The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors...The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors such as doctors’knowledge and experience.The accuracy is difficult to guarantee and has a serious lag.In this paper,a mixture prediction model is proposed for perioperative adverse events of heart failure,which combined with the advantages of the Deep Pyramid Convolutional Neural Networks(DPCNN)and Extreme Gradient Boosting(XGBOOST).The DPCNN was used to automatically extract features from patient’s diagnostic texts,and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients,then the XGBOOST algorithm was used to construct the prediction model of heart failure.An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018.The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3%and 31%compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.展开更多
By applying the Fourier analysis, we study the spectral properties of R- filters. Further, we prove that R-filters are a generalization of least squares polynomial adjustment, and we give the geometric interpretation ...By applying the Fourier analysis, we study the spectral properties of R- filters. Further, we prove that R-filters are a generalization of least squares polynomial adjustment, and we give the geometric interpretation of R-filters.展开更多
We introduce a concept for the majorization order on monomials. With the help of this order, we derive a necessary condition on the positive termination of a general successive difference substitution algorithm （KSDS...We introduce a concept for the majorization order on monomials. With the help of this order, we derive a necessary condition on the positive termination of a general successive difference substitution algorithm （KSDS） for an input form f.展开更多
Blockchain is an emerging decentralized technology of electronic voting.The current main consensus protocols are not flexible enough to manage the distributed blockchain nodes to achieve high efficiency of consensus.F...Blockchain is an emerging decentralized technology of electronic voting.The current main consensus protocols are not flexible enough to manage the distributed blockchain nodes to achieve high efficiency of consensus.For practical implementation,the consensus based on random linear block code(RLBC)is proposed and applied to blockchain voting scheme.Along with achieving the record correctness and consistency among all nodes,the consensus method indicates the active and inactive consensus nodes.This ability can assist the management of consensus nodes and restrain the generating of chain forks.To achieve end-to-end verifiability,cast-or-audit and randomized partial checking(RPC)are used in the proposed scheme.The voter can verify the high probability of correctness in ballot encryption and decryption.The experiments illustrate that the efficiency of proposed consensus is suitable for blockchain.The proposed electronic voting scheme is adapted to practical implementation of voting.展开更多
In order to generate an efficient common bitmap in single bitmap block truncation coding(SBBTC)of color images,an improved SBBTC scheme based on weighted plane(W-plane)method and hill climbing algorithm is proposed.Fi...In order to generate an efficient common bitmap in single bitmap block truncation coding(SBBTC)of color images,an improved SBBTC scheme based on weighted plane(W-plane)method and hill climbing algorithm is proposed.Firstly,the incoming color image is partitioned into non-overlapping blocks and each block is encoded using the W-plane method to get an initial common bitmap and quantization values.Then,the hill climbing algorithm is applied to optimize an initial common bitmap and generate a near-optimized common bitmap.Finally,the quantization values are recalculated by the near-optimized common bitmap and the considered color image is reconstructed block by block through the common bitmap and the new quantization values.Since the processing of each image block in SBBTC is independent and identical,parallel computing is applied to reduce the time consumption of this scheme.The simulation results show that the proposed scheme has better visual quality and time consumption than those of the reference SBBTC schemes.展开更多
The existence of closed orbits for the biochemical reaction model was discussed. It was also pointed out that the equation has no closed orbits or has stable limit cycles arising from Hopf bifurcations under a certain...The existence of closed orbits for the biochemical reaction model was discussed. It was also pointed out that the equation has no closed orbits or has stable limit cycles arising from Hopf bifurcations under a certain condition.展开更多
The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundes...The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.展开更多
In natural language processing(NLP),managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models,leading to practical inefficiencies.To address thi...In natural language processing(NLP),managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models,leading to practical inefficiencies.To address this challenge,we introduce AdaptForever,a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism.Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities,facilitating effective knowledge transfer from previous tasks to new ones.By combining Elastic Weight Consolidation(EWC)for knowledge preservation with specialized regularization terms,AdaptForever successfully maintains performance on earlier tasks while acquiring new capabilities.Experimental results demonstrate that AdaptForever achieves superior performance across a continuous sequence of NLP tasks compared to existing parameter-efficient methods,while effectively preventing catastrophic forgetting and enabling positive knowledge transfer between tasks.展开更多
