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FAIR Machine Learning Model Pipeline Implementation of COVID-19 data

data Intelligence 2022年第4期4卷 971-990,1036页

作者： Sakinat Folorunso Ezekiel Ogundepo Mariam Basajja Joseph Awotunde Abdullahi Kawu Francisca Oladipo Abdullahi Ibrahim Department of Mathematical Sciences Olabisi Onabanjo UniversityP.M.B 2002Ago-IwoyeOgun StateNigeria 120005Nigeria Data Science Nigeria Lagos 105102Nigeria Leiden University 1011NCAmsterdamthe Netherlands Department of Computer Science University of IlorinIlorinKwara State240103Nigeria Department of Computer Science Ibrahim Badamosi UniversityLapaiNiger State911101Nigeria Kampala International University 260101Uganda Federal University Lokoja Nigeria Virus Outbreak Data Network-Africa

Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines(that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and(re)usability of data, so that new and robust analytical tools, such as machine learning(ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.

关键词： FAIRification Semantic data model Cluster analysis FAIR data Metadata Machine learning model

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Anchor data augmentation 23

Anchor data augmentation

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Nora Schneider Shirin Goshtasbpour Fernando Perez-Cruz Computer Science Department ETH Zurich Zurich Switzerland Computer Science Department ETH Zurich Zurich Switzerland and Swiss Data Science Center Zurich Switzerland

We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions. Our Python implementation of ADA is available at: https://***/noraschneider/anchordataaugmentation/

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Anchor data Augmentation

arXiv

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arXiv 2023年

作者： Schneider, Nora Goshtasbpour, Shirin Perez-Cruz, Fernando Computer Science Department ETH Zurich Zurich Switzerland Swiss Data Science Center Zurich Switzerland

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

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2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Hasan, M. Mahmud, M. Poudel, S. Donthula, K. Poudel, K. University of Memphis Department of Electrical and Computer Engineering TN United States Senior Software Engineer Optum Inc MurfreesboroTN United States Middle Tennessee State University Computational and Data Science MurfreesboroTN United States Middle Tennessee State University Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350341256

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface. © 2023 IEEE.

关键词： Infrared devices

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Position-aware Hypergraph Message-Passing Neural Network

Position-aware Hypergraph Message-Passing Neural Network

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinyu Zhang Qize Jiang Hanyuan Zhang Weiwei Sun School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hypergraph neural networks can model more flexible connectivity relationships, are used to model higher-order interactions, and have produced strong results in many real-world applications. However, the currently existing hypergraph neural networks need more exploration in capturing the global positional information of nodes in hypergraphs. Although there have been many explorations of the problem in graph neural networks, extending these approaches to hypergraphs is fraught with challenges. The major challenge is that hyperedges in hypergraphs are the other dimensional element of the incidence structure, have more flexible definitions than edges in graphs, and require more attention when learning global positional information. We propose a novel position-aware hypergraph message-passing neural network framework to address the above challenges. Specifically, we propose a global positional embedding learning approach that can separately model global positional information for nodes and hyperedges. At the same time, we also optimize the learning of local structures with hyperedges. Experiments on several publicly available benchmark datasets find that our proposed method outperforms many state-of-the-art methods.

关键词： Neural networks Transforms Benchmark testing Signal processing Graph neural networks Acoustics Speech processing

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AI based Stock Market Analysis and Decision Making System using Design Thinking Approach 8

AI based Stock Market Analysis and Decision Making System us...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kumar, M.Venkatesh Umamaheswari, M. Bharathi, C. Hema Maruthaveni, R. Devi, M.Nirmala Prasanna, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Dr. Sns Rajalakshmi College of Arts and Science Department of Business Administration Tamilnadu Coimbatore India Tamil Nadu Agricultural University Department of Physical Science and Information Technology Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science with Data Analytics Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Management Studies Tamilnadu Coimbatore India

ISBN: (纸本)9798350386578

Stock market is a dynamic and ever-changing environment that can be both exciting and challenging for investors. Equities, primarily referred to as stocks, are traded on stock exchanges around the world and reflect ownership in a business. The stock market is an essential tool for companies to raise capital, and investors use it to grow their wealth. Investing in stocks requires a thorough understanding of the market and the individual companies in which you are interested. The financial health of a company must be taken into account and can be analyzed using financial statements and other data. Another critical factor is the overall economic climate, as market conditions can significantly impact stock prices. It's crucial to have a long-term outlook and avoid being influenced by momentary market changes when making equity investments. A random walk is a mathematical model used to describe a sequence of steps or movements where each step is determined by chance. It is often used in various fields, including physics, finance, and statistics. Random walk theory has important implications in various fields, including the efficient market hypothesis in finance and the modeling of diffusion processes in physics. To effectively predict the future stock price using conventional and modern machine learning models. Compare the performance of different models and identify a higher accuracy prediction model using the selected important factors from disparate data sources. © 2024 IEEE.

