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Power Consumption Dashboard Using Iot

Harbin Gongcheng Daxue Xuebao/Journal of Harbin engineering University 2023年第10期44卷 327-335页

作者： Sowmiya, S. Sakthivel, P. Nithya, C. Ravi, P. RanjithKumar, M. Computer Science and Engineering Mahendra Engineering College Mahendrapuri Mallasamudhram Namakkal India Information Technology Sona College of Technology Salem India Computer Science and Business System Knowledge Institute of Technology Namakkal India Information Technology K S R Institute for Engineering and Technology Tiruchengode Namakkal India Computer Science and Business Systems Knowledge Institute of Technology Namakkal India

"Power Consumption Dash Board Using IoT" was developed using a web application. The planned system includes a home energy monitoring system and cloud service notifications. An Internet of Things (IoT) platform was implemented to monitor energy consumption data at home and send the energy consumption data to the IoT cloud. The system uses the WiFi connected to the RaspberryPiPico to measure the energy consumption of the house. Energy consumption data is collected and stored in IoT cloud services. Consumers can monitor the energy consumption of their home on a daily basis and thus control how much energy is used on a daily basis. The data collection experiment aims to determine household energy consumption. Also, the website shows data on energy consumption before and after the load is connected to the system. IoT dashboard available online that allows users to check their daily energy consumption using IoT dashboard. IoT Cloud performs event analysis based on collected energy consumption data and alerts the owner with an alert, an email recommendation and a telegram notifying the customer when the energy consumption limit is exceeded, helping to reduce excessive energy consumption. © 2023 Editorial Board of Journal of Harbin engineering. All rights reserved.

关键词： Internet of things

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Encryption Traffic Classification Based on Mining Traffic Context and Transport Relationship

Encryption Traffic Classification Based on Mining Traffic Co...

引用

IEEE Conference on Wireless Communications and Networking

作者： Weilin Gai Runqing Zhang Huiyuan Zhang Yu Guo Jun Yin Peng Zhang TCA Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response TechnicalTeam/Coordination Center of China Beijing China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper proposes a novel ETC-MTCTR, which is designed to enable more accurate, versatile and efficient traffic classification in the context of multi-scenario, low-resource encrypted traffic. Through three modules of Datagram Token conversion, pretraining and fine-tuning, the method uses large-scale unlabeled encrypted traffic for pretraining, mining and learning the traffic context and transmission relationship of encrypted traffic classification tasks, so that a small number of labeled data samples can be effectively used in the fine-tuning stage. Significantly improve the performance of the model on specific downstream classification tasks, enhance the accuracy, adaptability and robustness of the model in diverse environments, limited resources and new encryption security protocols, and realize efficient encryption traffic classification in multi-scenario and low-resource background. The results show that ETC-MTCTR achieves the best performance on three tasks: encryption malware classification, VPN encrypted traffic classification, and TLS 1.3 encryption application classification. Its F1 score is improved by 0.22% in the classification task of encrypted malware, 1.4% in the classification task of VPN encrypted traffic App, 4.56% in the classification task of VPN encrypted traffic Service, and 9.89% in the classification task of TLS 1.3 encrypted application, which is significantly better than other comparison methods.

关键词： Adaptation models Accuracy Protocols Malware Robustness Encryption Virtual private networks Data mining

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Extended Properties of Nonlinear Power System Models that are Symmetrically Configured in the Narrow Sense

SSRN

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SSRN 2024年

作者： Segev, Elior Zhuk, Vladimir Ginzburg-Ganz, Elinor Belikov, Juri Levron, Yoash Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akademia tee 15a Tallinn12618 Estonia

Three-phase circuits that are Symmetrically Configured in the Narrow Sense (SCNS) have been defined in previous works as circuits which dq0 dynamic model does not depend on the transformation's reference angle. This property leads to dynamic models that are linear and time-invariant in the dq0 reference frame, or at least suitable for low-complexity numerical analysis, which is crucial when studying large-scale power systems. The current work is an extension of two previous works on the same topic. We continue here to explore properties of SCNS circuits, and provide two crucial extensions: first we explore circuits that are not passive, such as generators and inverters, and second we extend the mathematical formulation to apply to circuits that have more then one frame of reference. Our primary objective is to further enrich and extend the theoretical and practical tools introduced in previous works. The theoretical tools presented here are followed by a detailed case-study. © 2024, The Authors. All rights reserved.

