluated. Experimental results show that the algorithm is feasible, and it still has a good inversion effect when the aircraft turns at a small angle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aircraft

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Confidential Inference in Decision Trees: FPGA Design and Implementation 30

Confidential Inference in Decision Trees: FPGA Design and Im...

引用

30th IFIP/IEEE international conference on Very Large Scale Integration (VLSI-SoC)

作者： Karn, Rupesh Raj Elfadel, Ibrahim M. Khalifa Univ Ctr Cyber Phys Syst C2PS Abu Dhabi U Arab Emirates

ISBN: (数字)9781665490054

ISBN: (纸本)9781665490054

In confidential computing, algorithms operate on encrypted inputs to produce encrypted outputs. Specifically, in confidential inference, Alice has the parameters of the machine-learning model but does not want to reveal them to Bob who has the data. Bob wants to use Alice's model for inference but does not want to reveal his data. Alice and Bob agree to use homomorphic encryption for running the inference engine in full confidence without revealing either model or data. They find that full homomorphic encryption is very time consuming and very challenging to accelerate on hardware. In this particular case, homomorphic encryption can be made computationally efficient and can even be readily accelerated on hardware. In this paper, we reveal how Alice and Bob run the inference engine in full confidence and show an FPGA implementation of the specialized homomorphic computing algorithm they used. We further evaluate the resources needed to implement the encrypted decision tree and compare them with those of a plain decision tree. Confidential inference tests are run on the encrypted FPGA design using the MNIST dataset.

关键词： Computational modeling Very large scale integration data models Inference algorithms Hardware Cryptography Decision trees

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WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion models 17th

WordStylist: Styled Verbatim Handwritten Text Generation wit...

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17th international conference on Document Analysis and Recognition (ICDAR)

作者： Nikolaidou, Konstantina Retsinas, George Christlein, Vincent Seuret, Mathias Sfikas, Giorgos Smith, Elisa Barney Mokayed, Hamam Liwicki, Marcus Lulea Univ Technol Lulea Sweden Natl Tech Univ Athens Athens Greece Friedrich Alexander Univ Erlangen Germany Univ West Attica Egaleo Greece Univ Ioannina Ioannina Greece

ISBN: (纸本)9783031416781;9783031416798

Text-to-Image synthesis is the task of generating an image according to a specific text description. Generative Adversarial Networks have been considered the standard method for image synthesis virtually since their introduction. Denoising Diffusion Probabilistic models are recently setting a new baseline, with remarkable results in Text-to-Image synthesis, among other fields. Aside its usefulness per se, it can also be particularly relevant as a tool for data augmentation to aid training models for other document image processing tasks. In this work, we present a latent diffusion-based method for styled text-to-text-content-image generation on word-level. Our proposed method is able to generate realistic word image samples from different writer styles, by using class index styles and text content prompts without the need of adversarial training, writer recognition, or text recognition. We gauge system performance with the Frechet Inception Distance, writer recognition accuracy, and writer retrieval. We show that the proposed model produces samples that are aesthetically pleasing, help boosting text recognition performance, and get similar writer retrieval score as real data. Code is available at: https://***/koninik/WordStylist.

关键词： Diffusion models Synthetic Image Generation Text Content Generation Handwriting Generation data Augmentation Handwriting Text Recognition

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Sentiment Classification Based on RoBERTa and data Augmentation 9

Sentiment Classification Based on RoBERTa and Data Augmentat...

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9th IEEE international conference on Cloud Computing and Intelligence Systems, CCIS 2023

作者： Wang, Xiaoyi Xue, Siyuan Liu, Jie Zhang, Jing Wang, Jincheng Zhou, Jianshe Capital Normal University School of Literature Beijing100048 China China Language Intelligence Research Center Beijing100048 China North China University of Technology School of Information Science Beijing100144 China Beijing Institute of Control and Electronics Technology Beijing100038 China

ISBN: (纸本)9798350304428

Sentiment analysis is one of the hot research topics in natural language processing, aiming to analyze the subjective sentiment expressed in text through inductive reasoning. Common deep learning-based sentiment classification models often require the integration of word embedding techniques. However, conventional approaches to generating word embeddings struggle to effectively capture the bidirectional semantic features of text. RoBERTa, with its unique attention mechanism and masked technique, enables the capture of more comprehensive features in the text. In this paper, we introduce the RoBERTa model to the task of Chinese comments sentiment classification and address the limited data issue by employing data augmentation methods to expand the dataset. Through a series of experiments conducted on three different datasets, we demonstrate the effectiveness of the sentiment classification model that combines RoBERTa with data augmentation methods. © 2023 IEEE.

