信息工程最新文献

{"title":"A binary-domain recurrent-like architecture-based dynamic graph neural network","authors":"Zi-chao Chen,&nbsp;Sui Lin","doi":"10.1007/s43684-024-00067-9","DOIUrl":"10.1007/s43684-024-00067-9","url":null,"abstract":"<div><p>The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00067-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure-based discovery of F. religiosa phytochemicals as potential inhibitors against Monkeypox (mpox) viral protein","authors":"Ranjan K. Mohapatra ,&nbsp;Ahmed Mahal ,&nbsp;Pranab K. Mohapatra ,&nbsp;Ashish K. Sarangi ,&nbsp;Snehasish Mishra ,&nbsp;Meshari A. Alsuwat ,&nbsp;Nada N. Alshehri ,&nbsp;Sozan M. Abdelkhalig ,&nbsp;Mohammed Garout ,&nbsp;Mohammed Aljeldah ,&nbsp;Ahmad A. Alshehri ,&nbsp;Ahmed Saif ,&nbsp;Mohammed Abdulrahman Alshahrani ,&nbsp;Ali S. Alqahtani ,&nbsp;Yahya A. Almutawif ,&nbsp;Hamza M.A. Eid ,&nbsp;Faisal M Albaqami ,&nbsp;Mohnad Abdalla ,&nbsp;Ali A. Rabaan","doi":"10.1016/j.jobb.2024.05.004","DOIUrl":"https://doi.org/10.1016/j.jobb.2024.05.004","url":null,"abstract":"<div><p>Outbreaks of Monkeypox (mpox) in over 100 non-endemic countries in 2022 represented a serious global health concern. Once a neglected disease, mpox has become a global public health issue. A42R profilin-like protein from mpox (PDB ID: 4QWO) represents a potential new lead for drug development and may interact with various synthetic and natural compounds. In this report, the interaction of A42R profilin-like protein with six phytochemicals found in the medicinal plant <em>Ficus religiosa</em> (abundant in India) was examined. Based on the predicted and compared protein–ligand binding energies, biological properties, IC₅₀ values and toxicity, two compounds, kaempferol (C-1) and piperine (C-4), were selected. ADMET characteristics and quantitative structure–activity relationship (QSAR) of these two compounds were determined, and molecular dynamics (MD) simulations were performed. <em>In silico</em> examination of the kaempferol (C-1) and piperine (C-4) interactions with A42R profilin-like protein gave best-pose ligand-binding energies of –6.98 and –5.57 kcal/mol, respectively. The predicted IC₅₀ of C-1 was 7.63 μM and 82 μM for C-4. Toxicity data indicated that kaempferol and piperine are non-mutagenic, and the QSAR data revealed that piperlongumine (5.92) and piperine (5.25) had higher log P values than the other compounds examined. MD simulations of A42R profilin-like protein in complex with C-1 and C-4 were performed to examine the stability of the ligand–protein interactions. As/C and C-4 showed the highest affinity and activities, they may be suitable lead candidates for developing mpox therapeutic drugs. This study should facilitate discovering and synthesizing innovative therapeutics to address other infectious diseases.</p></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258893382400030X/pdfft?md5=ec15123379db8c297e57ae0d9b373a79&pid=1-s2.0-S258893382400030X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction","authors":"Ao Xiao,&nbsp;Wei Yan,&nbsp;Xumei Zhang,&nbsp;Ying Liu,&nbsp;Hua Zhang,&nbsp;Qi Liu","doi":"10.1007/s43684-024-00072-y","DOIUrl":"10.1007/s43684-024-00072-y","url":null,"abstract":"<div><p>The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00072-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘The trivial tickets build the trust’: a co-design approach to understanding security support interactions in a large university","authors":"Albesë Demjaha, David Pym, Tristan Caulfield, Simon Parkin","doi":"10.1093/cybsec/tyae007","DOIUrl":"https://doi.org/10.1093/cybsec/tyae007","url":null,"abstract":"Increasingly, organizations are acknowledging the importance of human factors in the management of security in workplaces. There are challenges in managing security infrastructures in which there may be centrally mandated and locally managed initiatives to promote secure behaviours. We apply a co-design methodology to harmonize employee behaviour and centralized security management in a large university. This involves iterative rounds of interviews connected by the co-design methodology: 14 employees working with high-value data with specific security needs; seven support staff across both local and central IT and IT-security support teams; and two senior security decision-makers in the organization. We find that employees prefer local support together with assurances that they are behaving securely, rather than precise instructions that lack local context. Trust in support teams that understand local needs also improves engagement, especially for employees who are unsure what to do. Policy is understood by employees through their interactions with support staff and when they see colleagues enacting secure behaviours in the workplace. The iterative co-design approach brings together the viewpoints of a range of employee groups and security decision-makers that capture key influences that drive secure working practices. We provide recommendations for improvements to workplace security, including recognizing that communication of the policy is as important as what is in the policy.","PeriodicalId":44310,"journal":{"name":"Journal of Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human feedback enhanced autonomous intelligent systems: a perspective from intelligent driving","authors":"Kang Yuan,&nbsp;Yanjun Huang,&nbsp;Lulu Guo,&nbsp;Hong Chen,&nbsp;Jie Chen","doi":"10.1007/s43684-024-00071-z","DOIUrl":"10.1007/s43684-024-00071-z","url":null,"abstract":"<div><p>Artificial intelligence empowers the rapid development of autonomous intelligent systems (AISs), but it still struggles to cope with open, complex, dynamic, and uncertain environments, limiting its large-scale industrial application. Reliable human feedback provides a mechanism for aligning machine behavior with human values and holds promise as a new paradigm for the evolution and enhancement of machine intelligence. This paper analyzes the engineering insights from ChatGPT and elaborates on the evolution from traditional feedback to human feedback. Then, a unified framework for self-evolving intelligent driving (ID) based on human feedback is proposed. Finally, an application in the congested ramp scenario illustrates the effectiveness of the proposed framework.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00071-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review","authors":"Abed Mutemi, F. Bação","doi":"10.26599/bdma.2023.9020023","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020023","url":null,"abstract":": The e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unstructured Big Data Threat Intelligence Parallel Mining Algorithm","authors":"Zhihua Li, Xinye Yu, Tao Wei, Junhao Qian","doi":"10.26599/bdma.2023.9020032","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020032","url":null,"abstract":"","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks","authors":"Selvam Saravanan, Uma Maheswari Balasubramanian","doi":"10.26599/bdma.2023.9020027","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020027","url":null,"abstract":"","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt","authors":"Junwen Duan, Xincheng Liao, Ying An, Jianxin Wang","doi":"10.26599/bdma.2023.9020036","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020036","url":null,"abstract":"","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARGA-Unet: Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation","authors":"Siyi XUN ,&nbsp;Yan ZHANG ,&nbsp;Sixu DUAN ,&nbsp;Mingwei WANG ,&nbsp;Jiangang CHEN ,&nbsp;Tong TONG ,&nbsp;Qinquan GAO ,&nbsp;Chantong LAM ,&nbsp;Menghan HU ,&nbsp;Tao TAN","doi":"10.1016/j.vrih.2023.05.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.05.001","url":null,"abstract":"<div><h3>Background</h3><p>Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious.</p></div><div><h3>Methods</h3><p>To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered.</p></div><div><h3>Results</h3><p>In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images.</p></div><div><h3>Conclusions</h3><p>In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000232/pdfft?md5=5e16730452951aa1e3b2edacee01d06e&pid=1-s2.0-S2096579623000232-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}