With the acceleration of network communication in the 5G era,the volume of data communication in cyberspace has increased unprecedentedly.The speed of data transmission will accelerate.Subsequently,the security of net...With the acceleration of network communication in the 5G era,the volume of data communication in cyberspace has increased unprecedentedly.The speed of data transmission will accelerate.Subsequently,the security of network communication data becomes more and more serious.Among them,malicious cross⁃site scripting leading to the leakage of user information is very serious.This article uses URL attribute analysis method and YARA rule to process data for cross⁃site scripting based on the long short⁃term memory(LSTM)characteristics of LSTM model.The results show that the LSTM classification model adopted in this paper has higher recall rate and F1⁃score than other machine learning methods,which proves that the method adopted in this paper is feasible.展开更多
<span><span><b><span style="font-family:"">Purpose:</span></b></span></span><span><span><span><span style="font-family:"&qu...<span><span><b><span style="font-family:"">Purpose:</span></b></span></span><span><span><span><span style="font-family:""> Linac quality assurance (QA) can be time consuming involving set up, execution, analysis and subject to user variability. The purpose of this study i</span></span></span></span><span><span><span><span style="font-family:"">s to develop qualitative automation tools for mechanical and imaging QA to improve efficiency, consistency, and accuracy. <b>Methods and Materials: </b>Traditionally QA ha</span></span></span></span><span><span><span><span style="font-family:"">s</span></span></span></span><span><span><span><span style="font-family:""> been performed with graph paper, film, and multiple phantoms. Analysis consists of ruler and vendor provided software. We have developed a single four-phantom<b> </b>method for QA procedures including light-radiation coincidence, imaging quality, table motion and Isocentricity an</span></span></span></span><span><span><span><span style="font-family:"">d separately cone beam computed tomography. XML scripts were developed to execute a series of tasks using Varian’s Truebeam Developer Mode. Non-phantom QA procedures have also been developed including field size, dose rate, MLC position, MLC and gantry speed, star shot, Winston-Lutz and Half Beam Block. All analysis is performed using inhouse MATLAB codes. <b>Results: </b>Overall time savings were 2.2 hours per Linac per month. Consistency improvements (standard deviation, STD) were observed for some tests. For example: field size improved from 0.11</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.04</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm and table motion improved from 0.17</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.12</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. CBCT STD improved from 0.99</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.61</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for slice thickness. No STD change was observed for Isocentricity test. We noticed an increase in STD from 0.33</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.41</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for light-radiation coincidence test. There was a small drop in field size accuracy. Isocentricity showed an increase in measurement accuracy from 0.47</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.15</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. Table motion increased in accuracy from 0.20</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.16</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. <b>Conclusion: </b>Automation is a viable, accurate and efficient option for monthly and annual QA.展开更多
Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for ...Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.展开更多
Submission Manuscripts should be submitted by one of the authors of the manuscript through the online Manuscript Tracking System(MTS)(http://mc03.man uscriptcentral.com/epphy).Regardless of the source of the word-proc...Submission Manuscripts should be submitted by one of the authors of the manuscript through the online Manuscript Tracking System(MTS)(http://mc03.man uscriptcentral.com/epphy).Regardless of the source of the word-processing tool,only electronic PDF(.pdf) or Word(.doc,.docx,.rtf) files can be submitted through the MTS.There is no page limit.Only online submissions are accepted.Submissions by anyone other than one of the authors will not be accepted.The submitting author takes responsibility for the paper during submission and peer review.If for some technical reason,submission through the MTS is not possible,the author can contact epp@mail.iggcas.ac.cn for support.展开更多
JShellLab is an easy to use MATLAB-like environment for the Java Virtual Machine(JVM).It implements scientific scripting based on the JShell Application Programming Interface(API)of modern Java.The paper illustrates t...JShellLab is an easy to use MATLAB-like environment for the Java Virtual Machine(JVM).It implements scientific scripting based on the JShell Application Programming Interface(API)of modern Java.The paper illustrates that JShellLab can significantly facilitate and simplify the development of complex computational demanding scientific software at the JVM.The novelty at the JShellLab is that it completely hides the complexity and the intricate dependencies of optimized scientific software.As an example,the demanded field of deep learning is exploited.Specifically,the implementation of effective practical deep learning-based systems using the JShellLab environment and the Deeplearning4j Java library is considered.展开更多
Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accu...Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.展开更多
文摘With the acceleration of network communication in the 5G era,the volume of data communication in cyberspace has increased unprecedentedly.The speed of data transmission will accelerate.Subsequently,the security of network communication data becomes more and more serious.Among them,malicious cross⁃site scripting leading to the leakage of user information is very serious.This article uses URL attribute analysis method and YARA rule to process data for cross⁃site scripting based on the long short⁃term memory(LSTM)characteristics of LSTM model.The results show that the LSTM classification model adopted in this paper has higher recall rate and F1⁃score than other machine learning methods,which proves that the method adopted in this paper is feasible.
