信息工程最新文献

{"title":"Face animation based on multiple sources and perspective alignment","authors":"Yuanzong Mei ,&nbsp;Wenyi Wang ,&nbsp;Xi Liu ,&nbsp;Wei Yong ,&nbsp;Weijie Wu ,&nbsp;Yifan Zhu ,&nbsp;Shuai Wang ,&nbsp;Jianwen Chen","doi":"10.1016/j.vrih.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.vrih.2024.04.002","url":null,"abstract":"<div><h3>Background</h3><p>Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving video. This technology could be beneficial in multiple medical fields, such as diagnosis and privacy protection<em>.</em> Previous studies on face animation often relied on a single source image to generate an output video. With a significant pose difference between the source image and the driving frame, the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature map.</p></div><div><h3>Methods</h3><p>In this study, we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these issues. We first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving video. We then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving frame.</p></div><div><h3>Conclusions</h3><p>The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation methods. It indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 3","pages":"Pages 252-266"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579624000202/pdfft?md5=2a9475967792588ba319db5427a9033d&pid=1-s2.0-S2096579624000202-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484842","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":"A review of medical ocular image segmentation","authors":"Lai WEI,&nbsp;Menghan HU","doi":"10.1016/j.vrih.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2024.04.001","url":null,"abstract":"<div><p>Deep learning has been extensively applied to medical image segmentation, resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U-Net in 2015. However, the application of deep learning models to ocular medical image segmentation poses unique challenges, especially compared to other body parts, due to the complexity, small size, and blurriness of such images, coupled with the scarcity of data. This article aims to provide a comprehensive review of medical image segmentation from two perspectives: the development of deep network structures and the application of segmentation in ocular imaging. Initially, the article introduces an overview of medical imaging, data processing, and performance evaluation metrics. Subsequently, it analyzes recent developments in U-Net-based network structures. Finally, for the segmentation of ocular medical images, the application of deep learning is reviewed and categorized by the type of ocular tissue.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 3","pages":"Pages 181-202"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209657962400010X/pdfft?md5=c30a9952442a34ae8a35e52683ed1214&pid=1-s2.0-S209657962400010X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484810","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":"Extending OpenStack Monasca for Predictive Elasticity Control","authors":"Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella","doi":"10.26599/bdma.2023.9020014","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020014","url":null,"abstract":"","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"7 9","pages":""},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph","authors":"Muhammad Ajmal Azad, J. Arshad, Farhan Riaz","doi":"10.26599/bdma.2023.9020020","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020020","url":null,"abstract":"—Robo or unsolicited calls have become a persistent issue in telecommunication networks, posing significant challenges to individuals, businesses, and regulatory authorities. These calls not only trick users to disclose their private and financial information but also affect their productivity through unwanted phone ringing. A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential harm. Therein, this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a network. The trust score of the callers is then used along with machine learning models to classify them as legitimate or robo-caller. We used a large anonymized data set (call detailed records) from a large telecommunication provider containing more than 1 billion records collected over 10 days. We have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error rate. Specifically, the proposed features when used collectively achieve a true-positive rate of around 97% with a false-positive rate of less than 0.01%.","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"26 23","pages":""},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-varying nexus and causality in the quantile between Google investor sentiment and cryptocurrency returns","authors":"Fatma Ben Hamadou,&nbsp;Taicir Mezghani,&nbsp;Mouna Boujelbène Abbes","doi":"10.1016/j.bcra.2023.100177","DOIUrl":"10.1016/j.bcra.2023.100177","url":null,"abstract":"<div><p>Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research. Indeed, this study aims to uncover the role of Google investor sentiment on cryptocurrency returns (including Bitcoin, Litecoin, Ethereum, and Tether), especially during the 2017–18 bubble (January 01, 2017, to December 31, 2018) and the COVID-19 pandemic (January 01, 2020, to March 15, 2022). To achieve this, we use two techniques: quantile causality and wavelet coherence. First, the quantile causality test revealed that investors’ optimistic sentiments have notably higher cryptocurrency returns, whereas pessimistic sentiments have significantly opposite effects. Moreover, the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant. This result supports the role of Tether as a stablecoin in portfolio diversification strategies. In fact, the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 2","pages":"Article 100177"},"PeriodicalIF":5.