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An Evolutionary Multi-Objective Neural Architecture Search Approach to Advancing Cognitive Diagnosis in intelligent Education

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yang, Shangshang Ma, Haiping Bi, Ying Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi School of Artificial Intelligence Anhui University Hefei China Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electrical and Information Engineering Computational Intelligence Laboratory Zhengzhou University Zhengzhou China School of Computer Science and Technology Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei Anhui China School of Engineering Westlake University Hangzhou China

As a pivotal technique in intelligent education systems, cognitive diagnosis (CD) serves to reveal students’ knowledge proficiency for better tackling subsequent tasks. Unfortunately, due to pursuing high model interpretability, existing manually designed models for CD often hold simplistic architectures, which cannot cope with intricate data in modern education platforms. Furthermore, the bias of human design limits the emergence of novel and effective CD models. To develop interpretable and more effective models, thus this paper proposes an evolutionary multi-objective neural architecture search (NAS) approach for CD. Specifically, we first adopt a comprehensive search space for the NAS task of CD: all candidate models can be encompassed by a general model that deals with three distinct types of inputs. Then, an innovative model interpretability objective is devised to formulate the architecture search task as a bi-objective optimization problem (BOP). To solve the BOP, we employ a multi-objective genetic programming (MOGP) as the search strategy to explore the search space. To make the employed MOGP search well, all architectures are first encoded by trees for easy optimization, and we devise a genetic operation and a population initialization strategy to expedite its convergence. Finally, the proposed approach is actually a MOGP-based NAS approach for CD. Extensive experiments show that CD models searched by the proposed approach exhibit significantly better performance than existing models and hold as good interpretability as handcrafted models. Besides, the effectiveness of the proposed MOGP search strategy, the devised objective, and tailored strategies are validated. IEEE

关键词： Computer architecture

Stable Attribute Group Editing for Reliable Few-shot Image Generation

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arXiv 2023年

作者： Ding, Guanqi Han, Xinzhe Wang, Shuhui Wu, Shuzhe Jin, Xin Tu, Dandan Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China Huawei Cloud EI Innovation Lab China

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully benefit from this task, such as low-data detection and few-shot classification. To achieve this goal, the generated images should guarantee category retention for classification beyond the visual quality and diversity. In our preliminary work, we present an "editing-based" framework Attribute Group Editing (AGE) for reliable few-shot image generation, which largely improves the performance compared with existing methods that require re-training a GAN with limited data. Nevertheless, AGE's performance on downstream classification is not as satisfactory as expected. This paper investigates the class inconsistency problem and proposes Stable Attribute Group Editing (SAGE) for more stable class-relevant image generation. Different from AGE which directly edits from a one-shot image, SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary. Meanwhile, according to the projection weights on the category-relevant attribute dictionary, we can select category-irrelevant attributes from the similar seen categories. Consequently, SAGE injects the whole distribution of the novel class into StyleGAN's latent space, thus largely remains the category retention and stability of the generated images. Going one step further, we find that class inconsistency is a common problem in GAN-generated images for downstream classification. Even though the generated images look photo-realistic and requires no category-relevant editing, they are usually of limited help for downstream classification. We systematically discuss this issue from both the generative model and classification model perspectives, and propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components. Extensive experime

关键词： Generative adversarial networks

Comparative study of Hermitian and non-Hermitian topological dielectric photonic crystals

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arXiv 2021年

作者： Chen, Menglin L.N. Jiang, Li Jun Zhang, Shuang Zhao, Ran Lan, Zhihao Sha, Wei E.I. Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London United Kingdom Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

The effects of gain and loss on the band structures of a bulk topological dielectric photonic crystal (PC) with C6v symmetry and the PC-air-PC interface are studied based on first-principle calculation. To illustrate the importance of parity-time (PT) symmetry, three systems are considered, namely the PT-symmetric, PT-asymmetric, and lossy systems. We find that the system with gain and loss distributed in a PT symmetric manner exhibits a phase transition from a PT exact phase to a PT broken phase as the strength of the gain and loss increases, while for the PT-asymmetric and lossy systems, no such phase transition occurs. Furthermore, based on the Wilson loop calculation, the topology of the PT-symmetric system in the PT exact phase is demonstrated to keep unchanged as the Hermitian system. At last, different kinds of edge states in Hermitian systems under the influences of gain and loss are studied and we find that while the eigenfrequencies of nontrivial edge states become complex conjugate pairs, they keep real for the trivial defect states. Copyright © 2021, The Authors. All rights reserved.

