With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article ana...With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.展开更多
Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited n...Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.展开更多
Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other...Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.展开更多
In order to combine the advantages of online teaching and traditional offline classroom teaching,this paper optimizes the teaching design by taking Musculoskeletal Rehabilitation for undergraduates as the carrier,and ...In order to combine the advantages of online teaching and traditional offline classroom teaching,this paper optimizes the teaching design by taking Musculoskeletal Rehabilitation for undergraduates as the carrier,and reconstructs the course according to five parts:basic theory course,practical training course,standardized patient,case report,and course evaluation.Through analyzing the classroom quality and teaching effect,the innovation and practical effect of course reconstruction are explored.With students as the main body and goals as the guide,this model gives full play to the initiative and creativity of students,meets the individual needs of students at different levels,and provides reference ideas for improving the advanced,innovative and challenging creation of the course.展开更多
Taking construction of the online and offline integrated first-class undergraduate curriculum teaching modes of Histology and Embryology in Guangxi as an opportunity,under the guidance of student-centered teaching con...Taking construction of the online and offline integrated first-class undergraduate curriculum teaching modes of Histology and Embryology in Guangxi as an opportunity,under the guidance of student-centered teaching concept,efforts were made to innovate online and offline integrated teaching mode to overcome the shortcomings and dilemma of traditional Histology and Embryology teaching,with attention paid to the competence education in students including schematic knowledge,professional techniques,analytical thinking,and ideological and political theories,which would be of great significance for the cultivation of high-quality professionals specialized in traditional Chinese medicine.展开更多
Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online interaction.One of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the m...Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online interaction.One of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the mismatch between the knowledge of the learned policy and the reality of the underlying environment.Recent works usually handle this in a too pessimistic manner to avoid out-of-distribution(OOD)queries as much as possible,but this can influence the robustness of the agents at unseen states.In this paper,we propose a simple but effective method to address this issue.The key idea of our method is to enhance the robustness of the new policy learned offline by weakening its confidence in highly uncertain regions,and we propose to find those regions by simulating them with modified Generative Adversarial Nets(GAN)such that the generated data not only follow the same distribution with the old experience but are very difficult to deal with by themselves,with regard to the behavior policy or some other reference policy.We then use this information to regularize the ORL algorithm to penalize the overconfidence behavior in these regions.Extensive experiments on several publicly available offline RL benchmarks demonstrate the feasibility and effectiveness of the proposed method.展开更多
With the deepening development of educational informatization, online and offline blended teaching, as a new teaching mode, is increasingly receiving widespread attention from educators [1]. At present, the reform of ...With the deepening development of educational informatization, online and offline blended teaching, as a new teaching mode, is increasingly receiving widespread attention from educators [1]. At present, the reform of the “online and offline blended teaching” of ideological and political education courses directly affects the quality of talent cultivation in universities. The article takes the course “Introduction to Basic Principles of Marxism” as an example to explore the reform mode of “online and offline blended teaching” in ideological and political theory courses in universities from the aspects of reasonable allocation of class hours, design of online teaching activities, how to deepen classroom teaching offline, and diversified assessment modes. Furthermore, the article summarizes the experience of mode reform and promotes the deep development of the people-oriented education concept in ideological and political courses in universities, so as to achieve the ultimate goal of moral education in ideological and political education in universities.展开更多
以高职航海技术专业为例,分析现代学徒制培养模式下的瓶颈问题,探究引入O2O(Online to Offline)教学方式,在线上线下形成学生、学校和企业共同参与的工学结合、产教融合的教学生态,提高航海技术专业学徒制学生的自主学习能力、专业技能...以高职航海技术专业为例,分析现代学徒制培养模式下的瓶颈问题,探究引入O2O(Online to Offline)教学方式,在线上线下形成学生、学校和企业共同参与的工学结合、产教融合的教学生态,提高航海技术专业学徒制学生的自主学习能力、专业技能和综合素质,培养满足社会和航运企业需求的航海人才。展开更多
Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification.A dimension-enhanced strategy,by offline two-dimensi...Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification.A dimension-enhanced strategy,by offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry(2 D-LC/IM-QTOF-MS)enabling four-dimensional separations(2 D-LC,IM,and MS),is proposed.In combination with in-house database-driven automated peak annotation,this strategy was utilized to characterize ginsenosides simultaneously from white ginseng(WG)and red ginseng(RG).An offline 2 DLC system configuring an Xbridge Amide column and an HSS T3 column showed orthogonality 0.76 in the resolution of ginsenosides.Ginsenoside analysis was performed by data-independent high-definition MSE(HDMSE)in the negative ESI mode on a Vion?IMS-QTOF hybrid high-resolution mass spectrometer,which could better resolve ginsenosides than MSEand directly give the CCS information.An in-house ginsenoside database recording 504 known ginsenosides and 58 reference compounds,was established to assist the identification of ginsenosides.Streamlined workflows,by applying UNIFI?to automatedly annotate the HDMSEdata,were proposed.We could separate and characterize 323 ginsenosides(including 286 from WG and 306 from RG),and 125 thereof may have not been isolated from the Panax genus.The established 2 D-LC/IM-QTOF-HDMSEapproach could also act as a magnifier to probe differentiated components between WG and RG.Compared with conventional approaches,this dimensionenhanced strategy could better resolve coeluting herbal components and more efficiently,more reliably identify the multicomponents,which,we believe,offers more possibilities for the systematic exposure and confirmative identification of plant metabolites.展开更多
Under the Kyoto Protocol,Japanwas supposed to reduce six percent of the green house gas (GHG) emission in 2012. However, until the year 2010, the statistics suggested that the GHG emission increased 4.2%. What is more...Under the Kyoto Protocol,Japanwas supposed to reduce six percent of the green house gas (GHG) emission in 2012. However, until the year 2010, the statistics suggested that the GHG emission increased 4.2%. What is more challenge is, afterFukushimacrisis, without the nuclear energy,Japanmay produce about 15 percent more GHG emissions than1990 inthis fiscal year. It still has to struggle to meet the target set by Kyoto Protocol. The demonstration area of “smart community” suggests Japanese exploration for new low carbon strategies. The study proposed a demand side response energy system, a dynamic tree-like hierarchical model for smart community. The model not only conveyed the concept of smart grid, but also built up a smart heat energy supply chain by offline heat transport system. Further, this model promoted a collaborative energy utilization mode between the industrial sector and the civil sector. In addition, the research chose the smart community inKitakyushuas case study and executed the model. The simulation and the analysis of the model not only evaluate the environmental effect of different technologies but also suggest that the smart community inJapanhas the potential but not easy to achieve the target, cut down 50% of the CO2 emission.展开更多
Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentat...Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.展开更多
In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has b...In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.展开更多
With the rapid development of information technology, it appears that a single online channel or a single offline is not able to meet the consumers＇ shopping needs, the enterprises will have a better development only...With the rapid development of information technology, it appears that a single online channel or a single offline is not able to meet the consumers＇ shopping needs, the enterprises will have a better development only by making the online and offline channels be in fusion and promote each other. This paper will combine the general business modes with corporate channel integration pattern, build out different forms of the market mode including market, enterprise and customers basing on different influence level, and analyze out the enterprise how to implement the business mode of integration basing on the influence factors under the background of different businessmodes, then apply the corresponding O2Oprocess to the mode, so as to broaden the enterprise selling channels for a better development.展开更多
Handwriting recognition is one of the most significant problems in pattern recognition,many studies have been proposed to improve this recognition of handwritten text for different languages.Yet,Fewer studies have bee...Handwriting recognition is one of the most significant problems in pattern recognition,many studies have been proposed to improve this recognition of handwritten text for different languages.Yet,Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts.The present paper suggests a feature extraction technique for offlineArabic handwriting recognition.A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function(RBF)neural networks is proposed.The methods of feature extraction are central to achieve high recognition performance.The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton(subwords,diacritics,loops,ascenders,and descenders).In order to reach our purpose,we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names(wilayas).Finally,a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction.The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.展开更多
This paper presents an off-line handwritten signature verification system based on the Siamese network,where a hybrid architecture is used.The Residual neural Network(ResNet)is used to realize a powerful feature extra...This paper presents an off-line handwritten signature verification system based on the Siamese network,where a hybrid architecture is used.The Residual neural Network(ResNet)is used to realize a powerful feature extraction model such that Writer Independent(WI)features can be effectively learned.A single-layer Siamese Neural Network(NN)is used to realize a Writer Dependent(WD)classifier such that the storage space can be minimized.For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively,we propose a method of selecting a reference signature as one of the inputs for the Siamese network.To take full advantage of the reference signature,we modify the conventional contrastive loss function to enhance the accuracy.By using the proposed techniques,the accuracy of the system can be increased by 5.9%.Based on the GPDS signature dataset,the proposed system is able to achieve an accuracy of 94.61%which is better than the accuracy achieved by the current state-of-the-art work.展开更多
