Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production exp...Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.展开更多
There are similarities and differences about the order of the attributes between English sentences and Chinese sentenc es. As a result, mistakes can often be observed; those mistakes are the results of unfamiliarity w...There are similarities and differences about the order of the attributes between English sentences and Chinese sentenc es. As a result, mistakes can often be observed; those mistakes are the results of unfamiliarity with the proper order of the attri butes. But here, special attention is paid to the EST(English for Science and Technology) translation in which their differences are much more distinct. Generally speaking, the attributes in English can be divided into pre-attributes and post-attributes in terms of their positions in a sentence. But in Chinese an attribute is usually put before a noun, and rarely put after it, so several simple comparisons are tried to find out some rules to translate English attributes into Chinese.展开更多
Contrastive linguistics is a branch of linguistics which mainly involves contrast or comparison,and it can leave us some useful insights into our problems,especially for translation work.This paper discusses similarit...Contrastive linguistics is a branch of linguistics which mainly involves contrast or comparison,and it can leave us some useful insights into our problems,especially for translation work.This paper discusses similarities and differences between English attributes and Chinese attributes from the perspective of the location,the composition,and the function,for the purpose of presenting a sound version for the original text.展开更多
Comparative study between English and Chinese attributes has been a research hotspot.The differences between English and Chinese attributes are elaborated by examples and translation strategies of English and Chinese ...Comparative study between English and Chinese attributes has been a research hotspot.The differences between English and Chinese attributes are elaborated by examples and translation strategies of English and Chinese attributes are discussed in this paper,from which the implications for translation teaching are stated at the same time.展开更多
The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which ...The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which are suitable for fractured and caved carbonate reservoir prediction is discussed,including amplitude,coherence analysis,spectra decomposition,seismic absorption attenuation analysis and impedance inversion.Moreover,3-D optimization of these attributes is achieved by integration of multivariate discriminant analysis and principle component analysis,where the logging data are taken as training samples.Using the optimized results,the spatial distribution and configuration features of the caved reservoirs can be characterized in detail.This technique not only improves the understanding of the spatial distribution of current reservoirs but also provides a significant basis for the discovery and production of carbonate reservoirs in the Tarim Basin.展开更多
Attribute-based encryption is drawing more attention with its inherent attractive properties which are potential to be widely used in the newly developing cloud computing. However, one of the main obstacles for its ap...Attribute-based encryption is drawing more attention with its inherent attractive properties which are potential to be widely used in the newly developing cloud computing. However, one of the main obstacles for its application is how to revoke the attributes of the users, though some ABE schemes have realized revocation, they mostly focused on the user revocation that revokes the user＇s whole attributes, or attribute revocation under the indirect revocation model such that all the users＇ private keys will be affected by the revocation. In this paper, we define the model of CP-ABE supporting the attribute revocation under the direct revocation model, in which the revocation list is embed in the ciphertext and none of the users＇ private keys will be affected by the revocation process. Then we propose a generic construction, and prove its security with the decision q-BDHE assumption.展开更多
This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists ...This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.展开更多
Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one....Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.展开更多
In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption an...In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption and decryption operations that depend at least linearly on the number of attributes involved in the access policy. We propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our scheme has constant ciphertext length and a constant number of pairing computations. Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user's attributes revocation occurs, the scheme transfers most re-encryption work to the cloud service provider, reducing the data owner's computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure access of sensitive data stored in the cloud server, and be able to cope with the dynamic changes of users' access privileges in large-scale systems. Besides, the multi-authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the effi ciency of the encryption and decryption operations.展开更多
