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Trustworthy semi-supervised anomaly detection for online‐tooffline logistics business in merchant identification

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 544-556页

作者： Yong Li Shuhang Wang Shijie Xu Jiao Yin business School China University of Political Science and LawBeijingChina JD Logistics BeijingChina Southeast University NanjingChina Institute for Sustainable Industries and Liveable Cities Victoria UniversityMelbourneVictoriaAustralia

The rise of online-to-offline(O2O)e-commerce business has brought tremendous opportunities to the logistics *** the online-to-offline logistics business,it is essential to detect anomaly merchants with fraudulent shipping behaviours,such as sending other merchants'packages for profit with their low *** can help reduce the financial losses of platforms and ensure a healthy *** anomaly detection studies have mainly focused on online fraud behaviour detection,such as fraudulent purchase and comment behaviours in ***,these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package-sending behaviours and the interpretable requirements of offline deployment in ***,a semi-supervised multiview fusion-based Anomaly detection framework in online-to-offline logistics is proposed,which consists of a basic version SemiDet and an attention-enhanced multi-view fusion *** SemiDet,pair-wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly ***,SemiDet calculates the anomaly scoring of each merchant with an auto-encoder *** the multi-relationships among logistics merchants,a multi-view attention fusion-based anomaly detection network is further designed to capture merchants'mutual influences and improve the anomaly merchant detection performance.A post-hoc perturbation-based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end-to-end anomaly *** framework based on an eight-month real-world dataset collected from one of the largest logistics platforms in China is evaluated,involving 6128 merchants and 16 million historical order consignor records in *** results show that the proposed model outperforms other baselines in both AUC-ROC and AUC-PR metrics.

关键词： artificial intelligence techniques data fusion deep learning

GMAT:A Graph Modeling Method for Group Preference Prediction

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Journal of Systems Science and Systems Engineering 2024年第4期33卷 475-493页

作者： Xiangyu Li Xunhua Guo Guoqing Chen International business School Jinan UniversityZhuhai519070China School of Economics and Management Tsinghua UniversityBeijing100084China

Preference prediction is the building block of personalized services,and its implementation at the group level helps enterprises identify their target customers *** methods for preference prediction mainly focus on behavioral interactions to extract the associations between groups and products,ignoring the importance of other auxiliary records(e.g.,online reviews and social tags)in association *** paper proposes a novel method named GMAT for group preference prediction,aiming to collectively detect the sophisticated association patterns from user generated content(UGC)and behavioral *** doing so,we construct a tripartite graph to collaborate these two types of data,and design a deep-learning algorithm with mutual attention module for generating the contextualized representations of groups and *** experiments on two real-world datasets show that GMAT is superior to other baselines in terms of group preference ***,GMAT is able to improve prediction accuracy compared with its different variants,further verifying the proposed method’s effectiveness on association pattern detection.

关键词： Group preference UGC tripartite graph deep learning

Phishing Attacks detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

Depressive semantic awareness from vlog facial and vocal streams via spatio-temporal transformer

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Digital Communications and Networks 2024年第3期10卷 577-585页

作者： Yongfeng Tao Minqiang Yang Yushan Wu Kevin Lee Adrienne Kline Bin Hu School of Information Science and Engineering Lanzhou UniversityLanzhouChina The School of Accounting Auditing and TaxationBusiness SchoolUNSW SydneyAustralia Department of Preventive Medicine Northwestern UniversityChicagoILUnited States

With the rapid growth of information transmission via the Internet,efforts have been made to reduce network load to promote *** such application is semantic computing,which can extract and process semantic *** media has enabled users to share their current emotions,opinions,and life events through their mobile ***,people suffering from mental health problems are more willing to share their feelings on social ***,it is necessary to extract semantic information from social media(vlog data)to identify abnormal emotional states to facilitate early identification and *** studies do not consider spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as *** solve this problem,this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of ***,a module with spatio-temporal data is embedded into the transformer encoder,which is utilized to obtain a representation of spatio-temporal ***,a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression *** are conducted on the D-Vlog *** results show that the method is effective,and the accuracy rate can reach 70.70%.This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.

关键词： Emotional computing Semantic awareness Depression recognition Vlog data

Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks

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Chinese Physics B 2024年第7期33卷 158-169页

作者：杨鹏范如国王奕博张应青 School of Economics and Management Wuhan UniversityWuhan 430072China Postdoctoral Research Station China International Engineering Consulting CorporationBeijing 100000China School of business Administration Guizhou University of Finance and EconomicsGuiyang 550025China

We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.

关键词： propagation dynamics complex networks public health events community structure

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A machine learning approach for predictings stroke

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Medical Data Mining 2024年第3期7卷 8-16页

作者： Yubo Fu Hasan School of business Colorado State University PuebloPuebloCO 81001USA

Background:Stroke is one of the most dangerous and life-threatening disease as it can cause lasting brain damage,long-term disability,or even *** early detection of warning signs of a stroke can help save the life of a *** this paper,we adopted machine learning approaches to predict strokes and identify the three most important factors that are associated with ***:This study used an open-access stroke prediction *** developed 11 machine learning models and compare the results to those found in prior ***:The accuracy,recall and area under the curve for the random forest model in our study is significantly higher than those of other *** learning models,particularly the random forest algorithm,can accurately predict the risk of stroke and support medical decision ***:Our findings can be applied to design clinical prediction systems at the point of care.

