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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is availab.e at https://***/Darkbblue/generic-diffusion-feature.

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HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection 24

HD-KT: Advancing Robust Knowledge Tracing via Anomalous Lear...

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33rd ACM Web Conference, WWW 2024

作者： Ma, Haiping Yang, Yong Qin, Chuan Yu, Xiaoshan Yang, Shangshang Zhang, Xingyi Zhu, Hengshu Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Beijing China School of Artificial Intelligence Anhui University Anhui Hefei China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin Beijing China

ISBN: (纸本)9798400701719

Knowledge tracing (KT) is a crucial task in online learning, aimed at tracing and predicting each student's knowledge states throughout their learning process. Over the past decade, it has garnered widespread attention due to it provides the potential for more tailored and adaptive online learning experiences. Although most current KT methodologies emphasize optimizing network structures to enhance predictive accuracy for future student performance, they often neglect anomalous interactions in students' learning processes, which may arise from low data quality (i.e., inferior question quality) and abnormal student behaviors (i.e., guessing and mistakes). To this end, in this paper, we propose a novel framework, termed HD-KT, designed to enhance the robustness of existing KT methodologies with Hybrid learning interactions Denoising approach. Specifically, we introduce two detectors for anomalous learning interactions, namely knowledge state-guided anomaly detector and student profile-guided anomaly detector. In the first detection module, we design a sequential autoencoder to identify anomalous learning interactions by detecting atypical student knowledge states. In the second module, we incorporate an attention mechanism by modeling a student's long-term profile to capture irregular interactions. Extensive experiments on four real-world benchmark datasets have decisively shown our HD-KT markedly boosts the robustness of numerous prevailing KT models, consequently increasing the accuracy of future student performance predictions. Additionally, our case studies highlight the versatility of HD-KT in addressing diverse downstream tasks, such as exercise quality analysis and learning behavior-based student clustering. © 2024 ACM.

关键词： Students

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images

A Multi-input Deep Neural Network Framework for Non-invasive...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIElab.color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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High-Resolution ISAR Imaging Based on 2-D Fast Sparse Bayesian Learning

High-Resolution ISAR Imaging Based on 2-D Fast Sparse Bayesi...

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2021 CIE International Conference on Radar, Radar 2021

作者： Zhang, Yujie Bai, Xueru Li, Xiaoyong Xidian University National Laboratory of Radar Signal Processing Xi'an China Xidian University Key Lab. of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi'an China

ISBN: (纸本)9781665498142

We address the problem of high-resolution ISAR imaging in a Bayesian inference framework. Firstly, the probabilistic graphical model is constructed by imposing the sparsity-promoting Gamma-Gaussian prior to the distribution of scattering centers. In order to eliminate the issue of matrix inversion in conventional Sparse Bayesian Learning (SBL), a 2-D fast SBL is then proposed to improve the computational efficiency. Experimental results have demonstrated the effectiveness of the proposed method in sparse-aperture and low signal-to-noise ratio scenarios. © 2021 IEEE.

关键词： Inverse synthetic aperture radar (ISAR) sparse Bayesian learning (SBL) variational inference (VI)

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Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images

arXiv

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arXiv 2024年

作者： Shan, Lianlei Wang, Weiqiang Lv, Ke Luo, Bin The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China School of Engineering Science University of Chinese Academy of Sciences Beijing100049 China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei230601 China

Semantic segmentation requires pixel-level annotation, which is time-consuming. Active Learning (AL) is a promising method for reducing data annotation costs. Due to the gap between aerial and natural images, the previous AL methods are not ideal, mainly caused by unreasonable lab.ling units and the neglect of class imbalance. Previous lab.ling units are based on images or regions, which does not consider the characteristics of segmentation tasks and aerial images, i.e., the segmentation network often makes mistakes in the edge region, and the edge of aerial images is often interlaced and irregular. Therefore, an edge-guided lab.ling unit is proposed and supplemented as the new unit. On the other hand, the class imbalance is severe, manifested in two aspects: the aerial image is seriously imbalanced, and the AL strategy does not fully consider the class balance. Both seriously affect the performance of AL in aerial images. We comprehensively ensure class balance from all steps that may occur imbalance, including initial lab.led data, subsequent lab.led data, and pseudo-lab.ls. Through the two improvements, our method achieves more than 11.2% gains compared to state-of-the-art methods on three benchmark datasets, Deepglobe, Potsdam, and Vaihingen, and more than 18.6% gains compared to the baseline. Sufficient ablation studies show that every module is indispensable. Furthermore, we establish a fair and strong benchmark for future research on AL for aerial image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Text-Queried Target Sound Event Localization