关键词： Photonic crystals

Image projective invariants

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arXiv 2017年

作者： Li, Erbo Mo, Hanlin Xu, Dong Li, Hua EON Reality Inc IrvineCA92618 United States Key lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Ambry Genetics Aliso ViejoCA92656 United States

In this paper, we propose relative projective differential invariants (RPDIs) which are invariant to general projective transformations. By using RPDIs and the structural frame of integral invariant, projective weighted moment invariants (PIs) can be constructed very easily. It is first proved that a kind of projective invariants exists in terms of weighted integration of images, with relative differential invariants as the weight functions. Then, some simple instances of PIs are given. In order to ensure the stability and discriminability of PIs, we discuss how to calculate partial derivatives of discrete images more accurately. Since the number of pixels in discrete images before and after the geometric transformation may be different, we design the method to normalize the number of pixels. These ways enhance the performance of PIs. Finally, we carry out some experiments based on synthetic and real image datasets. We choose commonly used moment invariants for comparison. The results indicate that PIs have better performance than other moment invariants in image retrieval and classification. With PIs, one can compare the similarity between images under the projective transformation without knowing the parameters of the transformation, which provides a good tool to shape analysis in image processing, computer vision and pattern recognition. Copyright © 2017, The Authors. All rights reserved.

关键词： Object recognition

Manifold-Manifold Distance with Application to Face Recognition based on Image Set

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Manifold-Manifold Distance with Application to Face Recognit...

26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.9

作者： Ruiping Wang Shiguang Shan Xilin Chen Wen Gao Graduate School of Chinese Academy of Sciences Beijing China Digital Media Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy and Sciences Beijing China School of EE&CS Peking University Beijing China

In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD, we also propose a novel manifold learning approach, which expresses a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between pair of subspaces respectively from one of the involved manifolds. The proposed MMD method is evaluated on the task of Face Recognition based on Image Set (FRIS). In FRIS, each known subject is enrolled with a set of facial images and modeled as a gallery manifold, while a testing subject is modeled as a probe manifold, which is then matched against all the gallery manifolds by MMD. Identification is achieved by seeking the minimum MMD. Experimental results on two public face databases, Honda/UCSD and CMU MoBo, demonstrate that the proposed MMD method outperforms the competing methods.

关键词： Face recognition Probes Image converters Content addressable storage Testing Cameras Image storage Video sequences Image recognition Object recognition

Continuous Distributed processing of Software Defined Radar

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Continuous Distributed Processing of Software Defined Radar

IEEE International Conference on Radar

作者： Bing Li Qiang Qiu Shiqi Gong Yongjun Liu Yu Lei CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Institute of Computing Technology Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China National Laboratory of Radar Signal Processing Xidian University Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665468893

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing.

关键词： Protocols Scalability Streaming media Radar imaging Data processing Software Hardware

Unified-modal Salient Object Detection via Adaptive Prompt Learning

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arXiv 2023年

作者： Wang, Kunpeng Li, Chenglong Tu, Zhengzheng Liu, Zhengyi Luo, Bin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China The Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing completely different models for different tasks leads to labor and time consumption, as well as high computational and practical deployment costs. In this paper, we attempt to address both single-modal and multi-modal SOD in a unified framework called UniSOD, which fully exploits the overlapping prior knowledge between different tasks. Nevertheless, assigning appropriate strategies to modality variable inputs is challenging. To this end, UniSOD learns modality-aware prompts with task-specific hints through adaptive prompt learning, which are plugged into the proposed pre-trained baseline SOD model to handle corresponding tasks, while only requiring few learnable parameters compared to training the entire model. Each modality-aware prompt is generated from a switchable prompt generation block, which adaptively performs structural switching based on single-modal and multi-modal inputs without human intervention. Through end-to-end joint training, UniSOD achieves overall performance improvement on 14 benchmark datasets for RGB, RGB-D, and RGB-T SOD, which demonstrates that our method effectively and efficiently unifies single-modal and multi-modal SOD tasks. The code and results are available at https://***/Angknpng/UniSOD. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Hierarchical Extreme Learning Machine for Unsupervised Representation Learning

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Hierarchical Extreme Learning Machine for Unsupervised Repre...

International Joint Conference on Neural Networks

作者： Wentao Zhu Jun Miao Laiyun Qing Guang-Bin Huang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Electrical & Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798

ISBN: (纸本)9781479919611

Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.

关键词： Hierarchical Extreme Learning Machine Unsupervised Representation Learning deep learning models Learning machines Dataset representation method Recognition (Psychology) Receptive field Learning representations Religious Missions Hierarchical

Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring

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Making Sense of Audio Vibration for Liquid Height Estimation...

2019 IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Hongzhuo Liang Shuang Li Xiaojian Ma Norman Hendrich Timo Gerkmann Fuchun Sun Jianwei Zhang TAMS (Technical Aspects of Multimodal Systems) Universität Hamburg Center for Vision Cognition Learning and Autonomy University of California Los Angeles SP (Signal Processing) Universität Hamburg Beijing National Research Center for Information Science and Technology (BNRist) State Key Lab on Intelligent Technology and Systems Tsinghua University

In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle these drawbacks, we propose to make use of audio vibration sensing and design a deep neural network PouringNet to predict the liquid height from the audio fragment during the robotic pouring task. PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task. Each record represents a complete pouring procedure. We conduct several evaluations on PouringNet with our dataset and robotic hardware. The results demonstrate that our PouringNet generalizes well across different liquid containers, positions of the audio receiver, initial liquid heights and types of liquid, and facilitates a more robust and accurate audio-based perception for robotic pouring.

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