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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Runhan Wang Ruiwei Zhao Weijia Fu Xiaobo Zhang Yuejie Zhang Rui Feng School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai Academy for Engineering and Technology Fudan University Shanghai National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Multi-contrast high quality high-resolution (HR) Magnetic Resonance (MR) images enrich available information for diagnosis and analysis. Deep convolutional neural network methods have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. Methods taking HR images as references (Ref) have made progress to enhance the effect of MR images SR. However, existing multi-contrast MR image SR approaches are based on contrasting-expanding backbones, which lose high frequency information of Ref image during downsampling. They also failed to transfer textures of Ref image into target domain. In this paper, we propose Edge Mask Transformer UNet (EMFU) for accelerating MR images SR. We propose Edge Mask Transformer (EMF) to generate global details and texture representation of target domain. Dual domain fusion module in UNet aggregates semantic information of the representation and LR image of target domain. Specifically, we extract and encode edge masks to guide the attention in EMF by re-distributing the embedding tensors, so that the network allocates more attention to image edge area. We also design a dual domain fusion module with self-attention and cross-attention to deeply fuse semantic information of multiple protocols for MRI. Extensive experiments show the effectiveness of our proposed EMFU, which surpasses state-of-the-art methods on benchmarks quantitatively and visually. Codes will be released to the community.

关键词： Tensors Protocols Fuses Image edge detection Magnetic resonance imaging Superresolution Semantics

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PEVTS: Privacy-Preserving Electric Vehicles Test-Bedding Scheme

PEVTS: Privacy-Preserving Electric Vehicles Test-Bedding Sch...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xu Yang Joseph K. Liu Wei Wu Man Ho Au Willy Susilo Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University China and State Key Laboratory of Cryptology Beijing China Faculty of Information Technology Monash University Australia Department of Computing Hong Kong Polytechnic University Hong Kong School of Computer Science and Software Engineering University of Wollongong Australia

Electric Vehicle (EV) infrastructure is relatively new in many countries. Due to the recency of an EV infrastructure, it is important to carry out a series of testing programs. Furthermore, authenticity for collection of data is necessary for testing programs in order to provide accurate results. At the same time, user privacy should not cease since tracing one's daily logistic movements or behaviour from the EV testing programs means breaching one's privacy. In this paper, we propose a novel solution PEVTS for enabling both data authenticity and user privacy concurrently. Our proposed system provides great flexibility to the authority to choose any arbitrary set of authenticated users for testing in every time period. At the same time, it provides anonymity for all participating users. Yet it can trace any vehicle within a time period for statistical purpose. We give a detailed description of our system. We also implement the prototype of our system to show its practicality.

关键词： Testing Privacy Cryptography Electric vehicles Data privacy Authentication

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Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images

arXiv

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arXiv 2022年

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University China

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the segmentation of DR lesions task, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the image quality assessment task, we create an ensemble of Inception-V3, SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our model. In the DR grading task, we build a Vision Transformer (ViT) and find that the ViT model pre-trained on color fundus images serves as a useful substrate for OCTA images. Our proposed methods ranked 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. The source code will be made available at https://***/FDU-VTS/DRAC. © 2022, CC BY.

关键词： Angiography

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Coherence-Based Distributed Document Representation Learning for Scientific Documents

arXiv

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arXiv 2022年

作者： Tan, Shicheng Zhao, Shu Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province 230601 China

Distributed document representation is one of the basic problems in natural language processing. Currently distributed document representation methods mainly consider the context information of words or sentences. These methods do not take into account the coherence of the document as a whole, e.g., a relation between the paper title and abstract, headline and description, or adjacent bodies in the document. The coherence shows whether a document is meaningful, both logically and syntactically, especially in scientific documents (papers or patents, etc.). In this paper, we propose a coupled text pair embedding (CTPE) model to learn the representation of scientific documents, which maintains the coherence of the document with coupled text pairs formed by segmenting the document. First, we divide the document into two parts (e.g., title and abstract, etc) which construct a coupled text pair. Then, we adopt negative sampling to construct uncoupled text pairs whose two parts are from different documents. Finally, we train the model to judge whether the text pair is coupled or uncoupled and use the obtained embedding of coupled text pairs as the embedding of documents. We perform experiments on three datasets for one information retrieval task and two recommendation tasks. The experimental results verify the effectiveness of the proposed CTPE model. Copyright © 2022, The Authors. All rights reserved.

关键词： information retrieval

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Achieving Privacy-Preserving and Scalable Graph Neural Network Prediction in Cloud Environments

Achieving Privacy-Preserving and Scalable Graph Neural Netwo...

