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A Comprehensive distributed Framework for Cross-silo Federated Learning using Blockchain 5

A Comprehensive Distributed Framework for Cross-silo Federat...

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5th international conference on Blockchain computing and Applications, BCCA 2023

作者： Durgapal, Prateek Kataria, Parth Garg, Garv Anand, Anshu S. Indian Institute of Information Technology Department of IT Uttar Pradesh Allahabad India

ISBN: (纸本)9798350339239

the interest in cross-silo federated learning (FL), which involves a small number of client organizations that collaboratively train a model, has considerably increased in recent times. Although there exists a multitude of works on cross-silo FL, most of them focus on a small subset of the desired features, namely, security, privacy, performance, and scalability. In this work, we propose a comprehensive distributed framework for deploying cross-silo FL by cohesively integrating various technologies and techniques to realize these features. the proposed approach aims to overcome the challenges associated with the centralized model of federated learning by leveraging the distributed nature of blockchain networks to enable secure and efficient data sharing among multiple silos. the use of Kubernetes enables efficient management of resources required for training machine learning models. We also propose two simple, intuitive, and novel reputation-based algorithms-RepSecAgg and RepFedAvg for improved security and performance of the models, respectively. the proposed approach has the potential to significantly improve the efficiency and security of cross-silo FL, which can be applied in various fields, including healthcare, IoT, and finance. the feasibility of the proposed approach is evaluated through simulations and experiments, demonstrating its effectiveness in improving the performance and scalability of cross-silo FL. © 2023 ieee.

关键词： Blockchain

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Enhancement of Network Lifetime in Wireless sensor Networks Using Modified Cluster Head Selection Algorithm 9

Enhancement of Network Lifetime in Wireless Sensor Networks ...

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9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Satyanarayana, P. Guptha Maremalla, Gokul Sriram Phanindra, G. Devi, A.Ch.R.R. Anantha Rajya Lakshmi, K. Sriramam, Yadavalli S S V. R. Siddhartha Engineering College Department of ECE Vijayawada India S.R.K.R. Engineering College Department of ECE A.P Bhimavaram India

ISBN: (纸本)9798350397376

A significant number of sensor nodes that have been distributed around the monitoring area make up the wireless sensor network (WSN), which is a smart an autonomous *** creating a wireless sensor network, one of the most challenging aspects is working out how to maintain a consistent energy usage amount while simultaneously extending the life of the network, presuming the nodes in the network can only be powered by batteries in the majority of scenarios. With the objective of lowering the network's overall energy consumption and improving its lifespan, we suggest an augmentation technique. Enhancing the energy distribution in groups across all the sensor nodes has been carried out with the goal of lowering the amount of energy lost during network connections. A cluster head selection technique serves as the basis for the newly developed and enhanced methodology. Additionally, a modified version of the TDMA schedule has been implemented. By using mathematical analysis and the outcomes of a MATLAB 2015a simulation, the feasibility of the proposed technique was demonstrated. © 2023 ieee.

关键词： Energy efficiency

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Incorporate Personas and Emotions into Dialog Generation 5

Incorporate Personas and Emotions into Dialog Generation

引用

5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Wu, Yufeng Liu, Chengyu Guo, Wenjian Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350357738

Since the Turing test, creating believable open-domain dialogues has been a basic problem in the study of artificial intelligence. Achieving success with dialogue models can greatly improve natural human-computer interactions and open up new possibilities for applications such as chatbots for therapy. Incorporating emotional information into dialogue systems and giving them personas can enhance user satisfaction by fostering more natural communication and human-like conversations. Both of these study directions are comparatively well-established, with important accomplishments in them. On the other hand, various roles in real-life discussion scenarios portray emotions in different ways. In order to create more fulfilling and human-like discussions, we investigate in this study the integration of personas and emotions in dialogue creation systems. through the combination of explicit and implicit persons, we tackle the problem of individualized and emotional conversation. While implicit personas are gleaned from linguistic style, colloquial idioms, and personality attributes, explicit personas are derived from user profiles. Explicit personas and emotional labels are incorporated into the dialogue agent, and implicit personas are captured through the use of latent variables in a pre-trained Transformer model. Our suggested method enhances the authenticity of discussions and user experience by producing responses that are consistent with personas and emotions, hence improving the quality of communication. © 2023 ieee.

