Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint r...Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.展开更多
This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognit...This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.展开更多
Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only...Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The dataset consists of 14,186 images across 19 activity classes,from dynamic activities such as running and swimming to static activities such as sitting and sleeping.Preprocessing included resizing all images to 512512 pixels,annotating them in YOLO’s bounding box format,and applying data augmentation methods such as flipping,rotation,and cropping to enhance model generalization.The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement,with a mAP@0.5 of 74.93%and a mAP@0.5-0.95 of 64.11%,outperforming previous versions of YOLO(v10,v9,and v8)and general-purpose architectures like ResNet50 and EfficientNet.It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running,0.79 for swimming,0.80 for sitting,and 0.81 for sleeping,and was tested for real-time deployment with an inference time of 8.9 ms per image,being computationally light.Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process,better attention modules,and an anchor-free detection mechanism.While YOLOv10 was extremely stable in static activity recognition,YOLOv9 performed well in dynamic environments but suffered from overfitting,and YOLOv8,while being a decent baseline,failed to differentiate between overlapping static activities.The experimental results determine proposed YOLOv11 to be the most appropriate model,providing an ideal balance between accuracy,computational efficiency,and robustness for real-world deployment.Nevertheless,there exist certain issues to be addressed,particularly in discriminating against visually similar activities and the use of publicly available datasets.Future research will entail the inclusion of 3D data and multimodal sensor inputs,such as depth and motion information,for enhancing recognition accuracy and generalizability to challenging real-world environments.展开更多
This paper briefly introduces the main ideas of a sustainable development OCR system based on open architecture techniques and then describes the construction of an optical character recognition (OCR) center built on ...This paper briefly introduces the main ideas of a sustainable development OCR system based on open architecture techniques and then describes the construction of an optical character recognition (OCR) center built on computer clusters, for the purpose of dynamically improving the recognition precision of the digitized texts of a million volumes of books produced by the China-US Million Books Digital Library (CADAL) Project. The practice of this center will provide helpful reference for other digital library projects.展开更多
目的在影像归档和通信系统(Picture Archiving and Communication System,PACS)数据库文件丢失或损坏后,实现影像资料和PDF报告关键信息的快速识别和重组,供患者回诊使用。方法利用基于深度学习的光学字符识别技术和Pydicom技术分别读取...目的在影像归档和通信系统(Picture Archiving and Communication System,PACS)数据库文件丢失或损坏后,实现影像资料和PDF报告关键信息的快速识别和重组,供患者回诊使用。方法利用基于深度学习的光学字符识别技术和Pydicom技术分别读取PDF和DCOM文件中的基本信息,重新建立起患者、影像、报告三者之间的联系,并将关联数据写入数据库。结果经抽样验证,该方法识别同类图像精度的准确度、精准度及召回率均为100%,综合指标F1值为1,在不同组别独立样本间的识别精度表现出一致性。平均每份报告识别时间约为0.14 s(t=-1.005,P=0.315),说明不同组别独立样本间的识别时间表现出一致性。结论该方法的使用能有效缩短数据库故障后患者等待时长,能够在短时间内恢复医疗秩序,可用于PACS数据库数据丢失后的应急处置,也为PACS的数据整合提供依据,为医学影像数据恢复和数据整合提供一种新思路。展开更多
Distributed speech recognition (DSR) applications have certain QoS (Quality of service) requirements in terms of latency, packet loss rate, etc. To deliver quality guaranteed DSR application over wirelined or wireless...Distributed speech recognition (DSR) applications have certain QoS (Quality of service) requirements in terms of latency, packet loss rate, etc. To deliver quality guaranteed DSR application over wirelined or wireless links, some QoS mechanisms should be provided. We put forward a RTP/RSVP transmission scheme with DSR-specific payload and QoS parameters by modifying the present WAP protocol stack. The simulation result shows that this scheme will provide adequate network bandwidth to keep the real-time transport of DSR data over either wirelined or wireless channels.展开更多