BACKGROUND Polycystic ovary syndrome(PCOS) is characterized by hyperandrogenism, hyperinsulinemia, ovarian polycystic changes, and irregular ovulation, often occurring in women of childbearing age for whom it can be a...BACKGROUND Polycystic ovary syndrome(PCOS) is characterized by hyperandrogenism, hyperinsulinemia, ovarian polycystic changes, and irregular ovulation, often occurring in women of childbearing age for whom it can be a cause of infertility. Hypothalamus-pituitary-ovarian axis dysregulation is important in the pathogenesis of PCOS and the associated chronic excess of sex hormones can lead to cardiovascular and cerebrovascular diseases, diabetes, and malignancies such as endometrial cancer, and breast cancer. At present, most scholars agree that lifestyle interventions in conjunction with drug treatment can help PCOS patients achieve their goals of successful pregnancy and childbirth.AIM To investigate the clinical effect of an online and offline(O2O) preventive health management model on PCOS with kidney deficiency and phlegm dampness.METHODS A total of 82 patients with PCOS of the kidney deficiency and phlegm dampness type who were admitted to Beijing Luhe Hospital Affiliated to Capital Medical University from April 2019 to June 2020 were randomly divided into two groups. The treatment group was treated with oral Diane-35 for 3 mo and received preventive O2O medical health management for 6 mo(including eating and living, exercise, drug management). The control group was treated with oral Diane-35 for 3 mo and completed outpatient health education. The traditional Chinese medicine(TCM) syndrome score, acne score, hair score, sex hormone level and clinical effects were compared between the two groups before and after the intervention. RESULTS After treatment, the TCM syndrome score, acne score, and serum luteinizing hormone/follicle stimulating hormone level were significantly lower in the treatment group than in the control group(P < 0.05). After 3 mo of treatment, the TCM syndrome curative effect index in the treatment group was 97.30% compared to 54.05% in the control group(P < 0.05), whereas the total treatment effect in the treatment group was 91.89%, compared to 54.05% in the control group(P < 0.05).CONCLUSION An integrated therapeutic approach incorporating medication, TCM methods and social media is more effective than standard treatment for PCOS.展开更多
The paper introduces a new type of cutting system: the Offline Program Profile Lining and Cutting Robot System (OPPLCRS). The system adopts the offline program technology without teaching. The system can measure and c...The paper introduces a new type of cutting system: the Offline Program Profile Lining and Cutting Robot System (OPPLCRS). The system adopts the offline program technology without teaching. The system can measure and compensate every profile’s deformation automatically, so it is suit to process any batch of profiles, and the order of processing different profiles can be arranged at will. The OPPLCRS, firstly appearing in China, will have wide application in profile cutting fields.展开更多
文摘With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.
文摘Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.
基金Supported by Guangxi Higher Education Undergraduate Teaching Reform Project(2023JGB238)Hospital Education Teaching Reform and Research Project(2022JG03B)School-level Teaching Reform Project of Guangxi University of Chinese Medicine(2022B029)。
文摘In order to combine the advantages of online teaching and traditional offline classroom teaching,this paper optimizes the teaching design by taking Musculoskeletal Rehabilitation for undergraduates as the carrier,and reconstructs the course according to five parts:basic theory course,practical training course,standardized patient,case report,and course evaluation.Through analyzing the classroom quality and teaching effect,the innovation and practical effect of course reconstruction are explored.With students as the main body and goals as the guide,this model gives full play to the initiative and creativity of students,meets the individual needs of students at different levels,and provides reference ideas for improving the advanced,innovative and challenging creation of the course.
基金Supported by the Project for Undergraduate Education and Teaching Reform and Research at the District Level of Guangxi (2020JGB233)the Key Project for Undergraduate Education and Teaching Reform and Research of Guangxi University of Chinese Medicine (2018B07)。
文摘Taking construction of the online and offline integrated first-class undergraduate curriculum teaching modes of Histology and Embryology in Guangxi as an opportunity,under the guidance of student-centered teaching concept,efforts were made to innovate online and offline integrated teaching mode to overcome the shortcomings and dilemma of traditional Histology and Embryology teaching,with attention paid to the competence education in students including schematic knowledge,professional techniques,analytical thinking,and ideological and political theories,which would be of great significance for the cultivation of high-quality professionals specialized in traditional Chinese medicine.
基金supported by the National Key R&D program of China under Grant No.2021ZD0113203National Science Foundation of China under Grant No.61976115.
