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Dynamic hidden-variable network models

Physical Review E 2021年第5期103卷 052307-052307页

作者： Harrison Hartle Fragkiskos Papadopoulos Dmitri Krioukov Network Science Institute Northeastern University Boston 02115 Massachusetts USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Northeastern University Departments of Physics Mathematics and Electrical & Computer Engineering Boston 02115 Massachusetts USA

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

关键词： Complex systems Evolving networks Network structure Block models Evolving network models Fitness models

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz

Investigation of the Impact of Height and Bandwidth on Path ...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving

arXiv

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arXiv 2022年

作者： Liao, Brian Hsuan-Cheng Cheng, Chih-Hong Esen, Hasan Knoll, Alois DENSO AUTOMOTIVE Deutschland GmbH Eching85386 Germany The Department of Computer Science and Engineering Chalmers University of Technology Göteborg41296 Sweden The Department of Informatics Technical University of Munich Garching85748 Germany

We consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. Specifically, despite impressive results shown by the mass literature, developers often find it hard to ensure the safe deployment of these learning-based perception models. Attributing the challenge to the lack of safety-oriented metrics, we hereby present uncompromising spatial constraints (USC), which characterize a simple yet important localization requirement demanding the predictions to fully cover the objects when seen from the autonomous vehicle. The constraints, as we formulate using the perspective and bird's-eye views, can be naturally reflected by quantitative measures, such that having an object detector with a higher score implies a lower risk of collision. Finally, beyond model evaluation, we incorporate the quantitative measures into common loss functions to enable safety-oriented fine-tuning for existing models. With experiments using the nuScenes dataset and a closed-loop simulation, our work demonstrates such considerations of safety notions at the perception level not only improve model performances beyond accuracy but also allow for a more direct linkage to actual system safety. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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Semi-supervised Embedding for Scalable and Accurate Time Series Clustering

Semi-supervised Embedding for Scalable and Accurate Time Ser...

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IEEE International Conference on Big Data

作者： Andrew Hill Russell Bowler Katerina Kechris Farnoush Banaei-Kashani Department of Computer Science and Engineering University of Colorado Denver Denver CO USA National Jewish Health Denver CO USA Department of Biostatistics and Informatics University of Colorado Denver—Anschutz Aurora CO USA

ISBN: (纸本)9781665480468

While time series data are abundant in numerous real world applications, large labeled time series datasets are scarce. Semi-supervised models, which leverage small amounts of labeled data along with a large set of unlabeled data, have been shown to significantly outperform unsupervised learning models that only rely on unlabeled data for time series clustering. However, existing semi-supervised time series clustering algorithms suffer from lack of scalability as they are limited to perform learning operations within the original data space. We propose a scalable and accurate autoencoder-based semi-supervised learning model for time series clustering in the embedded space. With this model, we also introduce multiple semi-supervised objective functions that leverage only a small number of labeled examples but significantly improve the quality of the autoencoder’s learned latent space for clustering. Our experiments on a variety of datasets show that our methods can often improve performance of a typical clustering method (namely, k-means). We demonstrate that our methods achieve a maximum average Adjusted Rand Index (ARI) of 0.897, a 140% increase over an unsupervised Convolutional Autoencoder (CAE) model. Finally, our proposed methods also achieve a maximum improvement of 44% over an existing semi-supervised model.

关键词： Training Adaptation models Veins Time series analysis Predictive models Big Data Semisupervised learning

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Fine-Grained Activity Classification in Assembly Based on Multi-Visual Modalities

Research Square

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Research Square 2022年

作者： Chen, Haodong Zendehdel, Niloofar Leu, Ming C. Yin, Zhaozheng Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology RollaMO65409 United States Department of Biomedical Informatics Department of Computer Science Stony Brook University Stony Brook NY117943 United States

Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red-green-blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. © 2022, CC BY.

关键词： Forecasting

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Artificial neural network architecture and orthogonal arrays in estimation of software projects efforts

Artificial neural network architecture and orthogonal arrays...

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2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

作者： Rankovic, Nevena Rankovic, Dragica Ivanovic, Mirjana Lazic, Ljubomir Chair of Computer Science School of Computing Union University Belgrade Serbia Chair of Mathematics School of Computing Union University Belgrade Serbia Faculty of Sciences University of Novi Sad Department of Mathematics and Informatics Novi Sad Serbia Chair of Computer Engineering School of Computing Union University Belgrade Serbia

ISBN: (纸本)9781665436038

Accurate assessment of software project development using the proper artificial intelligence tools can be a significant challenge for success in the software industry. This paper aims to minimize the relative error in software estimation using the proposed model of an artificial neural network (ANN) based on Taguchi's orthogonal vector plan. By selecting methods of clustering and fuzzification of different project values within several used datasets such as COCOMO2000, NASA60, and Kemerer15, reducing the number and time of iterations minimizes Mean Magnitude Relative Error (MMRE) and include a wide range of observed data. Additional criteria, such as monitoring prediction, correlation, and comparison with RBF (Radial Basis Function) relative error, were used to confirm that the proposed model gives two to three times better results depending on the observed cluster. Based on the obtained results, the accuracy and reliability of the proposed model for estimating software projects were determined. © 2021 IEEE.