Four parameters of chemical bond havebeen used to span a feature space to classifyquasicrystal-forming Al-alloys from thatalloys without quasicrystal formationwith good result. Since the first quasicrystal-formingsyst...Four parameters of chemical bond havebeen used to span a feature space to classifyquasicrystal-forming Al-alloys from thatalloys without quasicrystal formationwith good result. Since the first quasicrystal-formingsystem, Al-Mn system, discovered by She-chtman in 1984[1], a series of quasicrystal-forming binary alloy systems have beenfound. Most of these systems are Al-contain-ing systems. Bancel has indicated thatthere are three factors affecting theformability of quasicrystals [2]: (1) ele-ctrochemical factor (this factor can be展开更多
The purpose of the paper is to develop a mobile Android application--＂Car Log＂ that gives to users the ability to track all the costs for a vehicle and the ability to add fuel cost data by taking a photo of the cash...The purpose of the paper is to develop a mobile Android application--＂Car Log＂ that gives to users the ability to track all the costs for a vehicle and the ability to add fuel cost data by taking a photo of the cash receipt from the respective gas station where the charging was performed. OCR （optical character recognition） is the conversion of images of typed, handwritten or printed text into machine-encoded text. Once we have the text machine-encoded we can further use it in machine processes, like translation, or extracted, meaning text-to-speech transformed, helping people in simple everyday tasks. Users of the application will be able to enter other completely different costs grouped into categories and other charges. Car Log application quickly and easily can visualize, edit and add different costs for a ear. It also supports the ability to add multiple profiles, by entering data for all ears in a single family, for example, or a small business. The test results are positive thus we intend to further develop a cloud ready application.展开更多
为提升办公效率,增加文档数据信息录入、数据整合准确性,引入光学字符识别(Optical Character Recognition,OCR)技术,可有效地实现文档智能化运用。文本针对OCR识别技术基本特征、在不同行业中应用现状、技术优势等方面进行分析,重点关...为提升办公效率,增加文档数据信息录入、数据整合准确性,引入光学字符识别(Optical Character Recognition,OCR)技术,可有效地实现文档智能化运用。文本针对OCR识别技术基本特征、在不同行业中应用现状、技术优势等方面进行分析,重点关注OCR识别技术的功能升级,研究其在文档智能化领域的应用,以期能够增加OCR识别技术的扩展性。展开更多
基金supported in part by the National Science Foundation of China under Grants U22B2027,62172297,62102262,61902276 and 62272311,Tianjin Intelligent Manufacturing Special Fund Project under Grant 20211097the China Guangxi Science and Technology Plan Project(Guangxi Science and Technology Base and Talent Special Project)under Grant AD23026096(Application Number 2022AC20001)+1 种基金Hainan Provincial Natural Science Foundation of China under Grant 622RC616CCF-Nsfocus Kunpeng Fund Project under Grant CCF-NSFOCUS202207.
文摘Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.
基金supported by National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006。
文摘This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.
基金supported by King Saud University,Riyadh,Saudi Arabia,under Ongoing Research Funding Program(ORF-2025-951).
文摘Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The dataset consists of 14,186 images across 19 activity classes,from dynamic activities such as running and swimming to static activities such as sitting and sleeping.Preprocessing included resizing all images to 512512 pixels,annotating them in YOLO’s bounding box format,and applying data augmentation methods such as flipping,rotation,and cropping to enhance model generalization.The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement,with a mAP@0.5 of 74.93%and a mAP@0.5-0.95 of 64.11%,outperforming previous versions of YOLO(v10,v9,and v8)and general-purpose architectures like ResNet50 and EfficientNet.It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running,0.79 for swimming,0.80 for sitting,and 0.81 for sleeping,and was tested for real-time deployment with an inference time of 8.9 ms per image,being computationally light.Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process,better attention modules,and an anchor-free detection mechanism.While YOLOv10 was extremely stable in static activity recognition,YOLOv9 performed well in dynamic environments but suffered from overfitting,and YOLOv8,while being a decent baseline,failed to differentiate between overlapping static activities.The experimental results determine proposed YOLOv11 to be the most appropriate model,providing an ideal balance between accuracy,computational efficiency,and robustness for real-world deployment.Nevertheless,there exist certain issues to be addressed,particularly in discriminating against visually similar activities and the use of publicly available datasets.Future research will entail the inclusion of 3D data and multimodal sensor inputs,such as depth and motion information,for enhancing recognition accuracy and generalizability to challenging real-world environments.
