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IET Blockchain 2024年第S1期4卷 507-525页

作者： Cunningham, James Davies, Nigel Devaney, Sarah Holm, Søren Harding, Mike Neumann, Victoria Ainsworth, John Division of Informatics Imaging and Data Sciences University of Manchester Manchester United Kingdom Department of Computing and Communications Lancaster University Lancaster United Kingdom CSEP Department of Law School of Social Sciences The University of Manchester Manchester United Kingdom

Decentralized autonomous organizations (DAOs) have emerged as a novel governance mechanism that operates through distributed ledgers and smart contracts, enabling members to direct an organization's actions. The widespread adoption of DAOs has occurred in response to their utility in managing emergent semi-structured projects and has led to the development of various innovative governance mechanisms. The mechanisms employed by DAOs has the potential to be generalized beyond their core financial domain to a wide range of use cases. In the medical field the use of blockchain and DAOs can provide secure and transparent access to medical data, while ensuring patient privacy. Civic access to medical data is a growing area of interest, where individuals have control over their own medical data and can share it with healthcare providers, researchers, and other stakeholders. DAOs can facilitate this civic access, enabling individuals to share their data securely and selectively with authorized parties for research and other purposes. This paper explores the use of DAOs to medical data sharing, with a focus on ownership, governance, and transaction models. An application framework and API that enables the deployment of DAO-like organizations is derived and this approach is applied to the patient-centric management of medical data. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks 21

Forest-based Evolutionary Algorithm for Reconstructing Boole...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Stranieri, Nicolò Buffa, Francesca M. Tangherloni, Andrea Bocconi University Department of Computing Sciences Milan Italy University of Oxford Department of Oncology Oxford United Kingdom Bocconi University Bocconi Institute for Data Science and Analytics Department of Computing Sciences Milan Italy

ISBN: (纸本)9798350356632

Gene Regulatory Networks (GRNs) play a fundamental role in orchestrating the expression of our genes through complex interactions between DNA, RNA, proteins, and other molecules. Accurately reconstructing such networks from gene expression data is a critical yet challenging task in Systems Biology due to their intricate nature and limited data availability. In this work, we introduce a novel Forest-based Evolutionary Algorithm (FP) designed for reconstructing Boolean GRNs from time series data of gene expressions. Unlike traditional methods that struggle with scalability and accurate representation of regulatory interactions, FP utilizes a forest structure where each tree represents the logical relationships between genes, enhancing the model's capacity to depict complex networks efficiently. Our comprehensive testing indicates that FP rapidly converges towards potential solutions within a limited number of generations, although a higher fitness score does not always equate to a more accurate GRN representation. Implementing mini-batching techniques, inspired by their effectiveness in gradient descent optimization, shows promise in improving computational efficiency without sacrificing performance. A comparative analysis against the main state-of-the-art approaches reveals FP's tendency towards conservative predictions, emphasizing precision over recall, making it particularly suitable for contexts where the cost of false positives is high. These initial results suggest that FP is a robust and efficient tool for GRN inference. © 2024 IEEE.

关键词： Genetic programming

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Determination of future land use changes using remote sensing imagery and artificial neural network algorithm:A case study of Davao City,Philippines

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Artificial Intelligence in Geosciences 2023年第1期4卷 111-118页

作者： Cristina E.Dumdumaya Jonathan Salar Cabrera College of Information and Computing University of Southeastern PhilippinesBrgy.ObreroDavao CityPhilippines Faculty of Computing Data SciencesEngineering and TechnologyDavao Oriental State UniversityMati CityDavao Oriental8200Philippines

Land use and land cover(LULC)changes refer to alterations in land use or physical *** changes can be caused by human activities,such as urbanization,agriculture,and resource extraction,as well as natural phenomena,for example,erosion and climate *** changes significantly impact ecosystem services,biodiversity,and human *** this study,LULC changes in Davao City,Philippines,were simulated,predicted,and projected using a multilayer perception artificial neural network(MLP-ANN)*** MLP-ANN model was employed to analyze the impact of elevation and proximity to road networks(i.e.,exploratory maps)on changes in LULC from 2017 to *** predicted 2021 LULC map shows a high correlation to the actual LULC map of 2021,with a kappa index of 0.91 and a 96.68%*** MLP-ANN model was applied to project LULC changes in the future(i.e.,2030 and 2050).The results suggest that in 2030,the built-up area and trees are increasing by 4.50%and 2.31%,***,water will decrease by up to 0.34%,and crops is about to decrease by approximately 3.25%.In the year 2050,the built-up area will continue to increase to 6.89%,while water and crops will decrease by 0.53%and 3.32%,***,the results show that anthropogenic activities influence the land’s ***,the study illustrates how machine learning models can generate a reliable future scenario of land usage changes.

