From Entity Description to Semantic Analysis: The Case of Theodor Fontane’s Notebooks by Martin de la Iglesia, Mathias Göbel

“…Scholars all over the world are now able to use huge datasets for further research. There are now many digital editions available, but only a few tools to analyze them. …”
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Tracking algorithm of Siamese network based on online target classification and adaptive template update by Zhiwang CHEN, Zhongxin ZHANG, Juan SONG, Haipeng LEI, Yong PENG

“…Aiming at the problem that tracking algorithm of Siamese network learned the embedded features of the tracked target and the object in the offline training stage, and these embedded features often lacked the target-specific context information, which made these tracking algorithms less robust, a tracking algorithm of the Siamese network based on online target classification and adaptive template update was proposed, which used SiamRPN++ as the baseline algorithm.Firstly, a cross-correlation feature map supervision module for classification was designed in the offline training phase to learn more discriminative embedded features.Secondly, an online target classification module that included an attention mechanism in the online tracking phase was designed, and the online update filter strategy in the module was used to filter out the background noise.Finally, an adaptive template update module was designed to update the target template information using the UpdateNet.The results of experiments on VOT2018 and VOT2019 datasets verify the effectiveness of the proposed algorithm, which brings 13.5% and 18.2% (EAO) improvement respectively compared with the baseline algorithm SiamRPN++.…”
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Alpha Power Transformed Generalized Weibull Distribution With Its Detailed Properties and Applications by Merga Abdissa Aga, Ayele Taye Goshu

“…Simulation studies and analysis of real datasets with the new APTGWD model demonstrate the distribution’s potential as an alternative for modeling lifetime and reliability data. …”
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Motor Tasks Classification Using Phase Locking Value in a BCI Based EEG Paradigm by Oana-Diana Hrisca-Eva

“…An approach based on phase synchronization was tested on two datasets (one with EEG signals recorded from 15 healthy subjects and one with EEG signals recorded from 9 subjects with disabilities). …”
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Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning by REN Yonggong, ZHOU Pinglei, ZHANG Zhipeng

“…Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users.…”
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Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques by Dakhaz Mustafa Abdullah, Adnan Mohsin Abdulazeez, Amira Bibo Sallow

“…Basically, the informational indexes utilized as a part of this examination are taken from UCI datasets for patients affected by lung cancer. The principle point of this paper is to the execution investigation of the classification algorithms accuracy by WEKA Tool. …”
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Measurements of 4ΛH and 4ΛHe Production in √sNN = 3.0-3.5 GeV Au+Au Collisions at RHIC by Hu Chenlu

“…In these proceedings, the measurements of A=4 hypernuclei (4ΛH and 4ΛHe) production from the RHIC-STAR experiment utilizing the fixed target datasets are presented. The measured yields dN=dy of 4ΛH and 4ΛHe as a function of rapidity are shown from √sNN and 3.5 GeV Au+Au collisions. …”
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Semantic segmentation of 3D point cloud based on contextual attention CNN by Jun YANG, Jisheng DANG

“…Aiming at the under-segmentation of 3D point cloud semantic segmentation caused by the lack of contextual fine-grained information of the point cloud,an algorithm based on contextual attention CNN was proposed for 3D point cloud semantic segmentation.Firstly,the fine-grained features in local area of the point cloud were mined through the attention coding mechanism.Secondly,the contextual features between multi-scale local areas were captured by the contextual recurrent neural network coding mechanism and compensated with the fine-grained local features.Finally,the multi-head mechanism was used to enhance the generalization ability of the network.Experiments show that the mIoU of the proposed algorithm on the three standard datasets of ShapeNet Parts,S3DIS and vKITTI are 85.4%,56.7% and 38.1% respectively,which has good segmentation performance and good generalization ability.…”
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Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics by Vitali Chaiko

“…The methodology involves preprocessing large datasets from Twitter concerning major companies such as Amazon, Google, and Tesla, training LSTM models, and prompt engineering for ChatGPT-based predictions. …”
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Impact of Using Double Positive Samples in Deming Regression by Samuel Akwasi Adarkwa, Frank Kofi Owusu, Samuel Okyere

