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Cross-Domain Few-Shot Learning with Label Propagation for Hyperspectral Image Classification

Cross-Domain Few-Shot Learning with Label Propagation for Hy...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Haochen Zhang Wenxiu Diao Lingwu Meng Zhongjie Qian Zhixin Zhao Jingzhou Chen School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Huzhou Municipal Bureau of Natural Resources and Planning Huzhou China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

In practical applications, the acquisition cost of hyperspectral images is high, and acquiring substantial labeled data is difficult, which results in a restricted number of labeled samples using for training. Few-shot learning (FSL) serves as an efficient method to tackle the classification challenges of hyperspectral images (HSI) when labeled samples are scarce. It acquires transferable knowledge from abundant labeled data, that can be transferred to new domains. However, the distributional differences between domains with abundant labeled data and those with limited labeled data make the generalizable knowledge learned from the former less applicable to the classification tasks of the latter. To address this issue, this paper proposes a cross-domain few-shot hyperspectral image classification method based on label propagation. Firstly, the method employs a feature extraction module to capture complex information within samples, then applies a graph structure to the label propagation algorithm. The graph structure effectively captures local relationships between samples and avoids over-reliance on distant samples, thereby reducing the risk of overfitting. Furthermore, it incorporates a Tri-training structure to learn from different perspectives and complement each other, ultimately achieving classification of cross-domain few-shot data. Experimental results on hyperspectral datasets with Chikusei as the source domain, and Pavia University and Salinas as the target domains demonstrate that our method is effective and has good performance.

关键词： Training Adaptation models Network architecture Feature extraction Data models Few shot learning informatics Hyperspectral imaging Image classification Overfitting

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Adversarial Attacks For Multi Target Image Translation Networks

Adversarial Attacks For Multi Target Image Translation Netwo...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Zigang Fang Yu Yang Jialin Lin Rui Zhan School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Although image translation algorithms such as StarGAN, STGAN, StarGAN-v2, etc. based on the generative adversarial networks, bring enormous convenience to people's work and life, they also have greater security risks in terms of privacy and security ethics. We tackle this problem of generating adversarial attacks against image translation models, which disrupt the resulting output image. The existing adversarial technology can only generate adversarial samples for a single image translation model, but in actual applications, it is unknown to the user which model an attacker will use. The paper draws on the idea of multi-task learning and proposes a multi-objective adversarial sample generation algorithm to generate images that can simultaneously defend against tampering with multiple state- of-the-art image translation models. The simulation result shows that compared with the original images, adversarial images have no heavy visual distortion with PSNR of 27.17dB and SSIM of 0.70 which means that normal usages of images, such as showing something interesting, amazing, funny, cool or etc., are not affected. The adversarial samples generated by our proposed algorithm reduce the risk of malicious tampering and abuse of images. For the mainstream image translation models StarGAN, STGAN and StarGAN-v2, the attack success rates of the adversarial images generated by the algorithm are 100%, 66.67%, and 73.33%, respectively. It can be seen that the algorithm in this paper has obvious effects on protecting the reasonable and safe use of multimedia resources on social platforms.

关键词： Visualization Privacy Simulation Distortion Security informatics Strain

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Memory Optimization Techniques for Multithreaded Operating System on Wireless Sensor Nodes

Memory Optimization Techniques for Multithreaded Operating S...

引用

2014 ieee international conference on progress in informatics and computing

作者： Xing Liu Kun Mean Hou Christophe de Vaulx Hailun Zhu Xin Liu LIMOS Laboratory UMR 6158 CNRS Blaise Pascal University Electronic Information School Wuhan University

Multithreaded operating system(OS) is essential tobe used for many wireless sensor network(WSN) applications. However, the RAM consumption of the multithreaded OS is high. Thus, itis infeasible toapply the multithreaded OS on many high RAM constrained wireless sensor nodes(WNs). Toaddress this challenge, several memory optimization strategies are investigated in this paper. On one hand, the subfunctiongranularity thread switch and hybrid OS scheduling model are proposed todecrease the RAM consumption of the thread stacks. On the other hand, different dynamic memory allocation mechanisms are presented toreduce the heap memory size. With the implementation of these optimization techniques, the total RAM consumption of the multithreaded WSN OSs can be brought down greatly. In result, the multithreaded WSN OSs become suitable tobe used even on the high RAM constrained WNs.

关键词： memory optimization dynamic memory allocation hybrid operating system wireless sensor network

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A two-column Convolutional Neural Network for Facial Point Detection

A two-column Convolutional Neural Network for Facial Point D...