关键词： Dynamic models

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Impact of saturation on evaporation-driven density instabilities in porous media: Mathematical and numerical analysis

arXiv

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arXiv 2025年

作者： Bringedal, C. Kiemle, S. van Duijn, C.J. Helmig, R. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Science Bergen Norway Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Stuttgart Germany Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands

Evaporation from a porous medium partially saturated with saline water, causes the salinity (salt concentration) to increase near the top of the porous medium as water leaves while salt stays behind. As the density of the water increases with increased salt concentration, the evaporation leads to a gravitational unstable setting, where density instabilities can form. Whether density instabilities form, depends on a large range of parameters like the evaporation rate and intrinsic permeability of the porous medium, but also on the water saturation. As water saturation decreases, the storage, convection and diffusion of salt also decrease, which all influence the onset of instabilities. By performing a linear stability analysis on the governing equations, we give criteria for onset of instabilities. Numerical simulations give information about the further development of these instabilities. With this knowledge we can predict whether and when density instabilities occur, and how they will influence the further development of salt concentration in the porous medium. © 2025, CC BY.

关键词： Stability criteria

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Adaptive watermark generation mechanism based on time series prediction for stream processing

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Frontiers of computer science 2021年第6期15卷 59-73页

作者： Yang SONG Yunchun LI Hailong YANG Jun XU Zerong LUAN Wei LI Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China College of Life Sciences and Bioengineering Beijing University of TechnologyBeijing 100083China

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in *** reality,streaming data usually arrives out-of-order due to factors such as network *** data stream processing framework commonly adopts the watermark mechanism to address the data *** is a special kind of data inserted into the data stream with a timestamp,which helps the framework to decide whether the data received is late and thus be *** watermark generation strategies are periodic;they cannot dynamically adjust the watermark distribution to balance the responsiveness and *** paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above *** mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result *** implement the proposed mechanism on top of Flink and evaluate it with realworld *** experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

关键词： data stream processing watermark time series based prediction dynamic adjustment

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AI Trustworthy: Ethical Challenges and Strategies

AI Trustworthy: Ethical Challenges and Strategies

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2024 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2024

作者： Liu, Jian Sandjaja, Iwan Wunsch, Donald C. Kummer Institute Center for Artificial Intelligence and Autonomous Systems Missouri University of Science and Technology Rolla United States Missouri University of Science and Technology Department of Electrical and Computer Engineering Rolla United States

This paper explores the pivotal role of trust in the widespread application of Artificial Intelligence (AI) across various domains. We review AI applications in sectors like energy, healthcare, and autonomous vehicles and discuss the crisis of human trust they face. This paper introduces a novel framework that delineates the relationship between AI transparency and user trust, highlighting specific industry applications. Through a systematic review of recent literature, we first delve into factors such as emotional response, acceptance, transparency, accuracy, and interpretability that shape human trust in AI. We then underscore the necessity of ethical AI practices and highlight the importance of regulatory measures to keep pace with technological advancements. Finally, we propose strategic measures for maintaining trust in AI, focusing on stringent regulation, ethical authorization, and effective human-AI cooperation, thereby contributing to the ongoing discourse on AI trust. © 2024 IEEE.