Because of the incompleteness and uncertainty in the information on overseas oil-gas projects, project evaluation needs models able to deal with such problems. A new model is, therefore, presented in this paper based ...Because of the incompleteness and uncertainty in the information on overseas oil-gas projects, project evaluation needs models able to deal with such problems. A new model is, therefore, presented in this paper based on interval multi-attribute decision-making theory. Analysis was made on the important attributes （index） and the re- lationships affecting the basic factors to the project eco- nomic results were described. The interval numbers are used to describe the information on overseas oil and gas projects. On these bases, an improved TOPSIS model is introduced for the evaluation and ranking of overseas oil and gas projects. The practical application of the new model was carried out for an oil company in selecting some promising blocks from 13 oil and gas blocks in eight dif- ferent countries in the Middle East. Based on these inno- vative studies, some conclusions are given from theoretical and application aspects. The practical application shows that the introduction of interval numbers into the evaluation and ranking of the overseas oil and gas projects can lead to more reasonable decisions. The users can do the project evaluation based on the comprehensive values as well as based on some preferred index in the project evaluation and ranking.展开更多
In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by ...In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by Chen Q S. In the model, firstly, the author establishes the attribute space matrix and determines the weight based on Shannon entropy theory; secondly, calculates attribute measure; thirdly, evaluates that with confidence criterion and score criterion; finally, an application example is given. The results show that the water quality of the groundwater sources for the city comes up to the grade II or III standard. There is no pollution that obviously exceeds the standard and the water can meet people’s needs .The results from an evaluation of this model are in basic agreement with the observed situation and with a set pair analysis (SPA) model.展开更多
Feature selection（FS） aims to determine a minimal feature（attribute） subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory（RST） has been us...Feature selection（FS） aims to determine a minimal feature（attribute） subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory（RST） has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone,requiring no additional information. This paper describes the fundamental ideas behind RST-based approaches,reviews related FS methods built on these ideas,and analyses more frequently used RST-based traditional FS algorithms such as Quickreduct algorithm,entropy based reduct algorithm,and relative reduct algorithm. It is found that some of the drawbacks in the existing algorithms and our proposed improved algorithms can overcome these drawbacks. The experimental analyses have been carried out in order to achieve the efficiency of the proposed algorithms.展开更多
Nowadays, there is the tendency to outsource data to cloud storage servers for data sharing purposes. In fact, this makes access control for the outsourced data a challenging issue. Ciphertext-policy attribute-based e...Nowadays, there is the tendency to outsource data to cloud storage servers for data sharing purposes. In fact, this makes access control for the outsourced data a challenging issue. Ciphertext-policy attribute-based encryption（CP-ABE） is a promising cryptographic solution for this challenge. It gives the data owner（DO） direct control on access policy and enforces the access policy cryptographically. However,the practical application of CP-ABE in the data sharing service also has its own inherent challenge with regard to attribute revocation. To address this challenge, we proposed an attribute-revocable CP-ABE scheme by taking advantages of the over-encryption mechanism and CP-ABE scheme and by considering the semitrusted cloud service provider（CSP） that participates in decryption processes to issue decryption tokens for authorized users. We further presented the security and performance analysis in order to assess the effectiveness of the scheme. As compared with the existing attributerevocable CP-ABE schemes, our attribute-revocable scheme is reasonably efficient and more secure to enable attribute-based access control over the outsourced data in the cloud data sharing service.展开更多
By using the dynamic characteristic of one direction S-rough sets（one direction singular rough sets） and dual of one direction S-rough sets（dual of one direction singular rough sets）, the concepts of attribute dis...By using the dynamic characteristic of one direction S-rough sets（one direction singular rough sets） and dual of one direction S-rough sets（dual of one direction singular rough sets）, the concepts of attribute disturbance of knowledge, the attribute disturbance degree of knowledge, and the disturbance coefficient of knowledge are given. By employing these concepts, the cardinal order theorem of the attribute disturbance knowledge, the unit circle theorem of the attribute disturbance knowledge, and the discernible theorem of the attribute disturbance knowledge are presented.展开更多
Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator...Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.展开更多
The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system f...The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.展开更多
AVO (Amplitude variation with offset) technology is widely used in gas hydrate research. BSR (Bottom simulating reflector), caused by the huge difference in wave impedance between the hydrate reservoir and the underly...AVO (Amplitude variation with offset) technology is widely used in gas hydrate research. BSR (Bottom simulating reflector), caused by the huge difference in wave impedance between the hydrate reservoir and the underlying free gas reservoir, is the bottom boundary mark of the hydrate reservoir. Analyzing the AVO attributes of BSR can evaluate hydrate reservoirs. However, the Zoeppritz equation which is the theoretical basis of conventional AVO technology has inherent problems: the Zoeppritz equation does not consider the influence of thin layer thickness on reflection coefficients;the approximation of the Zoeppritz equation assumes that the difference of wave impedance between the two sides of the interface is small. These assumptions are not consistent with the occurrence characteristics of natural gas hydrate. The Brekhovskikh equation, which is more suitable for thin-layer reflection coefficient calculation, is used as