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International Conference on Computational Intelligence and Security

作者： Xiaobo Song Xinyi Zhang Xiaoli Wang Yuan Li The 20th Research Institute of China Electronics Technology Group Corporation Xi'an Shaanxi China School of Computer Science and Technology Xidian University Xi'an Shaanxi China Basic Course Teaching Department Xi'an Vocational and Technical College Xi'an Shaanxi China

Since climate changes and air pollution have a direct impact on the number of emergency respiratory patients visited, in this paper, we study predicting methods of daily patient visits in respiratory department based on deep learning. We validate our methods via experiments on the actual data of the First Affiliated Hospital of Xi'an Medical University. To improve the accuracy of prediction, we propose four data preprocessing methods, including meteorological lag and time sensitive method, sliding window method, seasonal classification method, as well as correlation analysis method. Experimental results show that the prediction accuracy of the deep learning model is improved from 76.28% to 86.37% after adopting our four preprocessing methods. The maximum error between predicted and actual visits is less than 20.

关键词： Climate change Data processing Patient monitoring Deep learning Medical services Respiratory system Predictive models

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Sequen-C: A multilevel overview of temporal event sequences

arXiv

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arXiv 2021年

作者： Castaneda, Jessica Gisela Magallanes Stone, Tony Morris, Paul D. Mason, Suzanne Wood, Steven Villa-Uriol, Maria-Cruz Department of Computer Science Centre for Urgent and Emergency Care Research Department of Infection Immunity and Cardiovascular Disease University of Sheffield United Kingdom Sheffield Teaching Hospitals NHS Foundation Trust

Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build a multilevel overview of event sequences, whose granularity can be transformed across sequence clusters (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric - then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III;which demonstrate how the technique can aid users to obtain a summary of common and deviating pathways, and explore data attributes for selected patterns. © 2021, CC BY.

关键词： Data visualization

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Interventional Contrastive Learning with Meta Semantic Regularizer

arXiv

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arXiv 2022年

作者： Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner. Although the prevailing CL model has achieved great progress, in this paper, we uncover an ever-overlooked phenomenon: When the CL model is trained with full images, the performance tested in full images is better than that in foreground areas;when the CL model is trained with foreground areas, the performance tested in full images is worse than that in foreground areas. This observation reveals that backgrounds in images may interfere with the model learning semantic information and their influence has not been fully eliminated. To tackle this issue, we build a Structural Causal Model (SCM) to model the background as a confounder. We propose a backdoor adjustment-based regularization method, namely Interventional Contrastive Learning with Meta Semantic Regularizer (ICL-MSR), to perform causal intervention towards the proposed SCM. ICL-MSR can be incorporated into any existing CL methods to alleviate background distractions from representation learning. Theoretically, we prove that ICL-MSR achieves a tighter error bound. Empirically, our experiments on multiple benchmark datasets demonstrate that ICL-MSR is able to improve the performances of different state-of-the-art CL methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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A privacy preserving algorithm for mining rare association rules by homomorphic encryption 6

A privacy preserving algorithm for mining rare association r...

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6th International Conference on Systems and Informatics, ICSAI 2019

作者： Ouyang, Weimin Huang, Qinhua Shanghai University of Political Science and Law Department of Computer Teaching Shanghai China

ISBN: (纸本)9781728152561

Privacy-preserving data mining have greate significance in the era of big data. The Privacy-preserving condition on rare association rules mining is about the sensitive information regarding participants. Each side have a private dataset, aims to collaboratively find rare association rules on data set like a logically unified frame, but actually composed of distributed private data set. We proposed a new efficient algorithm to discover privacy-preserving rare association rule mining technique. The main principle idea is that with the secure two-party computation theory we employ homomorphic encryption to hide the private information. © 2019 IEEE.

关键词： Association rules

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Big Data Security and Privacy Protection in Colleges and Universities

Big Data Security and Privacy Protection in Colleges and Uni...

