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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Li Yirui Wu Hexuan Hu Hu Lu Yong Lai Shaohua Wan Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University College of Computer and Information Hohai University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Computer Science and Communication Engineering Jiangsu University Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like ”black box” without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods.

关键词： Deep learning Training Visualization Image analysis Semantics Feature extraction Risk management

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Analysis of the spatial and temporal evolution of PM2.5 pollution in China during COVID -19 epidemic

Analysis of the spatial and temporal evolution of PM2.5 poll...

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2020 International Conference on Big data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2020

作者： Li, ChuanLin Huang, Fenghua Digital China Research Institute Key Laboratory of Spatial Data Mining Information Sharing Ministry of Education Fuzhou University Fuzhou China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University Fuzhou China

ISBN: (纸本)9781728164991

Environmental health has recently become more and more important in the discussion of international health issues. Due to the impact of haze pollution on human health, ecological environment and climate change, it has become the first problem to be solved in the process of protecting environmental health. PM2.5 is the main cause of haze pollution. Based on the observation data of PM2.5 concentration in 368 cities in China from February to march in 2020, this paper uses the statistical model of spatial data to reveal the spatial and temporal pattern distribution of PM2.5 in China during the period of severe outbreak and spread (February 3 to March 29) of 8 weeks and 56 days. The analysis results show that 1) during the epidemic period, the average concentration of PM2.5 in China ranged from 32.876 to 42.173, which generally showed a trend of rapid decrease and then rebound and continued decline after the outbreak of the epidemic. 2) in terms of space, the concentration of PM2.5 is the highest in the central region, higher in the north than in the south, and higher in the eastern bohai rim region and northern China than in the western qinghai-tibet plateau and Himalayan mountain belt. The high-value areas were mainly distributed in the north China plain, the beijing-tianjin-hebei region, urumqi and hotan regions of xinjiang. 3) the hot spot analysis shows that the hot spots of pollution distribution are the beijing-tianjin-hebei region and the north China plain. The cold spots are the pearl river delta and the yunnan-guizhou region. 4) spatial autocorrelation analysis shows that the average weekly concentration of PM2.5 shows a strong local spatial autocorrelation. © 2020 IEEE.

关键词： COVID-19

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Relative manifold based semi-supervised dimensionality reduction

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Frontiers of Computer Science 2020年第1期14卷 236-236页

作者： Xianfa CAI Guihua WEN Jia WEI Zhiwen YU School of Medical Information Engineering Guangdong Pharmaceutical UniversityGuangzhou 510006China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Shenzhen Key Laboratory of High Performance Data Mining Shenzhen 518055China

Figure 8 of this article shows YaleB and CMU PIE with incorrect legend titles:YaleB(Tr=1900,Te=514,NOC=100)should be YaleB(Tr=1900,Te=514,d=100)(Fig.8(a));TIE(Tr=1200,Te=2880,d=100)should be PIE(Tr=1200,Te=2880,d=100)... 详细信息

关键词： Figure dimensionality correct

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Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

arXiv

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arXiv 2022年

作者： Fan, Zhaoxin Song, Zhenbo Xu, Jian Wang, Zhicheng Wu, Kejian Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China Nreal China

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Innovative Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes

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Wireless Communications and Mobile Computing 2023年第1期2023卷 p1-3页

作者： Sung, Tien-Wen Lee, Chao-Yang Gaber, Tarek Nassar, Hamed Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan School of Science Engineering & Environment University of Salford Manchester United Kingdom Department of Computer Science Suez Canal University Ismailia Egypt

The integration of IoT systems and AI technology is very suitable for interconnected things to enhance intelligence, thus the artificial intelligence of things (AIoT), which enables the establishment of innovative IoT systems and applications for the modern smart city and smart home. The Internet of Things (IoT) consists of interconnected things with built-in and function-oriented sensors, essentially constituting a network of physical devices. The paper titled "Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency" by F. A. Almalki et al. proposes a drone system with AI framework to help clean solar panels mounted on the top of buildings and enhance their efficiency. [Extracted from the article]

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Human-in-The-Loop Optimization for Vehicle Body Lightweight Design

SSRN

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SSRN 2024年

作者： Hao, Jia Deng, Ruofan Jia, Liangyue Li, Zuoxuan Alizadeh, Reza Soltanisehat, Leili Liu, Binyi Sun, Zhibin Shao, Yiping Industrial and Systems Engineering Laboratory Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China School of Industrial and Systems Engineering University of Oklahoma NormanOK United States Charlton College of Business University of Massachusetts- Dartmouth MA United States College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China

Automatic optimization algorithms are crucial for vehicle body lightweight design;however, existing methods remain inefficient leading to excessive iterations that increase both time and costs. Current interactive optimization strategies partially mitigate this issue but lack a broad range of manipulation points and auxiliary information models. As such, we introduce a novel approach, "Human-in-the-Loop based method for Vehicle Body Lightweight Design" (HIL-VBLD). This method integrates human decision-making with optimization algorithms to reduce unproductive iterations. HIL-VBLD comprises two key components: (1) an innovative interaction mode that provides multiple manipulation points including constraint modification, algorithm switching, and selection of solutions of interest (SOI);(2) A comprehensive auxiliary information model that supports decision-making for designers. Our analysis demonstrates HIL-VBLD's efficacy, showing a 54.5% reduction in iteration cycles for genetic algorithm using SOI selection. Algorithm switching led to a 4.5% mass reduction, avoiding local optimum pitfalls associated with gradient algorithms. Additionally, the auxiliary information model achieved a further 1.25% mass reduction, enhancing optimization robustness. Compared to conventional automatic algorithm switching strategies, HIL-VBLD maintains equivalent accuracy with 23.9% fewer iterations. © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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An Empirical Study on Information Extraction using Large Language Models

arXiv

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arXiv 2023年

作者： Han, Ridong Yang, Chaohao Peng, Tao Tiwari, Prayag Wan, Xiang Liu, Lu Wang, Benyou College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Information Technology Halmstad University Sweden

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI’s GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs’ information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs’ human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods’ effectiveness and some of their remaining issues in improving GPT-4’s information extraction ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Pre-training for ad-hoc retrieval: Hyperlink is also you need

arXiv

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arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Distributed and Parallel Software Lab Huawei Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Hypertext systems

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