In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of ...In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.展开更多
Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.Wit...Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.With the development of wireless communication,the signal transmission environment has become increasingly bad,causing more difficulties in parameter estimation.It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related.In practice,it is impossible to calculate the cyclic spectrum of infinite length data signals.When using finite length data to obtain a cycle spectrum,the truncation noise is induced,resulting in interference.It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines.An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.展开更多
In this paper, an improved algorithm, web-based keyword weight algorithm （WKWA）, is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of t...In this paper, an improved algorithm, web-based keyword weight algorithm （WKWA）, is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF＊IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model （IVSM）. A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.展开更多
With the development of mobile technology, Internet and GIS, LBS plays an important role in various applications. From the perspective of LBS, it is one of the main tasks of matching 3-dimensional spatial-temporal tra...With the development of mobile technology, Internet and GIS, LBS plays an important role in various applications. From the perspective of LBS, it is one of the main tasks of matching 3-dimensional spatial-temporal trajectories. We present an interpoiation based Modified Hausdorff Distance algorithm for 3-dimensional spatial-temporal Trajectory Matching （IMHD-ST）. It adopts interpolation algorithm to shield the impact to the distance between trajectories due to different position updating porices, sampling granularity, initial position and so on in Moving Object Database （MOD）. Besides, it uses MHD to deal with the implicit spatial information and structural information of weighted position updating points in various trajectories and reflects the discrepancy of moving results through the spatial distance between trajectories. In addition, it adopts temporal distance corresponding to the spatial distance between trajectories to reflect the differences including direction, speed and so on during moving process. The experimental results show that the algorithrn can reflect the trajectory similarity between 3-dimensional mobile objects more correctly, accurately and robustly.展开更多
Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ...Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.展开更多
Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese senti...Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper,we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization.First of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a by-product.Secondly,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis.By combining FastText and Bi-LSTM,we have developed a new fast sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and speed.In particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.展开更多
The Hardy integral inequality is one of the most important inequalities in analysis. The present paper establishes some new Copson-Pachpatte （C-P） type inequalities, which are the generalizations of the Hardy integr...The Hardy integral inequality is one of the most important inequalities in analysis. The present paper establishes some new Copson-Pachpatte （C-P） type inequalities, which are the generalizations of the Hardy integral inequalities on binary functions.展开更多
This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling ...This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,（1） training data set（1871-1960）, and（2） testing data set（1961-2014）.Statistical analyzes reflect the dynamic nature of the ISMR, which couldn＇t be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network（ANN）. Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.展开更多
Group key management technique is a fundamental building block for secure and reliable group communication systems.In order to successfully achieve this goal, group session key needs to be generated and distributed to...Group key management technique is a fundamental building block for secure and reliable group communication systems.In order to successfully achieve this goal, group session key needs to be generated and distributed to all group members in a secure and authenticated manner.The most commonly used method is based on Lagrange interpolating polynomial over the prime field F p={0,1,2,…, p-1}. A novel approach to group key transfer protocol based on a category of algebraic-geometry code is presented over the infinite field GF(2 m). The attractive advantages are obvious. Especially, the non-repeatability, confidentiality, and authentication of group key transfer protocols are obtained easily. Besides, a more generalized and simple mathematical construction model is proposed which also can be applied perfectly to related fields of information security.展开更多