文摘Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online interaction.One of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the mismatch between the knowledge of the learned policy and the reality of the underlying environment.Recent works usually handle this in a too pessimistic manner to avoid out-of-distribution(OOD)queries as much as possible,but this can influence the robustness of the agents at unseen states.In this paper,we propose a simple but effective method to address this issue.The key idea of our method is to enhance the robustness of the new policy learned offline by weakening its confidence in highly uncertain regions,and we propose to find those regions by simulating them with modified Generative Adversarial Nets(GAN)such that the generated data not only follow the same distribution with the old experience but are very difficult to deal with by themselves,with regard to the behavior policy or some other reference policy.We then use this information to regularize the ORL algorithm to penalize the overconfidence behavior in these regions.Extensive experiments on several publicly available offline RL benchmarks demonstrate the feasibility and effectiveness of the proposed method.
文摘With the deepening development of educational informatization, online and offline blended teaching, as a new teaching mode, is increasingly receiving widespread attention from educators [1]. At present, the reform of the “online and offline blended teaching” of ideological and political education courses directly affects the quality of talent cultivation in universities. The article takes the course “Introduction to Basic Principles of Marxism” as an example to explore the reform mode of “online and offline blended teaching” in ideological and political theory courses in universities from the aspects of reasonable allocation of class hours, design of online teaching activities, how to deepen classroom teaching offline, and diversified assessment modes. Furthermore, the article summarizes the experience of mode reform and promotes the deep development of the people-oriented education concept in ideological and political courses in universities, so as to achieve the ultimate goal of moral education in ideological and political education in universities.
基金the National Natural Science Foundation of China(Grant No.81872996)the State Key Research and Development Project(Grant No.2017YFC1702104)+1 种基金the State Key Project for the Creation of Major New Drugs(2018ZX09711001-009-010)the Tianjin Municipal Education Commission Research Project(Grant No.2017ZD07)。
文摘Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification.A dimension-enhanced strategy,by offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry(2 D-LC/IM-QTOF-MS)enabling four-dimensional separations(2 D-LC,IM,and MS),is proposed.In combination with in-house database-driven automated peak annotation,this strategy was utilized to characterize ginsenosides simultaneously from white ginseng(WG)and red ginseng(RG).An offline 2 DLC system configuring an Xbridge Amide column and an HSS T3 column showed orthogonality 0.76 in the resolution of ginsenosides.Ginsenoside analysis was performed by data-independent high-definition MSE(HDMSE)in the negative ESI mode on a Vion?IMS-QTOF hybrid high-resolution mass spectrometer,which could better resolve ginsenosides than MSEand directly give the CCS information.An in-house ginsenoside database recording 504 known ginsenosides and 58 reference compounds,was established to assist the identification of ginsenosides.Streamlined workflows,by applying UNIFI?to automatedly annotate the HDMSEdata,were proposed.We could separate and characterize 323 ginsenosides(including 286 from WG and 306 from RG),and 125 thereof may have not been isolated from the Panax genus.The established 2 D-LC/IM-QTOF-HDMSEapproach could also act as a magnifier to probe differentiated components between WG and RG.Compared with conventional approaches,this dimensionenhanced strategy could better resolve coeluting herbal components and more efficiently,more reliably identify the multicomponents,which,we believe,offers more possibilities for the systematic exposure and confirmative identification of plant metabolites.
文摘Under the Kyoto Protocol,Japanwas supposed to reduce six percent of the green house gas (GHG) emission in 2012. However, until the year 2010, the statistics suggested that the GHG emission increased 4.2%. What is more challenge is, afterFukushimacrisis, without the nuclear energy,Japanmay produce about 15 percent more GHG emissions than1990 inthis fiscal year. It still has to struggle to meet the target set by Kyoto Protocol. The demonstration area of “smart community” suggests Japanese exploration for new low carbon strategies. The study proposed a demand side response energy system, a dynamic tree-like hierarchical model for smart community. The model not only conveyed the concept of smart grid, but also built up a smart heat energy supply chain by offline heat transport system. Further, this model promoted a collaborative energy utilization mode between the industrial sector and the civil sector. In addition, the research chose the smart community inKitakyushuas case study and executed the model. The simulation and the analysis of the model not only evaluate the environmental effect of different technologies but also suggest that the smart community inJapanhas the potential but not easy to achieve the target, cut down 50% of the CO2 emission.
基金National Natural Science Foundation of China (Project No. 61273365)111 Project (No. B08004) are gratefully acknowledged
文摘Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.