基金Project supported by China-US Million Books Digital Library Project
文摘This paper briefly introduces the main ideas of a sustainable development OCR system based on open architecture techniques and then describes the construction of an optical character recognition (OCR) center built on computer clusters, for the purpose of dynamically improving the recognition precision of the digitized texts of a million volumes of books produced by the China-US Million Books Digital Library (CADAL) Project. The practice of this center will provide helpful reference for other digital library projects.
文摘目的在影像归档和通信系统(Picture Archiving and Communication System,PACS)数据库文件丢失或损坏后,实现影像资料和PDF报告关键信息的快速识别和重组,供患者回诊使用。方法利用基于深度学习的光学字符识别技术和Pydicom技术分别读取PDF和DCOM文件中的基本信息,重新建立起患者、影像、报告三者之间的联系,并将关联数据写入数据库。结果经抽样验证,该方法识别同类图像精度的准确度、精准度及召回率均为100%,综合指标F1值为1,在不同组别独立样本间的识别精度表现出一致性。平均每份报告识别时间约为0.14 s(t=-1.005,P=0.315),说明不同组别独立样本间的识别时间表现出一致性。结论该方法的使用能有效缩短数据库故障后患者等待时长,能够在短时间内恢复医疗秩序,可用于PACS数据库数据丢失后的应急处置,也为PACS的数据整合提供依据,为医学影像数据恢复和数据整合提供一种新思路。
文摘Distributed speech recognition (DSR) applications have certain QoS (Quality of service) requirements in terms of latency, packet loss rate, etc. To deliver quality guaranteed DSR application over wirelined or wireless links, some QoS mechanisms should be provided. We put forward a RTP/RSVP transmission scheme with DSR-specific payload and QoS parameters by modifying the present WAP protocol stack. The simulation result shows that this scheme will provide adequate network bandwidth to keep the real-time transport of DSR data over either wirelined or wireless channels.
文摘Four parameters of chemical bond havebeen used to span a feature space to classifyquasicrystal-forming Al-alloys from thatalloys without quasicrystal formationwith good result. Since the first quasicrystal-formingsystem, Al-Mn system, discovered by She-chtman in 1984[1], a series of quasicrystal-forming binary alloy systems have beenfound. Most of these systems are Al-contain-ing systems. Bancel has indicated thatthere are three factors affecting theformability of quasicrystals [2]: (1) ele-ctrochemical factor (this factor can be
文摘The purpose of the paper is to develop a mobile Android application--＂Car Log＂ that gives to users the ability to track all the costs for a vehicle and the ability to add fuel cost data by taking a photo of the cash receipt from the respective gas station where the charging was performed. OCR （optical character recognition） is the conversion of images of typed, handwritten or printed text into machine-encoded text. Once we have the text machine-encoded we can further use it in machine processes, like translation, or extracted, meaning text-to-speech transformed, helping people in simple everyday tasks. Users of the application will be able to enter other completely different costs grouped into categories and other charges. Car Log application quickly and easily can visualize, edit and add different costs for a ear. It also supports the ability to add multiple profiles, by entering data for all ears in a single family, for example, or a small business. The test results are positive thus we intend to further develop a cloud ready application.
文摘为提升办公效率,增加文档数据信息录入、数据整合准确性,引入光学字符识别(Optical Character Recognition,OCR)技术,可有效地实现文档智能化运用。文本针对OCR识别技术基本特征、在不同行业中应用现状、技术优势等方面进行分析,重点关注OCR识别技术的功能升级,研究其在文档智能化领域的应用,以期能够增加OCR识别技术的扩展性。