“…Each of the simulation is made up of 100 datasets with 300 observations. Simulation studies suggest that the traditional Deming regression which deletes censored observations gives biased estimates and a low coverage, whereas the adapted Deming regression that takes censoring into account gives estimates that are close to the true value making them unbiased and gives a high coverage. …”
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Multi-view graph neural network for fraud detection algorithm by Zhuo CHEN, Miao ZHU, Junwei DU

“…Aiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-view graph neural network for fraud detection (MGFD) algorithm was proposed.First, A structure-independent encoder was used to encode the attributes of nodes in the network to learn the difference between the fraud node and the normal node.The hierarchical attention mechanism was designed to integrate the multi-view information in the network, and made full use of the interaction information between different perspectives in the network to model the nodes on the basis of learning differences.Then, based on the data imbalance ratio sampled subgraph, the sample was constructed according to the connection characteristics of fraud nodes for classification, which solved the problem of imbalance sample labels.Finally, the prediction label was used to identify whether a node is fraudulent.Experiments on real-world datasets have shown that the MGFD algorithm outperforms the comparison method in the field of graph-based fraud detection.…”
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Ensemble Classification Approach for Sarcasm Detection by Jyoti Godara, Isha Batra, Rajni Aron, Mohammad Shabaz

“…The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.…”
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LEO satellite Internet resource allocation strategy based on terminal traffic prediction by SHEN Fei, LYU Chengcheng, ZHANG Jiaxuan, RUAN Xiaoting

“…An improved LSTM-ARIMA algorithm was proposed with real datasets by the strategy to accurately predict the data traffic generated in the ground area over a certain period of time in the future. …”
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Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5 by Lijie Zhou, Weihai Yu

“…Based on convolution operation, pooling operation, softmax classifier, and network optimization algorithm in improved convolutional neural network of LeNet-5, this paper conducts image recognition experiments on handwritten digits and face datasets, respectively. A method combining local binary pattern and convolutional neural network is proposed for face recognition research. …”
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Robust deepfake detection method based on siamese network by LIN Shanhe

“…The proposed method demonstrated an average accuracy exceeding 90% across various datasets with different compression levels, surpassing several existing detection techniques. …”
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Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge by Linda-Sophie Schneider, Adrian Waldyra, Yipeng Sun, Andreas K. Maier

“…Experiments on real-world datasets demonstrate the efficacy of the approach. …”
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A Mixture of Regular Vines for Multiple Dependencies by Fadhah Amer Alanazi

“…However, the variation of complex hidden correlations from one pair of variables to another is more likely to be present in many real datasets. Single-type bivariate copulas are unable to deal with such a problem. …”
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The global Multidimensional Poverty Index: Harmonised level estimates and their changes over time by Nicolai Suppa, Usha Kanagaratnam

“…The estimates are based on 211 individual survey datasets, provided primarily by the Demographic Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS). …”
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Defending Deep Neural Networks Against Backdoor Attack by Using De-Trigger Autoencoder by Hyun Kwon

“…Experiments were conducted using MNIST, Fashion-MNIST, and CIFAR-10 as the experimental datasets and TensorFlow as the machine learning library. …”
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Design of nodes importance assessment method for complex network based on neighborhood information by Xing LI, Jie ZHAN, Baoquan REN, Siqi ZHU

“…Accurate identification of influential nodes in complex networks is crucial for network management and network security.The local centrality method is concise and easy to use, but ignores the topological relationship between neighboring nodes and cannot provide globally optimal results.A node importance assessment method was proposed to correlate the node edge relationship and topology, which firstly applied the H-index and information entropy to assess the nodes, then combined the structural holes of the nodes with the structural characteristics of the nodes, and took into account the attribute of “bridging” while focusing on the node’s own quality and the amount of information about the neighboring nodes.The algorithm was validated by simulating the propagation process using the SIR model, and the Kendall correlation coefficient, complementary cumulative distribution function and propagation influence were applied to validate the validity and applicability of the method.Comparison of the experimental results on six real network datasets shows that the proposed method is more accurate than the traditional centrality methods in identifying and ordering the key nodes in the network.…”
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