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arXiv 2024年

作者： Göktürk, Gökhan Kaya, Kamer Faculty of Engineering and Natural Sciences Sabancı University Istanbul34956 Turkey Center of Excellence in Data Analytics Sabancı University Turkey

Influence Maximization (IM) aims to find a given number of "seed" vertices that can effectively maximize the expected spread under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are often used for IM. However, these algorithms require a large number of simulations to find good seed sets. In this work, we propose DiFuseR, a blazing-fast, high-quality IM algorithm that can run on multiple GPUs in a distributed setting. DiFuseR is designed to increase GPU utilization, reduce inter-node communication, and minimize overlapping data/computation among the nodes. Based on the experiments with various graphs, containing some of the largest networks available, and diffusion settings, the proposed approach is found to be 3.2× and 12× faster on average on a single GPU and 8 GPUs, respectively. It can achieve up to 8× and 233.7× speedup on the same hardware settings. Furthermore, thanks to its smart load-balancing mechanism, on 8 GPUs, it is on average 5.6× faster compared to its single-GPU performance. © 2024, CC BY.

关键词： Expectation maximization algorithm

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MVLA-Net:A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

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Computers, Materials & Continua 2025年第4期83卷 1173-1193页

作者： Tariq Mahmood Tanzila Saba Faten S.Alamri Alishba Tahir Noor Ayesha Artificial Intelligence and Data Analytics(AIDA)Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Shifa College of Medicine Shifa Tameer-E-Millat UniversityIslamabad44000Pakistan Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic *** traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic *** the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior ***,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale *** paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based *** proposed model aims to improve comprehensive segmentation for retinal images with varying vessel *** employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency *** that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy *** evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental *** models achieved approximately 85%accuracy with the same image preparation ***,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,*** results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurat

关键词： Diabetic retinopathy grading retinal vessel segmentation dual-modal deep learning attention mecha-nism health risks

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Checks and Balances and Institutional Gridlock: Implications for Authoritarianism

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Governance 2025年第2期38卷

作者： Adiguzel, Fatih Serkant Faculty of Arts and Social Sciences Sabancı University İstanbul Turkey Center of Excellence in Data Analytics Sabancı University İstanbul Turkey

Recently, the world saw a wave of elected leaders attack democracy. Why do people support leaders who remove checks and balances? I argue that aspiring autocrats gain more popular support when they present these institutions as obstacles to getting things done. In doing so, they exploit a critical tension between the possibility of gridlock and the abuse of power, which is inherent in democratic institutions. Using cross-national data and leveraging an original survey experiment from Turkey, I show that effective checks and balances decrease democracy satisfaction and that aspiring autocrats gain more popular support when they present these institutions as obstacles. More interestingly, respondents perceive the aspiring autocrats' gridlock justification to dismantle checks and balances as a pro-democratic attempt to remove the obstacles to a policy-responsive regime. These results show that aspiring autocrats exploit the tension in democracies, making it harder for citizens to perceive the threat they face. © 2025 Wiley Periodicals LLC.

关键词： abuse of power democratic backsliding gridlock

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Integrating semantic features in fruit recognition based on perceptual color and semantic template

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Information Processing in Agriculture 2022年第2期9卷 316-334页

作者： Ema Rachmawati Iping Supriana Masayu Leylia Khodra Fauzan Firdaus School of Computing Universitas TelkomBandung 40257Indonesia School of Electrical Engineering and Informatics Institut Teknologi BandungBandung 40132Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing&Big Data Analytics(U-CoE AI-VLB)Institut Teknologi BandungBandung 40132Indonesia

Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts givenby predefined text caption or keywords. Two common fruit recognition models include bagof-features (BoF) and convolutional neural network (ConvNet), which achieve highperformance results. In most cases, the overfitting problem is unavoidable. This problemmakes it difficult to generalize new instances with only a slightly different appearance,although belonging to the same category. This article proposes a new fruit recognitionmodel by associating an object’s low-level features in an image with a high-level *** define a perceptual color for each fruit species to construct a relationship between fruitcolor and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visualfeature is adopted in this model. The experiment was conducted on three different fruitimage datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantictemplate (PCoST), is significantly better than the BoF and ConvNet models in reducing theoverfitting problem.

关键词： Multi-class fruit recognition Perceptual color Semantic template Bag-of-features

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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet Machine Learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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A data-driven approach to estimating post-discovery parameters of unexplored oilfields

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Petroleum 2023年第2期9卷 285-300页

作者： Fransiscus Pratikto Sapto Indratno Kadarsah Suryadi Djoko Santoso Industrial Engineering Department Bandung Institute of TechnologyJl Ganesha 10Bandung40132Indonesia Mathematics Department Bandung Institute of TechnologyJl Ganesha 10Bandung40132Indonesia Geophysical Engineering Department Bandung Institute of TechnologyJl Ganesha 10Bandung40132Indonesia University Center of Excellence on Artificial Intelligence for Vision Institut Teknologi BandungNatural Language Processing&Big Data Analytics(U-CoE AI-VLB)Bandung 40132West JavaIndonesia

Consider a typical situation where an investor is considering acquiring an unexplored *** oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are *** this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and *** call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir *** the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is *** came up with 12 reservoir ***,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir *** on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability *** investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.

关键词： data-driven Pre-discovery data Post-discovery parameters Gaussian mixture model Artificial neural network

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Rahadian, Helmy Burohman, Azka Muji Widyotriatmo, Augie Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Bandung Indonesia Universitas Dian Nuswantoro Electrical Engineering Study Program Semarang Indonesia Research Center for Photonics Tangerang Selatan Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia University Center of Excellence Ai on Vision Nlp & Big Data Analytics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309225

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed. © 2023 IEEE.