关键词： Sentiment analysis

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A Novel Framework for data Trading Markets based on Blockchain-enabled Federated Learning 25

A Novel Framework for Data Trading Markets based on Blockcha...

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IEEE 25th international conference on Intelligent Transportation Systems (ITSC)

作者： Li, Cheng Yuan, Yong Wang, Fei-Yue Renmin Univ China Coll Math Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

In recent years, with the continued technical advances and increasing prosperity of digital economy ecosystems, the value of data has been widely recognized, leading to a huge potential demand of establishing data markets for sharing and trading private data resources. The existing efforts of centralized data trading systems, however, cannot completely solve the longstanding issues of data security and privacy leakage, which motivates our work. In this paper, we designed a novel framework for data trading marketplace based on blockchain and federated learning, aiming at combining their technical advantages and solving the key issue of secured and decentralized data trading with privacy protection. Specially, we proposed a six-layered conceptual model for data markets, and each layer is focused on the key elements and technologies in digital assets, technical architecture, scheduling algorithms, incentive mechanisms, trading models and application scenarios, respectively. We also discuss the potential applications of our framework in areas including intelligent transportation and Internet of vehicles. Our work is targeted to offer useful references and guidance for designing a decentralized, secured and trusted data trading market, and also promoting the development of digital economy and industries.

关键词： blockchain federated learning smart contract data markets intelligent transportation

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Detection of Distributed Denial of Service Attacks in the Cloud Using Machine Learning Method

Detection of Distributed Denial of Service Attacks in the Cl...

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international conference on Web Research (ICWR)

作者： Ghazaleh Farjood Ehsan Akhtarkavan Shiva Kamkar Department of Computer Engineering Khatam University Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Cyber-attack classification using deep learning and machine learning models is very suitable for classifying digital threats based on their characteristics and behaviors because these models examine system logs, network traffic, or other relevant data patterns to distinguish between standard activities and malicious actions. The main challenge in developing a machine learning-based intrusion detection system is the lack of large and labeled datasets. Using suitable features to train the algorithms is also critical. In this paper, we benefited from the Salp Swarm Optimization algorithm as a method to select important features. Then, a support vector machine is used to classify network traffic as either normal or abnormal. We compared the proposed method to state-of-the-art approaches on the BUETDDoS2020, Bot-IoT, and CICDDOS datasets. Results show that the suggested approach leads to a high true positive rate, a low false positive rate, and scores close to one in terms of precision, recall, and F-measure.

关键词： Support vector machines Deep learning Machine learning algorithms Intrusion detection Telecommunication traffic data models Classification algorithms Particle swarm optimization Standards Cyberattack

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Review on Technological Advancement and Textual data Management algorithms in NLP and CBIR Systems 3rd

Review on Technological Advancement and Textual Data Managem...

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3rd international conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Diviya, M. Karmel, A. SCOPE VIT Chennai Campus Tamilnadu Chennai India

ISBN: (纸本)9789811664472

Natural language processing (NLP) and content-based image retrieval (CBIR) systems functioned efficiently with textual input. This offers a broader framework for on-going work such as text processing applications that include spam identification, content visualization, and image retrieval for the text being queried. The preprocessing of the text handling algorithm includes part of speech tagging (POS), text encoding, and text extraction function using the word embedding encoding algorithms like Word2vec, boot strapping, hidden Markov model (HMM), and so on. The derived textual input or image attribute plays a crucial role. Following this processing, the future phase can include either of the following approach, such as content-based recovery, image recovery, or speech processing. The techniques, such as content-based retrieval, image retrieval or speech processing, each of these algorithms requires their own training period in continuation with the available technical orientations. Convolutional neural network (CNN) and recurrent neural network (RNN) play a prominent role in analyzing the input dataset when it comes to testing. Generative adversarial networks (GANs) have now launched a new era in managing NLP applications in its place. This survey provides a glimpse of various algorithms that respond to text data and return with prominent accuracy involving CBIR issues and applications for text-based image synthesis. The examination of the methodologies deployed paves way for researchers to analyze and process textual data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Anomaly Detection of Industrial data Based on Multivariate Multi Scale Analysis 1

引用

18th international conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Lu, Dan Li, Siao Zhao, Yingnan Han, Qilong College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