关键词： Decentralized finance

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Trajectory-Aware Task Coalition Assignment in Spatial Crowdsourcing (Extended Abstract)

Trajectory-Aware Task Coalition Assignment in Spatial Crowds...

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International Conference on data Engineering

作者： Yuan Xie Fan Wu Xu Zhou Wensheng Luo Yifang Yin Roger Zimmermann Keqin Li Kenli Li College of Computer Science and Electronic Engineering Hunan University Hunan China Institute of Data Science National University of Singapore Department of Computer Science State University of New York New Paltz USA School of Data Science Chinese University of Hong Kong Shenzhen (IR) A *STAR Institute for Infocomm Research Singapore

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. To tackle the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

关键词： Crowdsourcing Industries Costs data engineering Trajectory Planning Task analysis

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Multi Criteria Decision Making in Fantasy Sports

Multi Criteria Decision Making in Fantasy Sports

引用

IEEE Punecon

作者： Dhruvi Shah Parth Kapadia Himanshu Kakwani Kanchan Dabre Computer Science and Engineering(Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

Football is a very famous sport worldwide and continues gaining traction to this day. As the interest in the game rises, so do the methods of interacting with the game. One such avenue is the FPL (Football Premier League). Recent years have seen a huge spike in interest for virtual, fantasy playing applications like the FPL. A person participating is usually heavily biased towards certain players or teams. This inherent bias causes them to make certain irrational decisions which may not provide the best results. There exists a huge gap in the market for an algorithm-based approach to team selection and the aim of this research is to make an optimal application capable of filling that void. The premise of this research is to create a team recommender system that optimizes one's team selection strategy for the FPL. This research works by introducing objectivity and eliminating biases in the team selection process. It incorporates the usage of TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and criteria-based metrics along with an XG-Boost regression predictor, with the aim of filling the gaps in the current systems and to maximize the returns while minimizing the cost associated with it. The approach implemented involved the usage of XGBoost, yielding a standard deviation of 0.041188.

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EVIT: Event-Oriented Instruction Tuning for Event Reasoning

arXiv

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arXiv 2024年

作者： Tao, Zhengwei Chen, Xiancai Jin, Zhi Bai, Xiaoying Zhao, Haiyan Lou, Yiwei MOE China School of Computer Science Peking University China Advanced Institute of Big Data

Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations and predict future events. The cutting-edge techniques for event reasoning play a crucial role in various natural language processing applications. Large language models (LLMs) have made significant advancements in event reasoning owing to their wealth of knowledge and reasoning capabilities. However, smaller instruction-tuned models currently in use do not consistently demonstrate exceptional proficiency in managing these tasks. This discrepancy arises from the absence of explicit modeling of events and the interconnections of them within their instruction data. Consequently, these models face challenges in comprehending event structures and semantics while struggling to bridge the gap between their interpretations and human understanding of events. Additionally, their limitations in grasping event relations lead to constrained event reasoning abilities to effectively deduce and incorporate pertinent event knowledge. In this paper, we propose Event-Oriented Instruction Tuning to train our LLM named EVIT specializing in event reasoning tasks. Specifically, we first propose a novel structure named event quadruple which contains the structure and semantics of events and is complete in the event representation. We then design event-relation learning based on the structures. We encapsulate the learning into the instruction-tuning formulation to better stimulate the event reasoning capacity of our model. We design a heuristic unsupervised method to mine event quadruple from a large-scale corpus. At last, we finetune a Llama model on our Event-Oriented Instruction Tuning. We conduct extensive experiments on event reasoning tasks on several datasets. Automatic and human evaluations demonstrate EVIT achieves competitive performances on event reasoning. Copyright © 2024, The Authors. All rights reser

关键词： Semantics

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Test Reuse Based on Adaptive Semantic Matching across Android Mobile Applications

arXiv

引用

arXiv 2023年

作者： Liu, Shuqi Zhou, Yu Han, Tingting Chen, Taolue College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Data Science Birkbeck University of London United Kingdom

Automatic test generation can help verify and develop the behavior of mobile applications. Test reuse based on semantic similarities between applications of the same category has been utilized to reduce the manual effort of Graphical User Interface (GUI) testing. However, most of the existing studies fail to solve the semantic problem of event matching, which leads to the failure of test reuse. To overcome this challenge, we propose TRASM (Test Reuse based on Adaptive Semantic Matching), a test reuse approach based on adaptive strategies to find a better event matching across android mobile applications. TRASM first performs GUI events deduplication on the initial test set obtained from test generation, and then employs an adaptive strategy to find better event matching, which enables reusing the existing test. Preliminary experiments with comparison to baseline methods on 15 applications demonstrate that TRASM can improve the precision of GUI event matching while reducing the failure of test reuse and the running time required for test reuse. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphical user interfaces

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