关键词： LULC Artificial neural network Remote sensing Land use land cover prediction Multilayer perception Philippines

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Inductive Link Prediction in N-ary Knowledge Graphs 31

Inductive Link Prediction in N-ary Knowledge Graphs

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

N-ary Knowledge Graphs (NKGs), where a fact can involve more than two entities, have gained increasing attention. Link Prediction in NKGs (LPN) aims to predict missing elements in facts to facilitate the completion of NKGs. Current LPN methods implicitly operate under a closed-world assumption, meaning that the sets of entities and roles are fixed. These methods focus on predicting missing elements within facts composed of entities and roles seen during training. However, in reality, new facts involving unseen entities and roles frequently emerge, requiring completing these facts. Thus, this paper proposes a new task, Inductive Link Prediction in NKGs (ILPN), which aims to predict missing elements in facts involving unseen entities and roles in emerging NKGs. To address this task, we propose a Meta-learning-based Nary knowledge Inductive Reasoner (MetaNIR), which employs a graph neural network with meta-learning mechanisms to embed unseen entities and roles adaptively. The obtained embeddings are used to predict missing elements in facts involving unseen elements. Since no existing dataset supports this task, three datasets are constructed to evaluate the effectiveness of MetaNIR. Extensive experimental results demonstrate that MetaNIR consistently outperforms representative models across all datasets. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Large Language Model-Based Event Relation Extraction with Rationales 31

Large Language Model-Based Event Relation Extraction with Ra...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (纸本)9798891761964

Event Relation Extraction (ERE) aims to extract various types of relations between different events within texts. Although Large Language Models (LLMs) have demonstrated impressive capabilities in many natural language processing tasks, existing ERE methods based on LLMs still face three key challenges: (1) Time Inefficiency: The existing pairwise method of combining events and determining their relations is time-consuming for LLMs. (2) Low Coverage: When dealing with numerous events in a document, the limited generation length of fine-tuned LLMs restricts the coverage of their extraction results. (3) Lack of Rationale: Essential rationales concerning the results that could enhance the reasoning ability of the model are overlooked. To address these challenges, we propose LLMERE, an LLM-based approach with rationales for the ERE task. LLMERE transforms ERE into a question-and-answer task that may have multiple answers. By extracting all events related to a specified event at once, LLMERE reduces time complexity from O(n2) to O(n), compared to the pairwise method. Subsequently, LLMERE enhances the coverage of extraction results by employing a partitioning strategy that highlights only a portion of the events in the document at a time. In addition to the extracted results, LLMERE is also required to generate corresponding rationales/reasons behind them, in terms of event coreference information or transitive chains of event relations. Experimental results on three widely used datasets show that LLMERE achieves significant improvements over baseline methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Relay Observation Scheduling of Global Distributed Telescope Array Based on Integer Programming

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Research in Astronomy and Astrophysics 2025年第1期25卷 209-225页

作者： Junhan Ju Ce Yu Yi Hu Yajie Zhang Chao Sun Jizeng Wei College of Intelligence and Computing Tianjin UniversityTianjin 300350China Technical R&D Innovation Center National Astronomical Data CenterTianjin 300350China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Certain transients require regular observations over several days at intervals of hours or shorter,which cannot be accomplished by telescopes at a single *** deployment of globally distributed telescopes at geographic locations of different longitudes enables the periodic monitoring of transients through relay ***,the simultaneous relay observation of numerous targets requires a telescope array of multiple telescopes that can be efficiently coordinated,and an automated scheduler for the *** paper proposes IPROS,an integer programming model relay observation scheduler for a telescope array,that accounts for the entire process of relay observation and is consistent with the practical *** introduce the integer programming mathematical model for the relay observation scheduling problem with the telescope array,upon which the scheduler is ***,we propose an algorithm to provide a comprehensive formulation of the optimization objective of minimizing cadence deviation in the *** results demonstrate that the relay observation scheduler based on the integer programming model can effectively address the telescope array relay observation *** shows superiority over a scheduler with non-specific consideration of relay observation in the modeling and a scheduler based on greedy thought.

关键词： telescopes-surveys-methods data analysis-techniques miscellaneous

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LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs

LINKAGE: Listwise Ranking among Varied-Quality References fo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yang, Sihui Bi, Keping Cui, Wanqing Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761681

Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like ROUGE or BERTScore cannot accurately measure semantic similarities or answers from different perspectives. Recently, Large Language Models (LLMs) have been resorted to for NFQA evaluation due to their compelling performance on various NLP tasks. Common approaches include pointwise scoring of each candidate answer and pairwise comparisons between answers. Inspired by the evolution from pointwise to pairwise to listwise in learning-to-rank methods, we propose a novel listwise NFQA evaluation approach, that utilizes LLMs to rank candidate answers in a list of reference answers sorted by descending quality. Moreover, for NF questions that do not have multi-grade or any golden answers, we leverage LLMs to generate the reference answer list of various quality to facilitate the listwise evaluation. Extensive experimental results on three NFQA datasets, i.e., ANTIQUE, the TREC-DL-NF, and WebGLM show that our method has significantly higher correlations with human annotations compared to automatic scores and common pointwise and pairwise approaches. Our code and dataset can be found at https://***/babyyang525/LINKAGE-Listwise-NFQA-Evaluation. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Aerial Image-Based Semantic Segmentation for Forest Fire Identification

