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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Toward Digital Twin: Leveraging Pre-training Approaches for Multivariate Time Series Forecasting 6

Toward Digital Twin: Leveraging Pre-training Approaches for ...

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6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Yan, Leon Zhang, Zhenwei Wang, Xin Zhang, Yu Gu, Yuantao Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350399059

Time series forecasting has been an active research area, particularly in the context of Digital Twin (DT) systems. Despite the excellent results yielded by pre-trained models in Natural Language Processing (NLP) and Computer Vision (CV), only a few studies have researched pre-training strategies for time series forecasting networks within DT systems. Recent studies demonstrate that transfer learning across multiple time series datasets does not always provide promising results, making self-supervised pre-training directly on the downstream dataset a temporarily considered optimal solution. To the best of our knowledge, the only general pre-training task in the time series field is the Masked Autoencoder. However, this approach may lead to redundant representations for downstream forecasting within DT systems. therefore, we propose three pre-training tasks specially designed for time series forecasting within DT frameworks: Inverse Forecasting (IF), Coarser Forecasting (CF), and Anomaly Forecasting (AF). these tasks are respectively designed to capture bidirectional dependency, reduce noise, and augment data, all crucial aspects in enhancing the predictive capabilities of DT systems. By integrating the three tasks, we obtain a composite pre-training task which generally improves the forecasting results of time series models within DT systems across multiple datasets. this work contributes to the ongoing efforts to improve the accuracy and efficiency of DT systems, paving the way for more robust and reliable digital representations of physical systems. © 2023 IEEE.

关键词： Time series

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LLMParser: An Exploratory Study on Using Large Language models for Log Parsing 24

LLMParser: An Exploratory Study on Using Large Language Mode...

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44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： Ma, Zeyang Chen, An Ran Kim, Dong Jae Chen, Tse-Hsun Peter Wang, Shaowei Lab MontrealQC Canada University of Alberta Electrical and Computer Engineering Department EdmontonAB Canada University of Manitoba Department of Computer Science WinnipegMB Canada

ISBN: (纸本)9798400702174

Logs are important in modern software development with runtime information. Log parsing is the first step in many log-based analyses, that involve extracting structured information from unstructured log data. Traditional log parsers face challenges in accurately parsing logs due to the diversity of log formats, which directly impacts the performance of downstream log-analysis tasks. In this paper, we explore the potential of using Large Language models (LLMs) for log parsing and propose LLMParser, an LLM-based log parser based on generative LLMs and few-shot tuning. We leverage four LLMs, Flan-T5-small, Flan-T5-base, LLaMA-7B, and ChatGLM-6B in LLMParsers. Our evaluation of 16 open-source systems shows that LLMParser achieves statistically significantly higher parsing accuracy than state-of-the-art parsers (a 96% average parsing accuracy). We further conduct a comprehensive empirical analysis on the effect of training size, model size, and pre-training LLM on log parsing accuracy. We find that smaller LLMs may be more effective than more complex LLMs;for instance where Flan-T5-base achieves comparable results as LLaMA-7B with a shorter inference time. We also find that using LLMs pre-trained using logs from other systems does not always improve parsing accuracy. While using pre-trained Flan-T5-base shows an improvement in accuracy, pre-trained LLaMA results in a decrease (decrease by almost 55% in group accuracy). In short, our study provides empirical evidence for using LLMs for log parsing and highlights the limitations and future research direction of LLM-based log parsers. © 2024 ACM.

