Substructure correlation adaptation transfer learning method based on K-means clustering by Haoshuang LIU, Yong ZHANG, Yingbo CAO

“…Domain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-level matching can lead to insufficient adaptation, and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly, multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then, the matching of the second-order statistics of the subdomain center was sought.Finally, the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method.…”
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IT diagnostics of Parkinson's disease based on voice markers and decreased motor activity by U. V. Vishniakou, X. Yiwei

“…The KNN algorithm and the algorithm of a two-layer neural network were used for training and testing on publicly available datasets on speech changes and motion retardation in Parkinson's disease. …”
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A social media geolocation method based on comparative learning by Yongchang XU, Shiduo HUANG, Haojun AI

“…Previous work on social media text-based geolocation focused on mapping language semantic space to geospatial space, which ignores the semantic correlation between social media texts and the distance correlation between geographical locations.To take advantage of these correlations, mCLF, a new unsupervised multiple-level contrastive learning framework was proposed, three contrastive learning modules were designed: a semantic learning module, a location learning module, and a cross-learning module.Transformer encoder was used to obtain semantic representation of posts, utilizing unsupervised contrastive learning method to decrease the distance of semantic representations and location representations of posts with near locations, and then fine-tuned the model with supervised method for geographic location regression or classification outputs.Compared with five baseline methods, extensive experiments based on four datasets demonstrate the effectiveness of the proposed framework.…”
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Eight quick tips for biologically and medically informed machine learning. by Luca Oneto, Davide Chicco

“…While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased. …”
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Do Local and World COVID-19 Media Coverage Drive Stock Markets? Time-Frequency Analysis of BRICS by Ahmed Bossman, Tamara Teplova, Zaghum Umar

“…., Brazil, Russia, India, China, and South Africa). With datasets covering January 2020 to March 2022, we employ the wavelet coherence technique on two major subsamples, viz. initial outbreak year sample and the “new normal” era sample. …”
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Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. by Lang Chai, Rui Huang

“…The study was conducted using multiple real-world and synthetic datasets to validate the proposed algorithm's performance. …”
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Research on federated learning approach based on local differential privacy by Haiyan KANG, Yuanrui JI

“…As a type of collaborative machine learning framework, federated learning is capable of preserving private data from participants while training the data into useful models.Nevertheless, from a viewpoint of information theory, it is still vulnerable for a curious server to infer private information from the shared models uploaded by participants.To solve the inference attack problem in federated learning training, a local differential privacy federated learning (LDP-FL) approach was proposed.Firstly, to ensure the federated model training process was protected from inference attacks, a local differential privacy mechanism was designed for transmission of parameters in federated learning.Secondly, a performance loss constraint mechanism for federated learning was proposed and designed to reduce the performance loss of local differential privacy federated model by optimizing the constraint range of the loss function.Finally, the effectiveness of proposed LDP-FL approach was verified by comparative experiments on MNIST and Fashion MNIST datasets.…”
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HybridFA:a memory reduction technique for the AC automata based on statistics by Gang XIONG, Hui-min HE, Jing YU, Yan-bing LIU, Li GUO

“…Despite the fast speed in multiple string matching tasks,the advanced Aho-Corasick(AC) automata wastes storage memory to a great extent.Study indicated that the automata states have specific statistical access characteristics in practice.Accordingly,a series of algorithms based on statistical characteristics for building hybrid finite automata,named HybridFA,are proposed.This work completes partial states of the AC automata according to different features,including access frequency,state hierarchy,and combined characteristics respectively.Experimental results on the real-world datasets like Snort,ClamAV,and URL show that the storage space of HybridAC is reduced to less than 5% of the space cost by the advanced AC automata.Furthermore,HybridFA based on combined characteristics achieves the superior performance on matching speed and robustness comparing to other proposed algorithms.…”
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Learning of the user behavior structure based on the time granularity analysis model by Lin Guo, Xiaoying Liu

“…Extensive experiments on various domains demonstrate that our proposed method outperforms state-of-the-art baselines on synthetic and real-world datasets.…”
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A Missing Sensor Data Estimation Algorithm Based on Temporal and Spatial Correlation by Zhipeng Gao, Weijing Cheng, Xuesong Qiu, Luoming Meng

“…Simulation results on different sensor datasets verify that the proposed approach outperforms existing solutions in terms of estimation accuracy.…”
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Patch-based domain adversarial training for speech enhancement by WANG Hongtao, LU Zhihua, YE Qingwei, ZHANG Lianjun

“…These mismatches can include differences in speakers, speech content, noise types, and signal-to-noise ratios between the datasets. Severe data mismatches can significantly degrade the performance of speech enhancement. …”
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IDS Transposer: A Users Guide by Emily Klancher Merchant, George Alter

“…The IDS Transposer is a software tool that automates this process for source data in any format, allowing database administrators to specify how their datasets are to be represented in IDS. This article describes how the IDS Transposer works, first by going through an example step-bystep, and then by discussing each part of the process and potential options and exceptions in detail.…”
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Harmonic Classification with Enhancing Music Using Deep Learning Techniques by Wen Tang, Linlin Gu

“…Then, traditional classification is used to create the key classifier which is better than others or manually one. Datasets used to evaluate the proposed model show good achievement results compared with existing one.…”
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An Introduction to the Teaching Reform of Data Science Courses in the Context of Big Data by Shang Du, Su Shuai

“…It aims to develop students’ statistical acumen and their ability to manage complex datasets within the big data landscape. By doing so, it seeks to elevate students’ overall proficiency and empower them with the skills to apply contemporary mathematical and statistical methods to tackle realworld challenges effectively.…”
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Dual-channel attribute graph clustering beyond the homogeneity assumption by AN Junxiu, LIU Yuan, YANG Linwang

“…Compared to other methods, DCAGC demonstrates significant clustering performance when handling heterogeneous graph datasets and exhibits strong resilience to anomalous connections.…”
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Mean Estimation of a Sensitive Variable under Nonresponse Using Three-Stage RRT Model in Stratified Two-Phase Sampling by Ronald Onyango, Brian Oduor, Francis Odundo

“…The efficiency of the proposed estimator is studied theoretically and numerically using two real datasets. From the numerical analysis, the proposed generalized class of exponential ratio-type estimators outperforms ordinary mean estimators, usual ratio estimators, and exponential ratio-type estimators. …”
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IDS Transposer: A Users Guide by Emily Klancher Merchant, George Alter

“…The IDS Transposer is a software tool that automates this process for source data in any format, allowing database administrators to specify how their datasets are to be represented in IDS. This article describes how the IDS Transposer works, first by going through an example step-bystep, and then by discussing each part of the process and potential options and exceptions in detail.…”
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IDS Transposer: A Users Guide by Emily Klancher Merchant, George Alter

“…The IDS Transposer is a software tool that automates this process for source data in any format, allowing database administrators to specify how their datasets are to be represented in IDS. This article describes how the IDS Transposer works, first by going through an example step-bystep, and then by discussing each part of the process and potential options and exceptions in detail.…”
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Two-Layer Hidden Markov Model for Human Activity Recognition in Home Environments by M. Humayun Kabir, M. Robiul Hoque, Keshav Thapa, Sung-Hyun Yang

“…We perform comparison with other activity recognition models using three real datasets to validate the proposed model. The results show that the proposed model achieves significantly better recognition performance than other models.…”
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Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
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