Bayesian Inference of Elevation to Reduce Large Interpolation Errors in 2-d Road Features Draped Over Digital Elevation Models by Crispin H. V. Cooper

“…The usual approach for adding elevation data to two dimensional (2-d) vector features in a Geographic Information System (GIS) is to infer heights from a Digital Elevation Model (DEM), either through traditional (naïve) interpolation, Kriging, or deep learning. Where the terrain contains steep slopes, however, any of these approaches can generate large errors due to the limited resolution of the DEM, and model error in the DEM concept itself. …”
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Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition by Zhichao Wang, Yu Jiang, Jiaxin Liu, Siyu Gong, Jian Yao, Feng Jiang

“…The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. …”
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Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review by Henrique Henriques, Luis Nobre Pereirsa

“…Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). …”
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Intelligent task-oriented semantic communication method in artificial intelligence of things by Chuanhong LIU, Caili GUO, Yang YANG, Chunyan FENG, Qizheng SUN, Jiujiu CHEN

“…With the integration and development of Internet of things (IoT) and artificial intelligence (AI) technologies, traditional data centralized cloud computing processing methods are difficult to effectively remove a large amount of redundant information in data, which brings challenges to the low-latency and high-precision requirements of intelligent tasks in the artificial intelligence of things (AIoT).In response to this challenge, a semantic communication method oriented to intelligent tasks in AIoT was proposed based on the deep learning method.For image classification tasks, convolutional neural networks (CNN) were used on IoT devices to extract image feature maps.Starting from semantic concepts, semantic concepts and feature maps were associated to extract semantic relationships.Based on the semantic relationships, semantic compression was implemented to reduce the pressure of network transmission and the processing delay of intelligent tasks.Experimental and simulation results show that, compared with traditional communication scheme, the proposed method is only about 0.8% of the traditional scheme, and at the same time it has higher classification task performance.Compared with the scheme that all feature maps are transmitted, the transmission delay of the proposed method is reduced by 80% and the effective accuracy of image classification task is greatly improved.…”
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Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

“…With the popularity of intelligent chip in the application of edge terminal devices, a large number of AI applications will be deployed on the edge of networks closer to data sources in the future.The method based on DNN partition can realize deep learning model training and deployment on resource-constrained terminal devices, and solve the bottleneck problem of edge AI computing ability.Thekernel based partition method (KPM) was proposed as a new scheme on the basis of traditional workload based partition method (WPM).The quantitative analysis of inference performance was carried out from three aspects of computation FLOPS, memory consumption and communication cost respectively, and the qualitative analysis of the above two schemes was carried out from the perspective of flexibility, robustness and privacy of inference process.Finally, a software and hardware experimental platform was built, and AlexNet and VGG11 networks were implemented using PyTorch to further verify the performance advantages of the proposed scheme in terms of delay and energy consumption.It was concluded that, compared with the WPM scheme, the KPM scheme had better DNN reasoning acceleration effect in large-scale computing scenarios.And it has lower memory usage and energy consumption.…”
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An Event Causality Identification Framework Using Ensemble Learning by Xiaoyang Wang, Wenjie Luo, Xiudan Yang

“…The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problems such as data imbalance and insufficient event content richness. …”
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Adversarial method for malicious ELF file detection based on deep reinforcement learning by SUN He, ZHANG Bocheng, GENG Jiaxuan, WU Di, WANG Junfeng, FANG Zhiyang

“…In recent years, research on detecting malicious executable and linkable format (ELF) files based on deep learning had made significant progress. At the same time, adversarial attacks on models had also gained widespread attention. …”
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Advances in modeling cellular state dynamics: integrating omics data and predictive techniques by Sungwon Jung

“…This review provides a comprehensive overview of current approaches for modeling cellular state dynamics, focusing on techniques ranging from dynamic or static biomolecular network models to deep learning models. We highlight how these approaches integrated with various omics data such as transcriptomics, and single-cell RNA sequencing could be used to capture and predict cellular behavior and transitions. …”
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Artificial Intelligence in Emotion Quantification : A Prospective Overview by Feng Liu

“…Multi-modal data sources, including facial expressions, speech, text, gestures, and physiological signals, are combined with machine learning and deep learning methods in modern emotion recognition systems. …”
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Automated Shape Analysis and DEM Study on Graded Crushed Stone by Hao Bai, Ruidong Li, Xiangyu Hu, Fei Chen, Zhiyong Liao