Anomaly detection stands as a crucial facet within the domain of data quality assurance. Notably, significant strides have been made within the realm of existing anomaly detection algorithms, encompassing notable techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and anomaly detection models founded upon Generative Adversarial Networks (GANs). However, a notable gap lies in the inadequate consideration of interdependencies and correlations inherent in multidimensional time-series data. This becomes particularly pronounced within the context of industrial evolution, where industrial data burgeons in complexity. To address this lacuna, a novel hybrid model has been introduced, synergizing the capabilities of GRU with structural learning methodologies and graph neural networks. The model capitalizes on graph structural learning to unearth dependencies linking data points across distinct spatial dimensions. Concurrently, GRU extracts temporal correlations embedded within data along a single dimension. Through the incorporation of graph attention networks, the model employs a dual-faceted correlation perspective for data prediction. Discrepancies between predicted values and ground truth are utilized to gauge errors. The amalgamation of predictive and scoring mechanisms enhances the model’s versatility. Empirical validation on two authentic sensor datasets unequivocally demonstrates the superior efficacy of this approach in anomaly detection compared to alternative methodologies. A notable augmentation is observed particularly in the recall rate, underscoring the method’s potency in identifying anomalies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Machine Learning Model for Predicting Non-Performing Agricultural Loans in an Imbalanced Lending Environment

Machine Learning Model for Predicting Non-Performing Agricul...

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

作者： Mohamed Ahmed Elnaggar Mostafa Abd El Azeem Fahima A. Maghraby Computer Science Dept. College of Computing & Information Technology Arab Academy for Science Technology and Maritime Transport (AASTMT) Cairo Egypt

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Granting a variety of agricultural loans to clients is necessary to support both markets and the agricultural sector. There are many types of risks that affect the banking sector. The most common and important one is the credit risk. The prediction of agricultural credit risk (ACR) has become more important for credit-granting institutions. ACR models seek to predict rather the clients will default on a loan (bad applicant) or non-default (good applicant). Predicting agricultural credit is thus an important part of the decision-making process to consider and to approve loans by the bank. In this paper, we propose a None Performing Agricultural Loans model (NPALSM). We focus only on the binary classifications whether the loan will be default or not by using machine learning classification techniques to build a proactive model to deal with credit approval. Proposed NPALSM consists of oversampling technique to balance data distribution. Empirical results show that the use of methods of resampling the amount of data consistently improves the performance of the original imbalanced data and outperforms other existing base methods using Egyptian credit data from an agricultural bank in Egypt. The meta classifier extra tree algorithm produces the highest accuracy with, 0.97% after sampling.

关键词： Support vector machines Accuracy Computational modeling Training data Predictive models Prediction algorithms data models Classification algorithms Partitioning algorithms Random forests

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One-Shot Object Detection in Heterogeneous Artwork datasets 11

One-Shot Object Detection in Heterogeneous Artwork Datasets

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11th international conference on Image processing Theory, Tools and Applications (IPTA)

作者： Madhu, Prathmesh Meyer, Anna Zinnen, Mathias Muhrenberg, Lara Suckow, Dirk Bendschus, Torsten Reinhardt, Corinna Bell, Peter Verstegen, Ute Kosti, Ronak Maier, Andreas Christlein, Vincent Friedrich Alexander Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Christian Archaeol Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Class Archaeol Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Inst Art Hist Erlangen Germany Philipps Univ Marburg German & Art Studies Marburg Germany

ISBN: (纸本)9781665469647

Christian archeologists face many challenges in understanding visual narration through artwork images. This understanding is essential to access underlying semantic information. Therefore, narrative elements (objects) need to be labeled, compared, and contextualized by experts, which takes an enormous amount of time and effort. Our work aims to reduce labeling costs by using one-shot object detection to generate a labeled database from unannotated images. Novel object categories can be defined broadly and annotated using visual examples of narrative elements without training exclusively for such objects. In this work, we propose two ways of using contextual information as data augmentation to improve the detection performance. Furthermore, we introduce a multi-relation detector to our framework, which extracts global, local, and patch-based relations of the image. Additionally, we evaluate the use of contrastive learning. We use data from Christian archeology (CHA) and art history - IconArt-v2 (IA). Our context encoding approach improves the typical fine-tuning approach in terms of mean average precision (mAP) by about 3.5% (4 %) at 0.25 intersection over union (IoU) for UnSeen categories, and 6% (1.5%) for Seen categories in CHA (IA). To the best of our knowledge, our work is the first to explore few shot object detection on heterogeneous artistic data by investigating evaluation methods and data augmentation strategies. We will release the code and models after acceptance of the work.

关键词： one-shot object detection digital humanities computational humanities data augmentation

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