the theoretical basis for AVO analysis. The reflection coefficients calculated by the Brekhovskikh equation are complex numbers with phase angles. Therefore, attributes of the reflection coefficient and its phase angle changing with offset are used to analyze the hydrate reservoir's porosity, saturation, and thickness. Finally, the random forest algorithm is used to predict the reservoir porosity, hydrate saturation, and thickness of the hydrate reservoir. In the synthetic data, the inversion results based on the four attributes of the Brekhovskikh equation are better than the conventional inversion results based on the two attributes of Zoeppritz, and the thickness can be accurately predicted. The proposed method also achieves good results in the application of Blake Ridge data. According to the method proposed in this paper, the hydrate reservoir in the area has a high porosity (more than 50%), and a medium saturation (between 10% and 20%). The thickness is mainly between 200m and 300m. It is consistent with the previous results obtained by velocity analysis.展开更多
By employing the knowledge（R-element equivalence class） in one direction S-rough sets and dual of one direction S-rough sets, the concept of knowledge law is given; the generation theorem of knowledge law, the excur...By employing the knowledge（R-element equivalence class） in one direction S-rough sets and dual of one direction S-rough sets, the concept of knowledge law is given; the generation theorem of knowledge law, the excursion theorem of knowledge law, and the attribute disturbance discernible theorem of knowledge law are proposed. Knowledge law is a new characteristic of S-rough sets.展开更多
The logging attribute optimization is an important task in the well-logging interpretation. A method of attribute reduction is presented based on rough set. Firstly, the core information of the sample by a general red...The logging attribute optimization is an important task in the well-logging interpretation. A method of attribute reduction is presented based on rough set. Firstly, the core information of the sample by a general reductive method is determined. Then, the significance of dispensable attribute in the reduction-table is calculated. Finally, the minimum relative reduction set is achieved. The typical calculation and quantitative computation of reservoir parameter in oil logging show that the method of attribute reduction is greatly effective and feasible in logging interpretation.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
基金the financially supported by the National Natural Science Foundation of China(Grant No.52104013)the China Postdoctoral Science Foundation(Grant No.2022T150724)。
文摘Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.
文摘There are similarities and differences about the order of the attributes between English sentences and Chinese sentenc es. As a result, mistakes can often be observed; those mistakes are the results of unfamiliarity with the proper order of the attri butes. But here, special attention is paid to the EST(English for Science and Technology) translation in which their differences are much more distinct. Generally speaking, the attributes in English can be divided into pre-attributes and post-attributes in terms of their positions in a sentence. But in Chinese an attribute is usually put before a noun, and rarely put after it, so several simple comparisons are tried to find out some rules to translate English attributes into Chinese.
文摘Contrastive linguistics is a branch of linguistics which mainly involves contrast or comparison,and it can leave us some useful insights into our problems,especially for translation work.This paper discusses similarities and differences between English attributes and Chinese attributes from the perspective of the location,the composition,and the function,for the purpose of presenting a sound version for the original text.
文摘Comparative study between English and Chinese attributes has been a research hotspot.The differences between English and Chinese attributes are elaborated by examples and translation strategies of English and Chinese attributes are discussed in this paper,from which the implications for translation teaching are stated at the same time.
基金co-supported by the National Basic Resarch Program of China (Grant No.2011CB201103)the National Scince and Technology Major Project (Grant No.2011ZX05004003)
文摘The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which are suitable for fractured and caved carbonate reservoir prediction is discussed,including amplitude,coherence analysis,spectra decomposition,seismic absorption attenuation analysis and impedance inversion.Moreover,3-D optimization of these attributes is achieved by integration of multivariate discriminant analysis and principle component analysis,where the logging data are taken as training samples.Using the optimized results,the spatial distribution and configuration features of the caved reservoirs can be characterized in detail.This technique not only improves the understanding of the spatial distribution of current reservoirs but also provides a significant basis for the discovery and production of carbonate reservoirs in the Tarim Basin.
文摘Attribute-based encryption is drawing more attention with its inherent attractive properties which are potential to be widely used in the newly developing cloud computing. However, one of the main obstacles for its application is how to revoke the attributes of the users, though some ABE schemes have realized revocation, they mostly focused on the user revocation that revokes the user＇s whole attributes, or attribute revocation under the indirect revocation model such that all the users＇ private keys will be affected by the revocation. In this paper, we define the model of CP-ABE supporting the attribute revocation under the direct revocation model, in which the revocation list is embed in the ciphertext and none of the users＇ private keys will be affected by the revocation process. Then we propose a generic construction, and prove its security with the decision q-BDHE assumption.
