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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wansong Zhang Xin Dong Kuo Yang Lei Zhang Xinyu Wang Rouye Huang Runshun Zhang Xuezhong Zhou Department of Artificial Intelligence Institute of Medical Intelligence Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing Jiaotong University Beijing China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing China Guang’Anmen Hospital China Academy of Chinese Medical Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Herbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling. In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, alleviating the sparsity of data, and neural collaborative filtering is utilized to carry out prescription recommendations. Comprehensive experiments demonstrate the superiority of the proposed PresRecCD model over the SOTA model. This study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment.

关键词： Learning systems Accuracy Collaborative filtering Biological system modeling Predictive models Bioinformatics Medical diagnostic imaging Context modeling

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Context-Aware Multi-Level Question Embedding Fusion for Visual Question Answering

SSRN

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SSRN 2023年

作者： Li, Shengdong Gong, Chen Zhu, Yuqing Luo, Chuanwen Hong, Yi Lv, Xueqiang School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration 100083 China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing Information Science and Technology University 100101 China

Question model has been widely concerned as the cornerstone of constructing Visual Question Answering (VQA) models. Existing question models attempt to exploit word context to extract multi-level concepts for modeling multi-level questions. However, they still have many defects. For example, most question models utilize simple fusion methods to fuse shallow modules and extract parameter-unshared low-level concepts, leading to poor modeling of multi-level questions;although some question models use external knowledge transfer and deep bidirectional Transformer encoder in BERT for multi-level questions, their complexity is still high. To solve these issues, we propose a novel low-complex contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF). We formalize its concepts and theories, deduce its modeling process, optimization process and feature extraction process, analyze its low complexity and high expressiveness, and prove that it defines a new way to solve parameter non-sharing for extracting parameter-shared multi-level concepts and optimize the tradeoff between expressiveness and complexity in question models. Extensive experiments on VQAv2 and VQA-CPv2 validate that comparing with the state-of-the-art, our CMQEF outperforms it, reduces the language priors of VQA models, and has preferable interpretability. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Group Distributionally Robust Optimization with Flexible Sample Queries

arXiv

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arXiv 2025年

作者： Bai, Haomin Yu, Dingzhi Li, Shuai Luo, Haipeng Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China John Hopcroft Center Shanghai Jiao Tong University China Thomas Department of Computer Science University of Southern California United States

Group distributionally robust optimization (GDRO) aims to develop models that perform well across m distributions simultaneously. Existing GDRO algorithms can only process a fixed number of samples per iteration, either 1 or m, and therefore can not support scenarios where the sample size varies dynamically. To address this limitation, we investigate GDRO with flexible sample queries and cast it as a two-player game: one player solves an online convex optimization problem, while the other tackles a prediction with limited advice (PLA) problem. Within such a game, we propose a novel PLA algorithm, constructing appropriate loss estimators for cases where the sample size is either 1 or not, and updating the decision using follow-the-regularized-leader. Then, we establish the first high-probability regret bound for non-oblivious PLA. Building upon the above approach, we develop a GDRO algorithm that allows an arbitrary and varying sample size per round, achieving a high-probability optimization error bound of O (Formula Present) , where rt denotes the sample size at round t. This result demonstrates that the optimization error decreases as the number of samples increases and implies a consistent sample complexity of O(m log(m)/ϵ2) for any fixed sample size r ∈ [m], aligning with existing bounds for cases of r = 1 or m. We validate our approach on synthetic binary and real-world multi-class datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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LAVS: A LIGHTWEIGHT AUDIO-VISUAL SALIENCY PREDICTION MODEL

LAVS: A LIGHTWEIGHT AUDIO-VISUAL SALIENCY PREDICTION MODEL

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhu, Dandan Zhao, Defang Min, Xiongkuo Han, Tian Zhou, Qiangqiang Yu, Shaobo Chen, Yongqing Zhai, Guangtao Yang, Xiaokang MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China School of Software Engineering Tongji University China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Department of Computer Science Stevens Institute of Technology United States School of Software Jiangxi Normal University China Information Technology Services East China Normal University China College of Information and Communication Hainan University China

ISBN: (纸本)9781665438643

Audio information is essential for guiding human attention and visual perception, which has been verified by many comprehensive psychological studies. However, the audio modality has been rather neglected in modeling visual attention, most of the current visual attention models heavily depend on visual information. Additionally, current existing high-performing visual attention models rely on deeper convolution neural networks (CNNs), benefiting from their extraordinary feature learning ability but incurring high computational cost. To this end, we propose a novel lightweight audio-visual saliency (LAVS) model to efficiently address the problem of fixation prediction in videos. To the best of our knowledge, our proposed model constitutes the first attempt to exploit a lightweight network and combines the visual and audio cues to perform saliency estimation in videos. Specifically, our proposed model consists of four modules, which are spatial-temporal visual saliency estimation module, audio features extraction module, source sound localization module, and audio-visual saliency fusion module. Extensive experiments across datasets validate the effectiveness and real-time performance of the proposed LAVS model, which outperforms the other state-of-the-art methods. © 2021 IEEE

