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A cloud-based architecture for explainable big data analytics using self-structuring Artificial Intelligence

Discover Artificial Intelligence 2024年第1期4卷 33页

作者： Mills, Nishan Issadeen, Zafar Matharaarachchi, Amali Bandaragoda, Tharindu De Silva, Daswin Jennings, Andrew Manic, Milos Centre for Data Analytics and Cognition La Trobe University MelbourneVIC3086 Australia Department of Computer Science Virginia Commonwealth University RichmondVA23284 United States

big data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed big data into this modern context. With these new manifestations of big data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of big data. In this paper we propose a cloud-based architecture for explainable big data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale big data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture. © The Author(s) 2024.

关键词： data analytics

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Launching a data analytics Major: How Collaboration and Agility Built a Successful Program

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American Mathematical Monthly 2025年第1期132卷 77-88页

作者： Forrester, Richard Marshall, Emily Forrester, Jeffrey Richeson, David Schaefer, Jennifer Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Economics Denison University Granville 43023 OH United States Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States

In this article, we share the details of Dickinson College’s journey to establish a data analytics major. Designed to provide students with the technical proficiency required to become a data scientist, Dickinson’s data analytics major also incorporates values of a liberal arts education, including interdisciplinary learning, critical thinking, thoughtful analysis, effective communication, and ethical considerations. We describe how sustained collaboration, a detailed plan to engage the campus community, partnerships with alumni, and support from senior leaders were essential to this achievement. We also discuss the challenges and successes encountered before and after the approval of the major. © 2024 Mathematical Association of America.

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction 30

A Semantic Mention Graph Augmented Model for Document-Level ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an China National University of Singapore Singapore

ISBN: (纸本)9782493814104

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Small Sample Estimation of Classification Metrics

Small Sample Estimation of Classification Metrics

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2022 International Conference on Interdisciplinary Research in Technology and Management, IRTM 2022

作者： Manna, Soumen Goa Institute Of Management Department Of Big Data Analytics Goa India

ISBN: (纸本)9781665478861

Estimation of classification evaluation metrics for small sample classification data is one of the most challenging task in Machine Learning model building process. The value of an evaluation metric based on the test data is considered to be the generalized performance measure of a classification model. Measuring the performance of a model based only on the test data works well when we have a large data set and both train and test data follow the same population distribution. However, this assumption does not always work for small data set. Therefore, measuring performance of a model based on test data set may mislead us for small sample classification problems. To deal with such situations, in this paper, we propose a modified method to estimate classification evaluation metrics. The new method provides an improved and consistent estimation of classification evaluation metrics. © 2022 IEEE.

关键词： Classification (of information)

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Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL 31

Solid-SQL: Enhanced Schema-linking based In-context Learning...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Geling Tan, Yunzhi Zhong, Ruichao Xie, Yuanzhen Zhao, Lingchen Wang, Qian Hu, Bo Li, Zang School of Cyber Science and Engineering Wuhan University China Big Data and AI Platform Department Tencent China

ISBN: (纸本)9798891761964

Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6% compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks. © 2025 Association for Computational Linguistics.

关键词： Adversarial machine learning

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Modelling of Poor Household in East Kalimantan Using Zero Inflated Ordered Probit (ZIOP) Approach 7

Modelling of Poor Household in East Kalimantan Using Zero In...

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7th Information Systems International Conference, ISICO 2023

作者： Rejeki, Noviana Ratnasari, Vita Ahsan, Muhammad Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya60111 Indonesia

Discrete ordinal response variables often exhibit an "excess" of zeros, which can be attributed to two different data conditions. Conventional ordinal probit models are limited in their ability to explain these excess zeros. The Zero Inflated Ordered Probit (ZIOP) model, which combines binary probit and ordinal probit regression, can be used to address this limitation. This study reviews the empirical analysis of the factors that contribute to poor household levels in East Kalimantan. The goodness-of-fit of the ZIOP and ordinal probit models was assessed using the Vuong test. © 2023 The Authors. Published by Elsevier B.V.