基金supported in part by the National Natural Science Foundation of China under Grant 62072392,Grant 61822602,Grant 61772207,Grant 61802331,Grant 61602399,Grant 61702439,Grant 61773331,and Grant 62062034the China Postdoctoral Science Foundation under Grant 2019T120732 and Grant 2017M622691+2 种基金the Natural Science Foundation of Shandong Province under Grant ZR2016FM42the Major scientific and technological innovation projects of Shandong Province under Grant 2019JZZY020131the Key projects of Shandong Natural Science Foundation under Grant ZR2020KF019.
文摘In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.
文摘With the rapid development of information technology, it appears that a single online channel or a single offline is not able to meet the consumers＇ shopping needs, the enterprises will have a better development only by making the online and offline channels be in fusion and promote each other. This paper will combine the general business modes with corporate channel integration pattern, build out different forms of the market mode including market, enterprise and customers basing on different influence level, and analyze out the enterprise how to implement the business mode of integration basing on the influence factors under the background of different businessmodes, then apply the corresponding O2Oprocess to the mode, so as to broaden the enterprise selling channels for a better development.
文摘Handwriting recognition is one of the most significant problems in pattern recognition,many studies have been proposed to improve this recognition of handwritten text for different languages.Yet,Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts.The present paper suggests a feature extraction technique for offlineArabic handwriting recognition.A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function(RBF)neural networks is proposed.The methods of feature extraction are central to achieve high recognition performance.The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton(subwords,diacritics,loops,ascenders,and descenders).In order to reach our purpose,we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names(wilayas).Finally,a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction.The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.
文摘This paper presents an off-line handwritten signature verification system based on the Siamese network,where a hybrid architecture is used.The Residual neural Network(ResNet)is used to realize a powerful feature extraction model such that Writer Independent(WI)features can be effectively learned.A single-layer Siamese Neural Network(NN)is used to realize a Writer Dependent(WD)classifier such that the storage space can be minimized.For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively,we propose a method of selecting a reference signature as one of the inputs for the Siamese network.To take full advantage of the reference signature,we modify the conventional contrastive loss function to enhance the accuracy.By using the proposed techniques,the accuracy of the system can be increased by 5.9%.Based on the GPDS signature dataset,the proposed system is able to achieve an accuracy of 94.61%which is better than the accuracy achieved by the current state-of-the-art work.
文摘BACKGROUND Polycystic ovary syndrome(PCOS) is characterized by hyperandrogenism, hyperinsulinemia, ovarian polycystic changes, and irregular ovulation, often occurring in women of childbearing age for whom it can be a cause of infertility. Hypothalamus-pituitary-ovarian axis dysregulation is important in the pathogenesis of PCOS and the associated chronic excess of sex hormones can lead to cardiovascular and cerebrovascular diseases, diabetes, and malignancies such as endometrial cancer, and breast cancer. At present, most scholars agree that lifestyle interventions in conjunction with drug treatment can help PCOS patients achieve their goals of successful pregnancy and childbirth.AIM To investigate the clinical effect of an online and offline(O2O) preventive health management model on PCOS with kidney deficiency and phlegm dampness.METHODS A total of 82 patients with PCOS of the kidney deficiency and phlegm dampness type who were admitted to Beijing Luhe Hospital Affiliated to Capital Medical University from April 2019 to June 2020 were randomly divided into two groups. The treatment group was treated with oral Diane-35 for 3 mo and received preventive O2O medical health management for 6 mo(including eating and living, exercise, drug management). The control group was treated with oral Diane-35 for 3 mo and completed outpatient health education. The traditional Chinese medicine(TCM) syndrome score, acne score, hair score, sex hormone level and clinical effects were compared between the two groups before and after the intervention. RESULTS After treatment, the TCM syndrome score, acne score, and serum luteinizing hormone/follicle stimulating hormone level were significantly lower in the treatment group than in the control group(P < 0.05). After 3 mo of treatment, the TCM syndrome curative effect index in the treatment group was 97.30% compared to 54.05% in the control group(P < 0.05), whereas the total treatment effect in the treatment group was 91.89%, compared to 54.05% in the control group(P < 0.05).CONCLUSION An integrated therapeutic approach incorporating medication, TCM methods and social media is more effective than standard treatment for PCOS.
文摘The paper introduces a new type of cutting system: the Offline Program Profile Lining and Cutting Robot System (OPPLCRS). The system adopts the offline program technology without teaching. The system can measure and compensate every profile’s deformation automatically, so it is suit to process any batch of profiles, and the order of processing different profiles can be arranged at will. The OPPLCRS, firstly appearing in China, will have wide application in profile cutting fields.
