为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学...为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。展开更多
A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, framedropping coding, video redundancy coding, and huma...A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, framedropping coding, video redundancy coding, and human visual model. According to packet loss and available bandwidth of the network, the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model, rate shaping, and periodically inserting key frame. The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm. It is shown that RAVE is a very efficient robust video encoder that provides improved visual quality for the receiver and consumes equal or less network resource. Results are confirmed by subjective tests and simulation tests.展开更多
With the rise of the Internet of Things(IoT),various devices in life and industry are closely linked.Because of its high payload,stable error correction capability,and convenience in reading and writing,Quick Response...With the rise of the Internet of Things(IoT),various devices in life and industry are closely linked.Because of its high payload,stable error correction capability,and convenience in reading and writing,Quick Response(QR)code has been widely researched in IoT.However,the security of privacy data in IoT is also a very important issue.At the same time,because IoT is developing towards low-power devices in order to be applied to more fields,the technology protecting the security of private needs to have the characteristics of low computational complexity.Visual Secret Sharing(VSS),with its features of safety and low computational cost,can fully meet the requirements of communication security in IoT.Therefore,a VSS scheme with QR code(VSS-QR)was proposed and has been applied to some extent.In VSS-QR,the secret is shared into a series of shares.These shares are usually common QR codes,which cannot cause the attention of the attacker.However,if there is dishonesty among participants,the secret cannot be recovered,which will lead to VSS-QR cannot be widely used due to its inadequate security.In this paper,we propose a visual secret sharing scheme with authentication based on QR code(VSSA-QR).Both the reconstructed secret QR code and shares can be verified whether they are forged by attackers.The above-mentioned operations conveniently are performed on low-power QR scanning devices.Not only does the proposed scheme prevent some dishonest participants or attackers from cheating,but also prevent all participants from conspiring.In addition,the payload is the QR code itself,which is higher than other schemes.Theoretical analysis and experiments prove that the proposed scheme is effective.展开更多
In order to apply the Human Visual System (HVS) model to JPEG2000 standard,several implementation alternatives are discussed and a new scheme of visual optimization isintroduced with modifying the slope of rate-distor...In order to apply the Human Visual System (HVS) model to JPEG2000 standard,several implementation alternatives are discussed and a new scheme of visual optimization isintroduced with modifying the slope of rate-distortion. The novelty is that the method of visual weighting is not lifting the coefficients in wavelet domain, but is complemented by code stream organization. It remains all the features of Embedded Block Coding with Optimized Truncation (EBCOT) such as resolution progressive, good robust for error bit spread and compatibility of lossless compression. Well performed than other methods, it keeps the shortest standard codestream and decompression time and owns the ability of VIsual Progressive (VIP) coding.展开更多
The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect predicti...The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.展开更多
文摘为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。
基金Supported by Innovation Fund of China(00C26224210641)
文摘A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, framedropping coding, video redundancy coding, and human visual model. According to packet loss and available bandwidth of the network, the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model, rate shaping, and periodically inserting key frame. The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm. It is shown that RAVE is a very efficient robust video encoder that provides improved visual quality for the receiver and consumes equal or less network resource. Results are confirmed by subjective tests and simulation tests.
基金This work was supported in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under Grant 2016r055in part by the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.
文摘With the rise of the Internet of Things(IoT),various devices in life and industry are closely linked.Because of its high payload,stable error correction capability,and convenience in reading and writing,Quick Response(QR)code has been widely researched in IoT.However,the security of privacy data in IoT is also a very important issue.At the same time,because IoT is developing towards low-power devices in order to be applied to more fields,the technology protecting the security of private needs to have the characteristics of low computational complexity.Visual Secret Sharing(VSS),with its features of safety and low computational cost,can fully meet the requirements of communication security in IoT.Therefore,a VSS scheme with QR code(VSS-QR)was proposed and has been applied to some extent.In VSS-QR,the secret is shared into a series of shares.These shares are usually common QR codes,which cannot cause the attention of the attacker.However,if there is dishonesty among participants,the secret cannot be recovered,which will lead to VSS-QR cannot be widely used due to its inadequate security.In this paper,we propose a visual secret sharing scheme with authentication based on QR code(VSSA-QR).Both the reconstructed secret QR code and shares can be verified whether they are forged by attackers.The above-mentioned operations conveniently are performed on low-power QR scanning devices.Not only does the proposed scheme prevent some dishonest participants or attackers from cheating,but also prevent all participants from conspiring.In addition,the payload is the QR code itself,which is higher than other schemes.Theoretical analysis and experiments prove that the proposed scheme is effective.
文摘In order to apply the Human Visual System (HVS) model to JPEG2000 standard,several implementation alternatives are discussed and a new scheme of visual optimization isintroduced with modifying the slope of rate-distortion. The novelty is that the method of visual weighting is not lifting the coefficients in wavelet domain, but is complemented by code stream organization. It remains all the features of Embedded Block Coding with Optimized Truncation (EBCOT) such as resolution progressive, good robust for error bit spread and compatibility of lossless compression. Well performed than other methods, it keeps the shortest standard codestream and decompression time and owns the ability of VIsual Progressive (VIP) coding.
基金supported by the NationalNatural Science Foundation of China(Grant No.61867004)the Youth Fund of the National Natural Science Foundation of China(Grant No.41801288).
文摘The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.