关键词： Marginal Effect Poor Household Rate Vuong Test Zero Inflated Ordered Probit (ZIOP)

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Challenges and Future Applications for IRNSS with Machine Learning Algorithms 2

Challenges and Future Applications for IRNSS with Machine Le...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Patel, Mehul S. Shah, Ankit Sankalchand Patel University Department of Information Technology Gujarat Visnagar India Adani Institute of Digital Technology Management Department of Big Data Analytics Gandhinagar India

ISBN: (纸本)9798350360523

The interest in machine learning (ML) research and its potential applications in many fields has also led to several studies on its use in Indian Regional Navigation Satellite Systems (IRNSS). The traditional IRNSS survey algorithms and models are further developed to improve their reliability and efficiency using machine learning techniques. We review how ML can improve the efficiency and usability of IRNSS, and also discuss areas of IRNSS where ML algorithms have been applied. Potential areas of IRNSS where ML can be applied to improve efficiency, accuracy, and robustness are explored, providing fertile ground for new research. The results show reasonable performance of the machine learning techniques for several IRNSS applications. However, the use of ML models in industry is still limited. In addition, we discuss the application areas, challenges, risks, and futures of using ML techniques in IRNSS. © 2024 IEEE.

关键词： Efficiency

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Enhanced Conductive and Optical Properties of TiO2/PANI Composites Synthesized by in-situ Chemical Oxidation Polymerization Route 3

Enhanced Conductive and Optical Properties of TiO2/PANI Comp...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Latifah, Umi Salsabila, Nisrina K. Yudhoyono, Gatut Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Surabaya60111 Indonesia

Polyaniline titania composites have been investigated due to their unique chemical and physical properties. This study synthesized several PANI/TiO2 composites with different mol% of PANI using in-situ oxidative polymerization methods. TiO2 was obtained by coprecipitation methods. The functional group analysis carried out using Fourier transform Infrared reflectance, it was found that PAni/TiO2 had been successfully created, which was indicated by the absence of new peaks. Based on the results of measuring absorbance in the Ultraviolet-Visible wavelength, it was found that the composite material made had a wider absorption range compared to its constituents. The electrical conductivity of composites was shown to have a higher value than TiO2. From these results, it was found that the PANI/TiO2 composite material has greater photoactivity than TiO2. © Published under licence by IOP Publishing Ltd.

关键词： Titanium dioxide

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Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Chowdam, Jayanth Panigrahi, Sibarama National Institute of Technology Intelligent Data Analytics Laboratory Department of Computer Science and Engineering Odisha Rourkela India

ISBN: (纸本)9798350369175

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL. © 2024 IEEE.

关键词： Dimensionality reduction

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Customer Profiling Based on Mobile Apps GPS data : A Case Study on Westfield Shopping Malls 30

Customer Profiling Based on Mobile Apps GPS Data : A Case St...

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30th International Conference on Geoinformatics, Geoinformatics 2023

作者： Zhou, Fangzhou Zhang, Xianghui Wang, Xinglei Cheng, Tao University College London SpaceTimeLab for Big Data Analytics Department of Civil Environmental and Geomatic Engineering London United Kingdom

ISBN: (纸本)9798350344240

In order to provide a personalised experience to customers, it's essential for shopping centers to understand its customer base and their shopping behaviors. Building a well-developed customer profile is critical for improving marketing efficiency, expanding market share, and building long-term, stable business ties with trading partners. Currently most shopping malls or retail business use footfall or customer surveys to grasp the customer behaviors, which are insufficient to obtain accurate and representative information about the customers. This study aims to provide a detailed customer profile for shopping centers using GPS datasets. We choose the two Westfield shopping malls in London as the case study area. In order to uncover additional customer information, this study focuses four research questions:(1) Origin places of customers;(2) Their transportation mode to the mall;(3) The average dwell time of customers;(4) The pattern of return visitors. According to the results, malls can develop a range of marketing initiatives to provide a better shopping experience for customers and attract more of them. © 2023 IEEE.

关键词： Shopping centers

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