This paper presents a set of algorithms capable of locating main facial features automatically and effectively. Based on integral projection of local binary image pixels and pixel clustering techniques, a set of a p...This paper presents a set of algorithms capable of locating main facial features automatically and effectively. Based on integral projection of local binary image pixels and pixel clustering techniques, a set of a priori knowledge based algorithms have succeeded in locating eyes, nose and mouth, and uprighting the tilt face. The proposed approach is superior to other methods as it takes account of photos with glasses and sha dows, therefore suitable for processing real ID type photos.展开更多
In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones...In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.展开更多
A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approa...A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.展开更多
When correcting a fault, adding a new concept or feature, or adapting a system to conform to a new platform, software engineers must first find the relevant parts of the code that correspond to a particular change. Th...When correcting a fault, adding a new concept or feature, or adapting a system to conform to a new platform, software engineers must first find the relevant parts of the code that correspond to a particular change. This is termed as concept or feature location process. Several techniques have been introduced which automate some or all of the process of concept location. Those techniques rely heavily on code comprehension as it is considered a prerequisite when attempting to maintain any software system. It provides a comprehensive overview of large body work which is beneficial to researchers and practitioners. This paper presents an overview of code comprehension categorization and consequence. A systematic literature survey of concept location enhancement techniques is also presented. Moreover, the paper presents an overview of the role of concept location in program comprehension and maintenance and discusses information retrieval techniques to advance concept location.展开更多
Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed...Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.展开更多
With the implementation of the“Internet+”strategy,electronic medi-cal records are generally applied in the medicalfield.Deep mining of electronic medical record content data is an effective means to obtain medical kn...With the implementation of the“Internet+”strategy,electronic medi-cal records are generally applied in the medicalfield.Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’states,but the existing methods for extracting entities from electronic medical records have problems of redundant information,overlapping entities,and low accuracy rates.Therefore,this paper proposes an entity extrac-tion method for electronic medical records based on the network framework of BERT-BiLSTM,which incorporates a multichannel self-attention mechanism and location relationship features.First,the text input sequence was encoded using the BERT-BiLSTM network framework,and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mech-anism.Then,the position relation characteristic was used to extract the local semantic message of the text,and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained.Next,the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix.Finally,the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus,and the exper-imental results indicate that the proposed method surpasses existing methods in terms of precision,recall,F1 value and training time.展开更多
文摘This paper presents a set of algorithms capable of locating main facial features automatically and effectively. Based on integral projection of local binary image pixels and pixel clustering techniques, a set of a priori knowledge based algorithms have succeeded in locating eyes, nose and mouth, and uprighting the tilt face. The proposed approach is superior to other methods as it takes account of photos with glasses and sha dows, therefore suitable for processing real ID type photos.
基金The National Basic Research Program of China(973 Program)(No.2010CB328104,2009CB320501)the National Natural Science Foundation of China(No.61272531,61070158,61003257,61060161,61003311,41201486)+4 种基金the National Key Technology R&D Program during the11th Five-Year Plan Period(No.2010BAI88B03)Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092130002)the National Science and Technology Major Project(No.2009ZX03004-004-04)the Foundation of the Key Laboratory of Netw ork and Information Security of Jiangsu Province(No.BM2003201)the Key Laboratory of Computer Netw ork and Information Integration of the Ministry of Education of China(No.93K-9)
文摘In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.
基金Project(90820302) supported by the National Natural Science Foundation of China
文摘A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.
文摘When correcting a fault, adding a new concept or feature, or adapting a system to conform to a new platform, software engineers must first find the relevant parts of the code that correspond to a particular change. This is termed as concept or feature location process. Several techniques have been introduced which automate some or all of the process of concept location. Those techniques rely heavily on code comprehension as it is considered a prerequisite when attempting to maintain any software system. It provides a comprehensive overview of large body work which is beneficial to researchers and practitioners. This paper presents an overview of code comprehension categorization and consequence. A systematic literature survey of concept location enhancement techniques is also presented. Moreover, the paper presents an overview of the role of concept location in program comprehension and maintenance and discusses information retrieval techniques to advance concept location.
基金supported by the Hunan University of Science and Technology Doctoral Research Foundation Project(E51873).
文摘Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.
基金This work is partly supported by the General Project of Scientific Research Funds of Liaoning Provincial Department of Education under Grant Nos.LJKZ0085,and LJKMZ20220447the Project of PublicWelfareResearch Fund for Science(Soft Science Research Program)of Liaoning Province under Grant No.2023JH4/10700056the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University under Grant No.93K172018K01.
文摘With the implementation of the“Internet+”strategy,electronic medi-cal records are generally applied in the medicalfield.Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’states,but the existing methods for extracting entities from electronic medical records have problems of redundant information,overlapping entities,and low accuracy rates.Therefore,this paper proposes an entity extrac-tion method for electronic medical records based on the network framework of BERT-BiLSTM,which incorporates a multichannel self-attention mechanism and location relationship features.First,the text input sequence was encoded using the BERT-BiLSTM network framework,and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mech-anism.Then,the position relation characteristic was used to extract the local semantic message of the text,and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained.Next,the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix.Finally,the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus,and the exper-imental results indicate that the proposed method surpasses existing methods in terms of precision,recall,F1 value and training time.
