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Stochastic Security Ephemeral Generation Protocol for 5G Enabled Internet of Things 1

7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021

作者： Al Sibahee, Mustafa A. Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq Faculty of Biological and Physical Sciences Tom Mboya University College Homabay Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen China

ISBN: (数字)9783030959876

ISBN: (纸本)9783030959869

To ensure secure access to the data held in internet of things, many lightweight authentication schemes have been developed using approaches such as symmetric cryptography or hashing operations. Although these schemes achieve forward key secrecy and user anonymity, de-synchronization is a major problem in these protocols. As such, many other schemes have been presented to address this pertinent security challenge. However, some of these schemes are still susceptible to smart card loss attacks among others. In this paper, stochastic security ephemeral generation protocol for 5G enabled internet of things is presented. It is demonstrated to offer mutual authentication and session key agreement. It is also robust against packet replays, eavesdropping and man-in-the-middle attacks. In terms of performance, it has the lowest computation and communication overheads. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Weisfeiler and Leman go machine learning: the story so far

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 15865-15923页

作者： Christopher Morris Yaron Lipman Haggai Maron Bastian Rieck Nils M. Kriege Martin Grohe Matthias Fey Karsten Borgwardt Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science and Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain View CA Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland and Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.

关键词： machine learning for graphs graph neural networks Weisfeiler-Leman algorithm expressivity equivariance

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Clustering Algorithms with Balanced Weights for Geographic data Processing

Clustering Algorithms with Balanced Weights for Geographic D...

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Smart World Congress (SWC), IEEE

作者： Qi Huang Jerome Yen Kenneth B. Kent Wenming Jin Yang Wang Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Faculty of Science and Technology University of Macau Faculty of Computer Science University of New Brunswick Fredericton NB Canada The SIAT-Suntang Big Data&AI Joint Innovation Laboratory Shenzhen China Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

With the rapid growth of geographic data, generated by various sensors and end equipment, new opportunities for research and practical applications can be found in various applications. However, effective utilization of this data often requires the division of geospatial space into smaller, manageable regions. An important challenge is to ensure that these regions with closed data points are balanced in terms of data size distribution (e.g., population density, resource allocation, etc.), creating a double optimization problem. The contributions of this paper are twofold. First, we propose a balance-driven partitioning algorithm, which is a coordinate-descent based algorithm using a dynamic programming technique. Second, we present a clustering-centric algorithm that improves the classic k-means algorithm with an imbalance-penalized function to allow the geographic data to be clustered together not only in terms of geographic location, but also in terms of the per-cluster total sizes in balance. Finally, to evaluate the efficiency of the proposed algorithms, we conducted experiments based on a trace geographic dataset and compared the results with those of the existing clustering algorithms. Our results demonstrate that the proposed algorithms can not only achieve the competitive clustering effects but also exhibit better performance in terms of data-size balance.

关键词： Shape Heuristic algorithms Clustering algorithms Transforms Partitioning algorithms Dynamic programming Sensors Resource management Population density Optimization

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Natural Gas Consumption Forecasting Based on Weighted Ensemble Learning 17th

Natural Gas Consumption Forecasting Based on Weighted Ensem...

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17th African Conference on Research in computer science and Applied Mathematics, CARI 2024

作者： Ait Taleb, Samiha Sebaa, Abderrazak Bouzera, Rafik Aksouh, Anais Ladlani, Randa Hadjout, Dalil Laboratoire LITAN École supérieure en Sciences et Technologies de l’Informatique et du Numérique RN 75 Amizour Bejaïa06300 Algeria LIMED Laboratory Computer Science Department University of Bejaïa Bejaïa06000 Algeria Computer Science Department Faculty of Exact Sciences University of Bejaïa Bejaia06000 Algeria Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783031882258

Algeria is a country renowned for its gas exports, but there are concerns that the increasing local demand may jeopardize its position as an exporter. In the face of these challenges, it is crucial to improve methods for predicting natural gas consumption. This article presents an innovative approach that combines long Short-Term memory (LSTM) and Seasonal AutoRegressive Integrated Moving Average (SARIMA) methods for predicting natural gas consumption. An accurate predictive model was developed using this approach, utilizing a dataset provided by SONELGAZ CD Bejaïa, which includes natural gas consumption data for high-pressure clients from 2015 to 2022. The results demonstrated a prediction error rate of 4.85% for individual client consumption and 4.76% for the entire client set. These findings showcase the effectiveness of the approach in accurately predicting future natural gas consumption. By combining LSTM and SARIMA, the approach offers a significant improvement over individual methods. By anticipating demand fluctuations, this approach contributes to effectively managing this valuable resource, enabling optimal utilization of natural gas in Bejaïa. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Forcasting prediction Gas Consumption LSTM SARIMA

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A Conversational Human-computer Interface for Smart Energy System Simulation Environments 23

A Conversational Human-Computer Interface for Smart Energy S...

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Proceedings of the Winter Simulation Conference

作者： Gabriel Dengler Peter Bazan Reinhard German Pooia Lalbakhsh Ariel Liebmann Laboratory of Computer Networks and Communication Systems Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr Erlangen Germany Department of Data Science and AI Faculty of Information Technology Monash University Clayton Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily.

