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Temperature change between neighboring days and hospital admissions in China

Advances in Climate Change Research 2023年第6期14卷 847-855页

作者： Yu-Shu HUANG He-Jia SONG Yi-Bin CHENG Shilu TONG Rui ZHANG Xiao-Yuan YAO Yong-Hong LI China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China Deparment of Big Data in Health Science School of Public Health and Centerof Clinical Big Data and Analytics of the Second Affiliated HospialZhejiang University School of MedicineHangzhou 310058China Jiaxing Center for Disease Control and Prevention Jiaxing 314050China Instinurte of Environmenr and Population Health School of Public HealrhAnhui Medical UniversityHefei 230032China School of Public Health and Social Work Queensland University of TechnologyBrisbane 4006Australia Information Center Chinese Center for Disease Control and PreventionBeiing 102206China

The short-term temperature fluctuation caused by global climate change is one of the risk factors affecting public *** the association between temperature fluctuation and diseases,which has received relatively limited research attention,can contribute to enhancing awareness of health risks and taking timely measures for health risk alert and ***,this study aims to investigate the relationship between temperature change between neighboring days(TCN)and hospitalizations,identify diseases sensitive to extreme TCN,and evaluate the related disease *** collected meteorological and hospitalization data from 2014 to 2019 in 23 sites of China to explore the impact of TCN on *** first quantified site-specific associations between TCN and hospitalizations and then conducted meta-analysis to pool the results,to assess the relative risk of extreme TCN for susceptible diseases,and to estimate the related disease burden attributed to *** analyses were undertaken by age,sex,and disease *** showed that all-cause hospital admission was significantly linked to TCN.A negative TCN(below-1.9℃)in the cool season and a positive TCN(above 1.0℃)in the warm season increased the risk of *** aged 15-64 years,men,and patients with musculoskeletal system or connective tissue diseases were more sensitive to extremely negative TCN during the cool *** aged over 65 years,men,and patients with respiratory diseases were more sensitive to extremely positive TCN during the warm *** attributable fraction to all-cause hospitalization from negative TCN in the cool season was 2.05%(95%CI:-0.90%,4.53%)and from positive TCN in the warm season was 5.79%(95%CI:2.98%,8.31%).Circulatory diseases in the cool season and respiratory diseases in the warm season had the highest disease *** findings indicate that awareness of TCN and its health risks should be promoted and evidence-informed policies are nee

关键词： Temperature change between neighboring days Morbidity Hospital admissions Sensitive diseases Vulnerable populations Disease burden

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"Even if" Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI

arXiv

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arXiv 2023年

作者： Aryal, Saugat Keane, Mark T. School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g., a customer refused a loan might be told "if you asked for a loan with a shorter term, it would have been approved"). Counterfactuals explain what changes to the input-features of an AI system change the output-decision. However, there is a sub-type of counterfactual, semi-factuals, that have received less attention in AI (though the Cognitive sciences have studied them more). This paper surveys semifactual explanation, summarising historical and recent work. It defines key desiderata for semifactual XAI, reporting benchmark tests of historical algorithms (as well as a novel, näıve method) to provide a solid basis for future *** Codes 68-11 © 2023, CC BY.

关键词： Benchmarking

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Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems

arXiv

引用

arXiv 2023年

作者： Cunningham, Eoghan Smyth, Barry Greene, Derek School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

In the recommender systems literature, novelty and diversity have been identified as key properties of useful recommendations. However, these properties have received limited attention in the specific sub-field of research paper recommender systems. In this work, we argue for the importance of offering novel and diverse research paper recommendations to scientists in order to reduce siloed reading, break down filter bubbles, and promote interdisciplinary research. We propose a novel framework for evaluating the novelty and diversity of research paper recommendations that leverages methods from network analysis and natural language processing. Using this framework, we show that the choice of representational method within a larger research paper recommendation system can have a measurable impact on the nature of downstream recommendations, specifically on their novelty and diversity. We describe a paper embedding method that provides more novel and diverse recommendations without sacrificing precision, compared to other state-of-the-art baselines. © 2023, CC BY.

关键词： Knowledge representation

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Counterfactual Explanation Through Constraint Relaxation

Counterfactual Explanation Through Constraint Relaxation

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Sharmi Dev Gupta Barry O'Sullivan Luis Quesada SFI Centre for Research Training in Artificial Intelligence School of Computer Science & IT University College Cork Cork Ireland Insight Centre for Data Analytics

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user constraints. A common approach to recover feasibility is to eliminate the constraints that cause the conflicts in the system. This approach allows the system to provide an explanation as: “if the user is willing to drop some of their constraints, there exists a solution”. However, this form of explanation might not be very informative. A counterfactual explanation is a type of explanation that can provide a basis for the user to recover feasibility by helping them understand what changes can be applied to their existing constraints rather than removing them. We propose an iterative method based on conflict detection and maximal relaxations in over-constrained constraint satisfaction problems to help compute a counterfactual explanation. We have evaluated our approach using well known instances that occur in industrial applications and demonstrated the relevance of multi-point relaxations.