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2016 ieee international conference on progress in informatics and computing

作者： Wei Wang Xiaobo Lu Jiaji Song Cong Chen School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper proposes a two-column convolutional neural network algorithm for face point detection, which proves better performance than single-column. Because of the deep level structure, global texture features are extracted at higher layers of the network, and the results of keypoint location remains high accuracy. In the first column, we take the channel component of original image as input to train the neural network respectively and calculate the average location results. In the second one, we use Sobel operator to extract first-order derivative feature of original image and regard it as the input to train another convolutional network. The location results of two columns above are fused with appropriate proportion to ensure better results. Experimental result on LFW dataset shows the performance of the proposed parallel structure better than single convolutional network, and it stays robust due to occlusions, large pose variations and extreme lighting.

关键词： facial point detection convolutional neural network Sobel information fusion

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News Hotspots Detection and Tracking Based on LDA Topic Model

News Hotspots Detection and Tracking Based on LDA Topic Mode...

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2016 ieee international conference on progress in informatics and computing

作者： Xiao Hu Capital Normal University

With the rapid spread of Internet and the mobile web, the number of news pages is increasing quickly as well as the content of news becomes highly dynamic. It’s difficult for normal users to obtain specific information contained in a mass of news streams. So it’s of great research significance to study how to analyze massive news, detect and track news hotspots automatically. This research proposes to apply LDA（Latent Dirichlet Allocation） model to the application of topic detection and tracking. The news articles collected by crawlers are modeled by the LDA model in a form of document-topic-word distribution. We propose a method to compute the heat of topics based on the distribution and to detect the news hotspots. In addition, we track the evolution of the topic trends in different time-slices. Jenson-Shannon distance is used to measure the similarity between topics to identify topic inheritance and topic mutation. We conducted experiments on a dataset consisting of 3462 news texts from news portals. The result revealed that the proposed model has a good effect both in detecting hotspots and discovering meaningful topical evolution trends.

关键词： LDA topic model news reports hotspots detection traking

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Macro-level Accident Fatality Prediction Using a Combined Model Based on ARIMA and Multivariable Linear Regression

Macro-level Accident Fatality Prediction Using a Combined Mo...

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2016 ieee international conference on progress in informatics and computing

作者： Zeyang Chen Zhen Gao Rongjie Yu Min Wang Ping Sun School of Software Engineering Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University

Prediction is one of the key functions of traffic safety management system and fatality prediction is the priority. In previous studies, time series theory was widely used in traffic fatality predication. Researchers have applied Auto-Regressive Integrated Moving Average(ARIMA) model to the traffic safety data. To introduce relevant features, which influences the model's reliability and accuracy, Multivariate Linear Regression(MLR) model is combined with ARIMA model. And the number of variable message signs and carriageway area are picked out as features in this study. The combined model not only inherits the accurate trend prediction of ARIMA model, but also calculates comprehensive relevant features. Traffic crash fatalities and fatalities per 10,000 vehicles in Shanghai are selected as targets and two models are trained respectively for the two targets above—ARIMA(1,1,0) and ARIMA(1,2,0). Then MLR model is introduced to take the two selected features into consideration. Experiments show that both combined models perform better and more reasonably than simple ARIMA model where the error rate of prediction is under 1%.

关键词： road accidents time series auto-regressive integrated moving average model multivariable linear regression model

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Accelerating Embarrassingly Parallel Algorithm on Intel MIC

Accelerating Embarrassingly Parallel Algorithm on Intel MIC

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2014 ieee international conference on progress in informatics and computing

作者： Qinglin Wang Jie Liu XiantuoTang Feng Wang Guitao Fu Zuocheng Xing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

The Embarrassingly Parallel(EP) algorithm which is typical of many Monte Carloapplications provides an estimate of the upper achievable limits for double precision performance of parallel supercomputers. Recently, Intel released Many Integrated Core(MIC) architecture as a many-core co-processor. MIC often offers more than 50 cores each of which can run four hardware threads as well as 512-bit vector instructions. In this paper,we describe how the EP algorithm is accelerated effectively on the platforms containing MIC using the offload execution model. The result shows that the efficientimplementation of EP algorithm on MIC can take full advantage of MIC's computational resources and achieves a speedup of 3.06 compared with that on Intel Xeon E5-2670 CPU. Based on the EP algorithm on MIC and an effective task distribution model, the implementation of EP algorithm on a CPU-MIC heterogeneous platform achieves the performance of up to2134.86 Mop/s and 4.04 times speedup compared with that on Intel Xeon E5-2670 CPU.

关键词： NPB embarrassingly parallel algorithm heterogeneous platform many integrated core architecture

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Bounded Asymmetric Gaussian Mixture Model with Spatial Constraint for Image Segmentation

Bounded Asymmetric Gaussian Mixture Model with Spatial Const...