At present,deep learning has been well applied in many fields.However,due to the high complexity of hypothesis space,numerous training samples are usually required to ensure the reliability of minimizing experience ri...At present,deep learning has been well applied in many fields.However,due to the high complexity of hypothesis space,numerous training samples are usually required to ensure the reliability of minimizing experience risk.Therefore,training a classifier with a small number of training examples is a challenging task.From a biological point of view,based on the assumption that rich prior knowledge and analogical association should enable human beings to quickly distinguish novel things from a few or even one example,we proposed a dynamic analogical association algorithm to make the model use only a few labeled samples for classification.To be specific,the algorithm search for knowledge structures similar to existing tasks in prior knowledge based on manifold matching,and combine sampling distributions to generate offsets instead of two sample points,thereby ensuring high confidence and significant contribution to the classification.The comparative results on two common benchmark datasets substantiate the superiority of the proposed method compared to existing data generation approaches for few-shot learning,and the effectiveness of the algorithm has been proved through ablation experiments.展开更多
After half a century research, the mechanical theorem proving in geometries has become an active research topic in the automated reasoning field. This review involves three approaches on automated generating readable ...After half a century research, the mechanical theorem proving in geometries has become an active research topic in the automated reasoning field. This review involves three approaches on automated generating readable machine proofs for geometry theorems which include search methods, coordinate-free methods, and formal logic methods. Some critical issues about these approaches are also discussed. Furthermore, the authors propose three further research directions for the readable machine proofs for geometry theorems, including geometry inequalities, intelligent geometry softwares and machine learning.展开更多
By means of dimension-decreasing method and cell-decomposition,a practical algorithm is proposed to decide the positivity of a certain class of symmetric polynomials,the numbers of whose elements are variable.This is ...By means of dimension-decreasing method and cell-decomposition,a practical algorithm is proposed to decide the positivity of a certain class of symmetric polynomials,the numbers of whose elements are variable.This is a class of mechanically decidable problems beyond Tarski model.To implement the algorithm,a program nprove written in maple is developed which can decide the positivity of these polynomials rapidly.展开更多
基金This study was approved by the Ethics Committee of the First Affiliated Hospital of Army Medical University,PLA,and the Approved No.of ethic committee is KY201936This work is supported by the National Key Research&Development Plan of China(2018YFC0116704)in data collectionIn addition,it is supported by Chongqing Technology Innovation and application research and development project(cstc2019jscx-msxmx0237)in the design of the study.
文摘The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors such as doctors’knowledge and experience.The accuracy is difficult to guarantee and has a serious lag.In this paper,a mixture prediction model is proposed for perioperative adverse events of heart failure,which combined with the advantages of the Deep Pyramid Convolutional Neural Networks(DPCNN)and Extreme Gradient Boosting(XGBOOST).The DPCNN was used to automatically extract features from patient’s diagnostic texts,and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients,then the XGBOOST algorithm was used to construct the prediction model of heart failure.An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018.The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3%and 31%compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.
基金Project supported by the National Basic Research Program of China (973 Program) (No. NKBRPC-2004CB318003)the Knowledge Innovation Program of the Chinese Academy of Sciences(No. KJCX2-YW-S02)the National Natural Science Foundation of China (No. 10771205)
文摘By applying the Fourier analysis, we study the spectral properties of R- filters. Further, we prove that R-filters are a generalization of least squares polynomial adjustment, and we give the geometric interpretation of R-filters.
基金Supported by the National Key Basic Research Project of China(Grant No.2011CB302402)the Fundamental Research Funds for the Central Universities,Southwest University for Nationalities(Grant No.12NZYTH04)
文摘We introduce a concept for the majorization order on monomials. With the help of this order, we derive a necessary condition on the positive termination of a general successive difference substitution algorithm （KSDS） for an input form f.
基金Supported by the National Natural Science Foundation of China(No.61501064)Sichuan Technology Support Program(No.2015GZ0088)Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(No.HCIC201502,HCIC201701)。
文摘Blockchain is an emerging decentralized technology of electronic voting.The current main consensus protocols are not flexible enough to manage the distributed blockchain nodes to achieve high efficiency of consensus.For practical implementation,the consensus based on random linear block code(RLBC)is proposed and applied to blockchain voting scheme.Along with achieving the record correctness and consistency among all nodes,the consensus method indicates the active and inactive consensus nodes.This ability can assist the management of consensus nodes and restrain the generating of chain forks.To achieve end-to-end verifiability,cast-or-audit and randomized partial checking(RPC)are used in the proposed scheme.The voter can verify the high probability of correctness in ballot encryption and decryption.The experiments illustrate that the efficiency of proposed consensus is suitable for blockchain.The proposed electronic voting scheme is adapted to practical implementation of voting.