关键词： Programming Iterative methods Artificial intelligence

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Case-based Explanation for Black-Box Time Series and Image Models with Applications in Smart Agriculture 30

Case-based Explanation for Black-Box Time Series and Image M...

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Doctoral Consortium of the 30th International Conference on Case-Based Reasoning, ICCBR-DC 2022

作者： Delaney, Eoin School of Computer Science University College Dublin Belfield Dublin 4 Ireland VistaMilk SFI Research Centre Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Black-box models are frequently deployed for high stakes prediction tasks in a variety of domains (e.g., disease diagnosis and agricultural prediction). The predictions of these opaque systems are often plagued by a lack of transparency, motivating novel research in eXplainable AI (XAI) aiming to understand why a certain prediction was made. One increasingly promising form of explanation is counterfactual explanation where the aim is to elucidate how a prediction could change, given some change in the input space. While the majority of existing work has focused on producing counterfactual explanations for tabular data, significantly less focus has been placed on generating and evaluating counterfactual explanations for time series and image data. Explaining predictions for these data types, arguably, presents a whole new set of issues for XAI, due to the complex and multi-dimensional nature of the data. In this research, we examine how leveraging case-based reasoning (CBR) techniques such as Nearest-Unlike-Neighbors (NUNs) can aid the generation and evaluation of explanations in these domains. We also demonstrate the inadequacies of many traditional techniques that are used to evaluate explanations and highlight the promise of CBR and user studies in the evaluation of explanations. © 2022 Copyright for this paper by its authors.

关键词： Forecasting

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Zero-Inflated Stochastic Block Modeling of Efficiency-Security Tradeoffs in Weighted Criminal Networks

arXiv

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arXiv 2024年

作者： Lu, Chaoyi Durante, Daniele Friel, Nial School of Mathematics and Statistics University College Dublin Ireland Department of Decision Sciences Bocconi Institute for Data Science and Analytics Bocconi University Italy

Criminal networks arise from the unique attempt to balance a need of establishing frequent ties among affiliates to facilitate the coordination of illegal activities, with the necessity to sparsify the overall connectivity architecture to hide from law enforcement. This efficiency-security tradeoff is also combined with the creation of groups of redundant criminals that exhibit similar connectivity patterns, thus guaranteeing resilient network architectures. State-of-the-art models for such data are not designed to infer these unique structures. In contrast to such solutions we develop a computationally-tractable Bayesian zero-inflated Poisson stochastic block model (ZIP-SBM), which identifies groups of redundant criminals with similar connectivity patterns, and infers both overt and covert block interactions within and across such groups. This is accomplished by modeling weighted ties (corresponding to counts of interactions among pairs of criminals) via zero-inflated Poisson distributions with block-specific parameters that quantify complex patterns in the excess of zero ties in each block (security) relative to the distribution of the observed weighted ties within that block (efficiency). The performance of the ZIP-SBM is illustrated in simulations and in a study of summits co-attendances in a complex Mafia organization, where we unveil efficiency-security structures adopted by the criminal organization that were hidden to previous analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Poisson distribution

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Tourism management synergies in Sub-Saharan Africa

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World Affairs 2024年第1期187卷 63-77页

作者： Asongu, Simplice A. Rahman, Mushfiqur Adu-Gyamfi, Richard Salahodjaev, Raufhon School of Economics University of Johannesburg Johannesburg South Africa Department of Economic & Data Science New Uzbekistan University Tashkent Uzbekistan University of Tashkent for Applied Sciences Tashkent Uzbekistan School of Business University of Wales Trinity Saint David London United Kingdom International Trade Centre Geneva Switzerland Tashkent State University of Economics Tashkent Uzbekistan ERGO Analytics Tashkent Uzbekistan

The purpose of this study is to assess how some governance dynamics, such as political stability and the rule of law, moderate the incidence of some macroeconomic factors (i.e., domestic investment and trade openness) on tourism development. The focus of this study is on 47 countries in sub-Saharan Africa with data from 2002 to 2018, and the Generalized Method of Moments is employed as the empirical strategy. From the findings, synergy effects are apparent in the role of the rule of law in moderating domestic investment for tourism development in terms of tourism receipts. It follows that, for the sampled countries, promoting tourism development can be most effective if policies for enhancing domestic investment and promoting the rule of law are implemented simultaneously. © 2024 The Authors. World Affairs published by Wiley Periodicals LLC on behalf of Policy Studies Organization.

关键词： economic growth Generalized Method of Moments GMM governance political stability rule of law sub-Saharan Africa synergy tourism development tourism management

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Introducing a novel bi-functional method for exploiting sentiment in complex information networks

Introducing a novel bi-functional method for exploiting sent...

