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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Shao, Changbin Huo, Jing Qi, Lei Feng, Zhen-Hua Li, Wenbin Dong, Chuanqi Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer Science Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9780999241165

To address the challenges posed by unknown occlusions, we propose a Biased Feature Learning (BFL) framework for occlusion-invariant face recognition. We first construct an extended dataset using a multi-scale data augmentation method. For model training, we modify the label loss to adjust the impact of normal and occluded samples. Further, we propose a biased guidance strategy to manipulate the optimization of a network so that the feature embedding space is dominated by non-occluded faces. BFL not only enhances the robustness of a network to unknown occlusions but also maintains or even improves its performance for normal faces. Experimental results demonstrate its superiority as well as the generalization capability with different network architectures and loss functions. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Face recognition

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UI-FAME: A deductive forgetting system for creating views of ALC-TBoxes 33

UI-FAME: A deductive forgetting system for creating views of...

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33rd International Workshop on Description Logics, DL 2020

作者： Wu, Xuan Lu, Chang Zhao, Yizheng Feng, Hao Schmidt, Renate A. Dai, Yiwei National Key Laboratory for Novel Software Technology Nanjing Univeristy China School of Artificial Intelligence Nanjing University China School of Physics Nanjing University China North China University of Science and Technology China Department of Computer Science University of Manchester United Kingdom

UI-FAME is a Java-based forgetting system for creating views of ALC-TBoxes. The system implements the method developed in our previous work for forgetting concept and role names from ALC-TBoxes. In this paper, we introduce UI-FAME and compare it with LETHE, a peer forgetting system for ALC-ontologies and many its extensions, over the ALC-TBox fragment of 494 realistic ontologies taken from the Oxford Ontology Repository. The comparison considers success rates, speed, memory consumption as the principal indicators to assess the performance of the two systems. For validation purpose, we also explore the semantic relationships between the views computed by the two systems. The experimental results showed that UI-FAME attained in general better success rates and performance results than LETHE. We found that in 97.08% cases UI-FAME and LETHE computed logically equivalent views, in 2.06% cases LETHE’s solution entailed UI-FAME’s solution but not the other way round, in 0.79% cases UI-FAME’s solution entailed LETHE’s solution but not the other way round, and in 0.07% cases they had no mutual entailment relationship. This was somewhat undesirable and thus needs further investigation. Copyright © 2020 for this paper by its authors.

关键词： Ontology

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Multiple Code Hashing for Efficient Image Retrieval

arXiv

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arXiv 2020年

作者： Li, Ming-Wei Jiang, Qing-Yuan Li, Wu-Jun National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University China

Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamming distance

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper aimed to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer architectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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A Self-adaptive PSO-Based Dynamic Scheduling Method on Hierarchical Cloud Computing 9th

A Self-adaptive PSO-Based Dynamic Scheduling Method on Hiera...

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Meng, Shunmei Huang, Weijia Xu, Xiaolong Li, Qianmu Dou, Wanchun Liu, Bowen Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030485122

Edge computing has been envisioned as an emerging and prospective computing paradigm for its advantage of low latency, which uses local resources. However, the edge resources are usually limited and could not meet end-users’ diversified requirement. Cloud computing paradigm could provide scalable and centralized resources with high computational capabilities, but it has latency issues. Thus it is suggested to combine both computing paradigms together to improve the performance of mobile applications. In this paper, we propose a self-adaptive dynamic scheduling approach based on hierarchical heterogeneous clouds. Our scheduling mechanism considers not only schedule planning but also dynamic scheduling on heterogeneous clouds. Firstly, a self-adaptive scheduling mechanism based on a meta-heuristic optimization algorithm, PSO (Particle Swarm Optimization), is presented for schedule planning. Then a dynamic scheduling mechanism on dynamic partial workflow model is proposed for dynamic optimization during the execution. Finally, external experiments compared with other methods are conducted to demonstrate the effectiveness of our proposal. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Mobile edge computing

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Hybrid Collaborative Filtering-Based API Recommendation

Hybrid Collaborative Filtering-Based API Recommendation

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yongchao Wang Yu Zhou Taolue Chen Jingxuan Zhang Wenhua Yang Zhiqiu Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Lab. for Novel Software Technology Nanjing University China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology China University of London Birkbeck UK

ISBN: (纸本)9781665458146

Automatic API recommendations can liberate software developers from labor-intensive programming tasks. Collaborative filtering (CF) techniques, which have been proved to be superior to other classic techniques, are widely used in recommendation tasks such as music, book, and goods recommendations, but are rarely used in the recommendation of APIs. In this paper, we employ the hybrid of CF techniques to build an API recommendation system. More precisely, We treat the API recommendation task as an item recommendation problem, where method declarations are regarded as users, API calls are regarded as items. First, we use the memory-based CF technique to find the most similar projects, collect the most similar declarations, and take API calls used by the considered declarations together to generate a rating matrix. Next, we use the model-based CF technique to complete the missing values in the rating matrix, then a ranked list of APIs is generated based on the completed rating matrix and sent to the developers as a recommendation result. Experimental results show that compared with the state-of-the-art work, the proposed approach can achieve better performance in terms of a comprehensive set of metrics, such as Success Rate, Precision, Recall, MRR, and NDCG for the top-1, top-3 and top-5 recommended APIs.