关键词： Computational linguistics

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Ontological engineering on Metagraphs Basis 6

Ontological Engineering on Metagraphs Basis

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6th international conference on Information Technologies in engineering Education, Inforino 2022

作者： Misnik, A.E. Inter-state Educational Institution of Higher Education Belarusian-Russian University Mogilev Belarus

ISBN: (纸本)9781665405775

the article is devoted to the problem of ontological engineering of a complex cyber-physical system. An example of such a system is a system that automates processes in higher education institutions. Ontological engineering is the process of designing and developing ontologies that combines two main technologies for designing complex cyber-physical systems-object-oriented and structural analysis. the goals of ontological engineering are to increase the level of integration of information necessary for making management decisions, increase the efficiency of information retrieval, and provide an opportunity for joint processing of knowledge based on a single semantic description of the knowledge space. Ontological engineering is carried out within the framework of the proposed approach to managing complex cyber-physical systems. the resulting ontology should be a convenient and flexible basis for modeling processes and ensuring the functioning of information and analytical processes in a complex system. Approaches to ontological engineering based on graphs are considered. the application of ordinary graphs, hypergraphs and metagraphs is described. the use of metagraphs in the construction of hierarchical ontologies has been substantiated. Metagraphs are considered as the basis for building an applied system ontology. A modification of the metagraph is proposed to include events and system methods in the ontology. this approach to the construction of a metagraph allows to include the process component in the ontological model of the system as an integral part of it, which allows to flexibly and with lower time expenses form process models based on segments of the general ontological model. An example of the implementation of a software-instrumental environment for ontological engineering and further construction of models of processes of a complex cyber-physical system is presented. the technology used to implement the ontological model in the database and the structur

关键词： Knowledge engineering Databases Object oriented modeling Buildings Semantics Knowledge based systems Ontologies

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Modeling and Simulation of Normal, Pathological or Post-surgical Situations of Human Anatomical Systems 11th

Modeling and Simulation of Normal, Pathological or Post-surg...

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11th international conference on E-Health and Bioengineering (EHB)

作者： Buciu, Gabriel Popa, Dragos-Laurentiu Vintila, Daniela Doina Berceanu, Mihaela Corina Calin, Daniel Cosmin Titu Maiorescu Univ Fac Gen Hlth Care Targu Jiu Romania Univ Craiova Fac Mech Craiova Romania Univ Med & Pharm Craiova Craiova Romania Cty Emergency Hosp Slatina Slatina Romania

ISBN: (纸本)9783031625190;9783031625206

In order for a simulation to be close to the reality of the behavior of a human system, sets of images obtained by computed tomography were used. these sets of images contain the information necessary for the three-dimensional reconstruction of the analyzed models. Using different techniques, which are based on the transformation of shades of gray from the tomographic images, it is possible to obtain primary geometries of the components and tissues contained in the so-called "cloud of points". this computer format is specific to reverse engineering, more precisely, to three-dimensional scanning. the primary geometry thus obtained is then processed in specific programs, since they may contain artifacts (elements that do not belong to the real model), self-intersecting surfaces or other non-conforming components that cannot be transformed into virtual solids. these transformation stages have the final goal of obtaining perfectly closed surfaces. Only such geometries can be transformed into virtual solids using a CAD program. Also in this software, using techniques and methods specific to direct engineering, implant or prosthetic elements can be added. Complete models are transferred to a program that allows their analysis using the finite element method. In this software, the specific materials are attached, constraints are indicated, mechanical loads or thermal or other flows are defined. Finally, the applications are run and the result maps that compose the behavior of the analyzed system are obtained. Based on them, conclusions can be drawn and decisions of a medical nature can be made.

关键词： Simulation Modeling 3D Reconstruction Reverse engineering Finite Element Method

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Deep Learning-Based APT Malware and Variants Detection with Attribution Analysis 6

Deep Learning-Based APT Malware and Variants Detection with ...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Binhui Leng, Peiyun Shen, Xuxiang Wei, Yuhang Sichuan University Chengdu School of Computer and Software Chengdu Jincheng College School of Cyber Science and Engineering China Chengdu Jincheng College School of Computer and Software Chengdu China