基金supported by ZTE Corporation and State Key Laboratory of Mobile Network and Mobile Multimedia Technology
文摘This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.
基金supported by National Key R&D Program of China(-NO.2017YFC0803700)National Nature Science Foundation of China(No.U1736206)+6 种基金National Nature Science Foundation of China(61671336)National Nature Science Foundation of China(61671332)Technology Research Program of Ministry of Public Security(No.2016JSYJA12)Hubei Province Technological Innovation Major Project(-No.2016AAA015)Hubei Province Technological Innovation Major Projec(2017AAA123)National Key Research and Development Program of China(No.2016YFB0100901)Nature Science Foundation of Jiangsu Province(No.BK20160386)
文摘Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.
基金supported by National Natural Science Foundation of China under Grant No.60873231Natural Science Foundation of Jiangsu Province under Grant No.BK2009426+1 种基金Major State Basic Research Development Program of China under Grant No.2011CB302903Key University Science Research Project of Jiangsu Province under Grant No.11KJA520002
文摘In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption and decryption operations that depend at least linearly on the number of attributes involved in the access policy. We propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our scheme has constant ciphertext length and a constant number of pairing computations. Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user's attributes revocation occurs, the scheme transfers most re-encryption work to the cloud service provider, reducing the data owner's computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure access of sensitive data stored in the cloud server, and be able to cope with the dynamic changes of users' access privileges in large-scale systems. Besides, the multi-authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the effi ciency of the encryption and decryption operations.
基金supported by the National Social Science Foundation key projects(13&ZD159,11&ZD164)
文摘Because of the incompleteness and uncertainty in the information on overseas oil-gas projects, project evaluation needs models able to deal with such problems. A new model is, therefore, presented in this paper based on interval multi-attribute decision-making theory. Analysis was made on the important attributes （index） and the re- lationships affecting the basic factors to the project eco- nomic results were described. The interval numbers are used to describe the information on overseas oil and gas projects. On these bases, an improved TOPSIS model is introduced for the evaluation and ranking of overseas oil and gas projects. The practical application of the new model was carried out for an oil company in selecting some promising blocks from 13 oil and gas blocks in eight dif- ferent countries in the Middle East. Based on these inno- vative studies, some conclusions are given from theoretical and application aspects. The practical application shows that the introduction of interval numbers into the evaluation and ranking of the overseas oil and gas projects can lead to more reasonable decisions. The users can do the project evaluation based on the comprehensive values as well as based on some preferred index in the project evaluation and ranking.
文摘In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by Chen Q S. In the model, firstly, the author establishes the attribute space matrix and determines the weight based on Shannon entropy theory; secondly, calculates attribute measure; thirdly, evaluates that with confidence criterion and score criterion; finally, an application example is given. The results show that the water quality of the groundwater sources for the city comes up to the grade II or III standard. There is no pollution that obviously exceeds the standard and the water can meet people’s needs .The results from an evaluation of this model are in basic agreement with the observed situation and with a set pair analysis (SPA) model.
基金supported by the UGC, SERO, Hyderabad under FDP during XI plan period, and the UGC, New Delhi for financial assistance under major research project Grant No. F-34-105/2008
文摘Feature selection（FS） aims to determine a minimal feature（attribute） subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory（RST） has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone,requiring no additional information. This paper describes the fundamental ideas behind RST-based approaches,reviews related FS methods built on these ideas,and analyses more frequently used RST-based traditional FS algorithms such as Quickreduct algorithm,entropy based reduct algorithm,and relative reduct algorithm. It is found that some of the drawbacks in the existing algorithms and our proposed improved algorithms can overcome these drawbacks. The experimental analyses have been carried out in order to achieve the efficiency of the proposed algorithms.