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International Conference on Intelligent Systems Applications in Multi-modal Information Analytics, 2019

作者： Guo, Jinlan Computer Teaching Department Shanghai University of Political Science and Law Shanghai China

ISBN: (纸本)9783030157388

An increasing number of universities choose to deploy big data and cloud computing to integrate information from various departments, and adopt data mining technology to improve teaching management levels and decision-making abilities. Based on the acquisition, storage, application and publishing of data, this paper proposes the whole technology framework of big data security system from three levels of network, equipment and application, and puts forward to strategies which can protect individual privacy in each link. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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Improving Lagos State emergency medical services by analysing road traffic accident data, response time, and efficient allocation of ambulances

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The Lancet. Global health 2022年 10卷 S26-S26页

作者： Salam, Sayeed, PhD Mehta, Avirut Kim, Dohyeong, PhD Seyi-Olajide, Justina, PhD Allo, Nicholas, PhD Malolan, Chenchita, MPH Nwariaku, Fiemu, MD Khan, Latifur, PhD Department of Computer Science University of Texas at Dallas Richardson TX United States Department of Public Policy and Political Economy Geospatial Information Sciences University of Texas at Dallas Richardson TX United States Lagos University Teaching HospitalLagos Nigeria Visual Earth London United Kingdom University of Texas Southwestern Medical Center University of Texas Dallas United States Department of Computer Science University of Texas at Dallas Richardson TX United States

BACKGROUND: Emergency medical services (EMS) play a critical role in the response to road traffic accidents (RTAs) by providing critical prehospital care to RTA victims, and thus helping to reduce the severity of injuries and casualties from RTAs. Analysing the efficiency of such systems and optimising the service (often with insufficient resources) would be highly beneficial to inform operating guidelines. In this study, we aimed to assess the ambulance utilisation rate for RTAs and provide insights about the system's efficiency and utilisation metrics in Lagos State, Nigeria. methods: We received hospital capacity information (type of hospital [eg, tertiary] and number of beds, nurses, operating rooms, and so on) from hospital survey data in Lagos. Additionally, we received RTA-specific intervention forms from the Lagos State Ambulance Service. With the limited amount of historical data, we highlighted methods to estimate the system parameters (eg, arrival rate, service rate, and so on) of RTA victims at various hospitals in Lagos. We also developed a parametric model and applied approximate dynamic programming approach for ambulance dispatch and location optimisation with a fixed number of base stations. Furthermore, our routing approach was accompanied by directions from OpenRouteService and queue analysis to determine the most appropriate hospital near an RTA for the EMS systems in Lagos. FINDINGS: Using patient-queue based wait-time analysis, we found that in over 80% of cases, one of the nearest three hospitals was the most appropriate recommendation for patient delivery. This exponentially decays with the next closest hospitals. Additionally, we found that optimising the ambulance relocation method, where the ambulance returns to a base selected by our model as opposed to its dedicated base or a random base, can reduce the average response time of the EMS system by 8%, or 3·5 min (from 41 min to 38 min). INTERPRETATION: Appropriate placement of mobile resour

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Mathematical Modeling of the Orthotropy Effect in the Problems of DC Electrical Exploration by Boundary and Near-boundary Element methods 15

Mathematical Modeling of the Orthotropy Effect in the Proble...

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15th IEEE International Scientific and Technical Conference on computer sciences and Information Technologies, CSIT 2020

作者： Zhuravchak, Liubov Zabrodska, Nataliya Lviv Polytechnic National University Software Department Institute of Computer Science and Information Technologies Lviv Ukraine National Academy of Sciences Department of Geoelectromagnetic Methods Carpathian Branch of S.I. Subbotin Institute of Geophysics Lviv Ukraine

ISBN: (纸本)9781728174433

Mathematical models of the electric field distribution of direct current (DC) in 2D and 3D orthotropic piecewise-homogeneous half-infinite media, taking into account an ideal contact between components, have been constructed. To find solutions of the respective problems of potential theory, indirect methods of boundary and near-boundary elements have been used. The orthotropy effect in homogeneous and piecewise-homogeneous half-spaces has been investigated. A software implementation of the proposed approaches has been carried out. © 2020 IEEE.

关键词： Boundary element method

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Evolving an efficient and effective off-the-shelf computing infrastructure for rural communities of South Africa 11th

Evolving an efficient and effective off-the-shelf computing ...