基金Supported by the National Natural Science Foundation of China(No.61402537)the Open Fund of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(No.HCIC201706)the Sichuan Science and Technology Programme(No.2018GZDZX0041)
文摘In order to generate an efficient common bitmap in single bitmap block truncation coding(SBBTC)of color images,an improved SBBTC scheme based on weighted plane(W-plane)method and hill climbing algorithm is proposed.Firstly,the incoming color image is partitioned into non-overlapping blocks and each block is encoded using the W-plane method to get an initial common bitmap and quantization values.Then,the hill climbing algorithm is applied to optimize an initial common bitmap and generate a near-optimized common bitmap.Finally,the quantization values are recalculated by the near-optimized common bitmap and the considered color image is reconstructed block by block through the common bitmap and the new quantization values.Since the processing of each image block in SBBTC is independent and identical,parallel computing is applied to reduce the time consumption of this scheme.The simulation results show that the proposed scheme has better visual quality and time consumption than those of the reference SBBTC schemes.
文摘The existence of closed orbits for the biochemical reaction model was discussed. It was also pointed out that the equation has no closed orbits or has stable limit cycles arising from Hopf bifurcations under a certain condition.
基金funded by Taif University,Taif,Saudi Arabia,Project No.(TUDSPP-2024-139).
文摘The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.
基金supported by the National Key R&D Program of China(No.2023YFB3308601)Sichuan Science and Technology Program(2024NSFJQ0035,2024NSFSC0004)the Talents by Sichuan provincial Party Committee Organization Department.
文摘In natural language processing(NLP),managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models,leading to practical inefficiencies.To address this challenge,we introduce AdaptForever,a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism.Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities,facilitating effective knowledge transfer from previous tasks to new ones.By combining Elastic Weight Consolidation(EWC)for knowledge preservation with specialized regularization terms,AdaptForever successfully maintains performance on earlier tasks while acquiring new capabilities.Experimental results demonstrate that AdaptForever achieves superior performance across a continuous sequence of NLP tasks compared to existing parameter-efficient methods,while effectively preventing catastrophic forgetting and enabling positive knowledge transfer between tasks.
基金Project supported by the National Natural Science Foundation of China (No. 60474064), and the Natural Science Foundation of Zhejiang Province (No. Y105694), China
文摘In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
基金supported by the National Key R&D Program of China(2016YFB0800203)
文摘Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.With the development of wireless communication,the signal transmission environment has become increasingly bad,causing more difficulties in parameter estimation.It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related.In practice,it is impossible to calculate the cyclic spectrum of infinite length data signals.When using finite length data to obtain a cycle spectrum,the truncation noise is induced,resulting in interference.It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines.An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.
基金Project supported by the Science Foundation of Shanghai Municipal Commission of Science and Technology （Grant No.055115001）
文摘In this paper, an improved algorithm, web-based keyword weight algorithm （WKWA）, is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF＊IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model （IVSM）. A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.
文摘With the development of mobile technology, Internet and GIS, LBS plays an important role in various applications. From the perspective of LBS, it is one of the main tasks of matching 3-dimensional spatial-temporal trajectories. We present an interpoiation based Modified Hausdorff Distance algorithm for 3-dimensional spatial-temporal Trajectory Matching （IMHD-ST）. It adopts interpolation algorithm to shield the impact to the distance between trajectories due to different position updating porices, sampling granularity, initial position and so on in Moving Object Database （MOD）. Besides, it uses MHD to deal with the implicit spatial information and structural information of weighted position updating points in various trajectories and reflects the discrepancy of moving results through the spatial distance between trajectories. In addition, it adopts temporal distance corresponding to the spatial distance between trajectories to reflect the differences including direction, speed and so on during moving process. The experimental results show that the algorithrn can reflect the trajectory similarity between 3-dimensional mobile objects more correctly, accurately and robustly.
