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26th international symposium on Mathematical Theory of networks and systems (MTNS)

作者： Bensussen, Antonio Alvarez-Buylla, Elena R. Carlos Martinez-Garcia, Juan Cinvestav IPN Dept Control Automat Mexico City DF Mexico Univ Nacl Autonoma Mexico Inst Ecol & C3 Mexico City DF Mexico C3 UNAM Mexico City DF Mexico

Studying large-scale Boolean networks is of interest for several areas of biology. One of the simplest ways to approach this type of systems is by implementing iterative synchronous update schemes. However, the complexity of such approach grows exponentially depending on the number of nodes included in the network, being almost impossible to characterize the complete topology of networks with more than 35 nodes. Consequently, in this paper we developed an algebraic method equivalent to the synchronous update scheme able to characterize the entire topology of Boolean networks. To do this, we explored the properties of transforming n-dimensional discrete non-parameterised Boolean systems into relations defined in integers, which can be visualized in the Cartesian plane. This visualization makes then easy to explore in a systematic manner the topology of the attractor landscape. Finally, we show examples of how to implement this method for topological analysis, which opens new possibilities for modeling large Boolean networks for biology and other areas of knowledge. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Boolean networks Topology Basins of Attraction Theoretical computer Science systems Biology

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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural networks: An Overview 17

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural...

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17th IEEE international symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc University of Arizona Department of Electrical & Computer Engineering TucsonAZ United States University of Arizona Departments of Psychology and Biomedical Engineering TucsonAZ United States

ISBN: (纸本)9798331530471

Spiking Neural networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuro-morphic systems that fully exploit the computational advantages of sparse SNNs. © 2024 IEEE.

关键词： Neural network models

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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Support Vector Machine with Linear and Radial Bias Function to Classify the Soil Erosion and Land Degradation

Support Vector Machine with Linear and Radial Bias Function ...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Borra, Subba Reddy Shreyas, A.V. Alsalami, Zaid Chanti, Yerrolla Ramesh, Gajula Malla Reddy Engineering College for Women Autonomous Hyderabad India Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

The analysis of Land Use and Land Cover (LULC) changes needed update for remotely sensed information for assessing its impacts in erosion. This is especially significant evaluation to regions with semi-arid landscapes the soils cause scarce and appropriate management requirements corresponding LULC for resident conditions to obtain the suitable land use. To addresses this issue, in this article developed the Support Vector Machine (SVM) with linear and Radial Bias Function (RBF) which classifies the land degradation and erosion effectively. The developed SVM with linear and RBF method which is able to handle the mask generation, ranking and overfitting problems. Moreover, it minimizes the computational overhead through dividing the samples from various classes. The images are uniformly rescaled by using min-max normalization and the meaningful features are extracted by utilizing Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) techniques. Then, it is effectively classified by using the developed SVM with linear and RBF technique. © 2024 IEEE.

关键词： Support vector machines

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Multi-agent deep reinforcement learning for online request scheduling in edge cooperation networks

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2023年 141卷 258-268页

作者： Zhang, Yaqiang Li, Ruyang Zhao, Yaqian Li, Rengang Wang, Yanwei Zhou, Zhangbing Inspur Beijing Elect Informat Ind Co Ltd Beijing 100085 Peoples R China Inspur Elect Informat Ind Co Ltd Jinan 250101 Peoples R China Shandong Mass Informat Technol Res Inst Jinan 250101 Peoples R China China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China

Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system per-formance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Edge cooperation networks Online request scheduling Multi -agent system Deep reinforcement learning Value decomposition Long-term performance

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19th international symposium on distributed Computing and Artificial Intelligence, DCAI 2022

19th International Symposium on Distributed Computing and Ar...

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19th international symposium on distributed Computing and Artificial Intelligence, DCAI 2022

ISBN: (纸本)9783031232091

The proceedings contain 22 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: computer Vision: A Review on 3D Object Recognition;An IoUT-Based Platform for Managing Underwater Cultural Heritage;overview: Security in 5G Wireless systems;a Study on the Application of Protein Language Models in the Analysis of Membrane Proteins;visualization for Infection Analysis and Decision Support in Hospitals;An Intelligent and Green E-healthcare Model for an Early Diagnosis of Medical Images as an IoMT Application;towards Highly Performant Context Awareness in the Internet of Things;adaptive System to Manage User Comfort Preferences and Conflicts at Everyday Environments;ML-Based Automation of Constraint Satisfaction Model Transformation and Solver Configuration;race Condition Error Detection in a Program Executed on a Device with Limited Memory Resources;the Impact of Covid-19 on Student Mental Health and Online Learning Experience;Threat Detection in URLs by Applying Machine Learning Algorithms*;an Approach to Simulate Malware Propagation in the Internet of Drones;the Use of Corporate Architecture in Planning and Automation of Production Processes;Towards Ontology-Based End-to-End Domain-Oriented KBQA System;TFEEC: Turkish Financial Event Extraction Corpus;denial of Service Attack Detection Based on Feature Extraction and Supervised Techniques;automating the Implementation of Unsupervised Machine Learning Processes in Smart Cities Scenarios;Intelligent Model Hotel Energy Demand Forecasting by Means of LSTM and GRU Neural networks;explainable Artificial Intelligence on Smart Human Mobility: A Comparative Study Approach.