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2016 ieee international conference on progress in informatics and computing

作者： Juanfen Sun Zexuan Ji School of Computer Science and Engineering Nanjing University of Science and Technology

Accurate image segmentation is an important issue in image processing, where spatially constrained Gaussian mixture models play an important part and have been proven to be effective. However, most GMM based methods suffer from limited noise robustness, over-smoothness for segmentations, and lack of flexibility to fit data. In order to address these issues, in this paper,we propose a rough set bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation. First, we partition the target image into three rough regions with two adaptively computed thresholds. Second, a new bounded indicator function is constructed to determine the bounded support regions of the observed data. Third, two novel prior factors are proposed by incorporating the spatial information among neighborhood pixels, which are constructed based on the prior and posterior probabilities of the within-and between-clusters, and considers the spatial direction. We compare our algorithm to state-of-theart segmentation approaches in both synthetic and real images to demonstrate the superior performance of the proposed algorithm.

关键词： Image segmentation Asymmetric Gaussian mixture model Markov random field Rough Set theory EM algorithm

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The Assessment of the Points Reward Mechanism in Online Course Forum

The Assessment of the Points Reward Mechanism in Online Cour...

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2016 ieee international conference on progress in informatics and computing

作者： Yunping Feng Di Chen Haopeng Chen Chengcheng Wan Puzhao Xi Shanghai Jiaotong University

Massive open online course is now a popular choice for online learning. Different variants of MOOC platforms have different targets of students. For example, MOOC platforms such as Coursera and ed X aim at students all over the world, while some local MOOC providers cooperate with local universities and credits earned on these platforms are acknowledged by those universities. Students usually learn by watching videos and doing online quizzes. Course forum is also an important component of MOOC. It is a good place for opinions sharing and discussion. Both students and TAs participate in course ***, students' participation in course forums sometimes is not totally voluntary. In some courses, students' participation in course forums is rewarded with extra points. This paper focuses on the points reward mechanism in online course forums. We analyze students and TAs' posting behavior and the contents of their posts. We find that the points reward mechanism can motivate students to write more posts, but students are also likely to write "spam posts", only to make themselves look like an active participator to earn the extra points. And the points reward mechanism has limited impacts on students' academic performance. It also leads to a high percentage of "spam posts".This paper talks about the problems of the points reward mechanism and presents some suggestions for future MOOC forums.

关键词： MOOC massive open online course participation forum

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Assertion Detection in Clinical Natural Language Processing Using Large Language Models

Assertion Detection in Clinical Natural Language Processing ...

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ieee international conference on Healthcare informatics (ICHI)

作者： Yuelyu Ji Zeshui Yu Yanshan Wang Dept. of Computing and Information University of Pittsburgh Pittsburgh US Dept. of Pharmaceutical Sciences University of Pittsburgh Pittsburgh US Dept. of Health Information Management University of Pittsburgh Pittsburgh US

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

In this study, we aim to address the task of assertion detection when extracting medical concepts from clinical notes, a key process in clinical natural language processing (NLP). Assertion detection in clinical NLP usually involves identifying assertion types for medical concepts in the clinical text, namely certainty (whether the medical concept is positive, negated, possible, or hypothetical), temporality (whether the medical concept is for present or the past history), and experiencer (whether the medical concept is described for the patient or a family member). These assertion types are essential for healthcare professionals to quickly and clearly understand the context of medical conditions from unstructured clinical texts, directly influencing the quality and outcomes of patient care. Although widely used, traditional methods, particularly rule-based NLP systems and machine learning or deep learning models, demand intensive manual efforts to create patterns and tend to overlook less common assertion types, leading to an incomplete understanding of the context. To address this challenge, our research introduces a novel methodology that utilizes Large Language Models (LLMs) pre-trained on a vast array of medical data for assertion detection. We enhanced the current method with advanced reasoning techniques, including Tree of Thought (ToT), Chain of Thought (CoT), and Self-Consistency (SC), and refine it further with Low-Rank Adaptation (LoRA) fine-tuning. We first evaluated the model on the i2b2 2010 assertion dataset. Our method achieved a micro-averaged F-1 of 0.89, with 0.11 improvements over the previous works. To further assess the generalizability of our approach, we extended our evaluation to a local dataset that focused on sleep concept extraction. Our approach achieved an F-1 of 0.74, which is 0.31 higher than the previous method. The results show that using LLMs is a viable option for assertion detection in clinical NLP and can potentially integrate w

关键词： Deep learning Medical conditions Large language models Manuals Natural language processing Cognition History

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