关键词： Behavioral research

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A Systematic Literature Review on Neural Code Translation

arXiv

引用

arXiv 2025年

作者： Chen, Xiang Xue, Jiacheng Xie, Xiaofei Liang, Caokai Ju, Xiaolin School of Artificial Intelligence and Computer Science Nantong University China and State Key Lab. for Novel Software Technology Nanjing University China School of Artificial Intelligence and Computer Science Nantong University China School of Computing and Information Systems Singapore Management University Singapore

Code translation aims to convert code from one programming language to another automatically. It is motivated by the need for multi-language software development and legacy system migration. In recent years, neural code translation has gained significant attention, driven by rapid advancements in deep learning and large language models. Researchers have proposed various techniques to improve neural code translation quality. However, to the best of our knowledge, no comprehensive systematic literature review has been conducted to summarize the key techniques and challenges in this field. To fill this research gap, we collected 57 primary studies covering the period 2020∼2025 on neural code translation. These studies are analyzed from seven key perspectives: task characteristics, data preprocessing, code modeling, model construction, post-processing, evaluation subjects, and evaluation metrics. Our analysis reveals current research trends, identifies unresolved challenges, and shows potential directions for future work. These findings can provide valuable insights for both researchers and practitioners in the field of neural code translation. Copyright © 2025, The Authors. All rights reserved.

关键词： software design

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Consistency and Diversity induced Human Motion Segmentation

arXiv

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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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Mining the Contributions Along the Lifecycles of Open-Source Projects 17th

Mining the Contributions Along the Lifecycles of Open-Source...

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16th national Conference on software and Applications, NASAC 2017 and 17th national Conference on software and Applications, NASAC 2018

作者： Zhou, Hang Xu, Lei Li, Yanhui State Key Laboratory for Novel Software Technology Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9789811503092

Recently the impact of developers’ behavior on the evolution of open-source software (OSS) has become a hot topic. When does the developer commit his/her code? Is there any regularity of the time distribution of commit along the lifecycles of open-source project? Will the change of the core member in a development team has an impact on software evolution process? We are quite interested in these above questions so we conducted an empirical study in this paper. We collect more than 50,000 commits from 6 open-source software in Github and design a formula to measure the contributor’s contribution value. We then take four major experiments to analyze some issues about inert intervals and the impact of the change of main contributors on software evolution. To make the result visible, we also design an automatic mining tool which can automatically mine the metadata from specified repository and make it graphically presented. Through the experiments we gained some interesting findings such as there is no inevitable statistical connection between a contributor’s inert interval and his contribution value, and main contributors’ change has a huge impact on the software evolution. We believe that these findings will have deeper research significance in the future. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Open source software

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

MIPI 2022 Challenge on Under-Display Camera Image Restorati...

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17th European Conference on computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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Hermes: Improving server utilization by colocation-aware runtime systems 21

Hermes: Improving server utilization by colocation-aware run...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Shenming Li, Kaiming Huang, Hao State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9781728120584

Improving server utilization is increasingly important to service providers. Latency-critical services have strict tail latency service-level objects and safe colocation of the latencycritical service with other workloads on the same machine is difficult. This would underutilize server resources. We present Hermes, a user-level resource management layer to address this dilemma. We implement two kinds of runtime systems in Hermes, one for latency-critical workloads (LC runtime) and one for besteffort workloads (BE runtime). LC runtime implements userlevel thread management and controls the dedicated computing resources occupied by latency-critical workload through a feedback-based controller. BE runtimes schedule threads of besteffort workloads to take advantage of simultaneous multithreading technology. Runtime systems are aware of their colocation and work in a cooperative approach to improve server utlization without violating the tail latency service-level objects of the latency-critical workload. Hermes is implemented entirely at user-level on Linux. We evaluate Hermes using memcached and several sythetic micro-benchmarks, the result shows that Hermes could achieve both safe colocation and improvement of core utilization. © 2019 IEEE.

关键词： Quality of service

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Provably robust metric learning 20

Provably robust metric learning

引用

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Lu Wang Xuanqing Liu Jinfeng Yi Yuan Jiang Cho-Jui Hsieh National Key Laboratory for Novel Software Technology Nanjing University Nanjing China and *** Beijing China Department of Computer Science University of California Los Angeles CA *** Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781713829546

Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied. In this paper, we show that existing metric learning algorithms, which focus on boosting the clean accuracy, can result in metrics that are less robust than the Euclidean distance. To overcome this problem, we propose a novel metric learning algorithm to find a Mahalanobis distance that is robust against adversarial perturbations, and the robustness of the resulting model is certifiable. Experimental results show that the proposed metric learning algorithm improves both certified robust errors and empirical robust errors (errors under adversarial attacks). Furthermore, unlike neural network defenses which usually encounter a trade-off between clean and robust errors, our method does not sacrifice clean errors compared with previous metric learning methods.

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