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EFL Teachers' Insights on AI Integration Exploring Chat GPT and the Graph Sage Model

EFL Teachers' Insights on AI Integration Exploring Chat GPT ...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Nayak, Ipsita Parasar, Anuradha Lamba, Anil Kumar Raghavendra Reddy, C. Tharini, C. Pulugu, Dileep Kiit D University Department of English Bhubaneswar India Galgotias University Uttar Pradesh India Geeta University School of Computer Science and Engineering Haryana India Mohan Babu University School of Liberal Arts and Sciences Department of English Tirupati India Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology Department of English Chennai India Malla Reddy College of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

The application of AI technology is revolutionizing the field of English as a foreign language (EFL) instruction. OpenAI's ChatGPT is an AI conversational tool with the potential to revolutionize EFL classrooms by bringing more interactive and tailored approaches to teaching the language. Examining how EFL teachers in Kazakhstan are using ChatGPT was the driving force behind the research. A range of feelings, from enthusiasm to hesitation, was shown by the results of twelve EFL educators' semi-structured *** suggested method consists of preprocessing, model training, and feature extraction. Three parts make up preprocessing: stemming, handling unstructured words, and standard preprocessing. By isolating documents with unique keywords, reducing background noise, and improving document representation for text analysis, TF-IDF calculates word relevance in feature extraction. The training made continuous use of E-GraphSAGE. This novel approach outperforms the SVM and the ELM, with an average accuracy of 94.32%. © 2024 IEEE.

关键词： Teaching

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A general framework and decentralised algorithms for collective computational processes

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2024年 158卷 11-27页

作者： Audrito, Giorgio Casadei, Roberto Torta, Gianluca Univ Torino Turin Italy Alma Mater Studiorum Univ Bologna Bologna Italy

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface ( spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

关键词： Collective adaptive systems Collective computing Dynamic ensembles Aggregate processes Decentralised systems distributed algorithms Neighbourhood communications

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DKWS: A distributed System for Keyword Search on Massive Graphs (Extended Abstract) 40

DKWS: A Distributed System for Keyword Search on Massive Gra...

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40th IEEE international Conference on Data Engineering, ICDE 2024

作者： Jiang, Jiaxin Choi, Byron Huang, Xin Xu, Jianliang Bhowmick, Sourav S School of Computing National University of Singapore Singapore Hong Kong Baptist University Department of Computer Science Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Addressing the complexities of querying unstructured graphs such as knowledge graphs and social networks, this paper introduces D KWS, a novel distributed keyword search system. Leveraging a monotonic property, we ensure correct parallelization of our advanced keyword search algorithm, which incorporates tight pruning bounds and is divided into monotonic backward and forward search phases. The system is further augmented by the notify-push paradigm and the PINE programming model, facilitating asynchronous communication and preemptive searches to mitigate staleness in distributed environments. Extensive experiments on real-world datasets demonstrate DKWS's performance advantage, being up to two orders of magnitude faster and incurring 7.6 times lower communication costs than the existing systems. © 2024 IEEE.

关键词： Search engines

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Some Method of Behavioral Energy Testing of distributed computer systems 13

Some Method of Behavioral Energy Testing of Distributed Comp...

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13th international Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Martynyuk, Oleksandr Drozd, Oleksandr Van Thuong, Bui Stepova, Hanna Sugak, Lyudmila Martynyuk, Dmitry National University Odesskaya Polytechnika Department of Computer Intellectual Systems and Networks Odessa Ukraine National University Odesskaya Polytechnika Educational and Methodological Department Odessa Ukraine Joint Venture Nippon Auto Odessa Ukraine

ISBN: (纸本)9798350396119

The method of behavioral energy-loaded testing considered in this paper are based on Petri nets, extended by temperature and volt/ampere characteristics, and have the features of a comprehensive consideration of behavioral and energy characteristics in their relationship, performed in event aspect. These features connect the allowable boundary temperature-voltage characteristics with behavioral events/actions in positions/transitions of Petri nets. The use of the proposed method of such a special additional analysis of energy characteristics in complex testing technologies makes it possible to increase the completeness and flexibility of testing computer systems while reducing its execution time. © 2023 IEEE.

关键词： distributed computer systems

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