Aerial Image-Based Semantic Segmentation for Forest Fire Ide...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Salma, Nadilla Asyifa Sulistiyo, Mahmud Dwi Risnandar Sthevanie, Febryanti Wulandari, Gia Septiana Telkom University School of Computing Bandung Indonesia Telkom University Research Center for Data and Information Sciences Brin School of Computing Bandung Indonesia

ISBN: (纸本)9798350327472

Fire is a serious disaster because it can endanger the safety of living things and cause great losses. Detection of forest fires using sensor-based devices or through analysis of weather data becomes less efficient because it is not real-time, its scope depends on sensor deployment, and sometimes it misses small fire spreads that are potentially dangerous. The key idea in this paper is to utilize a semantic segmentation approach to precisely detect forest fires and thus prevent the spread of larger fires. The proposed method uses aerial images since it is a practical way to tackle problems in fire monitoring and detection. Since similar research is still limited, let alone necessary to be implemented in countries potentially susceptible to forest fires, this study demonstrates deep learning-based semantic segmentation methods as preliminary research for identifying forest fires. We analyzed the implementation of the well-known FCN and U-Net models with some backbone options and used the FLAME dataset to train the models and evaluate them quantitatively. Based on the experimental results, FCN with VGG-16 backbone became the model with the highest Mean IoU value of 0.825. We also tested the best model on several aerial images arbitrarily taken from the internet to see the qualitative performance and analyze its possibility of being applied to situations outside the research dataset. © 2023 IEEE.

关键词： Semantic Segmentation

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Smartphone Fundus Photography Enhancement for Retinopathy of Prematurity Disease Diagnosis Using Deep Learning 6

Smartphone Fundus Photography Enhancement for Retinopathy of...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Mutua, Elizabeth Ndunge Kasamani, Bernard Shwabwabo Reich, Christoph School of Computing & Engineering Sciences Strathmore University Nairobi Kenya Institute for Data Science Cloud Computing & It Society Furtwangen University Furtwangen Germany

ISBN: (纸本)9798350345162

Retinopathy of Prematurity (ROP) is the highest cause of childhood blindness globally with babies born preterm having a higher probability of contracting the disease. The disease diagnosis remains an economic burden to many countries, lack of enough ophthalmologists for the disease diagnosis coupled with non-existent national screening guidelines still remains a challenge. To diagnose the disease, a fundus photography is conducted, printout images are analyzed to determine the presence or absence of the disease. With the increase in the development of smartphones having advanced image capturing and processing features, the utilization of smartphones to capture retina image for disease diagnosis is becoming a common trend. For regions where ophthalmologists are few and/or for low resource regions with few or no retina capturing equipment, the use of smartphones to capture retina images for retina diseases is an effective method. This, however, is challenged: different smartphones produce images of different resolutions;some images are darker others lighter and with different resolution. A smartphone retina image capturing has a smaller field of view ranging between 450-900 which is a major limitation. A lens to support a bigger view can be combined with this approach to provide a wide view of 1300. This enlargement however distorts the image quality and may result in losing some image features. To overcome these challenges, this work develops an improved U-Net model to preprocess images captured using smartphones for ROP disease diagnosis. Our focus is to determine the presence or absence of the disease from smartphone captured images. Because the images are captured under a smaller field of view (FOV), we develop an improved U-Net model by adding patches to enhance image circumference and extract all features from the image and use the extracted features to train a U-Net model for the disease diagnosis. The model results outperformed similar recent developments with

关键词： convolutional neural network fundus photography retina vessel enhancement smartphone photography

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Counseling Responses for Mental Health Forum Questions with Early Maladaptive Schema Prediction 27

Counseling Responses for Mental Health Forum Questions with ...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Gollapalli, Sujatha Das Ang, Beng Heng Du, Mingzhe Ng, See-Kiong Institute of Data Science National University of Singapore Singapore Integrative Sciences and Engineering Programme National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University China

ISBN: (纸本)9781643685489

State-of-the-art Large Language Models (LLMs) have shown remarkable capabilities for general Question Answering (QA) tasks. However, their practical use for answering mental health questions has been limited due to the missing link between LLM-generated answer responses and well-established theories and guiding principles from Psychology and Counseling. We present a first step in this direction with STeer, an AI-based method that supports Schema Therapy-enabled responses for mental health questions on community QA forums. STeer uses Early Maladaptive Schemas (EMSs), a fundamental concept from Schema Therapy that characterizes "self-defeating, unhealthy patterns of thought and behavior" in individuals, to effectively prompt state-of-the-art LLMs to generate specific, theoretically-grounded, empathetic counseling responses to mental health questions. We present EMSRank, a novel method based on the Personalized PageRank algorithm, to automatically predict the EMSs from mental health forum question texts. We show that EMSRank is computationally scalable and can be further combined with textual entailment to obtain high precision, explainable EMS labels for mental health forum questions. To address the current lack of annotated datasets, we also leveraged on EMSRank to create a first-of-its-kind, large dataset of about 23K EMS-annotated mental health questions from three diverse, currently operating, peer-support community forums for mental health. With the global rise in mental health issues, our work is a timely step towards enabling the use of AI-based assistive tools for counseling support on mental health community forums. © 2024 The Authors.

关键词： Electronic health record

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