ISBN: (纸本)9798350325485

Malware has become one of the most severe security threats in cyber security, among which APT malware attacks are more threatening than advanced sustainable threat attacks. In this paper, we perform APT malware and variant detection based on deep learning and analyze the results by detecting and attribution. the variant detection of APT malware can inhibit the spread of malicious code, which is important for network security detection and defense. this paper proposes a detection and classification method for APT malware and its variants based on deep learning. Firstly, the feature representation method of APT malware based on RGB images is proposed to solve the gradient explosion and gradient disappearance of grayscale map feature extraction and generate images with rich texture information, which can mine deeper features of APT malware attacks. Secondly, this paper also improves the convolutional neural network model by combining the Self-Attention mechanism with the spatial pyramid pool SPP-net to solve the problem of image input of different sizes, as the accuracy is above 92.14%. then, the experiment results prove the predictive analysis of the model to understand the organization to which the APT attack belongs and the way of its specific attack, providing the possibility of tracing the source. Finally, the visual analysis of APT threat characteristics is presented. © 2023 IEEE.

关键词： Convolutional neural networks

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Long-Term Water Level Prediction of Hongze Lake Based on ICEEMDAN-AdaBoost-GRU-LSTM 6

Long-Term Water Level Prediction of Hongze Lake Based on ICE...

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6th international conference on Information Communication and Signal Processing, ICICSP 2023

作者： Shan, Qingpeng Xie, Lingnan Liang, Xuechun College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China Bank of Nanjing Credit Approval Department Nanjing China

ISBN: (纸本)9798350339994

To overcome the limitations of traditional time series models in dealing with monthly average water level time series, such as high noise, strong nonlinearity, limited data, and low prediction accuracy, this study introduces a novel water level prediction model called ICEEMDAN-AdaBoost-GRU-LSTM. the proposed model combines the Adaptive Boosting (AdaBoost) ensemble strategy with Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and two neural network architectures: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). the modeling process involves the following steps: Firstly, a stacked GRU-LSTM model is established as the base model. then, an AdaBoost ensemble technique is applied to enhance the predictive power of the base model. the input data is preprocessed and decomposed into several Intrinsic Mode Functions (IMFs) and a residue (RES) using the ICEEMDAN method. the high-frequency noise component IMF1 is eliminated, and the remaining mid-to-low-frequency components are predicted simultaneously using the strong predictor. Finally, the predicted values of the middle and low frequency components are superimposed to get the final predicted value. Experimental results demonstrate that the proposed ICEEMDAN-AdaBoost-GRU-LSTM model outperforms other models such as LSTM, GRU, Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and others in terms of prediction accuracy. © 2023 IEEE.

关键词： AdaBoost GRU ICEEMDAN LSTM water level prediction

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A Lightweight Convolutional Network Combined with Channel Attention for Non-intrusive Load Monitoring 6

A Lightweight Convolutional Network Combined with Channel At...

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6th international conference on Energy, Electrical and Power engineering, CEEPE 2023

作者： Liu, Zhan Zhou, Gan Feng, Yanjun Zhang, Jian Zeng, Ying Jin, Long The College of Software Engineering Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China Communication Management Guangdong Power Grid Guangzhou China

ISBN: (纸本)9798350348279

Non-intrusive load monitoring (NILM) is the basis of end-side informatization applications in smart grids. At the same time, the fine-grained power consumption of equipment decomposed by non-intrusive load monitoring algorithms also plays an important role in adjusting the power consumption structure. At present, deep neural network has become the focus of research in the field of non-intrusive load identification, but most neural network models only focus on how to improve the identification accuracy, while ignoring the network size requirements of hardware monitoring devices in the actual deployment process. In this paper, we propose a lightweight convolutional neural network combined with channel attention mechanism (LACNet). through the serialized multi-scale dilated convolution structure, while increasing the receptive field, reducing the parameters to achieve the purpose of compressing the model size, and also using the channel attention mechanism to optimize different features to improve the model identification accuracy. Finally, we conducted experimental verification on the public dataset UK-DALE. the results showed that LACNet outperforms several existing load identification networks in terms of EA and other evaluation metrics. At the same time, the model parameters are also greatly reduced. © 2023 IEEE.