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11th International Development Informatics Association Conference, IDIA 2020

作者： Siebörger, Ingrid Terzoli, Alfredo Hodgkinson-Williams, Cheryl Department of Information Systems Rhodes University Makhanda South Africa Department of Computer Science Rhodes University Makhanda South Africa Centre for Innovation in Learning and Teaching University of Cape Town Cape Town South Africa

ISBN: (纸本)9783030520137

Information Communication Technologies (ICTs) have diffused into almost every area of life for citizens living in the Global North. Data is seen as a key element in the fourth industrial revolution and is the foundation of Information, Knowledge and Wisdom. Knowledge has become the new "make or break" asset and an inability to access the world’s data and technologies that facilitate access, synthesis and interrogation of data places one at a disadvantage. In 2013, the department of Communications in South Africa announced a new national broadband policy to promote the reduction of a digital divide and sup-port citizens and the economy in digital interactions. Implementation has thus far not been successful. Furthermore, the Broadband policy does not address how South Africans are to access the Internet once available. The Siyakhula Living Lab (SLL) provides an example of a computing infrastructure model for the introduction of ICTs into rural communities. Through the deployment of "broadband islands" and low-cost computing infrastructure to promote both knowledge creation and consumption, access to the proposed ubiquitous Internet connectivity can be unlocked. However, the initial computing infrastructure deployed to the SLL suffers from a single point of failure. In this paper, we discuss alternative computing infrastructure configurations that were tested and deployed within the SLL in order to determine a more appropriate computing infrastructure model for the SLL and potentially other rural South African communities;such that learners, teachers and community members can be active contributors and consumers of data, information, knowledge and wisdom. © Springer Nature Switzerland AG 2020.

关键词： Costs

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Event-based Pathology Data Prioritisation: A Study using Multi-variate Time Series Classification 13

Event-based Pathology Data Prioritisation: A Study using Mul...

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13th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021

作者： Qi, Jing Burnside, Girvan Charnley, Paul Coenen, Frans Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Department of Biostatistics University of Liverpool LiverpoolL69 3BX United Kingdom Wirral University Teaching Hospital NHS Foundation Trust Arrowe Park Hospital Wirral CH49 5PE United Kingdom

ISBN: (纸本)9789897585333

A particular challenge for any hospital is the large amount of pathology data that doctors are routinely presented with. Pathology result analysis is routine in hospital environments. Some form of machine learning for pathology result prioritisation is therefore desirable. Patients typically have a history of pathology results, and each pathology result may have several dimensions, hence time series analysis for prioritisation suggests itself. However, because of the resource required, labelled prioritisation training data is typically not readily available. Hence traditional supervised learning and/or ranking is not a realistic solution and some alternative solution is required. The idea presented in this paper is to use the outcome event, what happened to a patient, as a proxy for a ground truth prioritisation data set. This idea is explored using two approaches: kNN time series classification and Long Short-Term Memory deep learning. Copyright © 2021 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Long short-term memory

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Particle Swarm Optimization and L´evy Flight integration

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Procedia computer science 2021年 192卷 4658-4671页

作者： Joanna Kołodziejczyk Yuliia Tarasenko Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żołnierska 49 71-210 Szczecin Poland

Particle Swarm Optimization (PSO) is a well-known and popular stochastic optimization method. The Lévy Flight (LF) properties were used to improve the canonical PSO known as premature convergence. The Lévy Flight was applied to change each particle walk on the fitness landscape. We analyze the literature modifications that concluded that Levy flight improved the PSO providing better search space exploration. Based on this conclusion, we propose new approaches to integrate Lévy Flight with PSO by changing initial points in the search space and learning strategies as inertia and constriction coefficients. We use seven standard test functions for an experimental evaluation and scores based on ranking to compare PSO variants. The ranked benchmarks were average performance, standard deviation, and best and worst found solutions obtained from multiple trials. The main contributions are a systematic overview of LF modifications applied in PSO and three new LF applications in canonical PSO procedure. The new approaches are swarm initialization based on LF, lower dimension LF inertia coefficient, and LF-based constriction factor. Another contribution is numerical evaluations on various benchmark functions with diverse characteristics. Two of the proposed modifications performed better or equal, and the third was only 2% worse than the best canonical PSO from the trial.

关键词： Particle Swarm Optimization Lévy Flight stochastic optimization

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