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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Wang, Shiqi Qin, Jiaohua Jiang, Mingfang Gao, ZhiChen College of Computer Science and Information Technology Central South University of Forestry and Technology Changsha410004 China Department of Information Science and Engineering Hunan First Normal University Changsha410205 China Department of Applied Mathematics and Statistics College of Engineering and Applied Sciences Stony Brook University Stony BrookNY11794 United States

ISBN: (纸本)9783030786083

Rapid development and application of big data technology is speeding up the dissemination and utilization of visible media big data. The intensive application of visual media brings about some security issues. The building of copyright protection mechanism of visual media big data under Internet environment can provide safety guarantee for content services of visual media in a digital library, and enhance integrated serviceability of digital resources in the digital library. Firstly, sparse features are extracted by exploiting the data sparsity and perception sparsity of visual media, and then a sparse perception computing model for visual media big data is built. Then the design idea of visual media copyright encryption, copyright authentication, copyright notice, and audit trail scheme are given from the point of view of sparse feature computation of visual media. Finally, a copyright protection application scheme of visual media big data under the network environment is developed. Its application in the digital library validates the effectiveness of this copyright protection mechanism of visual media. It can safeguard the confidentiality, integrity, availability, and auditing of visual media big data in the digital library, and enhance content serviceability of visual media in the digital library under big data environment. © 2021, Springer Nature Switzerland AG.

关键词： Big data

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Analysis of Extreme Rainfall Trends Over 75 Locations (District Wise) of Uttar Pradesh, India using Daily Gridded Rainfall Data (1973-2023)

Analysis of Extreme Rainfall Trends Over 75 Locations (Distr...

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Sushil Kumar Arun Kumar Amarendra Singh Amar Pratap Bhaskar Anurag Singh Baghel Jagabandhu Panda Deparment of Applied Mathematics Gautam Buddha University Greater Noida India Department of Mathematics M.M.H. College Ghaziabad India Centre for Atmospheric Sciences IIT Delhi India Department of Computer Science and Engineering Gautam Buddha University Greater Noida India Department of Earth and Atmospheric Sciences National Institute of Technology Rourkela India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

This study analyzes rainfall patterns in selected districts of Uttar Pradesh, India, using daily gridded rainfall data from the India Meteorological Department (IMD) for the period 1973 to 2023. Trends in severe daily rainfall occurrences were identified using the Mann-Kendall (MK) test. The inverse distance weighting (IDW) interpolation method was used to visualise the geographical distribution of rainfall patterns. The results demonstrate that while monsoon patterns were declining in certain areas, they were increasing in others. The study sheds light on how Uttar Pradesh's agricultural planning and water resource management are affected by rainfall unpredictability.

关键词： Climate change Irrigation Interpolation Rain Disasters Monsoons Market research Planning Floods Water resources

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A heterogeneous graph cross-omics attention model for single-cell representation learning

A heterogeneous graph cross-omics attention model for single...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yue Liu Junfeng Zhang Shulin Wang Wei Zhang Xiangxiang Zeng Chee Keong Kwoh College of Computer Science and Electronic Engineering Hunan University Changsha Hunan Hangzhou Machine Learning Research and Development Center Sina Weibo Hangzhou Zhejiang College of Computer Engineering & Applied Mathematics Changsha University Changsha Hunan School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Single-cell multi-omics sequencing technologies allow simultaneous measurement of transcriptome and epigenome profiles in the same cell, providing unprecedented opportunities to dissect cell heterogeneity. Despite great efforts, conjoint analysis of single-cell multi-omics data still suffers from sparsity, high dimensionality and binary. In this study, we present a heterogeneous graph cross-omics attention model (scHGA), a computational tool based on a heterogeneous graph neural network combining two attention mechanisms to jointly analyze single-cell multi-omics data based on different protocols data, including SNARE-seq, scMT-seq and sci-CAR. To avoid the cell heterogeneity of single-omics data, scHGA automatically learns a cell association graph to capture neighbor information. The latent representation of aggregated cells generated by hierarchical attention can fuse knowledge across different omics to dissect cellular heterogeneity, providing a better scheme to characterize the features of cells. scHGA is an effective exploration of graph neural networks in single-cell multi-omics analysis, providing new insights into the understanding of single-cell sequencing data.

关键词： Heating systems Analytical models Sequential analysis Correlation Protocols Data analysis Biological system modeling

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Learning for Interval Prediction of Electricity Demand: A Cluster-Based Bootstrapping Approach

SSRN

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SSRN 2024年

作者： Dube, Rohit Gautam, Natarajan Banerjee, Amarnath Nagarajan, Harsha Industrial and Systems Engineering Texas A & M University College StationTX77843 United States Electrical Engineering and Computer Science Syracuse University SyracuseNY13210 United States Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los Alamos NM87545 United States

Accurate predictions of electricity demands are necessary for managing operations in a small aggregation load setting like a Microgrid. Due to low aggregation, the electricity demands can be highly stochastic and point estimates would lead to inflated errors. Interval estimation in this scenario, would provide a range of values within which the future values might lie and helps quantify the errors around the point estimates. This paper introduces a residual bootstrap algorithm to generate interval estimates of day-ahead electricity demand. A machine learning algorithm is used to obtain the point estimates of electricity demand and respective residuals on the training set. The obtained residuals are stored in memory and the memory is further partitioned. Days with similar demand patterns are grouped in clusters using an unsupervised learning algorithm and these clusters are used to partition the memory. The point estimates for test day are used to find the closest cluster of similar days and the residuals are bootstrapped from the chosen cluster. This algorithm is evaluated on the real electricity demand data from EULR (End Use Load Research) and is compared to other bootstrapping methods for varying confidence intervals. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autonomous Vehicles Under Multiple Weather Conditions

Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autono...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Safa Teboulbi Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Abdellatif Mtibaa Higher Institute of Computer Sciences and Mathematics of Monastir Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia Systems Integration & Emerging Energies Laboratory National Engineering School of Sfax Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.