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720923000520/pdfft?md5=98182819a759cd071a476d4ffe8e903a&pid=1-s2.0-S2096720923000520-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139196126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of blockchain technology in integrated IoT networks for constructing scalable ITS systems in India","authors":"Arya Kharche,&nbsp;Sanskar Badholia,&nbsp;Ram Krishna Upadhyay","doi":"10.1016/j.bcra.2024.100188","DOIUrl":"10.1016/j.bcra.2024.100188","url":null,"abstract":"<div><p>The implementation of blockchain technology in integrated IoT networks for constructing scalable Intelligent Transportation Systems (ITSs) in India has the potential to revolutionize the way we approach transportation. By leveraging the power of IoT and blockchain, we can create a highly secure, transparent, and efficient system that can transform the way we move people and goods. India, one of the world’s most populous countries, has a highly congested and inefficient transportation system that often leads to delays, accidents, and waste of time and resources. The integration of IoT and blockchain can help address these issues by enabling real-time monitoring, tracking, and optimization of traffic flows, thereby reducing congestion, improving safety, and increasing the overall efficiency of the transportation system. This paper explores the potential of blockchain technology in the context of integrated IoT networks for constructing scalable ITS systems in India. The methodology followed is to develop a proof-of-concept blockchain-based application for ITS, implement the blockchain solution into the existing ITS infrastructure, and ensure proper integration and compatibility with other systems. Conduct thorough research and maintenance to ensure the reliability and sustainability of such blockchain-based systems. This research discusses the various benefits and challenges of this approach and the various applications of this technology in the transportation sector, including the green sustainability concept. The results find various ways in which such implementations of blockchain and IoT-Machine Learning (IoT-ML) can revolutionize transportation systems.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 2","pages":"Article 100188"},"PeriodicalIF":6.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000010/pdfft?md5=f0df3bf2f2a306097761b6d525acf13d&pid=1-s2.0-S2096720924000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139393019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study of a blockchain-based judicial evidence preservation scheme","authors":"Shuaiqi Liu,&nbsp;Qingxiao Zheng","doi":"10.1016/j.bcra.2024.100192","DOIUrl":"10.1016/j.bcra.2024.100192","url":null,"abstract":"<div><p>To address the challenges of low credibility, difficult data sharing, and regulatory supervision issues involving electronic evidence storage in the judicial preservation process, this paper proposes a blockchain-based judicial evidence preservation scheme. The scheme utilizes the characteristics of blockchain’s immutability to achieve credible forensics of electronic evidence on the chain and employs the decentralized storage of the interplanetary file system for secure and efficient off-chain storage. Simultaneously, it resolves the problem of declining throughput due to limited block capacity. Additionally, it leverages smart contract technology to encompass major aspects of the judicial process, including user case registration, authority management, judicial evidence uploading and downloading, case data sharing, partial disclosure of case information, and regulatory review. Simulation experiments demonstrate that the scheme significantly improves throughput and stability. Performance tests indicate that the transfer speed of the interplanetary file system can meet the data-sharing needs of judicial organizations.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 2","pages":"Article 100192"},"PeriodicalIF":6.