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共 5726 条记录，以下是211-220 订阅

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An Empirical Study on the Membership Inference Attack against Tabular data Synthesis models 22

An Empirical Study on the Membership Inference Attack agains...

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31st ACM international conference on Information and Knowledge Management (CIKM)

作者： Hyeong, Jihyeon Kim, Jayoung Park, Noseong Jajodia, Sushil Northwestern Univ Evanston IL 60208 USA Yonsei Univ Seoul South Korea George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781450392365

Tabular data typically contains private and important information;thus, precautions must be taken before they are shared with others. Although several methods (e.g., differential privacy and k-anonymity) have been proposed to prevent information leakage, in recent years, tabular data synthesis models have become popular because they can well trade-off between data utility and privacy. However, recent research has shown that generative models for image data are susceptible to the membership inference attack, which can determine whether a given record was used to train a victim synthesis model. In this paper, we investigate the membership inference attack in the context of tabular data synthesis. We conduct experiments on 4 state-of-the-art tabular data synthesis models under two attack scenarios (i.e., one black-box and one white-box attack), and find that the membership inference attack can seriously jeopardize these models. We next conduct experiments to evaluate how well two popular differentially-private deep learning training algorithms, DP-SGD and DP-GAN, can protect the models against the attack. Our key finding is that both algorithms can largely alleviate this threat by sacrificing the generation quality.

关键词： tabular data synthesis membership inference attack differential privacy

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A PROBABILITY-BASED ALL-ZERO BLOCK EARLY TERMINATION ALGORITHM FOR QSHVC 30

A PROBABILITY-BASED ALL-ZERO BLOCK EARLY TERMINATION ALGORIT...

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30th IEEE international conference on Image processing (ICIP)

作者： Wang, Dayong Lu, Xin Dufaux, Frederic Wang, Qianmin Li, Weisheng Hang, Bo Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Paris France Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Hubei Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728198354

To seamlessly adapt to time-varying network bandwidths, Quality Scalable High-Efficiency Video Coding (QSHVC) is developed. However, its coding process is overwhelmingly complex, and this seriously limits its wide applications in realtime environments. Therefore, it is of great significance to study fast coding algorithms for QSHVC. In this paper, we propose a novel probability-based All-Zero Block (AZB) early termination algorithm for QSHVC. We observe that the generated residual coefficients follow the Laplace distribution if a CU is accurately predicted. Based on this observation, we derive the sum of squared differences-based AZB decision condition. Second, the probability of each coding mode and coding depth being chosen as the best ones are combined with AZBs to derive the probability-based early termination condition. The experimental results show that the proposed algorithm can improve the average coding speed by 74.95% with a 0.26% increase in BDBR.

关键词： SHVC probability AII-Zero Block

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YTCNet: A Real-time Algorithm for Parcel Damage Detection with Rich Features and Attention 27

YTCNet: A Real-time Algorithm for Parcel Damage Detection wi...

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27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Chen, Zhi Dui, Cuifeng Zhou, Yuyu Guan, Haoxuan Huang, Xiujie Li, Zhefu Liu, Changjiang Zhuang, Xiaotian Zhu, Xingyu Guan, Quanlong Jinan Univ Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Cetc Potevio Sci & Technol Co Ltd Guangzhou Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Guangzhou Peoples R China Guangdong Testing Inst Prod Qual Supervis Guangzhou Guangdong Peoples R China Beijing JD Zhenshi Informat Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In this study, we tackle the challenge of parcel damage detection and present YTCNet to improve both accuracy and real-time performance. We enhance the Yolov5 algorithm by integrating C3TR modules into the architecture to capture more comprehensive feature information. Furthermore, we introduce the CBAM attention module to focus on critical areas in parcel images, enhancing the model's feature extraction capabilities. By effectively combining C3TR and CBAM modules, we successfully developed an efficient parcel damage detection algorithm. Our experimental results demonstrate the superior performance of YTCNet in accuracy and real-time processing compared to various object detection models across multiple parcel damage scenarios. Our research significantly contributes to the logistics supply chain industry by aligning with IoT development. It enables practical applications to more effectively identify parcel damage, thereby enhancing customer satisfaction.

关键词： Object Detection YOLO Transformer IoT

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Research on anthrax toxin analysis based on big data mining technology

Research on anthrax toxin analysis based on big data mining ...