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IEEE International Conference on Communications (ICC)

作者： Yanli Yuan Dian Lei Chuan Zhang Ximeng Liu Zehui Xiong Liehuang Zhu School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Guangdong Provincial Key Laboratory Novel Security Intelligence Technologies Guangdong China College of Mathematics and Computer Science Fuzhou University Fuzhou China School of Information Systems Singapore Management University Singapore Information System and Technology Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Graph neural networks (GNNs) have been widely applied in various graph analysis tasks. To provide more convenient and faster predictive services, many enterprises are choosing to deploy GNNs in cloud environments. However, given the increasing privacy concerns about GNNs models and graph data, as well as the need to quickly generate embeddings for new nodes in real-world applications, a critical issue in this emerging paradigm is to ensure the security and scalability of GNN predictions. In this paper, we propose a privacy-preserving and scalable GNN prediction scheme, named PS-GNN, to address the privacy issues in cloud environments. Specifically, PS-GNN utilizes a customized array structure to store graph data and employs secret sharing to preserve the confidentiality of both the GNN model and graph data. Besides, the scalability of PS-GNN is achieved by aggregating feature information from local node neighborhoods in parallel. Through a detailed analysis, we demonstrate the security of PS-GNN. Extensive experiments on real-world datasets demonstrate that PS-GNN outperforms existing schemes in terms of computational and communication overhead, and reaches state-of-the-art performance on large graphs.

关键词： Privacy Data privacy Scalability Computational modeling Predictive models Graph neural networks Data models

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VIDA: HOMEOSTATIC VISUAL DOMAIN ADAPTER FOR CONTINUAL TEST TIME ADAPTATION

arXiv

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arXiv 2023年

作者： Liu, Jiaming Yang, Senqiao Jia, Peidong Zhang, Renrui Lu, Ming Guo, Yandong Xue, Wei Zhang, Shanghang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China AI2Robotics China The Chinese University of Hong Kong Hong Kong Hong Kong University of Science and Technology Hong Kong

Since real-world machine systems are running in non-stationary environments, Continual Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually changing target domains. Recently, existing methods mainly focus on model-based adaptation, which aims to leverage a self-training manner to extract the target domain knowledge. However, pseudo labels can be noisy and the updated model parameters are unreliable under dynamic data distributions, leading to error accumulation and catastrophic forgetting in the continual adaptation process. To tackle these challenges and maintain the model plasticity, we design a Visual Domain Adapter (ViDA) for CTTA, explicitly handling both domain-specific and domain-shared knowledge. Specifically, we first comprehensively explore the different domain representations of the adapters with trainable high-rank or low-rank embedding spaces. Then we inject ViDAs into the pre-trained model, which leverages high-rank and low-rank features to adapt the current domain distribution and maintain the continual domain-shared knowledge, respectively. To exploit the low-rank and high-rank ViDAs more effectively, we further propose a Homeostatic Knowledge Allotment (HKA) strategy, which adaptively combines different knowledge from each ViDA. Extensive experiments conducted on four widely used benchmarks demonstrate that our proposed method achieves state-of-the-art performance in both classification and segmentation CTTA tasks. Note that, our method can be regarded as a novel transfer paradigm for large-scale models, delivering promising results in adaptation to continually changing distributions. Project page: https://***/view/iclr2024-vida/home Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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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

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Hypergraph-enhanced dual semi-supervised graph classification 24

Hypergraph-enhanced dual semi-supervised graph classificatio...

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Proceedings of the 41st International Conference on Machine Learning

作者： Wei Ju Zhengyang Mao Siyu Yi Yifang Qin Yiyang Gu Zhiping Xiao Yifan Wang Xiao Luo Ming Zhang School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods.

关键词：

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Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network

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Advanced Photonics Nexus 2024年第2期3卷 8-17页

作者： Yi Xu Fu Li Jianqiang Gu Zhiwei Bi Bing Cao Quanlong Yang Jiaguang Han Qinghua Hu Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringMinistry of EducationKey Laboratory of Optoelectronic Information TechnologyTianjinChina Tianjin University College of Intelligence and ComputingTianjinChina Central South University School of Physics and ElectronicsHunan Key Laboratory of Nanophotonics and DevicesChangshaChina Guilin University of Electronic Technology School of Optoelectronic EngineeringGuangxi Key Laboratory of Optoelectronic Information ProcessingGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Recently,deep learning has been used to establish the nonlinear and nonintuitive mapping between physical structures and electromagnetic responses of meta-atoms for higher computational ***,to obtain sufficiently accurate predictions,the conventional deep-learning-based method consumes excessive time to collect the data set,thus hindering its wide application in this interdisciplinary *** introduce a spectral transfer-learning-based metasurface design method to achieve excellent performance on a small data set with only 1000 samples in the target waveband by utilizing open-source data from another spectral *** demonstrate three transfer strategies and experimentally quantify their performance,among which the“frozen-none”robustly improves the prediction accuracy by∼26%compared to direct *** propose to use a complex-valued deep neural network during the training process to further improve the spectral predicting precision by∼30%compared to its real-valued *** design several typical teraherz metadevices by employing a hybrid inverse model consolidating this trained target network and a global optimization *** simulated results successfully validate the capability of our *** work provides a universal methodology for efficient and accurate metasurface design in arbitrary wavebands,which will pave the way toward the automated and mass production of metasurfaces.

关键词： transfer learning complex-valued deep neural network metasurface inverse design conditioned adaptive particle swarm optimization terahertz

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