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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Saadin Oyucu Şeref Sağıroğlu Ahmet Aksöz Emre Biçer Department of Computer Engineering Faculty of Engineering Adıyaman Türkiye Gazi University Artificial Intelligence and Big Data Analytics Security R&D Center Ankara Türkiye Mobilers Team Sivas Cumhuriyet University Sivas Türkiye Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas University of Science and Technology Sivas Türkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM’s capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently.

关键词： Deep learning data privacy Recurrent neural networks Power system stability Stability analysis data models Smart grids

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Indonesian Abusive Tweet Classification based on Convolutional Neural Network and Long Short Term Memory Method

Indonesian Abusive Tweet Classification based on Convolution...

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2021 International Conference on Artificial Intelligence and big data Analytics, ICAIBDA 2021

作者： Gultom, Reinaldo Yosafat Zulkarnaen, Fahri Izzuddin Nurhasanah, Yahma Sholahuddin, Asep Universitas Padjadjaran Research Center for Artificial Intelligence and Big Data Bandung Indonesia Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Informatics Engineering Department UPN Veteran Jawa Timur Surabaya Indonesia Department of Statistics Universitas Padjadjaran Bandung Indonesia Department of Computer Science Universitas Padjadjaran Bandung Indonesia

ISBN: (纸本)9781665408905

Twitter is one of the famous social media applications in Indonesia. The study will classify data to detect hate speech on Twitter into two classes: abusive and non-abusive. The abusive class will contain tweets in the form of words that contain negative elements or hate speech, while the non-abusive class contains tweets in the form of words that do not contain negative elements. The desired purpose of this research is to help those who need this research to be able to use it to supervise others from hate speech and find the perpetrators who do it. The data used is secondary data taken from the Kaggle website. The data contains tweets that have been classified based on connotations of abusive or non-abusive. Several processes are carried out to achieve the desired goal, including data acquisition, preprocessing, classification, evaluation, and content detection. The algorithms used in the study were LSTM and CNN. Both algorithms produce the same performance relatively. It's just that CNN is slightly superior. The accuracy value of CNN is 0.9131 or 91.31%, the precision is 0.9131 or 91.31%, the recall is 0.9131 or 91.31%, and the F1-Score of 0.9131 or 91.31%. Meanwhile, for LSTM, the accuracy value is 0.9104 or 91.04%, the precision is 0.9104 or 91.04%, the recall is 0.9104 or 91.04%, and the F1-Score is 0.9104 or 91.04% © 2021 IEEE.

关键词： Social networking (online)

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lin Gu Junhao Huang Shaoxing Huang Deze Zeng Bo Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions.

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Zhejiang Lab Zhejiang-HUST Joint Research Center for Graph Processing China Department of Computer Science University of Warwick United Kingdom

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Sentiment Analysis of YouTube Video Comments with the Topic of Starlink Mission Using Long Short Term Memory

Sentiment Analysis of YouTube Video Comments with the Topic ...

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2021 International Conference on Artificial Intelligence and big data Analytics, ICAIBDA 2021

作者： Putri, Ayu Masyita Ananda Putra Basya, Daniel Ardiyanto, Muchammad Teguh Sarathan, Indra Padjadjaran University Research Center for Artificial Intelligence and Big Data Department of Chemistry Sumedang Indonesia Telkom University Department of Computer Engineer Bandung Indonesia Airlangga University Department Robotics and Artificial Intelligence Engineering Surabaya Indonesia Padjadjaran University Faculty of Cultural Studies Sumedang Indonesia

ISBN: (纸本)9781665408905

The launch of the Starlink Satellite by a private company from the United States (SpaceX) has become a hot topic for conversation. SpaceX is a project that aims to provide satellite-based internet services worldwide with high performance and affordable prices even in remote places. The satellite launch video was shared on the YouTube platform. Many comments were on the satellite launch video, and various reactions were written on the video's comments column. Therefore, in this study, sentiment analysis will be carried out to analyze the responses of internet users through comments on YouTube to the launch of the Starlink satellite using deep learning. The YouTube comment data used in this study amounted to 22,000 comments. The model used is Long Short Term Memory (LSTM). This study will produce an accuracy value of the LSTM model by applying different activation and optimization functions. This study shows that the highest accuracy is 86% using the LSTM model, Softmax activation function, and Adam's optimization. © 2021 IEEE.