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2020 international conference on data processing algorithms and models, icdpam 2020

作者： Chen, Jingfei Li, Hexi Jiang, Huaning Liang, Ting The Institute of NBC Defense Chinese PLA Army Beijing102205 China

Anthrax is still endemic in some areas around the world, and a large number of people and livestock are at risk of contracting anthrax. As the pathogenic substance of Bacillus anthracis, anthrax toxin plays an essential pathogenic role. Researchers have conducted detailed studies and reports on their genes, structures, and biological activities. However, few articles analyse the field from the perspective of bibliometrics. In the present study, we used bibliometric software such as Citespace to examine the literature data and aimed to provide a reference for researchers in this field. In terms of journals, P NATL ACAD SCI USA, INFECT IMMUN and J BIOL CHEM rank among the top 10 as to the number of published articles and the number of co-citations, and they have an important influence in the field of anthrax toxins. As for researchers, the quantity and quality of the articles published by the three researchers, LEPPLA SH, MOAYERI M, and COLLIER RJ, have advantages. Their research on anthrax toxin is worthy of attention. On the frontiers of research, future research may have various directions, such as the in-depth explanation of the mechanism of anthrax toxin-induced diseases, how to use anthrax toxin to enhance anti-tumour methods to guide the development of new vaccines based on the immunological mechanism of anthrax toxin, and use new materials and technologies to detect different aspects of development of anthrax toxins, etc. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Wireless Channel Prediction using Artificial Intelligence with constrained data Sets 24

Wireless Channel Prediction using Artificial Intelligence wi...

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24th international Microwave and Radar conference (MIKON)

作者： Javanmardi, Gowhar Samano-Robles, Ramiro Polytech Inst Porto CISTER ISEP Porto Portugal

ISBN: (纸本)9788395602030

The work in this paper deals with the study of artificial intelligence (AI) tools for purposes of vehicular wireless channel prediction. The objective is to test the ability of different types of AI algorithms under different types of implementation constraints that will be typical of vehicular applications. We focus particularly in scenarios where the channel changes relatively fast and therefore the relevant measurements or long-term statistical models are scarce. This means that the training of our models can be potentially inaccurate or incomplete and we need to investigate which AI algorithm behaves better in this challenging condition. In future work we aim to investigate also computation complexity constraints, real-time deadlines, and outdated/distorted or noisy data set samples. We also aim to correlate the main properties of the well-known Jakes' channel model with the effectiveness of the type of prediction and the parameters of the different algorithms being tested. The objective of channel prediction in vehicular networks is to reduce allocation and transmission errors, thereby reducing latency and improving message transmission reliability, which is crucial for future applications such as autonomous vehicles. Results show that even in situations with incomplete data sets, AI can provide good approximate predictions on the channel outcome. LSTM in multi-step and CNN in single-step between implemented algorithms have better performance and lower error in prediction.

关键词： channel prediction artificial intelligence Jake's model

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Evaluating data Augmentation for Medication Identification in Clinical Notes

Evaluating Data Augmentation for Medication Identification i...

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2023 international conference Recent Advances in Natural Language processing: Large Language models for Natural Language processing, RANLP 2023

作者： Koontz, Jordan Oronoz, Maite Pérez, Alicia Ixa UPV/EHU Basque Country Donostia Spain HiTZ - Ixa UPV/EHU Basque Country Donostia Spain

ISBN: (纸本)9789544520922

We evaluate the effectiveness of using data augmentation to improve the generalizability of a Named Entity Recognition model for the task of medication identification in clinical notes. We compare disparate data augmentation methods, namely mention-replacement and a generative model, for creating synthetic training examples. Through experiments on the n2c2 2022 Track 1 Contextualized Medication Event Extraction data set, we show that data augmentation with supplemental examples created with GPT-3 can boost the performance of a transformer-based model for small training sets. © 2023 Incoma Ltd. All rights reserved.

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Performance of Machine Learning models on Crime data 4th

Performance of Machine Learning Models on Crime Data

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Bhardwaj, Geetika Bawa, R.K. Punjabi University NH64 Next to Urban Estate Phase II Punjab Patiala147002 India

ISBN: (纸本)9789819914784

In India, the crime against women is increasing at a startling rate. The National Commission for Women reported that there was a 46% rise in crimes against women in the beginning of the year 2021 as compared to 2020. This pressing issue needs to be addressed and in order to do that crime data needs to be collected so as to understand and analyze the crime rate and make predictions by using the existing information so as to take some proactive measures to reduce the rate of crime against women in future. Machine learning models become an effective mechanism to predict the rate of crime in India for a variety of crime categories by doing analysis of patterns followed in a particular crime or a category, criminal-centric areas, and the comparative study of all those categories of crime. A dataset has been created by collecting data from Kaggle and NCRB, the government website of crime data collection. Various machine learning algorithms have been applied on the data after preprocessing, and performance of all these models is evaluated and compared using various metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter 13

PoliBERTweet: A Pre-trained Language Model for Analyzing Pol...