Text-Queried Target Sound Event Localization

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European signal processing Conference (EUSIPCO)

作者： Jinzheng Zhao Xinyuan Qian Yong Xu Haohe Liu Yin Cao Davide Berghi Wenwu Wang Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK Department of Computer Science and Technology University of Science and Technology Beijing China Tencent AI Lab Bellevue WA USA Department of Intelligent Science Xi'an Jiaotong Liverpool University China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Sound event localization and detection (SELD) aims to determine the appearance of sound classes, together with their Direction of Arrival (DOA). However, current SELD systems can only predict the activities of specific classes, for example, 13 classes in DCASE challenges. In this paper, we propose text- queried target sound event localization (SEL), a new paradigm that allows the user to input the text to describe the sound event, and the SEL model can predict the location of the related sound event. The proposed task presents a more user-friendly way for human-computer interaction. We provide a benchmark study for the proposed task and perform experiments on datasets created by simulated room impulse response (RIR) and real RIR to validate the effectiveness of the proposed methods. We hope that our benchmark will inspire the interest and additional research for text-queried sound source localization.

关键词： Location awareness Human computer interaction Direction-of-arrival estimation Accuracy Europe Benchmark testing signal processing Predictive models

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Holographic projection three-dimensional display with enlarged viewing angle using decentered lens group

Holographic projection three-dimensional display with enlarg...

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Digital Holography and Three-Dimensional Imaging, DH 2021 - Part of OSA Imaging and Applied Optics Congress 2021

作者： Xie, Taomin Yang, Guanglin Xie, Haiyan Lab. of Signal and Information Processing Dept. of Electronics School of Electronic Engineering and Computer Science Peking University Beijing China China Science Patent and Trademark Agent Beijing100083 China

A method of beam deflection using decentered lens group based on angle compensation is proposed to enlarge the viewing angle of three-dimensional (3D) image display for holographic projection, based on time-division m... 详细信息

ISBN: (纸本)9781557528209

关键词： Three dimensional displays

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Research on Images Recognition based on Deep Convolution Neural Network

Research on Images Recognition based on Deep Convolution Neu...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaobo Chang Linlin Zhao Jinxiang Wang College of Engineering Yanbian University Yanji China Intelligent Information Processing Lab. Dept. of Computer Science & Technology Yanbian University Yanji China Computer Science and Technology College of Engineering Yanbian University Yanji China

Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method.

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A Novel Miniaturized Wideband High-Gain Palm-Leaf Vivaldi Array Antenna

A Novel Miniaturized Wideband High-Gain Palm-Leaf Vivaldi Ar...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Wang, Min Li, Xuan Xiang, Ceng Chen, Zhengchuan Chongqing University of Posts and Telecommunications Postdoctoral Res. Ctr. of Chongqing Key Lab. of Optoelectron. Info. Sensing and Transmiss. Technol. Chongqing400065 China Guilin University of Electronic Technology Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin541004 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China

ISBN: (纸本)9798350336740

A novel miniaturized wideband high-gain palm-leaf Vivaldi array antenna is presented in this work. Firstly, a novel Vivaldi antenna element is designed. To miniaturize the element, three groups of arc-shaped slots of varying lengths are etched on the radiator and a transition structure from fan-shaped microstrip line to slotted line is introduced in the feeding structure. Besides, two rectangular slots are introduced to the both sides of the slotted line to suppress the sidelobe level. Then, in order to facilitate the cascading of elements, a 1×16 microstrip power division feed network with equal amplitude and same phase is designed. Finally, a wideband high-gain 4 × 4 Vivaldi array antenna is designed and simulated. Simulated results indicate that the relative bandwidth of the presented Vivaldi array antenna reaches 42.2% (3.6 GHz-5.6 GHz), and the gain reaches 15.8 dBi at 4.5 GHz. © 2023 IEEE.

关键词： Microwave antennas

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