文摘<span><span><b><span style="font-family:"">Purpose:</span></b></span></span><span><span><span><span style="font-family:""> Linac quality assurance (QA) can be time consuming involving set up, execution, analysis and subject to user variability. The purpose of this study i</span></span></span></span><span><span><span><span style="font-family:"">s to develop qualitative automation tools for mechanical and imaging QA to improve efficiency, consistency, and accuracy. <b>Methods and Materials: </b>Traditionally QA ha</span></span></span></span><span><span><span><span style="font-family:"">s</span></span></span></span><span><span><span><span style="font-family:""> been performed with graph paper, film, and multiple phantoms. Analysis consists of ruler and vendor provided software. We have developed a single four-phantom<b> </b>method for QA procedures including light-radiation coincidence, imaging quality, table motion and Isocentricity an</span></span></span></span><span><span><span><span style="font-family:"">d separately cone beam computed tomography. XML scripts were developed to execute a series of tasks using Varian’s Truebeam Developer Mode. Non-phantom QA procedures have also been developed including field size, dose rate, MLC position, MLC and gantry speed, star shot, Winston-Lutz and Half Beam Block. All analysis is performed using inhouse MATLAB codes. <b>Results: </b>Overall time savings were 2.2 hours per Linac per month. Consistency improvements (standard deviation, STD) were observed for some tests. For example: field size improved from 0.11</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.04</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm and table motion improved from 0.17</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.12</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. CBCT STD improved from 0.99</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.61</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for slice thickness. No STD change was observed for Isocentricity test. We noticed an increase in STD from 0.33</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.41</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for light-radiation coincidence test. There was a small drop in field size accuracy. Isocentricity showed an increase in measurement accuracy from 0.47</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.15</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. Table motion increased in accuracy from 0.20</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.16</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. <b>Conclusion: </b>Automation is a viable, accurate and efficient option for monthly and annual QA.
文摘Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.
文摘Submission Manuscripts should be submitted by one of the authors of the manuscript through the online Manuscript Tracking System(MTS)(http://mc03.man uscriptcentral.com/epphy).Regardless of the source of the word-processing tool,only electronic PDF(.pdf) or Word(.doc,.docx,.rtf) files can be submitted through the MTS.There is no page limit.Only online submissions are accepted.Submissions by anyone other than one of the authors will not be accepted.The submitting author takes responsibility for the paper during submission and peer review.If for some technical reason,submission through the MTS is not possible,the author can contact epp@mail.iggcas.ac.cn for support.
文摘JShellLab is an easy to use MATLAB-like environment for the Java Virtual Machine(JVM).It implements scientific scripting based on the JShell Application Programming Interface(API)of modern Java.The paper illustrates that JShellLab can significantly facilitate and simplify the development of complex computational demanding scientific software at the JVM.The novelty at the JShellLab is that it completely hides the complexity and the intricate dependencies of optimized scientific software.As an example,the demanded field of deep learning is exploited.Specifically,the implementation of effective practical deep learning-based systems using the JShellLab environment and the Deeplearning4j Java library is considered.
文摘Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.