关键词： Human computer interaction

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Temporal Graph Convolutional Network Soft sensor for Chemical Process with distributed Outputs 5

Temporal Graph Convolutional Network Soft Sensor for Chemica...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Guo, Weiwei Jia, Mingwei Zhou, Weihua Liu, Yi Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Taizhou Vocational and Technical College Department of Mechanical and Electrical Engineering Taizhou318000 China

ISBN: (纸本)9798350325294

To explore data-driven soft sensors and enhance the model explainability, a temporal graph convolutional network is proposed for chemical processes with distributed outputs. It can model process variable relationships in the form of a topology graph. Firstly, the subgraph from prior knowledge is constructed and supplemented using mutual information. In this way, the advantages of both prior knowledge and process data are explored to construct the topology graph. then, the spatial and temporal dependencies are captured by a graph convolutional layer and a gated recurrent unit, respectively. Finally, a soft sensor based on the spatiotemporal graph convolutional network is established for quality prediction of the chemical process with distributed outputs. the effectiveness of the proposed model is verified by molecular weight distribution prediction of a styrene radical polymerization process. © 2023 ieee.

关键词： Topology

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Optimized Power Scheduling in Microgrids Under Load Uncertainty 5

Optimized Power Scheduling in Microgrids Under Load Uncertai...

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5th international conference on Energy, Power, and Environment, ICEPE 2023

作者： Lakhina, Upasana Badruddin, Nasreen Jangra, Ajay Bao Huy, Truong Hoang Guerrero, Josep M. Universiti Teknologi Petronas Department of Electrical and Electronics Engineering Perak Malaysia University Institute of Engineering and Technology Kurukshetra University Kurukshetra India Soonchunhyang University Asan-si Department of Future Convergence Technology Chuncheongnam-do 31538 Korea Republic of Aalborg University Centre of Research on Microgrids Department of Energy Technology Denmark

ISBN: (纸本)9798350313123

Microgrid is a controllable system that supplies power generated from some distributed units operating in an islanded or grid-connected mode to a community load. Renewable energy sources are beneficial to microgrids for clean energy generation. this paper proposes a memory-based multi-verse optimization (MMVO) algorithm for optimal power scheduling in microgrids among various generation units under load uncertainty. the random nature of the uncertain load is modeled using stochastic programming, and the improved algorithm is tested on two different scale microgrids, i.e., ieee 37 -node and ieee 141-node test systems. the simulation results demonstrate that the proposed algorithm minimizes the energy production cost under uncertain load demand. © 2023 ieee.

关键词： distributed power generation

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Design and Analysis of Prediction method for FBG based Humidity sensor 5

Design and Analysis of Prediction method for FBG based Humid...

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5th international conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Raju, Bharathi Kumar, R. Dhanalakshmi, Samiappan Srm Institute of Science and Technology Srm Nagar Department of Electronics and Communication Engineering Kattankulathur Chengalpattu TN Chennai India

ISBN: (纸本)9781665474672

Technology best outfits in various applications including communication, sensing, etc., In sensing application, measurands are monitored through variation in optical characteristics of signal like wavelength, phase, amplitude and polarization which depends on optical technology used. the challenge in this process is mapping of optical attributes with measurands. Machine learning algorithms facilitate fibre optical sensors for further enhancing the operation. In this work, various regression algorithms were investigated to optimize the estimation of measurands based on the signal characteristics. A Fibre Bragg Grating sensor (FBG) humidity sensor was experimented to collect the sensor data for training the regression models. Four different types of regression models such as Decision Tree, Random Forest, K-Nearest Neighbour (KNN) and Gaussian process regression (GPR) models were tested to study the performance of algorithms. For estimating the sensor output GPR outperforms in predicting the sensor data. © 2023 ieee.

关键词： Forecasting

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. the use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 ieee.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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computing Confinement Loss of Open-Channels Based PCF-SPR sensor with ANN Approach 5

Computing Confinement Loss of Open-Channels Based PCF-SPR Se...

