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Adaptive Optimization and Resource Allocation (AORA) Model for IoT-Edge computing Using Hybrid Newton-Raphson and Dolphin Echolocation Algorithm (HNR-DEA) Technique

Adaptive Optimization and Resource Allocation (AORA) Model f...

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Advanced computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan A. Shiny K. Dharani Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The swift expansion of Internet of Things (IoT) applications demands the implementation of real-time data processing and low-latency communication, thereby posing challenges to conventional cloud computing paradigms. This study confronts these issues by amalgamating edge computing with hybrid optimization methodologies, and introduces the AORA (Adaptive Optimization and Resource Allocation) framework. The AORA framework capitalizes on intelligent task scheduling, dynamic resource allocation, and energy-efficient approaches to augment the performance of edge *** research methodology encompasses the collection and preprocessing of data derived from IoT devices, feature extraction, prediction of resource demand, and the optimization of task offloading. It employs the Hybrid Newton Raphson and Dolphin Echolocation Algorithm (HNR-DEA) methodology, which synergizes Newton Raphson optimization technique for faster convergence rate around local optima with bio-inspired Dolphin Echolocation Algorithm (DEA) for comprehensive global *** Experimental outcomes reveal considerable advancements, including a 45% reduction in average latency, a 38% decrease in overall energy consumption, and a 23% enhancement in resource utilization. Furthermore, the model attained a 97% task completion rate alongside consistently elevated network throughput. Simulations were executed in CloudSim with 100 IoT devices generating dynamic workloads and edge servers equipped with multi-core processors. The experimental framework utilized Google Cluster Data to replicate workload variations, thereby ensuring scalability and robustness. These results underscore the AORA model's potential as a resilient and scalable solution for real-time IoT applications.

关键词： Adaptation models Computational modeling Scalability Prediction algorithms Real-time systems Internet of Things Resource management Newton method Optimization Dolphins

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Creating illusive perceived assistive force using visual feedback

Creating illusive perceived assistive force using visual fee...

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IEEE International Conference on systems, Man and Cybernetics

作者： Swagata Das Velika Wongchadakul Ramin Tadayon Yuichi Kurita Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan School of Computing Informatics & Decision Systems Engineering Arizona State University Tempe Arizona USA

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

This paper ponders the effects of introducing an assistive force visual display on a subject's force perception. Correct and incorrect displays were tested and were found to significantly affect the subject's force perception as compared to no visual feedback case. The elbow joint was considered for this study and assisted with four wearable artificial muscles called pneumatic gel muscles (PGMs). It was observed that subjects tend to underestimate the assistive force. The introduction of a visual display could significantly reduce this force underestimation. Moreover, the level of force underestimation was higher in case of higher force values. In other words, the higher the actual force provided, the higher the level of force underestimation in all three conditions: correct, incorrect and no visual feedback. It was also observed that the perceived error (perceived force - actual force) was directly in proportion with the visual error (visual force - actual force).

关键词： Visualization Conferences Force Muscles Elbow Robots Cybernetics

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Decentralized attribution of generative models

arXiv

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arXiv 2020年

作者： Kim, Changhoon Ren, Yi Yang, Yezhou School of Computing Informatics and Decision Systems Engineering Arizona State University United States School for Engineering of Matter Transport and Energy Arizona State University United States

Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated from. Existing studies showed empirical feasibility of attribution through a centralized classifier trained on all user-end models. However, this approach is not scalable in reality as the number of models ever grows. Neither does it provide an attributability guarantee. To this end, this paper studies decentralized attribution, which relies on binary classifiers associated with each user-end model. Each binary classifier is parameterized by a user-specific key and distinguishes its associated model distribution from the authentic data distribution. We develop sufficient conditions of the keys that guarantee an attributability lower bound. Our method is validated on MNIST, CelebA, and FFHQ datasets. We also examine the trade-off between generation quality and robustness of attribution against adversarial post-processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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Identification of the K-most Vulnerable Entities in a Smart Grid System

Identification of the K-most Vulnerable Entities in a Smart ...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Sohini Roy Arunabha Sen School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona USA

ISBN: (数字)9781728187044

ISBN: (纸本)9781728187051

A smart grid system can be considered as a multi-layered network with power network in one layer and communication network in the other. The entities in both the layers exhibit complex intra-and-interdependencies between them. A reliable decision making by the smart grid operator is contingent upon correct analysis of such dependencies between its entities and also on accurate identification of the most critical entities in the system. The Modified Implicative Interdependency Model (MIIM) [1] successfully captures such dependencies using multi-valued Boolean Logic based equations called Interdependency Relations (IDRs) after most of the existing models made failed attempts in doing that. In this paper, for any given integer K, this model is used to identify the K-most vulnerable entities in a smart grid, failure of which can maximize the network damage. Owing to the problem being NP complete, an Integer Linear Programming (ILP) based solution is given here. Validation of the model [1] and the results of the ILP based solution is done by simulating a smart grid system of IEEE 14-Bus using MATPOWER and Java Network Simulator (JNS). Simulation results prove that not only the model MIIM [1] is correct but also it can predict the network damage for failure of K-most vulnerable entities more accurately than its predecessor Implicative Interdependency Model (IIM) [2].