关键词： Energy policy

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Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and systems

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computer Modeling in engineering & sciences 2023年第5期135卷 1053-1089页

作者： Sneha Basak Himanshi Agrawal Shreya Jena Shilpa Gite Mrinal Bachute Biswajeet Pradhan Mazen Assiri Computer Science and Information Technology Department Symbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South Wales2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Institute of Climate ChangeUniversity Kebangsaan MalaysiaBangiSelangor43600Malaysia

Speech recognition systems have become a unique human-computer interaction(HCI)*** is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation *** paper aims to present a retrospective yet modern approach to the world of speech recognition *** development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this *** review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal *** speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

关键词： Speech recognition automatic speech recognition(ASR) mel-frequency cepstral coefficients(MFCC) hidden Markov model(HMM) artificial neural network(ANN)

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Investigating Efficacy of Perplexity in Detecting LLM-Generated Code

arXiv

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arXiv 2024年

作者： Xu, Jinwei Zhang, He Yang, Yanjin Cheng, Zeru Lyu, Jun Liu, Bohan Zhou, Xin Yang, Lanxin Bacchelli, Alberto Chiam, Yin Kia Chiew, Thiam Kian State Key Laboratory of Novel Software Technology Software Institute Nanjing University Nanjing210008 China Department of Informatics University of Zürich Zürich8006 Switzerland Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Large language model-generated code (LLMgCode) has become increasingly prevalent in software development. Many studies report that LLMgCode has more quality and security issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully checked. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method (PERPLEXITY for short) is the state-of-the-art method. However, the efficacy evaluation of PERPLEXITY has focused on the detection accuracy. In this article, we are interested in whether PERPLEXITY is good enough in a wider range of realistic evaluation settings. To this end, we devise a large-scale dataset that includes 11,664 HaCode snippets and 13,164 LLMgCode snippets, and based on that, we carry out a family of experiments to compare PERPLEXITY against feature-based and pre-training-based methods from three perspectives: (1) detection accuracy in terms of programming language, degree of difficulty, and scale of solution, (2) generalization capability, and (3) inference efficiency. The experimental results show that PERPLEXITY has the best generalization capability while it has low accuracy and efficiency in most cases. Based on the experimental results and detection mechanism of PERPLEXITY, we discuss implications into both the strengths and limitations of PERPLEXITY, e.g., PERPLEXITY is unsuitable for high-level programming languages while it has good interpretability. As the first large-scale investigation on detecting LLMgCode from HaCode, this article provides a wide range of evidence for future improvement. Copyright © 2024, The Authors. All rights reserved.

关键词： High level languages

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Assessing Driver Gaze Location in a Dynamic Vehicle Environment

Assessing Driver Gaze Location in a Dynamic Vehicle Environm...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Aidan Lochbihler Bruce Wallace Kathleen Van Benthem Chris Herdman Will Sloan Kirsten Brightman Frank Knoefel Shawn Marshall Systems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM 3 Innovation Hub Ottawa Canada Department of Cognitive Science Carleton University Ottawa Canada Bruyère Research Institute Elizabeth Bruyère Hospital Systems and Computer Engineering Carleton University Ottawa Hospital Carleton University

Measuring the human gaze is an important area of research due to this measurement's ability to give insight into what or where a person is focused and/or paying attention to. However, gaze has been very challenging to measure effectively and then convert into a metric. The problem of measuring human gaze is challenging in the context of dynamic environments with motion of the subject or the environment itself. One domain of research that has sought these gaze-related attention metrics has been the area of automotive driver assessment. Being able to understand if a driver is looking at relevant areas as well as scanning the road for hazards is a valuable metric to evaluate if an individual is fit to drive. Eye-tracking glasses measure where a person is looking relative to their head position but do not map this information against important regions within the visual field. This paper provides a computationally scalable method to identify relevant regions within a dynamic visual field and allow for the measurement of what a driver is focused on, reducing the need for extensive manual segmentation. The paper provides a method of identifying the windshield and other key regions within a motor vehicle typical for a driver's field of view. The identification of key regions was accomplished through the application of convolutional neural networks (CNNs) with a Dice score of 0.9404 The model is then shown to allow for the assessment of visual focus for drivers.

关键词：

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MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation

arXiv

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arXiv 2024年

作者： Luo, Jialin Wang, Yuanzhi Gu, Ziqi Qiu, Yide Yao, Shuaizhen Wang, Fuyun Xu, Chunyan Zhang, Wenhua Wang, Dan Cui, Zhen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, MultiGSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://***/ljl5261/MMM-RS. © 2024, CC BY-NC-SA.

关键词： Visual languages

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