检索结果-内蒙古大学图书馆

Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wu, Fan Gao, Min Yu, Junliang Wang, Zongwei Liu, Kecheng Wang, Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing401331 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China School of Information Technology and Electrical Engineering The University of Queensland Queensland4072 Australia Informatics Research Centre Henley Business School University of Reading Berkshire ReadingRG6 6UD United Kingdom School of Mechanical Engineering Chongqing University Chongqing400044 China

To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects. Primitive attacks are highly feasible but less effective due to simplistic handcrafted rules, while upgraded attacks are more powerful but costly and difficult to deploy because they require more knowledge from recommendations. In this paper, we explore a novel shilling attack called Graph cOnvolution-based generative shilling ATtack (GOAT) to balance the attacks' feasibility and effectiveness. GOAT adopts the primitive attacks' paradigm that assigns items for fake users by sampling and the upgraded attacks' paradigm that generates fake ratings by a deep learning-based model. It deploys a generative adversarial network (GAN) that learns the real rating distribution to generate fake ratings. Additionally, the generator combines a tailored graph convolution structure that leverages the correlations between co-rated items to smoothen the fake ratings and enhance their authenticity. The extensive experiments on two public datasets evaluate GOAT's performance from multiple perspectives. Our study of the GOAT demonstrates technical feasibility for building a more powerful and intelligent attack model with a much-reduced cost, enables analysis the threat of such an attack and guides for investigating necessary prevention measures. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

Conditional Generative Modeling for Amorphous Multi-Element Materials

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

学校读者我要写书评

暂无评论

Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

学校读者我要写书评

暂无评论

SSRN

SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

Long-short Term Prediction for Occluded Multiple Object Tracking

学校读者我要写书评

暂无评论

Long-short Term Prediction for Occluded Multiple Object Trac...

2021 IEEE Global Communications Conference (GLOBECOM)

作者： Zhihong Sun Jun Chen Mithun Mukherjee Weijian Ruan Chao Liang Yi Yu Dan Zhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China College of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Digital Content and Media Sciences Research Division National Institute of Informatics Japan School of Computer Science Wuhan University China

ISBN: (纸本)9781728181059

Online multiple object tracking (MOT) is a challenging problem in complex scenes due to frequent occlusions. Most of the existing MOT methods tend to focus on addressing an individual type of occlusion, which cannot meet the requirements of real complex scenes. In this paper, we propose a unified MOT framework that combines long- and short-term prediction models for online multiple object tracking. Basically, The short-term prediction model consists of an appearance-based model and a motion-based model, aiming at exploiting the appearance and motion of objects to handle different types of occlusions jointly. Furthermore, we adopt a cubic spline interpolation as a long-term prediction model to estimate the trajectory of the target in occluded frames. To handle different lengths of occlusions, an adaptive weighted fusion model is proposed to combine the short-term prediction model, and the long-term prediction model. Experimental results on several challenging datasets demonstrate that the proposed method outperforms state-of-the-art methods.

关键词： Adaptation models Interpolation Target tracking Conferences Video sequences Predictive models Trajectory

MedAlign: A Clinician-Generated dataset for Instruction Following with Electronic Medical Records

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Fleming, Scott L. Lozano, Alejandro Haberkorn, William J. Jindal, Jenelle A. Reis, Eduardo Thapa, Rahul Blankemeier, Louis Genkins, Julian Z. Steinberg, Ethan Nayak, Ashwin Patel, Birju Chiang, Chia-Chun Callahan, Alison Huo, Zepeng Gatidis, Sergios Adams, Scott Fayanju, Oluseyi Shah, Shreya J. Savage, Thomas Goh, Ethan Chaudhari, Akshay S. Aghaeepour, Nima Sharp, Christopher Pfeffer, Michael A. Liang, Percy Chen, Jonathan H. Morse, Keith E. Brunskill, Emma P. Fries, Jason A. Shah, Nigam H. Department of Biomedical Data Science Stanford School of Medicine StanfordCA United States Department of Computer Science Stanford School of Engineering StanfordCA United States Department of Anesthesiology Peri-operative and Pain Medicine Stanford School of Medicine StanfordCA United States Department of Pediatrics Stanford School of Medicine StanfordCA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Department of Radiology Stanford School of Medicine StanfordCA United States Stanford University StanfordCA United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Technology and Digital Solutions Stanford Health Care Palo AltoCA United States Department of Electrical Enginering Stanford School of Engineering StanfordCA United States Department of Medicine Vanderbilt University School of Medicine NashvilleTN United States Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Medicine Stanford School of Medicine StanfordCA United States Department of Neurology Mayo Clinic RochesterMN United States Human-Centered Artificial Intelligence Institute Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Clinical Excellence Research Center Stanford School of Medicine StanfordCA United States

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. MedAlign is provided under a research data use agreement1 to enable LLM evaluations on tasks aligned with clinician needs and preferences. © 2023, CC BY.

关键词： Natural language processing systems

A review of data-driven and probabilistic algorithms for detection purposes in local power systems

学校读者我要写书评

暂无评论

A review of data-driven and probabilistic algorithms for det...

International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Sylvie Koziel Patrik Hilber Ryutaro Ichise School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Principles of Informatics Research Division NII National Institute of Informatics Tokyo Japan

ISBN: (数字)9781728128221

ISBN: (纸本)9781728128238

Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.

关键词： Feature extraction Power systems data mining Task analysis Classification algorithms Prediction algorithms Principal component analysis

ADAPTIVE GRAPH DIFFUSION NETWORKS

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Sun, Chuxiong Hu, Jie Gu, Hongming Chen, Jinpeng Yang, Mingchuan Big Data and AI Division China Telecom Research Institute Beijing China School of Software Engineering Beijing University of Posts and Telecommunications Beijing China

Graph Neural Networks (GNNs) have received much attention in the graph deep learning domain. However, recent research empirically and theoretically shows that deep GNNs suffer from overfitting and over-smoothing problems. The usual solutions either cannot solve extensive runtime of deep GNNs or restrict graph convolution in the same feature space. We propose the Adaptive Graph Diffusion Networks (AGDNs) which perform multi-layer generalized graph diffusion in different feature spaces with moderate complexity and runtime. Standard graph diffusion methods combine large and dense powers of the transition matrix with predefined weighting coefficients. Instead, AGDNs combine smaller multi-hop node representations with learnable and generalized weighting coefficients. We propose two scalable mechanisms of weighting coefficients to capture multi-hop information: Hop-wise Attention (HA) and Hop-wise Convolution (HC). We evaluate AGDNs on diverse, challenging Open Graph Benchmark (OGB) datasets with semi-supervised node classification and link prediction tasks. Until the date of submission (Aug 26, 2022), AGDNs achieve top-1 performance on the ogbn-arxiv, ogbn-proteins and ogbl-ddi datasets and top-3 performance on the ogbl-citation2 dataset. On the similar Tesla V100 GPU cards, AGDNs outperform Reversible GNNs (RevGNNs) with 13% complexity and 1% training runtime of RevGNNs on the ogbn-proteins dataset. AGDNs also achieve comparable performance to SEAL with 36% training and 0.2% inference runtime of SEAL on the ogbl-citation2 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks