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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain Science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research Institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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Rail Transit Event Extraction based on the Fusion of Semantic and Local Dependency Features 15

Rail Transit Event Extraction based on the Fusion of Semanti...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Chen, Qiang Meng, Haining Bao, Yihao Yang, Mingsong Tong, Xinyu Zhao, Qin Khan, Muhammad Bilal Hei, Xinhong Xi'an University of Technology School of Computer Science and Engineering Xi'an China Xi'an University of Technology School of Computer Science and Engineering Shaanxi Key Laboratory of Network Computing and Security Technology Xi'an China Xi'an University of Technology School of Civil Engineering and Architecture Xi'an China

ISBN: (纸本)9798331508821

Event extraction in rail transit plays a vital role in all stages of design, construction, and final acceptance. Event extraction is a key part of building event logic graph (ELG). However, the difficulty in obtaining semantic features and event local dependencies poses a challenge to accurate event extraction, which hinders the informatization and intelligent development of rail transit projects. To address these challenges, we proposes an event extraction method that fuses semantic and local dependency features. The study initially preprocesses the rail transit design codes dataset and performs sequence labeling. Then, the event trigger words are mined by combining the term frequency-inverse document frequency (TF-IDF) and minimum edit distance (MED) algorithms. Based on the semantic similarity measurement method and the trigger word mining results, the features representation of event semantic information is enhanced by using the Bidirectional Encoder Representations from Transformers (BERT) model and Bi-directional Long Short-Term Memory (BiLSTM) model. At the same time, event arguments are mined and a local dependency graph is generated based on their mining results, and then the enhanced local dependency features are obtained by using the graph convolutional network (GCN). Finally, the event semantic features and local dependency features are fused, and the conditional random field (CRF) is used to predict the labels to obtain the event extraction results. Through the comparison with five baseline methods on the datasets of rail transit design codes, it is found that the Precision, Recall, and F1 score of the proposed method are improved, with an average improvement of 9.604% on Precision, 9.342% on Recall, and 9.730% on F1 score, respectively. The contribution of the sub-modules to the overall performance is further evaluated through ablation experiments, which confirms the effectiveness and feasibility of the proposed method. © 2024 IEEE.

关键词： Semantics

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Analyze the encryption and decryption methods using the Catalan sequence 38

Analyze the encryption and decryption methods using the Cata...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Zdanski, Maciej Walusiak, Lukasz Rylko, Natalia Faculty of Computer Science and Telecommunications Cracow University of Technology Poland Faculty of Architecture Civil Construction and Applied Arts Academy of Silesia Poland

This paper concerns the project and implementation of a cryptographic tool based on the Catalan sequence. It explains the beginnings of cryptography and how people’s approach to transmitting messages has changed over the years. With the development of society, the need to conceal the meaning of messages from unauthorized individuals has grown. The paper addresses the issue of symmetric affine cipher, its complexity, and the secure transmission of the secret key through individuals using the tool. The key can be entered by the user-sender or generated automatically using the Catalan sequence. In contemporary times, the issue of secure data transmission, and consequently the appropriate methods of encryption and decryption, is crucial. The presented actions not only show an analysis of the methods but also the implementation of these as a functioning tool. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Cryptography

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Trajectory equation of a lump before and after collision with other waves for generalized Hirota–Satsuma–Ito equation

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Chinese Physics B 2023年第10期32卷 163-171页

作者：夏亚荣张开开姚若侠申亚丽 School of Information and Engineering Xi'an UniversityXi'an 710065China School of Science Xi'an University of Architecture and TechnologyXi'an 710055China School of Computer Science Shaanxi Normal UniversityXi'an 710062China Maths and Information Technology School Yuncheng UniversityYuncheng 044000China

Based on the Hirota bilinear and long wave limit methods,the hybrid solutions of m-lump with n-soliton and nbreather wave for generalized Hirota–Satsuma–Ito(GHSI)equation are ***,by approximating solutions of the GHSI equation along some parallel orbits at infinity,the trajectory equation of a lump wave before and after collisions with n-soliton and n-breather wave are studied,and the expressions of phase shift for lump wave before and after collisions are ***,it is revealed that collisions between the lump wave and other waves are elastic,the corresponding collision diagrams are used to further explain.

