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PIMIP: An open source platform for pathology information management and integration

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arXiv 2021年

作者： Wu, Jialun Mao, Anyu Bao, Xinrui Zhang, Haichuan Gao, Zeyu Wang, Chunbao Gong, Tieliang Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform, PIMIP (Pathology Information Management & Integration Platform). Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible. © 2021, CC BY.

关键词： Information management

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Recognition Framework

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Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

Communication and Networks (ASIANComNet), Asian Conference on

作者： Seelam Ch Vijaya Bhaskar Gedela Kalyani Ramy Al-Fatlawy Fatima Alsalamy Waleed Hadi Madhloom Kurdi H.M. Al-Aboudy G. Sujatha Department of IT MVSR Engineering College Hyderabad Telangana India Big Data Analytics Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq Medical Laboratories Techniques Department Al-Mustaqbal University Hillah Iraq Department of Computer Science Altoosi University College 2 Iraq Department of Computer Techniques Engineering Mazaya University College DhiQar Iraq Department of ECE Arunai Engineering College Tiruvannamalai Tamil Nadu India

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study’s results show that the ASHO-LGBM strategy’s performance analysis achieves the highest accuracy rate.

关键词： Wireless sensor networks Accuracy Cloning Intrusion detection Medical services Boosting Delays Performance analysis Environmental monitoring Manufacturing automation

DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

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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

A personalized diagnostic generation framework based on multi-source Heterogeneous data

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arXiv 2021年

作者： Wu, Jialun Gao, Zeyu Zhang, Haichuan Zhang, Ruonan Gong, Tieliang Wang, Chunbao Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Library of Xi’an Jiaotong University Xi’an China Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-today routine. This lead to the current generalized standards that are being continuously updated as new findings are reported. It is noticeable that these effective standards are developed based on a multi-source heterogeneous data, including whole-slide images and pathology and clinical reports. In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient. We use nuclei-level image feature similarity and content-based deep learning method to search for a personalized group of population with similar pathological characteristics, extract structured prognostic information from descriptive pathology reports of the similar patient population, and assign importance of different prognostic factors to generate a personalized pathological diagnosis result. We use multi-source heterogeneous data from TCGA (The Cancer Genome Atlas) database. The result demonstrate that our framework matches the performance of pathologists in the diagnosis of renal cell carcinoma. This framework is designed to be generic, thus could be applied for other types of cancer. The weights could provide insights to the known prognostic factors and further guide more precise clinical treatment protocols. © 2021, CC BY.

关键词： Diagnosis

big-data clustering: K-Means or k-indicators?

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arXiv 2019年

作者： Chen, Feiyu Yang, Yuchen Xu, Liwei Zhang, Taiping Zhang, Yin School of Big Data and Software Engineering Chongqing University Chongqing China Department of Computational and Applied Mathematics Rice University HoustonTX United States School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan China College of Computer Science Chongqing University Chongqing China Institute for Data and Decision Analytics Chinese University of Hong Kong Shenzhen China

The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck with respect to the number of clusters K as this number grows in big data applications. In this work, we promote a closely related model called K-indicators model and construct an efficient, semi-convex-relaxation algorithm that requires no randomized initializations. We present extensive empirical results to show advantages of the new algorithm when K is large. In particular, using the new algorithm to start the K-means algorithm, without any replication, can significantly outperform the standard K-means with a large number of currently state-ofthe-A rt random replications. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

Improving Power Allocation and Installation in WSN Using Novel Nature-Inspired Optimization for Cyber Security Applications

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Improving Power Allocation and Installation in WSN Using Nov...

Communication and Networks (ASIANComNet), Asian Conference on

作者： K. S. Balamurugan Gedela Kalyani Ramy Al-Fatlawy Waleed Hadi Madhloom Kurdi Fatima Alsalamy H.M. Al-Aboudy K. Manikandan Department of ECE Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Big Data Analytics Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Altoosi University College Najaf Iraq Medical Laboratories Techniques Department Al-Mustaqbal University Hillah Iraq Department of Computer Techniques Engineering Mazaya University College DhiQar Iraq Department of Biomedical Engineering Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection.

关键词： Resistance Wireless sensor networks System performance Power distribution Reliability engineering Multitasking Energy efficiency Resource management computer crime Optimization

Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

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arXiv 2024年

作者： Altin, Mahsun Cakir, Altan Department of Computer Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Department of Physics Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Artificial Intelligence Data Science Research and Application Center Istanbul Technical University Sariyer Istanbul34467 Turkey Parton Big Data Analytics and Consulting ITU Ariteknokent Sariyer Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models’ capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300% and 650% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection. © 2024, CC BY.

关键词： Anomaly detection

Weakly supervised learning for textbook question answering

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TechRxiv

TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

Emotion correlation mining through deep learning models on natural language text

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arXiv 2020年

作者： Wang, Xinzhi Kou, Luyao Sugumaran, Vijayan Luo, Xiangfeng Zhang, Hui School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijing Beijing100084 China Key Laboratory of City Integrated EmergencyResponse Science Tsinghua University Beijing100084 China Department of Decision and Information Sciences Oakland University RochesterMI48309 United States Center for Data Science and Big Data Analytics Oakland University RochesterMI48309 United States

Emotion analysis has been attracting researchers’ attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles;2) the bodies;and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love–anger and sadness–anger. In objective news, it is easy to recognize text emotion as love and cause fear–joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human–computer interaction. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks