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Generation and search for the shortest path in two-dimensional labyrinths as a model for optimizing routing processes in the packet switching network 4

Generation and search for the shortest path in two-dimension...

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4th International Conference on Future Networks and Distributed systems, ICFNDS 2020

作者： Dagaev, Alexander Borodyansky, Yuriy Pomogalova, Albina Department of Software Engineering and Computer Science The Bonch-Bruevich State University of Telecommunications St. Petersburg Russia Department of Protected Information Systems The Bonch-Bruevich State University of Telecommunications St. Petersburg Russia

ISBN: (纸本)9781450388863

The algorithms for generating and finding the shortest path in two-dimensional labyrinths and their characteristics are considered. The aim of the study is to determine the dependence of the working time of the shortest path search algorithms on the characteristics of mazes. This may allow these algorithms to be used to optimise routing processes in packet switching networks. All of the characteristics of the algorithms under consideration are described in detail. These search algorithms also allow for paralleling, so they can be used when creating games or any special applications. Such generation algorithms have been selected, the resulting mazes of which have different characteristics, thus making it possible to determine the required dependence. The peculiarity of applying the result of this research is that the fastest search algorithm can be selected for the generation algorithm or for specific characteristics of an already generated maze. The search algorithms can be improved to find paths in mazes in N-dimensional spaces. This search extension possibility can be widely used not only for working with mazes, but also with any other structure of a given coordinate system. © 2020 ACM.

关键词： Packet switching

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A Novel 6dof Pose Estimation Method Using Transformer Fusion

SSRN

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SSRN 2023年

作者： Wang, Huafeng Zhang, Haodu Liu, Wanquan Hu, Zhimin Gao, Haoqi Lv, Weifeng Gu, Xianfeng School of Information Technology North China University of Technology Beijing100122 China School of Software Beihang University Beijing100191 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510335 China University of Wisconsin Madison United States Department of Computer Science Stony Brook University NY11794-2424 United States

Integrating heterogeneous information in multimodal tasks presents challenges in effectively leveraging complementary information. This is particularly evident in the context of 6D pose estimation, where the effective utilization of RGB-D data is crucial for extracting supplementary information that complements 2D and 3D data. Another challenge is aligning modalities implicitly to obtain robust estimation results. To tackle these challenges, we propose a Transformer-based fusion module that facilitates hierarchical fusion across modalities at different stages. This approach effectively combines disparate features and complements global information in a non-convolutional manner. Additionally, we employ an attentional residual structure to address two primary challenges. Firstly, it mitigates information loss that may occur due to the stacking depth of layers. Secondly, it leverages attention weights to adapt the output of the preceding layer, thereby enhancing modal alignment. We validate our approach on two benchmark datasets: LineMOD [1] and YCB-Video [2]. Our method outperforms most existing methods on the LineMOD dataset and achieves state-of-the-art results on the YCB-Video dataset, showcasing its superior generalization ability. © 2023, The Authors. All rights reserved.

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Data-Driven Distributionally Robust Control for Interacting Agents under Logical Constraints

arXiv

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arXiv 2025年

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Gros, Sebastien Dimarogonas, Dimos V. Soudjani, Sadegh The Max Planck Institute for Software Systems Kaiserslautern Germany The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Thomas Lord Department of Computer Science the Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles United States Trondheim Norway

In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints expressed by signal temporal logic (STL). We formulate the control synthesis as a chance-constrained program (CCP) with STL specifications that must be satisfied with high probability under all uncertainty tubes induced by the other agents. To tackle the CCP, we propose two methods based on concentration of measure (CoM) theory and conditional value at risk (CVaR) and compare the required assumptions and resulting optimizations. These approaches convert the CCP into an expectation-constrained program (ECP), which is simpler to solve than the original CCP. To estimate the expectation using a finite set of observed data, we adopt a distributionally robust optimization (DRO) approach. The underlying DRO can be approximated as a robust data-driven optimization that provides a probabilistic under-approximation to the original ECP, where the probability depends on the number of samples. Therefore, under feasibility, the original STL constraints are satisfied with two layers of designed confidence: the confidence of the chance constraint and the confidence of the approximated data-driven optimization, which depends on the number of samples. We then provide details on solving the resulting robust data-driven optimization numerically. Finally, we compare the two proposed approaches through case studies. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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A Reinforcement Learning Based Approach to Identify Resource Bottlenecks for Multiple Services Interactions in Cloud Computing Environments 16th