关键词： Radial basis function networks

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Cooperative, Connected and Autonomous Mobility: Coordination at Intersections Using Reservation-based Mechanisms

Cooperative, Connected and Autonomous Mobility: Coordination...

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International Conference on Intelligent Transportation

作者： Francisco Filipe Zafeiris Kokkinogenis Pedro M. d'Orey Rosaldo J. F. Rossett Faculty of Engineering University of Porto Porto Portugal Department of Informatics Engineering Laboratory of Artificial Intelligence and Computer Science (LIACC) Faculty of Engineering University of Porto Porto Portugal CISTER ISEP Polytechnic Institute of Porto Portugal Departmento de Ciência de Computadores Faculdade de Ciências Universidade do Porto Porto Portugal

Road transportation is fundamental for the movement of individuals and goods, also contributing to economic development. A significant contributor to urban road congestion is poor intersection control using conventional traffic signals. In this work, we present a decentralized multi-agent system mechanism for road intersection management for connected autonomous vehicles, including the coordination of platoon formations. We propose a reservation-based mechanism able to maximize the overall vehicle throughput at intersections. The study introduces i) auctions as an alternative to the First-Come-First-Serve policy for assigning reservations to vehicles and ii) a method for resolving disputes between conflicting reservations. The results demonstrate the benefits of using platooning for improving throughput and the average delay in intersection control. The distributed nature of the approach increases scalability by shifting the majority of the computing burden from the intersection manager to the driving agents.

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Spatially temporally distributed informative path planning for multi-robot systems

arXiv

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arXiv 2024年

作者： Nguyen, Binh Nguyen, Linh Nghiem, Truong X. La, Hung Baca, José Rangel, Pablo Montoya, Miguel Cid Nguyen, Thang The Department of Engineering Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States The School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States The Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC3842 Australia Lab Department of Computer Science and Engineering University of Nevada RenoNV89557 United States

This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multistep prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets. © 2024, CC BY.

关键词： Motion planning

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Exploring DeepDream and XAI Representations for Classifying Histological Images

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SN computer Science 2024年第4期5卷 362页

作者： Martinez, João Manoel Cardoso Neves, Leandro Alves Longo, Leonardo Henrique da Costa Rozendo, Guilherme Botazzo Roberto, Guilherme Freire Tosta, Thaína Aparecida Azevedo de Faria, Paulo Rogério Loyola, Adriano Mota Cardoso, Sérgio Vitorino Silva, Adriano Barbosa do Nascimento, Marcelo Zanchetta Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) Rua Cristóvão Colombo 2265 SP São José do Rio Preto 15054-000 Brazil Department of Informatics Engineering - Faculty of Engineering University of Porto Dr. Roberto Frias sn Porto 4200-465 Portugal Science and Technology Institute (ICT) Federal University of São Paulo (UNIFESP) Av. Cesare Mansueto Giulio Lattes 1201 SP São José dos Campos 12247-014 Brazil Department of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU) Av. Amazonas S/N MG Uberlândia 38405-320 Brazil Area of Oral Pathology School of Dentistry Federal University of Uberlândia (UFU) R. Ceará - Umuarama MG Uberlândia 38402-018 Brazil Faculty of Computer Science (FACOM) Federal University of Uberlândia (UFU) Av. João Naves de Ávila 2121 MG Uberlândia 38400-902 Brazil

The application of machine learning methods to analyze histological images has led to significant advances in computer-aided diagnosis. In this work, a study was carried out to classify patterns present in histological samples stained with hematoxylin-eosin, representative of breast cancer, colorectal cancer, liver tissue, and oral dysplasia. The proposed method explored convolutional neural networks (VGG19, ResNet50, InceptionV3, and EfficientNet B2), gradient-weighted class activation mapping, local interpretable model-agnostic explanations and representations based on the inceptionism effect (DeepDream). The deep features were analyzed via multiple classifiers (rotation forest, multilayer perceptron, logistic, random forest, decorate, k-nearest neighbor, KStar, and support vector machine) to indicate the main associations. The best solution was obtained through the ResNet50 model with the representations from the VGG19 architecture, exploring feature vectors with 40 and 100 attributes. The results were AUC rates ranging from 0.994 (breast cancer dataset) to 1.0 (colorectal dataset). The most explored deep features were obtained via original images (highest occurrence), followed by local interpretable model-agnostic explanations, DeepDream, and gradient-weighted class activation mapping representations. These results can support the development of techniques addressed to pattern recognition of H &E images with more robust computer-aided diagnoses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Deep features DeepDream representations Histological images XAI

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