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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tao, Guihua Li, Haojiang Lu, Dandan Ling, Ziqin Liu, Lizhi Cai, Hongmin South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University Cancer Center Sun Yat-sen University State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou China

ISBN: (纸本)9781665468190

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients' survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features' estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods. © 2022 IEEE.

关键词： Deep neural networks

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FaultFuzz: A Coverage Guided Fault Injection Tool for Distributed Systems

FaultFuzz: A Coverage Guided Fault Injection Tool for Distri...

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International Conference on Software Engineering - Companion, ICSE, Companion

作者： Wenhan Feng Qiugen Pei Yu Gao Dong Wang Wensheng Dou Jun Wei Zheheng Liang Zhenyue Long State Key Lab of Computer Science at ISCAS University of CAS Beijing China Joint Laboratory on Cyberspace Security China Southern Power Grid GuangDong Power Grid China Nanjing Institute of Software Technology University of CAS Nanjing China

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Distributed systems are expected to correctly recover from various faults, e.g., node crash / reboot and network disconnection / reconnection. However, faults that occur under special timing can trigger fault recovery bugs that are rooted in incorrect fault recovery protocols and implementations. Existing random and brute-force fault injection approaches are not effective in revealing fault recovery bugs due to the combinatorial explosion of multiple faults in distributed systems. In this paper, we propose FaultFuzz, a coverage guided fault in-jection approach that can systematically and effectively test fault recovery behaviors in distributed systems. Based on runtime feed-backs collected from distributed system testing, e.g., code coverage and I/O information, FaultFuzz generates possible combinations of faults, and preferentially selects the combinations that are more likely to trigger new fault recovery behaviors and reveal new fault recovery bugs. We have applied FaultFuzz on three widely-used distributed systems, i.e., Zookeeper, HDFS and HBase and found 5 bugs in them. A video demonstration of FaultFuzz is available at https://***/SMw1ZF1vyXw.

关键词： System testing Runtime Protocols Codes computer bugs Explosions Timing

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Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS

arXiv

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arXiv 2023年

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Li, Jun Liu, Huaping Harbin150001 China The State Key Laboratory of Automotive Safety and Energy The School of Vehicle and Mobility Tsinghua University Beijing100084 China The School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Intelligent Technology and the Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments are conducted using the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug- and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer

arXiv

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arXiv 2024年

作者： Guan, ZhiHao Yang, Jia-Qi Yang, Yang Zhu, Hengshu Li, Wenjie Xiong, Hui the Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Career Science Lab BOSS Zhipin Beijing100028 China the Hong Kong Polytechnic University Hong Kong the Artificial Intelligence Thrust The Hong Kong University of Science and Technology Guangzhou China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user’s skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： User profile

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuqi Yang Lijuan Zhang Qing Lan Qiujie Ma Huan Huang Jiaqing Miao Shiyu Mou Cheng Luo Dezhong Yao Ying Tan The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu China The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse.

关键词： Autism Visualization Sparse approximation Mental disorders Noise Predictive models Multilayer perceptrons Brain modeling Spatiotemporal phenomena Sparse matrices

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Mixed Attention Densely Residual Network for Single Image Super-Resolution

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computer Systems science & Engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

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End-to-End Streaming Customizable keyword Spotting Based on Text-Adaptive Neural Search 1

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Yang, Baochen Guo, Jiaqi Li, Haoyu Xi, Yu Zhuo, Qing Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China AISpeech Ltd. Suzhou China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

Streaming keyword spotting (KWS) is an important technique for voice assistant wake-up. While KWS with a preset fixed keyword has been well studied, test-time customizable keyword spotting in streaming mode remains a great challenge due to the lack of pre-collected keyword-specific training data and the requirement of streaming detection output. In this paper, we propose a novel end-to-end text-adaptive neural search architecture with a multi-label trigger mechanism to allow any pre-trained ASR acoustic model to be effectively used for fast streaming customizable keyword spotting. Evaluation results on various datasets show that our approach significantly outperforms both traditional post-processing baseline and the neural search baseline, meanwhile achieving a 44x search speedup compared to the traditional post-processing method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Customizable keyword keyword spotting Streaming Text-adaptive search

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DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration

DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral M...

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International Conference on computer Vision (ICCV)

作者： Yuchun Miao Lefei Zhang Liangpei Zhang Dacheng Tao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Sydney AI Centre School of Computer Science The University of Sydney

Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing diffusion-based methods are trained on a large amount of training data and perform very well in-distribution, but can be quite susceptible to distribution shift. This is especially inappropriate for data-starved hyperspectral image (HSI) restoration. To tackle this problem, this work puts forth a self-supervised diffusion model for HSI restoration, namely Denoising Diffusion Spatio-Spectral Model (DDS2M), which works by inferring the parameters of the proposed Variational Spatio-Spectral Module (VS2M) during the reverse diffusion process, solely using the degraded HSI without any extra training data. In VS2M, a variational inference-based loss function is customized to enable the untrained spatial and spectral networks to learn the posterior distribution, which serves as the transitions of the sampling chain to help reverse the diffusion process. Benefiting from its self-supervised nature and the diffusion process, DDS2M enjoys stronger generalization ability to various HSIs compared to existing diffusion-based methods and superior robustness to noise compared to existing HSI restoration methods. Extensive experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate DDS2M’s superiority over the existing task-specific state-of-the-arts. Code is available at: https://***/miaoyuchun/DDS2M.

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Disentangled Noisy Correspondence Learning

arXiv

引用

arXiv 2024年

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China SGIT AI Lab State Grid Shaanxi Electric Power Company Limited State Grid Corporation of China Shaanxi China The School of Information Science and Technology University of Science and Technology China United Arab Emirates The Baidu Inc China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of MII and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal input for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation

arXiv

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arXiv 2024年

作者： Li, Hourun Wang, Yifan Xiao, Zhiping Yang, Jia Zhou, Changling Zhang, Ming Ju, Wei State Key Laboratory for Multimedia Information Processing School of Computer Science PKU-Anker LLM Lab Peking University Beijing China Computer Center Peking University Beijing China School of Information Technology & Management University of International Business and Economics Beijing China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States College of Computer Science Sichuan University Chengdu China

Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains. Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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