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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Temporal Feature Matters: A Framework for Diffusion Model Quantization

arXiv

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arXiv 2024年

作者： Huang, Yushi Gong, Ruihao Liu, Xianglong Liu, Jing Li, Yuhang Lu, Jiwen Tao, Dacheng Electrical and Computer Engineering Department Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Software Development Environment Beihang University China Department of Data Science and AI Faculty of IT Monash University Australia Electrical Engineering Department Yale University United States Department of Automation Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore

The Diffusion models, widely used for image generation, face significant challenges related to their broad applicability due to prolonged inference times and high memory demands. Efficient Post-Training Quantization (PTQ) is crucial to address these issues. However, unlike traditional models, diffusion models critically rely on the time-step for the multi-round denoising. Typically, each time-step is encoded into a hypersensitive temporal feature by several modules. Despite this, existing PTQ methods do not optimize these modules individually. Instead, they employ unsuitable reconstruction objectives and complex calibration methods, leading to significant disturbances in the temporal feature and denoising trajectory, as well as reduced compression efficiency. To address these challenges, we introduce a novel quantization framework that includes three strategies: 1) TIB-based Maintenance: Based on our innovative Temporal Information Block (TIB) definition, Temporal Information-aware Reconstruction (TIAR) and Finite Set Calibration (FSC) are developed to efficiently align original temporal features. 2) Cache-based Maintenance: Instead of indirect and complex optimization for the related modules, pre-computing and caching quantized counterparts of temporal features are developed to minimize errors. 3) Disturbance-aware Selection: Employ temporal feature errors to guide a fine-grained selection between the two maintenance strategies for further disturbance reduction. This framework preserves most of the temporal information and ensures high-quality end-to-end generation. Extensive testing on various datasets, diffusion models and hardware confirms our superior performance and acceleration. © 2024, CC BY.

关键词： Spatio-temporal data

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Realizability of Rectangular Euler Diagrams

arXiv

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arXiv 2024年

作者： Dürrschnabel, Dominik Priss, Uta Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design Kassel Germany Faculty of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany

Euler diagrams are a tool for the graphical representation of set relations. Due to their simple way of visualizing elements in the sets by geometric containment, they are easily readable by an inexperienced reader. Euler diagrams where the sets are visualized as aligned rectangles are of special interest. In this work, we link the existence of such rectangular Euler diagrams to the order dimension of an associated order relation. For this, we consider Euler diagrams in one and two dimensions. In the one-dimensional case, this correspondence provides us with a polynomial-time algorithm to compute the Euler diagrams, while the two-dimensional case is linked to an NP-complete problem which we approach with an exponential-time *** Codes 06A07, 68R05 Copyright © 2024, The Authors. All rights reserved.

关键词： Formal concept analysis

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Brain Age Estimation Using Structural MRI: A Clustered Federated Learning Approach

Brain Age Estimation Using Structural MRI: A Clustered Feder...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Seyyed Saeid Cheshmi Abtin Mahyar Anita Soroush Zahra Rezvani Bahar Farahani Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

Estimating brain age based on structural Magnetic Resonance Imaging (MRI) is one of the most challenging and prominent areas of research in recent medical imaging and neuroscience studies. The significance of brain age prediction in the early diagnosis of neurological disorders has fueled a resurgence of interest in this field. Various studies have addressed this issue using a spectrum of techniques, from traditional machine learning to deep neural networks. The majority of these techniques employ centralized paradigms, which do not adequately preserve privacy. To tackle this problem, a handful of studies have utilized a federated approach. In this study, we propose a novel hierarchical clustered federated learning approach that carefully captures and considers the similarities of the clients’ predictions on a certain benchmark dataset. This method enhances performance in Non-Independent and Identically Distributed (Non-IID) environments while preserving privacy. We use a multi-site dataset that provides a broad variety of MRI scans, characterizing a proper Non-IID environment. Our method achieves a Mean Absolute Error (MAE) of 3.86, while the non-clustered FedAvg federated approach attains a 4.14 MAE on the test set. 1 1 Code is available at https://***/Abtinmy/Clustered-FL-BrainAGE.

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Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review

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Informatics in Medicine Unlocked 2023年 38卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa

Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the treatment recommendations despite the counsel provided to patients on the importance of medication adherence (MA). Early detection of non-communicable disease (NCD) patients poorly adhering to recommended medications using analytics based on machine learning (ML) may improve the outcomes of NCD patients positively. This paper presents a systematic review of literature involving the application of ML in evaluating MA amongst NCD patients. The articles considered in this study were extracted from Web of science, Google Scholar, PubMed, and IEEE Explore. Twenty-five articles in total met the criteria for inclusion. These were articles that utilised ML techniques to analyse MA in NCDs, with patients suffering from diabetes (n = 8), hypertension (n = 3), cardiovascular disease (CVD) and statin adherence (n = 6), cancer (n = 3), respiratory diseases (n = 2), and other NCD conditions (n = 3). The proportion of days covered (PDC) was typically used to evaluate MA. It emerged from the study that for MA to be considered high, the adherence threshold should be at least 75% of the PDC, a universally accepted threshold. In MA analytics research and practice, a PDC ≥80% threshold is typically regarded as a high level of adherence to prescription medication. Logistic regression (LR) (n = 12), random forest (RF) (n = 11), support vector machine (SVM) (n = 7), neural net (n = 6), ensemble learning (n = 6), MLPs (n = 4), XGBoost (n = 3), Bayesian network (BN) (n = 3), and gradient boosting (n = 3) were the most frequently applied ML techniques in the analytics of MA amongst NCD patients. It should be underscored that leveraging standard ML, deep learning (DL), and ensemble learning has eno

关键词： Cancer Cardiovascular disease (CVD) Chronic disease Deep learning Diabetes Hypertension Machine learning Medication adherence NCD patients Non-communicable disease Respiratory disease

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