关键词： medical decision making machine learning predictive modeling stroke imbalanced data

In Situ Atomic Reconstruction Engineering Modulating Graphene-Like MXene-Based Multifunctional Electromagnetic Devices Covering Multi-Spectrum

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Nano-Micro Letters 2024年第9期16卷 247-261页

作者： Ting‑Ting Liu Qi Zheng Wen‑Qiang Cao Yu‑Ze Wang Min Zhang Quan‑Liang Zhao Mao‑Sheng Cao School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081People’s Republic of China Department of Physics Beijing Technology and Business UniversityBeijing 100048People’s Republic of China School of Mechanical and Materials Engineering North China University of TechnologyBeijing 100144People’s Republic of China

With the diversified development of big data,detection and precision guidance technologies,electromagnetic(EM)functional materials and devices serving multiple spectrums have become a hot *** the multispectral response of materials is a challenging and meaningful scientific *** this study,MXene/TiO_(2)hybrids with tunable conduction loss and polarization relaxation are fabricated by in situ atomic reconstruction *** importantly,MXene/TiO_(2)hybrids exhibit adjustable spectral responses in the GHz,infrared and visible spectrums,and several EM devices are constructed based on *** antenna array provides excellent EM energy harvesting in multiple microwave bands,with|S11|up to−63.2 dB,and can be tuned by the degree of *** ultra-wideband bandpass filter realizes a passband of about 5.4 GHz and effectively suppresses the transmission of EM signals in the *** infrared stealth device has an emissivity of less than 0.2 in the infrared spectrum at wavelengths of 6-14μ*** work can provide new inspiration for the design and development of multifunctional,multi-spectrum EM devices.

关键词： Graphene-like MXene hybrids Multi-spectral response Multi-function antenna Ultra-wideband bandpass filter Electromagnetic device

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YOLOv7-Bw: Enhancing Remote Sensing Small Object detection with Generative Model Data

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YOLOv7-Bw: Enhancing Remote Sensing Small Object Detection w...

Enhanced Photocatalytic Activity of Nanoparticle-Aggregated Ag–AgX(X=Cl, Br)@TiO_2 Microspheres Under Visible Light

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作者： Xuebo Jin Anshuo Tong Huijun Ma Beijing Technology and business university School of Computer and Artificial Intelligence

In recent years, deep learning technology has been increasingly applied in the detection of remote sensing ***, remote sensing images generally have large differences in object size and dense distribution, which requires high performance of detection algorithms. The current detection methods are generally inefficient, prone to missed detections and inaccurate detection boxes. Therefore, this article improves the YOLO algorithm and proposes a YOLOv7-based algorithm YOLOv7-bw, which achieves efficient detection of remote sensing images. Especially for object detection in small object dense and fuzzy remote sensing images, this algorithm significantly improves detection accuracy and robustness, promoting the application and development of object detection in the remote sensing industry. Meanwhile, compared with commonly used algorithms, our YOLOv7-bw performs better, proving that our proposed algorithm is feasible and can be better applied to remote sensing image detection.

关键词： remote sensing image YOLO object detection m AP

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Nano-Micro Letters 2017年第4期9卷 139-151页

作者： Cuiling Zhang Hao Hua Jianlin Liu Xiangyu Han Qipeng Liu Zidong Wei Chengbin Shao Chenguo Hu Chongqing Engineering Laboratory for detection Controland Integrated System Chongqing Technology and Business University College of Chemistry and Chemical Engineering College of Physics Chongqing University

Ag–AgX(X = Cl, Br)@TiO_2 nanoparticle-aggregated spheres with different mass ratio of R = TiO2/Ag(X) from 35:1 to 5:1 were synthesized by a facile sol–gel technique with post-photoreduction. The photocatalytic activities of both Ag–Ag Cl@TiO_2 and Ag–Ag Br@TiO_2 under visible light are effectively improved by ~3 times relative to TiO_2 NPAS under the simulated sunlight for the decomposition of methyl orange(MO). Ag–AgBr@TiO_2 showed 30% improvement and less stable in photocatalytic activity than that of AgCl@TiO_2. The role of Ag and Ag X nanoparticles on the surface of Ag–Ag X(X = Cl,Br)@TiO_2 was discussed. Ag on these samples not onlycan efficiently harvest visible light especially for Ag Cl, but also efficiently separate excited electrons and holes via the fast electron transfer from Ag X(X = Cl, Br) to metal Ag nanoparticles and then to TiO_2-aggregated spheres on the surface of heterostructure. On the basis of their efficient and stable photocatalytic activities under visible-light irradiation, these photocatalysts could be widely used for degradation of organic pollutants in aqueous solution.

关键词： Ag–AgX(X=Cl,Br)@TiO2 Nanoparticleaggregated spheres Methyl orange Visible light Photocatalysis

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