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作者： Koukaras, Paraskevas Rousidis, Dimitrios Tjortjis, Christos The Data Mining and Analytics Research Group School of Science and Technology International Hellenic University Thessaloniki Greece

This paper elaborates on multilayer Information Network (IN) modelling, utilising graph mining and machine learning. Although, Social Media (SM) INs may be modelled as homogeneous networks, real-world networks contain multi-typed entities, characterised by complex relations and interactions posing as heterogeneous INs. For mining data whilst retaining semantic context in such complex structures, we need better ways for handling multi-typed and interconnected data. This work conceives and performs several simulations on SM data. The first simulation models information, based on a bi-partite network schema. The second simulation utilises a star network schema, along with a graph database offering querying for graph metrics. The third simulation handles data from the previous simulations to generate a multilayer IN. The paper proposes a novel bi-functional method for sentiment extraction of user reviews/opinions across multiple SM platforms, considering the concepts of supervised/unsupervised learning and sentiment analysis. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

关键词： data mining

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Logic-Aware Knowledge Graph Reasoning for Structural Sparsity under Large Language Model Supervision 25

Logic-Aware Knowledge Graph Reasoning for Structural Sparsit...

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34th ACM Web Conference, WWW 2025

作者： Pan, Yudai Hong, Jiajie Zhao, Tianzhe Song, Lingyun Liu, Jun Shang, Xuequn School of Computer Science Northwest Polytechnical University Shaanxi Xi’an China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Shaanxi Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an China

ISBN: (纸本)9798400712746

Knowledge Graph (KG) reasoning aims to predict missing entities in incomplete triples, which requires adequate structural information to derive accurate embeddings. However, KGs in the real world are not as dense as the idealized benchmarks, where sparse graph structures restrict the comprehensive structural information for superior performance. Although the logical semantics in KGs shows its potential in alleviating the impact of structural sparsity, there still exist some challenges. The deficient supervision and the semantic gap of logic make it difficult to introduce logical semantics in sparse KG reasoning. To this end, we propose a novel KG reasoning approach LoLLM injecting logic with the supervised information supplied by the Large Language Model (LLM), which is proved to be effective in evaluating and scoring. Firstly, LoLLM derives structural embeddings employing a graph convolutional network (GCN) with relation-aware and triple-aware attention. LoLLM secondly constructs reasoning paths instantiated from the first-order logic rules extracted from sparse KGs, and injects the logical semantics by a designed LLM-enhanced tuning strategy. We propose a textual loss (TL) and a logical loss (LL) in the optimization and obtain logical tuning embeddings of KG in this process. Finally, LoLLM fuses structural embeddings from the GCN and logical tuning embeddings from the LLM-enhanced tuning for scoring and incomplete triple prediction. Extensive experiments on two sparse KGs and a benchmark show that LoLLM outperforms state-of-the-art structure-based and Language Model (LM)-augmented baselines. Moreover, the logic rules with corresponding confidences provide explicit explanations as an interpretable paradigm. © 2025 Copyright held by the owner/author(s).

关键词： Structural analysis

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Network Intrusion Detection using Supervised and Unsupervised Machine Learning 16

Network Intrusion Detection using Supervised and Unsupervise...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Shahbandayeva, Lala Mammadzada, Ulviyya Manafova, Ilaha Jafarli, Sevinj Adamov, Abzetdin Z. The George Washington University School of Engineering and Applied Science WashingtonDC United States Ada University School of It and Engineering Baku Azerbaijan Ada University Center for Data Analytics Research Baku Azerbaijan

ISBN: (纸本)9781665451628

Traditional intrusion detection systems may effectively detect known attacks and intrusions with predefined signatures. This requires training the systems to detect various versions of the same attack patterns and constantly keep updated databases of known attack signatures. However, as the skills of security researchers and practitioners expand, so do those of attackers. In order to detect attack types that are unknown, undefined, or designed to bypass the signature and pattern-based intrusion detection systems, the need for more intelligent systems arises. Machine learning is widely used in such systems for this purpose. While researchers and security professionals have designed approaches to this problem using various types of machine learning, our hybrid approach attempts to provide a novel way to effectively detect attacks. This is done by using a set of supervised learning algorithms to detect known attacks and unsupervised learning to detect unknown and zero-day attacks. By utilizing the CSE-CIC-IDS 2018 dataset, we have trained our classifiers to detect benign traffic and 14 known attacks with a selection of 23 features. The network traffic flows that are not classified with a specific level of certainty are sent to the clustering phase to be detected as benign or malicious traffic. Our results indicate that the three classification algorithms used, K-Nearest Neighbors, Random Forest, and Artificial Neural Networks, are able to successfully classify the known attacks with F1-scores between 0.93 and 0.969, and the clustering algorithm HDBSCAN is able to successfully cluster unclassified benign and malicious traffic with unknown labels with F1-scores between 0.85 and 0.957. © 2022 IEEE.

关键词： Classification (of information)

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