“…In this study, the realistic particle outline is first automatically extracted based on digital image processing and deep learning algorithms. Then, the elongation (EI), roundness (Rd), and roughness (Rg) of GCS are quantified by shape evaluation algorithms. …”
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Learning-Based Dark and Blurred Underwater Image Restoration by Yifeng Xu, Huigang Wang, Garth Douglas Cooper, Shaowei Rong, Weitao Sun

“…Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. …”
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Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning by Runzi LIU, Tianci MA, Weihua WU, Chenhong YAO, Qinghai YANG

“…In recent years, with the increasing number of various emergency tasks, how to control the impact on common tasks while ensuring system revenue has become a huge challenge for the dynamic scheduling of relay satellite networks.Aiming at this problem, with the goal of maximizing the total revenue of emergency tasks and minimizing the damage to common tasks, a dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning was proposed.Specifically, in order to take into account the long-term and short-term performance of the system at the same time, a two-layer scheduling framework implemented by upper-level and lower-level DQN was designed.The upper-level DQN was responsible for determining the temporary optimization goal based on long-term performance, and the lower-level DQN determined the scheduling strategy for current task according to the optimization goal.Simulation results show that compared with traditional deep learning methods and the heuristic methods dealing with dynamic scheduling problems, the proposed method can improve the total revenue of urgent tasks while reducing the damage to common tasks.…”
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Generalization of neural network models for complex network dynamics by Vaiva Vasiliauskaite, Nino Antulov-Fantulin

“…However, deploying deep learning models in unfamiliar settings-such as predicting dynamics in unobserved state space regions or on novel graphs-can lead to spurious results. …”
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Entity Linking Based on Sentence Representation by Bingjing Jia, Zhongli Wu, Pengpeng Zhou, Bin Wu

“…Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. …”
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Learning attribute network algorithm based on high-order similarity by Shaoqing WU, Yihong DONG, Xiong WANG, Yan CAO, Yu XIN

“…Due to the lack of deep-level information mining and utilization in the existing network representation learning methods,the potential pattern structure similarity was proposed by further exploring the potential information in the network.The similarity score between network structures was defined to measure the similarity between various structures so that nodes could cross irrelevant vertices to obtain high-order similarities on the global structure.In order to achieve the best effect,deep learning was used to fuse multiple information sources to participate in training together to make up for the deficiency of random walks.In the experiment,Lap,DeepWalk,TADW,SDNE and CANE were selected as comparison methods,and three real-world networks were used as data sets to verify the validity of the model,and experiments of node classification and link reconstruction are carried out.In the node classification,the average performance is improved by 1.7 percentage points for different datasets and training proportions.In the link reconstruction experiment,only half the dimension is needed to achieve better performance.Finally,the performance improvement of the model under different network depths was discussed.By increasing the depth of the model,the average performance of node classification increased by 1.1 percentage points.…”
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Lightweight and Efficient CSI-Based Human Activity Recognition via Bayesian Optimization-Guided Architecture Search and Structured Pruning by Sungkwan Youm, Sunghyun Go

“…This paper presents an integrated approach to developing lightweight, high-performance deep learning models for human activity recognition (HAR) using WiFi Channel State Information (CSI). …”
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An Efficient Data Analysis Framework for Online Security Processing by Jun Li, Yanzhao Liu

“…None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. …”
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Gearbox Fault Identification and Classification with Convolutional Neural Networks by ZhiQiang Chen, Chuan Li, René-Vinicio Sanchez

“…Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. …”
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Adaptive Contrast Enhancement for Digital Radiographic Images using Image-to-Image Translation by Popp Ann-Kathrin, Schumacher Mona, Himstedt Marian

“…The contrast enhancement of an image can be considered as a style transfer or an image-to-image translation which is an important field in deep learning. Based on common methods like the pix2pix network that only translate from one domain into one other, we propose a method (cc-pix2pix) for translating into multiple domains in one training. …”
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Attention-assisted dual-branch interactive face super-resolution network by Xujie Wan, Siyu Xu, Guangwei Gao

“…We propose a deep learning-based Attention-Assisted Dual-Branch Interactive Network (ADBINet) to improve facial super-resolution by addressing key challenges like inadequate feature extraction and poor multi-scale information handling. …”
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