关键词： Heuristic methods

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COVID-19 detection in computed tomography images with 2D and 3D approaches

arXiv

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arXiv 2021年

作者： Ali Ahmed, Sara Atito Can Yavuz, Mehmet Umut Sen, Mehmet Gulsen, Fatih Tutar, Onur Korkmazer, Bora Samancı, Cesur Şirolu, Sabri Hamid, Rauf Eryürekli, Ali Ergun Mammadov, Toghrul Yanikoglu, Berrin Sabanci University Istanbul34956 Turkey Urbanstat The Istanbul University-Cerrahpaşa Cerrahpaşa Faculty of Medicine Turkey The Computer Science and Engineering Program Sabanci University Istanbul34956 Turkey The Sabanci University Center of Excellence in Data Analytics

Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the definitive RT-PCR test. We present a deep learning ensemble for detecting COVID-19 infection, combining slice-based (2D) and volume-based (3D) approaches. The 2D system detects the infection on each CT slice independently, combining them to obtain the patient-level decision via different methods (averaging and long-short term memory networks). The 3D system takes the whole CT volume to arrive to the patient-level decision in one step. A new high resolution chest CT scan dataset, called the IST-C dataset, is also collected in this work. The proposed ensemble, called IST-CovNet, obtains 90.80% accuracy and 0.95 AUC score overall on the IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies;and 93.69% accuracy and 0.99 AUC score on the publicly available MosMed dataset that consists of COVID-19 scans and normal controls only.. The system is deployed at Istanbul university Cerrahpaşa School of Medicine. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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Real or Virtual: A Video Conferencing Background Manipulation-Detection System

arXiv

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arXiv 2022年

作者： Nowroozi, Ehsan Mekdad, Yassine Conti, Mauro Milani, Simone Uluagac, A. Selcuk Yanikoglu, Berrin Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Center of Excellence in Data Analytics Sabanci University Istanbul Turkey Department of Mathematics University of Padua Padua Italy Cyber-Physical Systems Security Lab Florida International University MiamiFL United States Department of Information Engineering University of Padua Padua Italy

Recently, the popularity and wide use of the last-generation video conferencing technologies created an exponential growth in its market size. Such technology allows participants in different geographic regions to have a virtual face-to-face meeting. Additionally, it enables users to employ a virtual background to conceal their own environment due to privacy concerns or to reduce distractions, particularly in professional settings. Nevertheless, in scenarios where the users should not hide their actual locations, they may mislead other participants by claiming their virtual background as a real one. Therefore, it is crucial to develop tools and strategies to detect the authenticity of the considered virtual background. In this paper, we present a detection strategy to distinguish between real and virtual video conferencing user backgrounds. We demonstrate that our detector is robust against two attack scenarios. The first scenario considers the case where the detector is unaware about the attacks (i.e., the forensically-edited frames are not part of the training set). In the second scenario, we make the detector aware of the adversarial attacks, which we refer to Adversarial Multimedia Forensics (i.e, the forensically-edited frames are included in the training set). Given the lack of publicly available dataset of virtual and real backgrounds for video conferencing, we created our own dataset and made them publicly available [1]. Then, we demonstrate the robustness of our detector against different adversarial attacks that the adversary considers. Ultimately, our detector's performance is significant against the CRSPAM1372 [2] features, and post-processing operations such as geometric transformations with different quality factors that the attacker may choose. Moreover, our performance results shows that we can perfectly identify a real from a virtual background with an accuracy of 99.80%. © 2022, CC BY.

关键词： Convolutional neural networks

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Seeing Your Stories:Visualization for Narrative Medicine

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Health data Science 2024年第1期4卷 390-405页

作者： Hua Ma Xiaoru Yuan Xu Sun Glyn Lawson Qingfeng Wang Faculty of Science and Engineering University of NottinghamNingbo 315100China Digital Art Department Art&Design Technology InstituteSuzhou 215104China National Key Laboratory of General Artiffcial Intelligence and School of Intelligence Science and Technology Peking UniversityBeijing 100871China Health Data Visualization and Visual Analytics Research Center National Institute of Health Data Science at PKUBeijing 100191China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo ChinaNingbo 315100China Human Factors Research Group Faculty of EngineeringUniversity of NottinghamNottingham NG72RDUK Nottingham University Business School China University of NottinghamNingbo 315100China

Importance:Narrative medicine(NM),in which patient stories play a crucial role in their diagnosis and treatment,can potentially support a more holistic approach to patient care than traditional scientiffc ***,there are some challenges in the implementation of narrative medicine,for example,differences in understanding illnesses between physicians and patients and physicians’increased workloads and overloaded *** paper ffrst presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization ***:The review is conducted from 2 perspectives:(a)the contexts and domains in which visualization has been explored for narrative medicine and(b)the forms and solutions applied in these *** applied domains are deffned,including understanding patients from narrative records,medical communication,medical conversation training in education,and psychotherapy and emotional wellness ***:A future work framework illustrates some opportunities for future research,including groups of speciffc directions and future points for the 4 domains and 3 technological exploration opportunities(combination of narrative and medical data visualization,taskaudience-based visual storytelling,and user-centered interactive visualization).Speciffcally,3 directions of future work in medical communication(asynchronous online physician-patient communication,synchronous face-to-face medical conversation,and medical knowledge dissemination)were concluded.

关键词： directions education visualization

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