关键词： Sentiment analysis

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How Graph Neural Networks Learn: Lessons from Training Dynamics

arXiv

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arXiv 2023年

作者： Yang, Chenxiao Wu, Qitian Wipf, David Sun, Ruoyu Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Amazon Web Services United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Application and Implementation of Traffic Violation Report Combined with Vision and Voice Kits

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Sensors and Materials 2021年第2期34卷 533-543页

作者： Wu, Wen-Chuan Chen, Yen-Lun Lin, Pei-Yu Department of Computer Science and Information Engineering National Ilan University No. 1 Sec. 1 Shennong Rd. Yilan County 260 Yilan Taiwan Department of Computer Science and Information Engineering Aletheia University No. 32 Zhen-Li St. Tamsui Dist. New Taipei City25103 Taiwan Department of Information Communication Innovation Center for Big Data and Digital Convergence Yuan Ze University No. 135 Yuan-Tung Rd. Chung-Li32003 Taiwan

The Internet of Things (IoT) is a technique to connect various devices together in a network and exchange data among themselves. Currently, there are many IoT applications, such as smart homes, smart cities, smart manufacturing, and so forth. Their purpose is to make life more convenient. In this paper, we use IoT technology to solve the problem of traffic violation reports. We utilize specific sensors and a signal processing technique to detect events or changes in the environment. Then, messages are sent to trigger a camera to obtain videos of the violation. These video clips and the related information are finally uploaded to the cloud space of the reporter of the violation. Experimental results showed that the proposed scheme can speed up traffic violation reporting and can increase the willingness of people to act as whistleblowers. © 2021 M Y U Scientific Publishing Division. All rights reserved.

关键词： Automation

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiong Wang Yuxin Chen Yuqing Li Xiaofei Liao Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients’ selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal ${{\mathcal{O}}}\left({{T^{ - 2/3}}}\right)$ convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques.

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A Secure Identity Authentication Protocol for Edge data in Smart Grid Environment 8

A Secure Identity Authentication Protocol for Edge Data in S...

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8th IEEE International Conference on Cyber Security and Cloud Computing and 2021 7th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2021

作者： Xiao, Lijun Cai, Jiahong Qiu, Meikang Liu, Meiqin Guangzhou College of Technology and Business Big Data Development and Research Center Guangzhou China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Texas AM University-Commerce Department of Computer Science CommerceTX75428 United States Zhejiang University College of Electrical Engineering Hangzhou310027 China

ISBN: (纸本)9781665443777

Edge computing uses devices deployed at the edge of the network to preprocess data to reduce the computing pressure on the central server. Through edge computing, smart grid can process large amounts of data faster, meeting real-time, reliability, and high-efficiency requirements. However, as the number of edge devices continues to increase, the problem of leakage and tampering of private data is gradually exposed. In order to solve this problem, this paper proposes a lightweight identity authentication scheme suitable for smart grid environment based on Elliptic Curve Encryption (ECC) technology. In the proposed protocol, we encrypt the identity and key information through ECC, and use the timestamp to verify the validity of the session. In addition, the proposed protocol is encrypted with a random number for each round of authentication, and an attacker cannot crack our protocol through a replay attack. Experimental analysis shows that the proposed protocol is safety and key information will not be leaked. Finally, the cost analysis shows that the proposed protocol has lower communication and lower calculation cost than other that of related protocols, and is suitable for deployment in large-scale intelligent smart grid environments. © 2021 IEEE.

关键词： Cryptography

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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