关键词： Hirota bilinear method long wave limit hybrid solutions trajectory equation

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Creating 3D Asset Variations Through 2D Style Transfer and Generated Texture Maps

Creating 3D Asset Variations Through 2D Style Transfer and G...

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44th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2023

作者： Nikolov, I. Computer Graphics Group Faculty of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9783038682110

Generating 3D object variations through style transfer models applied to their textures is an easy way for creating content for games and XR applications. Most workflows focus on either generating albedo textures only without changing the underlying surface and not touching the underlying surface or transforming the 3D object directly, which is computationally and resource-heavy. In this paper, we present an initial exploration of an in-between solution that aims to combine the style transfer of albedo textures with the generation of additional maps, such as normal, displacement, and roughness. The results show that the pipeline can generate variations based on different styles, which would enable the addition of smaller 3D-style surface features to objects without transforming their meshes. The project code and generated textures will be available at https://***/IvanNik17/3D-Assets-From-2D-Style-Transfer. © 2023 The Authors.

关键词： Textures

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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computer Modeling in Engineering & Sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu Xinhong Hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

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Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency 11

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Olekar, Vijay Patil, Yash Pawar, Vishvam Pitale, Shubham Jaiswal, V.R. Naranje, Vishal Pune Institute of Computer Technology Pune Dept. of Information Technology Pune India School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates

ISBN: (纸本)9798350350357

The processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. This survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications. © 2024 IEEE.

关键词： Cyber Physical System

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An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fitting Model 4th

An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fit...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Ling, Zhang Zongxin, Ma MingYu, Yan Wenchao, Jiang Muhammad Computer Faculty Guangdong University of Technology Guangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Management and Technology Lahore54770 Pakistan

ISBN: (纸本)9783031200984

Nowadays gaze tracking systems have several common drawbacks: low suitability, high complexity and low accuracy. To solve these problems, this paper presents a novel gaze tracking method, which improves the performance of gaze tracking system in two aspects. A 3D ocular quadratic surface model is constructed with eyeball’s point cloud data, which are collected by a pair of binocular cameras in a free space, allowing head movement and without any wearable devices. Geometrical features of ocular surface, including principal direction, Gaussian curvature and radius are exploited to calculate ocular optical axis direction and person-specific parameters, and then to estimate ocular visual axis direction, i.e. gaze direction without any personal calibration procedures. For this purpose, two person-specific parameters, the radius of cornea curvature and the distance between the center of cornea curvature and the pupil on the ocular model, are inputted into a GRNN network to estimate the value of kappa angle, which is an angle between ocular optical axis and visual axis and has important effect on estimation accuracy of the ocular visual axis direction by the ocular optical axis direction. Compared with the pupil center cornea reflection method, the following experimental results show that the method presented in this paper is effective and accurate in kappa angle estimating and gaze tracking. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Eye tracking

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Adaptive Segmentation for Unconstrained Iris Recognition

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computers, Materials & Continua 2024年第2期78卷 1591-1609页

作者： Mustafa AlRifaee Sally Almanasra Adnan Hnaif Ahmad Althunibat Mohammad Abdallah Thamer Alrawashdeh Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Faculty of Computer Studies Arab Open UniversityRiyadh11681Saudi Arabia Faculty of Science and Information Technology Al-Zaytoonah University of JordanAmman11733Jordan

In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture *** these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction ***,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained ***,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera *** image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous *** iris images are accordingly classified into seven categories based on their global RGB *** the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ***,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris *** experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

关键词： Image recognition color segmentation image processing localization

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