关键词： YOLO Adaptation models Accuracy Rain Smart cities Snow Roads Real-time systems Reliability Meteorology

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Netlocator: An Active Failure Localization System in Telecom Carrier Network

SSRN

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SSRN 2023年

作者： Ruan, Chang Tan, Xianchao Bao, Zhihong Cai, Zhihui Xie, Yin Hu, Jinbin Liao, Zhuofan He, Shiming Zhang, Tao School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Xunlei Network Technology Cooperation Shenzhen518000 China Hunan Customer Support Center China Telecom Changsha410000 China College of Computer Engineering and Applied Mathematics Changsha University Changsha410022 China

Modern telecom carrier networks connect hundreds of thousands of servers and carry a large amount of telecommunication service data. In such a crucial and complex fabric, network failures especially gray failures inevitably occur. However, the traditional monitoring mechanisms cannot locate gray failures or even discover them timely via using Simple Network Management Protocol (SNMP). To address the problem, several active failure localization systems are proposed, which actively probes the network failures in an end-to-end manner. In this paper, to better identify and locate the network failures, we design NetLocator, an active failure localization system which probes the network in a matrix manner, infers the network topology automatically and collects the probe data correctly by tackling several challenging problems. Furthermore, the failure localization algorithm in NetLocator does not use the preset threshold as in previous work deTector. We implement and test NetLocator in a real telecom carrier network and with simulation. The experimental results show that the NetLocator system effectively identifies and localize network failures especially gray failures. Compared with the recent schemes deTector and NetDiagnoser, NetLocator improves the accuracy of failure localization, including the device and link failure. Moreover, NetLocator is readily deployable in a real telecom carrier network. © 2023, The Authors. All rights reserved.

关键词： Probes

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CS-NET: STRUCTURAL APPROACH TO TIME-SERIES FORECASTING FOR HIGH-DIMENSIONAL FEATURE SPACE DATA WITH LIMITED OBSERVATIONS

arXiv

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arXiv 2022年

作者： Zong, Weiyu Feng, Mingqian Heyrich, Griffin Chin, Peter Boston University Department of Mathematics and Computer Science United States Johns Hopkins University Department of Applied Mathematics and Statistics United States Dartmouth College Thayer School of Engineering United States

In recent years, deep-learning-based approaches have been introduced to solving time-series forecasting-related problems. These novel methods have demonstrated impressive performance in univariate and low-dimensional multivariate time-series forecasting tasks. However, when these novel methods are used to handle high-dimensional multivariate forecasting problems, their performance is highly restricted by a practical training time and a reasonable GPU memory configuration. In this paper, inspired by a change of basis in the Hilbert space, we propose a flexible data feature extraction technique that excels in high-dimensional multivariate forecasting tasks. Our approach was originally developed for the National Science Foundation (NSF) Algorithms for Threat Detection (ATD) 2022 Challenge. Implemented using the attention mechanism and Convolutional Neural Networks (CNN) architecture, our method demonstrates great performance and compatibility. Our models trained on the GDELT Dataset finished 1st and 2nd places in the ATD sprint series and hold promise for other datasets for time series forecasting. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic

A Novel Methodology to Predict Flood and Landslide Disasters...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： G. Bhupal Raj Muzeeb Khan Patan Ashwani Kumar Gupta C.Lakshmi Gopinath D Ajay Singh Yadav School of Agriculture SR University Warangal Department of EEE College of Engineering and Technology University of Technology and Applied Sciences Muscat Oman Faculty of Business and Commerce Chandigarh University Unnao Uttar Pradesh Saveetha Engineering College Chennai Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad U.P

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques.

关键词： Deep learning Accuracy Biological system modeling Prevention and mitigation Disasters Predictive models Market research Terrain factors Floods Forecasting

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A Prescribed Paper Less Clinic to view the Patient's Documents 1

A Prescribed Paper Less Clinic to view the Patient's Documen...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： MacHap, Kamalakannan Prabhu, M. Ramkumar Rajalingam, A. Charulatha, G. Saranya, R. Renukadevi, S. School of Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Peri Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Shinas Oman R. M K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350397840

Diseases are a significant aspect of human beings in modern times. It plays a vital role in a safe life in the present era. At the same time, registration, missing medication letter, scans, etc., is among the most significant issues when interacting with physicians. Here it has to overcome these problems with Smartphone app control with user engagement innovations for the wellness climate. The Rx List Pill Identifier Tool recognizes generic, OTC, and marketed medicines by coloring pill, size, type, and medication print. Align and quickly identify the drugs with the images in the medication imprint (pill ID). In addition, the health details of patients, such as ECG, temperature, and BP, which are provided for reference purposes to cloud stocking, can be stored. The existing frameworks' outcome is to give guidance for transforming these traditional health services into lean systems that will aid in the creation of a poor hospital with satisfied customers. The centralized medical complex skin temperature tracking system comprises the access point and various control terminals, comprising a power supply, thermal signal array, ESP8266 transmission and reception, display, alarm, and motion sensor, among other things. The STM32 heart controls and operates the software. During operation, the host delivers the signal with the necessary addressing data. © 2022 IEEE.

关键词： Internet of things

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Quantum Hamiltonian Descent for Non-smooth Optimization

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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