A Reinforcement Learning Based Approach to Identify Resource...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Xu, Lingxiao Xu, Minxian Semmes, Richard Li, Hui Mu, Hong Gui, Shuangquan Tian, Wenhong Wu, Kui Buyya, Rajkumar School of Software and Information Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Co. Ltd. Chengdu China Department of Computer Science University of Victoria Victoria Canada CLOUDS Lab School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9783030675394

Cloud service providers are provisioning resources including a variety of virtual machine instances to support customers that migrate their services to the cloud. From the customers’ perspective, selecting the appropriate amount of resources is tightly coupled with performance and cost. By identifying the potential resource bottlenecks in the early stage of the service deployment process, resource planning can be significantly optimized. However, due to the unpredictable workloads and heterogeneous resources, it is difficult to identify resource bottlenecks that can degrade system performance. To support system non-functional requirements (NFR) in a better manner, we propose a reinforcement learning based approach to support the NFR management of system concerning the multiple services interactions scenario by identifying the potential resource bottleneck and optimizing the demanded resources. The proposed approach can predict the resource bottleneck for multiple services interactions, e.g. bottleneck in CPU or overloads in specific service, and provide guidance for resource planning. We modeled and simulated the proposed approach using an extended version of the CloudSim toolkit. Comprehensive evaluations with realistic use case from Siemens Digital Industries software’s MindSphere Solution on AliCloud show that our proposed approach can achieve high accuracy in terms of performance metrics, such as response time, queries per second (QPS), and resource usage. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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Managing Control software Variability in Cyber-Physical Production systems: The V4rdiac Approach

SSRN

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SSRN 2024年

作者： Fadhlillah, Hafiyyan Sayyid Meixner, Kristof Greiner, Sandra Fernández, Antonio Manuel Gutiérrez Rabiser, Rick Christian Doppler Laboratory VaSiCS Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Christian Doppler Laboratory SQI TU Wien Favoritenstraße 9-11 Vienna1040 Austria Information Systems Engineering TU Wien Favoritenstraße 9-11 Vienna1040 Austria Software Engineering Group Institute of Computer Science University of Bern Neubrückstraße 10 Bern3012 Switzerland Department of Computer Languages and Systems Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain

Cyber-Physical Production systems (CPPSs) are highly configurable and versatile production systems that utilize diverse hardware components through control software. Managing control software variants in CPPS development is challenging as variation points in the software stem from multiple engineering disciplines. Furthermore, there is a lack of mechanisms to explicitly describe control software variants as part of the software. In this paper, we present an extension of the Variability for 4diac (V4rdiac) approach and investigate its usefulness in managing control software variants based on multidisciplinary variability in CPPS development and delta modeling. We extend the approach by considering its application engineering activities to produce client-specific control software variants. We also present a novel mechanism to visualize deltas in control software to reduce the effort of implementing and maintaining deltas. We apply V4rdiac in two case studies and conduct an additional user study with four participants from industry to evaluate its usefulness. The evaluation results confirm that the engineers were able to successfully manage the control software variants. The results also show that V4rdiac can be easily learned and increases industrial practitioners’ productivity in managing control software. © 2024, The Authors. All rights reserved.

关键词： Application programs

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Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

Level of Trust and Privacy Management in 6G Intent-based Net...

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6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

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Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction

Comparative Analysis of Machine Learning Models for Diabetes...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Materwala, Huned United Arab Emirates University College of Information Technology Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu-Dhabi Al-Ain15551 United Arab Emirates

ISBN: (纸本)9781728176246

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper. © 2020 IEEE.

关键词： Medical informatics

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