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000058/pdfft?md5=2e348c2e2fdee9fd6d0a35ddadba3f13&pid=1-s2.0-S2096720924000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139880914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Detection of Malicious Behavior in Windows Portable Executables Using Multi-Head 2D Transformers","authors":"Sohail Khan, Mohammad Nauman","doi":"10.26599/bdma.2023.9020025","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020025","url":null,"abstract":": Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on systems. One of the major challenges in tackling this problem is the complexity of malware analysis, which requires expertise from human analysts. Recent developments in machine learning have led to the creation of deep models for malware detection. However, these models often lack transparency, making it difficult to understand the reasoning behind the model’s decisions, otherwise known as the black-box problem. To address these limitations, this paper presents a novel model for malware detection, utilizing vision transformers to analyze the opcode sequences of more than 350,000 Windows portable executable malware samples from real-world datasets. The model achieved a high accuracy of 0.9864, not only surpassing previous results but also providing valuable insights into the reasoning behind the classification. Our model is able to pinpoint specific instructions that lead to malicious behavior in malware samples, aiding human experts in their analysis and driving further advancements in the field. We report our findings and show how causality can be established between malicious code and actual classification by a deep learning model thus opening up this black-box problem for deeper analysis.","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"57 5","pages":""},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning","authors":"B.A.O. Lingyun ,&nbsp;Zhengrui HUANG ,&nbsp;Zehui LIN ,&nbsp;Yue SUN ,&nbsp;Hui CHEN ,&nbsp;You LI ,&nbsp;Zhang LI ,&nbsp;Xiaochen YUAN ,&nbsp;Lin XU ,&nbsp;Tao TAN","doi":"10.1016/j.vrih.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2024.02.001","url":null,"abstract":"<div><h3>Background</h3><p>Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications, particularly in visual recognition tasks such as image and video analyses. There is a growing interest in applying this technology to diverse applications in medical image analysis. Automated three-dimensional Breast Ultrasound is a vital tool for detecting breast cancer, and computer-assisted diagnosis software, developed based on deep learning, can effectively assist radiologists in diagnosis. However, the network model is prone to overfitting during training, owing to challenges such as insufficient training data. This study attempts to solve the problem caused by small datasets and improve model detection performance.</p></div><div><h3>Methods</h3><p>We propose a breast cancer detection framework based on deep learning (a transfer learning method based on cross-organ cancer detection) and a contrastive learning method based on breast imaging reporting and data systems (BI-RADS).</p></div><div><h3>Results</h3><p>When using cross organ transfer learning and BIRADS based contrastive learning, the average sensitivity of the model increased by a maximum of 16.05%.</p></div><div><h3>Conclusion</h3><p>Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced, and contrastive learning method based on BI-RADS can improve the detection performance of the model.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 3","pages":"Pages 239-251"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209657962400007X/pdfft?md5=a1bdf0d74f499e2548f6f5735dd9b5bf&pid=1-s2.0-S209657962400007X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484848","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":"Blockchain-based engine data trustworthy swarm learning management method","authors":"Zhenjie Luo,&nbsp;Hui Zhang","doi":"10.1016/j.bcra.2023.100185","DOIUrl":"10.1016/j.bcra.2023.100185","url":null,"abstract":"<div><p>Engine data management is of great significance for ensuring data security and sharing, as well as facilitating multi-party collaborative learning. Traditional data management approaches often involve decentralized data storage that is vulnerable to tampering, making it challenging to conduct multi-party collaborative learning under privacy protection conditions and fully leverage the value of data. Moreover, data with compromised integrity can lead to incorrect results if used for model training. Therefore, this paper aims to break down data sharing barriers and fully utilize decentralized data for multi-party collaborative learning under privacy protection conditions. We propose a trustworthy engine data management method based on blockchain technology to ensure data immutability and non-repudiation. To address the issue of limited data samples for some users resulting in poor model performance, we introduce swarm learning techniques based on centralized machine learning and design a trustworthy data management method for swarm learning, achieving trustworthy regulation of the entire process. We conduct research on engine models under swarm learning based on the NASA open dataset, effectively organizing decentralized data samples for collaborative training while ensuring data privacy and fully leveraging the value of data.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 2","pages":"Article 100185"},"PeriodicalIF":6.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209672092300060X/pdfft?md5=3cecec9b4347c0153afcf9159a3b9bdc&pid=1-s2.0-S209672092300060X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139129817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}