关键词： Measurement Weight measurement Collaborative filtering software quality Programming Reliability engineering software reliability

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DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations 39

DearLLM: Enhancing Personalized Healthcare via Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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WheaCha: A Method for Explaining the Predictions of Models of Code

arXiv

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arXiv 2021年

作者： Wang, Yu Wang, Ke Wang, Linzhang State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210023 China Visa Research Palo AltoCA United States

Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use for the prediction. In this paper, we present a new method, called WheaCha, for explaining the predictions of code models. Although WheaCha employs the same mechanism of tracing model predictions back to the input features, it differs from all existing attribution methods in crucial ways. Specifically, WheaCha divides an input program into "wheat" (i.e., the defining features that are the reason for which models predict the label that they predict) and the rest "chaff " for any prediction of a learned code model. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show (1) HuoYan is efficient — taking on average under twenty seconds to compute the wheat for an input program in an end-to-end fashion (i.e., including model prediction time);(2) the wheat that all models use to predict input programs is made of simple syntactic or even lexical properties (i.e., identifier names);(3) Based on wheat, we present a novel approach to explaining the predictions of code models through the lens of training data. © 2021, CC BY.

关键词： Forecasting

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Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment

arXiv

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arXiv 2023年

作者： Liu, Kangcheng Liu, Yong-Jin Chen, Baoquan Pasadena United States The BNRist MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing China The National Key Lab of Genaral AI Peking University Beijing China The School of Artificial Intelligence Peking University Beijing China

Deep neural network models have achieved remarkable progress in 3D scene understanding while trained in the closed-set setting and with full labels. However, the major bottleneck for the current 3D recognition approach is that these models do not have the capacity to recognize any unseen novel classes beyond the training categories in diverse real-world applications. In the meantime, current state-of-the-art 3D scene understanding approaches primarily require a large number of high-quality labels to train neural networks, which merely perform well in a fully supervised manner. Therefore, we are in urgent need of a framework that can simultaneously be applicable to both 3D point cloud segmentation and detection, particularly in the circumstances where the labels are rather scarce. This work presents a generalized and straightforward framework for dealing with 3D scene understanding when the labeled scenes are quite limited. To extract knowledge for novel categories from the pre-trained vision-language models, we propose a hierarchical feature-aligned pre-training and knowledge distillation strategy to extract and distill meaningful information from large-scale vision-language models, which helps benefit the open-vocabulary scene understanding tasks. To leverage the boundary information, we propose a novel energy-based loss with boundary awareness benefiting from the region-level boundary predictions. To encourage latent instance discrimination and to guarantee efficiency, we propose the unsupervised region-level semantic contrastive learning scheme for point clouds, using confident predictions of the neural network to discriminate the intermediate feature embeddings at multiple stages. In the limited reconstruction case, our proposed approach, termed WS3D++, ranks 1st on the large-scale ScanNet benchmark on both the task of semantic segmentation and instance segmentation. Also, our proposed WS3D++ achieves state-of-the-art data-efficient learning performance on the o

关键词： Semantic Segmentation

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Static code analyzer recommendation via preference mining

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Expert Systems with Applications 2025年 285卷

作者： Ge, Xiuting Fang, Chunrong Li, Xuanye Shang, Ye Zhang, Mengyao Pan, Ya China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education China The State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Southwest University of Science and Technology China

Static Code Analyzers (SCAs) have played a critical role in software quality assurance. However, SCAs with various static analysis techniques suffer from different levels of false positives and false negatives, thereby yielding varying effectiveness in SCAs. To detect more defects in a given project, it is a possible way to use more available SCAs for scanning this project. Due to producing unacceptable costs and overpowering warnings, invoking all available SCAs for the defect detection of a given project is impractical in real scenarios. To address the above problem, we are the first to propose a practical SCA recommendation approach via preference mining, which aims to select the most effective SCA for a given project. Specifically, our approach performs the SCA effectiveness evaluation to obtain the correspondingly optimal SCAs on projects under test. Subsequently, our approach performs the SCA preference mining via the project characteristics, thereby analyzing the intrinsic relation between projects under test and the correspondingly optimal SCAs. Finally, our approach constructs the SCA recommendation model based on projects under test and the associated characteristics. We conduct the experimental evaluation on three popular SCAs as well as 213 open-source and large-scale projects. The results present that our constructed SCA recommendation model outperforms four typical baselines by 2 ∼ 11 times. © 2025

关键词： Redundancy

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