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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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Noise-Tolerant Learning for Audio-Visual Action Recognition

arXiv

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arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

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Causal Inference with Correlation Alignment

Causal Inference with Correlation Alignment

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IEEE International Conference on Big data

作者： Umar I. Abdullahi Spyros Samothrakis Maria Fasli Institute for Analytics and Data Science School of Computer Science and Electronic Engineering University of Essex Colchester

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Estimating the effect of an intervention (i.e. causal inference) is a fundamental problem in fields like medicine, e-commerce, government (among others). An ideal approach requires experimental data, which is often hard to find or collect. Observational data on the other hand is in abundance, but contains systematic bias posing a great challenge for this task. In this paper, we introduce the use of two methods pioneered in domain adaptation: Euclidean and non-Euclidean correlation alignment in the context of causal inference. We evaluate their performance using widely used datasets and our results perform favourably against the current state of the art.

关键词： Correlation Systematics Sociology Big data Robustness Task analysis Standards

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Impact of Distributed Training on Mask R-CNN Model Performance for Image Segmentation 1

Impact of Distributed Training on Mask R-CNN Model Performan...

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1st International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2020

作者： Ranjit, Mercy Prasanna Ganapathy, Gopinath Manuel, Ranjit Frederick Microsoft Corporation India Ltd AI Machine Learning Bangalore India Bharathidasan University School of Computer Science and Engineering Trichy India ITC Infotech India Ltd Data Analytics AI Bangalore India

ISBN: (纸本)9781728141428

The paper compares and discusses the impact of distributed deep learning training using the Horovod framework on the performance metrics of image segmentation model trained using the Mask R-CNN (Region-based Convolutional Neural Network) architecture in comparison with the baseline metrics obtained from running the training on a single node. Image segmentation is a complex problem in comparison with image classification and the fluctuations introduced due to the distributed gradient computations while running the training distributed is less pronounced on simpler problems like image classification while it is more pronounced for image segmentation where we measure the performance at pixel level. It is important to know how the latest algorithms and distribution frameworks perform when training the model on distributed compute with parallel gradient calculations, adjusted learning rates and averaged neural network weight updates. The paper also aimed to understand if the effective batch size per training iteration while performing the training distributed did not negatively impact the ability of the model to generalize when increasing the batch size as indicated in the previous studies. The distributed training with Horovod was performed using the Azure machine learning service on scalable cloud compute cluster. The aim of the study was not comparison in-terms of the execution time which has been well established on previous studies but on understanding the impact on the quality of the predictions measured with standard validation metrics for image segmentation using the ISIC (International Skin Imaging Collaboration) 2018 Skin Lesion image dataset. © 2020 IEEE.

关键词： Image segmentation

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Reflections from the Workshop on AI-Assisted Decision Making for Conservation

arXiv

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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

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Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks

Image Analysis of MRI-based Brain Tumor Classification and S...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： R. Saranya R Pushpakumar P Srinivasan N Aparna K. Karthika Amit Chauhan Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of ECE Sona College of Technology Salem Tamilnadu India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Verappanur Tamil Nadu India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Life Sciences School of Sciences CHRIST (Deemed to be university) Bengaluru Karnataka India

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method.

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Using multiple drug similarity networks to promote adverse drug event detection

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Heliyon 2024年第22期10卷 e39728页

作者： Padhi, Biswajit Liu, Ruoqi Yang, Yuedi Peng, Xueqiao Li, Lang Zhang, Pengyue Zhang, Ping Department of Computer Science and Engineering The Ohio State University 2015 Neil Ave Columbus 43210 OH United States Department of Biostatistics and Health Data Science Indiana University School of Medicine 410 W. 10th Street HITS 3000 Indianapolis 46202 IN United States Department of Biomedical Informatics The Ohio State University 1800 Cannon Drive Columbus 43210 OH United States Translational Data Analytics institute The Ohio State University 1760 Neil Ave Columbus 43210 OH United States

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://***/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring. © 2024 The Authors

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

An Efficient Algorithm to Classify the Mitotic Cell using An...

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computer, Power and Communications (ICCPC), International Conference on

作者： R.G. Vidhya J. Seetha Sudhir Ramadass S. Dilipkumar Ajith Sundaram G. Saritha Department of ECE HKBK College of Engineering Bangalore India Department of Computer science and Business systems Panimalar Engineering College Chennai India Data analyst Sterck systems Chennai Department of Analytics Scope VIT Vellore Selection grade School of Business Kochi Department of ECE Sri Sai Ram institute of technology Chennai Tamilnadu India

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer; however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful.

关键词： Image segmentation Sensitivity Breast tissue Medical services Feature extraction Breast cancer Mammography

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The Future of data Protection: Empowering data Security with Identity-Centric Blockchain Technology

The Future of Data Protection: Empowering Data Security with...

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Communication & Computing (WCONF), World Conference on

作者： A. Heney Juliet Vinithra Banu T K. Suresh Kumar D Kirubakaran M Dinesh K. Thilagam Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of MBA Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Velammal Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

In response to the formidable challenges surrounding data security, particularly in the face of evolving threats and vulnerabilities, this study introduces the Advanced Blockchain-Enhanced data Security Protocol (ABE-DSP) as a cutting-edge solution. Acknowledging the complexities of safeguarding sensitive information, ABE-DSP is a meticulously crafted and sophisticated framework that aims to fortify the security and integrity of data transactions through the integration of blockchain technology. This protocol addresses the pressing challenge of ensuring robust data security by placing a primary focus on enhancing overall system security and refining user authentication processes. Within the context of these challenges, the study explores the intricacies of ABE-DSP's design and implementation. Furthermore, the proposed method demonstrates its effectiveness by achieving a remarkable success rate, processing 97% of received packets with precision. This high percentage underscores the protocol's efficacy and reliability, positioning it as a robust and advanced tool for overcoming the contemporary challenges associated with data security. This study contributes valuable insights to the ongoing discourse on leveraging blockchain technology for identity-centric data security, offering a promising avenue for addressing and mitigating the challenges faced in the dynamic landscape of information protection.

关键词： Protocols Navigation data security Face recognition Refining Authentication Pressing Blockchains Reliability Protection

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