基金supported by the Major International(Regional)Joint Research Project of China National Science Foundation under Grant No.61520106007National High Technology Research and Development Program of China(863)under Grant No.2015AA016007
文摘Nowadays, there is the tendency to outsource data to cloud storage servers for data sharing purposes. In fact, this makes access control for the outsourced data a challenging issue. Ciphertext-policy attribute-based encryption（CP-ABE） is a promising cryptographic solution for this challenge. It gives the data owner（DO） direct control on access policy and enforces the access policy cryptographically. However,the practical application of CP-ABE in the data sharing service also has its own inherent challenge with regard to attribute revocation. To address this challenge, we proposed an attribute-revocable CP-ABE scheme by taking advantages of the over-encryption mechanism and CP-ABE scheme and by considering the semitrusted cloud service provider（CSP） that participates in decryption processes to issue decryption tokens for authorized users. We further presented the security and performance analysis in order to assess the effectiveness of the scheme. As compared with the existing attributerevocable CP-ABE schemes, our attribute-revocable scheme is reasonably efficient and more secure to enable attribute-based access control over the outsourced data in the cloud data sharing service.
基金Foundation item： Supported by the Nature Science Foundation of Shandong Province（Y2007H02）
文摘By using the dynamic characteristic of one direction S-rough sets（one direction singular rough sets） and dual of one direction S-rough sets（dual of one direction singular rough sets）, the concepts of attribute disturbance of knowledge, the attribute disturbance degree of knowledge, and the disturbance coefficient of knowledge are given. By employing these concepts, the cardinal order theorem of the attribute disturbance knowledge, the unit circle theorem of the attribute disturbance knowledge, and the discernible theorem of the attribute disturbance knowledge are presented.
基金supported by National Natural Science Foundation of China(No.61603310)the Fundamental Research Funds for the Central Universities(No.XDJK2018B019).
文摘Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.
基金supported by the Natural Science Foundation of Tianjin(No.20JCQNJC00720)the Fundamental Research Fund for the Central Universities(No.3122021052)。
文摘The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.
基金The research is funded by the National Natural Science Foundation of China(No.12171455)the Original Innovation Research Program of the Chinese Academy of Sciences(CAS)under grant number ZDBS-LY-DQC003the Key Research Programs IGGCAS-2019031.
文摘AVO (Amplitude variation with offset) technology is widely used in gas hydrate research. BSR (Bottom simulating reflector), caused by the huge difference in wave impedance between the hydrate reservoir and the underlying free gas reservoir, is the bottom boundary mark of the hydrate reservoir. Analyzing the AVO attributes of BSR can evaluate hydrate reservoirs. However, the Zoeppritz equation which is the theoretical basis of conventional AVO technology has inherent problems: the Zoeppritz equation does not consider the influence of thin layer thickness on reflection coefficients;the approximation of the Zoeppritz equation assumes that the difference of wave impedance between the two sides of the interface is small. These assumptions are not consistent with the occurrence characteristics of natural gas hydrate. The Brekhovskikh equation, which is more suitable for thin-layer reflection coefficient calculation, is used as the theoretical basis for AVO analysis. The reflection coefficients calculated by the Brekhovskikh equation are complex numbers with phase angles. Therefore, attributes of the reflection coefficient and its phase angle changing with offset are used to analyze the hydrate reservoir's porosity, saturation, and thickness. Finally, the random forest algorithm is used to predict the reservoir porosity, hydrate saturation, and thickness of the hydrate reservoir. In the synthetic data, the inversion results based on the four attributes of the Brekhovskikh equation are better than the conventional inversion results based on the two attributes of Zoeppritz, and the thickness can be accurately predicted. The proposed method also achieves good results in the application of Blake Ridge data. According to the method proposed in this paper, the hydrate reservoir in the area has a high porosity (more than 50%), and a medium saturation (between 10% and 20%). The thickness is mainly between 200m and 300m. It is consistent with the previous results obtained by velocity analysis.
基金Foundation item： Supported by the Nature Science Foundation of Shandong Province（Y2007H02）
文摘By employing the knowledge（R-element equivalence class） in one direction S-rough sets and dual of one direction S-rough sets, the concept of knowledge law is given; the generation theorem of knowledge law, the excursion theorem of knowledge law, and the attribute disturbance discernible theorem of knowledge law are proposed. Knowledge law is a new characteristic of S-rough sets.
基金Supported by the National Natural Science Foundation of China (No.60308002)
文摘The logging attribute optimization is an important task in the well-logging interpretation. A method of attribute reduction is presented based on rough set. Firstly, the core information of the sample by a general reductive method is determined. Then, the significance of dispensable attribute in the reduction-table is calculated. Finally, the minimum relative reduction set is achieved. The typical calculation and quantitative computation of reservoir parameter in oil logging show that the method of attribute reduction is greatly effective and feasible in logging interpretation.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