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5th ieee international conference on Telecommunications and Photonics, ICTP 2023

作者： Islam, Nazrul Islam Khan, Md. Saikat Hasan, Md. Nahid Yousuf, Mohammad Abu Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9798350393477

Surface plasmon resonance (SPR) is a sensitive spectroscopic technique for measuring changes in the refractive index (RI) of a medium in contact with a sensor surface. Confinement loss computation is crucial for designing an SPR-based photonic crystal fiber (PCF) sensor as it directly impacts its performance and sensitivity. the study highlights the simple and fast-training feed-forward Artificial Neural Network (ANN) as a technique for calculating the confinement loss of an open-channels-based PCF-SPR sensor. Moreover, ANN demonstrates proficiency in generating precise predictions for the aforementioned optical properties within the conventional parameter range, encompassing wavelengths between 0.60 μm and 0.90 μm, as well as an analyte RI range spanning from 1.33 to 1.40. Additionally, this work demonstrates a higher speed in predicting output values for varying sensor structural parameters than direct numerical simulation techniques. this study signifies a noteworthy progression in the field through the utilization of ANN-driven optimization methods for integrated silicon photonics devices. this advancement leads to improved speed and accuracy in nredicting sensor outputs. © 2023 ieee.

关键词： Machine learning

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Modeling and Controlling of AC/DC Hybrid Microgrid 5

Modeling and Controlling of AC/DC Hybrid Microgrid

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5th ieee international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Ravi Kishore, D. Saimanoj, Muchhupalli Kishore, Adapala Sri Venkata Sai Veerendra, Devadi Hari Krishna Department of Electrical and Electronics Engineering Rajahmundry India

ISBN: (纸本)9781665493604

As a result of climate change, renewable energy sources like solar and wind have become the most popular choice for distributed generators (DGs). Power sources including wind, solar, biomass, mini-hydro, fuel cells, and microturbines will help boost decentralised power production. Electricity systems may benefit from distributed generation in a number of ways, including environmental friendliness, scalability, and the use of nonconventional microsources. A regulated microgrid is comprised of loads and distributed generators. Microgrids may function whether or not they are connected to the main power grid. Microgrids provide power system connections to variable renewable AC and DC sources and loads while minimising the need for reverse conversions in AC or DC grids. Connecting DGs to the utility/grid through power electronic converters raises questions about the security and integrity of the connected systems. Local dependability, feeder loss reduction, local voltage support, waste heat utilisation, voltage sag correction, and uninterruptible power supply are just some of the client demands that may be met by tailoring the microgrid to their specific requirements. In order to better understand how hybrid AC/DC microgrid systems function when connected to the mains, this research evaluates their efficiency. Microgrids use renewable energy sources such as solar panels, wind turbines, and batteries. the DC and AC sub-grids are synchronised by the manipulation of converters. MATLAB/Simulink is responsible for generating the results. © 2023 ieee.

关键词： distributed power generation

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Exploiting Spatial and Temporal Features for Deep Learning Based Human Activity Recognition 29

Exploiting Spatial and Temporal Features for Deep Learning B...

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29th ieee international conference on Parallel and distributed systems, ICPADS 2023

作者： Cao, Wenying Shi, Gaotao Zhang, Tieguan Tianjin University International Engineering Institute Tianjin China Tianjin University College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350330717

Human activity recognition using inertial sensors has gained significant popularity and widespread adoption in various fields, while deep learning has emerged as the dominant approach, playing a pivotal role in enhancing performance. Nevertheless, existing algorithms often neglect the heterogeneity of sensors and fail to effectively extract contextual features from long-term time series data, resulting in low accuracy in activity recognition, especially for complex activities. this paper proposes a novel method called FUsion Transformer hUman activity REcognition(FUTURE). FUTURE exploits the feature fusion mechanism and designs a multi-scaled DenseNet to extract the spatial features so that the relationship between heterogeneous sensors can be captured. Furthermore, a customized multi-head attention model with less computational complexity is employed in FUTURE to capture global dependencies within the sensor data. Experimental results on multiple datasets validate that FUTURE achieves an average recognition accuracy of 95% for complex activities and the evaluation against the state-of-the-art demonstrates the superior performance of the FUTURE model in accurately classifying complex activities. © 2023 ieee.

关键词： Attention mechanism Feature fusion Human Activity Recognition Inertial sensor

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