关键词： Electric power utilization

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Crop Yield Forecasting for A Resilient Food System: the Role of AI and Green Principles 6

Crop Yield Forecasting for A Resilient Food System: The Role...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Perumal, Iyyanar Kalaivani, P. Nandhini, P. Mouliraj, V. Anbalagan, Naveenkumar Akilandeswari, J. Sona College of Technology Department of Information Technology Salem India Kongu Engineering College Department of CSE Perundurai India Sri Shanmugha College of Engineering and Technology Department of CSE Sankari India

ISBN: (纸本)9798350306613

the agriculture sector is critical to satisfying the world's ever-increasing food need. Accurate crop yield prediction is critical for ensuring sustainable agriculture and optimizing agricultural production. Our system effectively analyses many elements influencing crop development, such as soil qualities, climate patterns, and historical agricultural data, by integrating environmentally conscious methods and utilizing the power of AI algorithms. the predictive model predicts crop production with high accuracy, allowing farmers and government or NGOs to make educated decisions about resource allocation and productivity while reducing environmental effect. Green principles combined with cutting-edge AI technology in crop yield prediction offer a viable answer for sustainable agriculture and promises a more robust food supply system in the future. In this article, the proposed system uses the Seasonal Adaptive Auto-Regressive Integrated Moving Average (ARIMA) time series model, which provides more accurate results than the prior models and can be used to predict the production of any type of crop. the outcomes show considerable increases in forecast accuracy and give useful information about the elements that affect crop output. © 2024 IEEE.

关键词： Auto-Regressive Integrated Moving Average crop Seasonal Adaptive Auto-Regressive Integrated Moving Average Time Series

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Enhancing Age and Gender Recognition Accuracy through Efficient Deep Learning models 6

Enhancing Age and Gender Recognition Accuracy through Effici...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Kathirisetty, Nikhila Kumar, Kottu Santosh Rangaiah, Yagateela Pandu Aluvalu, Rajanikanth Regulwar, Ganesh B. thakkar, Hiren Kumar Vardhaman College of Engineering Hyderabad India Marwadi University Gujarat Rajkot India Department of IT Vardhaman College of Engineering Hyderabad India Department of ECE Institute of Aeronautical Engineering Hyderabad India Department of IT Chaitanya Bharathi Institute of Technology Hyderabad India Department of IT Vardhaman College of Engineering Shamshabad Hyderabad India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

ISBN: (纸本)9798350369748

the growing interest in discerning age and gender from facial features has practical implications. While sophisticated deep learning techniques have been proposed, they often demand substantial resources and lack real-time capability. In our proposal, we advocate for leveraging YOLOv8 to construct a fully automated system that swiftly identifies faces, provides real-time age and gender determination, and facilitates gender identification in images. this versatile system aids retailers in identifying shoppers, tailors advertisements on TV and in ads enriches urban population insights, and tailors gym workouts based on gender and age. Our solution prioritizes accessibility, striving to minimize resource requirements and enhance real-time applicability, making it adaptable across diverse contexts. Moreover, a dedicated dataset is crafted for YOLO object detection models, featuring 1978 images, 10,000 annotations, and two classes (male and female). Employed across all YOLOv8 versions, experimental outcomes reveal the smaller YOLOv8 version achieved the highest mean Average Precision (mAP) at 89.8%. Furthermore, a modified lightweight YOLOv8 version with 168 layers surpassed the best model by 1.4%, achieving a remarkable 95.4% MAP. this modified version exhibited efficient processing, with 0.8ms preprocess, 12.8ms inference, 0.0ms loss, and 1.6ms post-process per image on the proposed dataset showcasing its effectiveness in real-world applications. © 2024 IEEE.

关键词： Contrastive Learning

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An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction 4

An Applied Artificial Intelligence Technique for Early-Stage...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Ali, Hassan Imtiaz, Hussain Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Alshammeri, Menwa Mir, Azka Kumar, Himanshu University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait College of CIS Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. this increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. this research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. the insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's disease Classification Health care Machine Learning OASIS Prediction

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