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13th international conference on Language Resources and Evaluation (LREC)

作者： Kawintiranon, Kornraphop Singh, Lisa Georgetown Univ Washington Dept Comp Sci Washington DC USA

ISBN: (纸本)9791095546726

Transformer-based models have become the state-of-the-art for numerous natural language processing (NLP) tasks, especially for noisy data sets, including social media posts. For example, BERTweet, pre-trained RoBERTa on a large amount of Twitter data, has achieved state-of-the-art results on several Twitter NLP tasks. We argue that it is not only important to have general pre-trained models for a social media platform, but also domain-specific ones that better capture domain-specific language context. Domain-specific resources are not only important for NLP tasks associated with a specific domain, but they are also useful for understanding language differences across domains. One domain that receives a large amount of attention is politics, more specifically political elections. Towards that end, we release PoliBERTweet, a pre-trained language model trained from BERTweet on over 83M US 2020 election-related English tweets. While the construction of the resource is fairly straightforward, we believe that it can be used for many important downstream tasks involving language, including political misinformation analysis and election public opinion analysis. To show the value of this resource, we evaluate PoliBERTweet on different NLP tasks. The results show that our model outperforms general-purpose language models in domain-specific contexts, highlighting the value of domain-specific models for more detailed linguistic analysis. We also extend other existing language models with a sample of these data and show their value for presidential candidate stance detection, a context-specific task. We release PoliBERTweet and these other models to the community to advance interdisciplinary research related to Election 2020.

关键词： Transformer BERT RoBERTa BERTweet Language Model Politics Election 2020 Twitter

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AUTH-PERSONS: A dataSET FOR DETECTING HUMANS IN CROWDS FROM AERIAL VIEWS 29

AUTH-PERSONS: A DATASET FOR DETECTING HUMANS IN CROWDS FROM ...

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IEEE international conference on Image processing (ICIP)

作者： Symeonidis, Charalampos Mademlis, Ioannis Pitas, Ioannis Nikolaidis, Nikos Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recent advances in artificial intelligence, control and sensing technologies have facilitated the development of autonomous Unmanned Aerial Vehicles (UAVs). Detecting humans from video input captured on-the-fly from UAVs is a critical task for ensuring flight safety, mostly handled with lightweight Deep Neural Networks (DNNs). However the detection of individual people in the case of dense crowds and/or distribution shifts (i.e., significant visual differences between the training and the test sets) is still very challenging. This paper presents AUTH-Persons, a new, annotated, publicly available video dataset, that consists of both real and synthetic footage, suitable for training and evaluating aerial-view person detection algorithms. The synthetic data were collected from 8 visually distinct photorealistic outdoor environments and they mostly contain scenes with crowded areas, where heavy occlusions and high person densities pose challenges to common detectors. This dataset is employed to evaluate the generalization performance of various state-of-the-art detection frameworks, by testing them on environments that are visually distinct from those they have been trained on. Finally, given that Non-Maximum Suppression (NMS) methods at the end of person detection pipelines typically suffer in crowded scenes, the performance of various NMS algorithms is also compared in AUTH-Persons.

关键词： person detection Unmanned Aerial Vehicles synthetic data generation Non-Maximum Suppression

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Transparent Machine Learning algorithms for Explainable AI on Motor fMRI data 10th

Transparent Machine Learning Algorithms for Explainable AI o...

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10th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Marques dos Santos, Jose Diogo Machado, David Fortunato, Manuel Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Abel Salazar Biomed Sci Inst R Jorge de Viterbo Ferreira P-4050313 Porto Portugal

ISBN: (纸本)9783031349591;9783031349607

With the emergence of explainable artificial intelligence (xAI), two main approaches for tackling model explainability have been put forward. Firstly, the use of inherently simple and transparent models that with easily understandable inner-workings (interpretability) and can readily provide useful knowledge about the model's decision making process (explainability). The second approach is the development of interpretation and explanation algorithms that may shed light upon black-box models. This is particularly interesting to apply on fMRI data as either approach can provide pertinent information about the brain's underlying processes. This study aims to explore the capability of transparent machine learning algorithms to correctly classify motor fMRI data, if more complex models inherently lead to a better prediction of the motor stimulus, and the capability of the Integrated Gradients method to explain a fully connected artificial neural network (FCANN) used to model motor fMRI data. The transparent machine learning models tested are Linear Regression, Logistic Regression, Naive Bayes, K-Neighbors, Support Vector Machine, and Decision Tree, while the Integrated Gradients method is tested on a FCANN with 3 hidden layers. It is concluded that the transparent models may accurately classify the motor fMRI data, with accuracies ranging from 66.75% to 85.0%. The best transparent model, multinomial logistic regression, outperformed the most complex model, FCANN. Lastly, it is possible to extract pertinent information about the underlying brain processes via the Integrated Gradients method applied to the FCANN by analyzing the spatial expression of the most relevant Independent Components for the FCANN's decisions.

关键词： Human Connectome Project HCP fMRI machine learning logistic regression linear regression decision tree naive bayes k-neighbors SVM artificial neural network

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