关键词： Smart grids Communication networks Mathematical model Integer linear programming Power system faults Power system protection Steady-state

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Structural Dependency Aware Service Chain Mapping for Network Function Virtualization

Structural Dependency Aware Service Chain Mapping for Networ...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Arunabha Sen Sandipan Choudhuri Kaustav Basu School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona USA

ISBN: (数字)9781728163000

ISBN: (纸本)9781728163017

Network Function Virtualization (NFV) has emerged as an attractive technology solution to improve the efficiency and flexibility of network services through a technique called Service Chain mapping. A Network Service may be composed of several components (sub-services) and a subset of the physical network nodes may be able to provide each of the individual sub-services. The goal of the Service Chain Mapping problem is to select a physical network node to get the individual sub-services executed and making sure that results from execution of one sub-service flows smoothly to the physical node selected for execution of the next sub-service. Recently, there is increasing awareness in the research community that there exists significant dependency between network nodes. In order to seamlessly deal with failures, provisions are made for redundant services (and sub-services). However, unless the Service Chain mapping is carried out carefully, both the primary and the backup service providers may be unavailable due to the failure of one single physical node. Even if the backup service provider is available, the increased delay to access the backup node may be unacceptably high. Recognizing the importance of structural dependency, researchers have recently started studying dependency-aware Service Chain mapping. In this paper, we draw attention to some of the shortcomings of a recently proposed metric for measuring structural dependency. In addition, we introduce a notion of utility of a path for data transfer from a source node u to a destination node v, to provide a dynamic programming based algorithm for mapping a service chain on a physical network which ensures that the loss of utility due to failure of physical network nodes is minimized.

关键词： Data transfer Indexes Reliability engineering Communication networks Delays

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Predictive modeling of periodic behavior for human-robot symbiotic walking

arXiv

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arXiv 2020年

作者： Clark, Geoffrey Campbell, Joseph Sorkhabadi, Seyed Mostafa Rezayat Zhang, Wenlong Amor, Heni Ben School of Computing Informatics and Decision Systems Engineering Arizona State University Polytechnic School Arizona State University MesaAZ85212 United States

We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21◦ in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects. Copyright © 2020, The Authors. All rights reserved.

关键词： Prosthetics

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Causal Deep Operator Networks for Data-Driven Modeling of Dynamical systems

TechRxiv

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TechRxiv 2023年

作者： Nghiem, Truong X. Nguyen, Thang Nguyen, Binh T. Nguyen, Linh School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Engineering Texas A&M University–Corpus Christi Corpus ChristiTX United States Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC Australia Northern Arizona University United States

The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality – a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control. © 2023, CC BY.

关键词： Dynamical systems

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Spatio-temporal anomaly detection, diagnostics, and prediction of the air-traffic trajectory deviation using the convective weather 11

Spatio-temporal anomaly detection, diagnostics, and predicti...

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11th Annual Conference of the Prognostics and Health Management Society, PHM 2019

作者： Zhao, Xinyu Yan, Hao Li, Jing Pang, Yutian Liu, Yongming School of Computing Informatics and Decision Systems Engineering TempeAZ85281 United States School for Engineering of Matter Transport and Energy TempeAZ85287 United States

ISBN: (纸本)9781936263295

With ahead-of-time aircraft management, we are able to reduce aircraft collision and improve air traffic capacity. However, there are various impact factors which will cause a large deviation between the actual flight and the original flight plan. Such uncertainty will result in an inappropriate decision for flight management. In order to solve this problem, most of the existing research attempt to build up a stochastic trajectory prediction model to capture the influence of the weather. However, the complexity of the weather information and various human factors make it hard to build up an accurate trajectory prediction framework. Our approach considers the problem of trajectory deviation as the "anomaly" and builds up an analytics pipeline for anomaly detection, anomaly diagnostics, and anomaly prediction. For anomaly detection, we propose to apply the CUSUM chart to detect the abnormal trajectory point which differs from the flight plan. For anomaly diagnostics, we would like to link the entire anomalous trajectory sequences with the convective weather data and extract important features based on time-series feature engineering. Furthermore, XGBoost was applied to detect the anomalous trajectory sequences based on the time-series features. For anomaly prediction, we will build up a point-wise prediction framework based on the Hidden Markov Model and Convectional LSTM to predict the probability that the pilot would deviate from the flight plan. Finally, we demonstrate the significance of the proposed method using real flight data from JFK to LAX. © 2019 Prognostics and Health Management Society. All rights reserved.

关键词： Anomaly detection

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Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

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Intelligent Medicine 2024年第3期4卷 141-152页

作者： Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang Marcin Grzegorzek Hongzan Sun Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical Informatics University of LuebeckLuebeckGermany Department of Knowledge Engineering University of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical University ShenyangLiaoning 110136China

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关键词： Gastric cancer Graph-curvature feature Image identification

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