基金supported by the Science and Technology Plan Projects of Sichuan Province of China under Grant No.2008GZ0003the Key Technologies R & D Program of Sichuan Province of China under Grant No.2008SZ0100
文摘Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
基金supported by the National Science Foundation of China(No.61771140)the 2017 Natural Science Foundation of Fujian Provincial Science&Technology Department(No.2018J01560)the 2016 Fujian Education and Scientific Research Project for Young and Middle-aged Teachers(JAT170522).
文摘Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper,we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization.First of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a by-product.Secondly,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis.By combining FastText and Bi-LSTM,we have developed a new fast sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and speed.In particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.
基金Project supported by the National Basic Research Program of China(No.2011CB302402)theNational Natural Science Foundation of China(No.11171053)
文摘The Hardy integral inequality is one of the most important inequalities in analysis. The present paper establishes some new Copson-Pachpatte （C-P） type inequalities, which are the generalizations of the Hardy integral inequalities on binary functions.
基金supported by the Department of Science and Technology (DST)-SERB, Government of India, under Grant EEQ/ 2016/000021
文摘This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,（1） training data set（1871-1960）, and（2） testing data set（1961-2014）.Statistical analyzes reflect the dynamic nature of the ISMR, which couldn＇t be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network（ANN）. Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.
基金Supported by the National Natural Science Foundation of China(No.61501064)Sichuan Technology Support Program(No.2015GZ0088)
文摘Group key management technique is a fundamental building block for secure and reliable group communication systems.In order to successfully achieve this goal, group session key needs to be generated and distributed to all group members in a secure and authenticated manner.The most commonly used method is based on Lagrange interpolating polynomial over the prime field F p={0,1,2,…, p-1}. A novel approach to group key transfer protocol based on a category of algebraic-geometry code is presented over the infinite field GF(2 m). The attractive advantages are obvious. Especially, the non-repeatability, confidentiality, and authentication of group key transfer protocols are obtained easily. Besides, a more generalized and simple mathematical construction model is proposed which also can be applied perfectly to related fields of information security.
基金This work was supported by The National Natural Science Foundation of China(No.61402537)Sichuan Science and Technology Program(Nos.2019ZDZX0006,2020YFQ0056)+1 种基金the West Light Foundation of Chinese Academy of Sciences(201899)the Talents by Sichuan provincial Party Committee Organization Department,and Science and Technology Service Network Initiative(KFJ-STS-QYZD-2021-21-001).
文摘At present,deep learning has been well applied in many fields.However,due to the high complexity of hypothesis space,numerous training samples are usually required to ensure the reliability of minimizing experience risk.Therefore,training a classifier with a small number of training examples is a challenging task.From a biological point of view,based on the assumption that rich prior knowledge and analogical association should enable human beings to quickly distinguish novel things from a few or even one example,we proposed a dynamic analogical association algorithm to make the model use only a few labeled samples for classification.To be specific,the algorithm search for knowledge structures similar to existing tasks in prior knowledge based on manifold matching,and combine sampling distributions to generate offsets instead of two sample points,thereby ensuring high confidence and significant contribution to the classification.The comparative results on two common benchmark datasets substantiate the superiority of the proposed method compared to existing data generation approaches for few-shot learning,and the effectiveness of the algorithm has been proved through ablation experiments.
基金supported by the Funds of the Chinese Academy of Sciences for Key Topics in Innovation Engineering under Grant No.KJCX2-YW-S02
文摘After half a century research, the mechanical theorem proving in geometries has become an active research topic in the automated reasoning field. This review involves three approaches on automated generating readable machine proofs for geometry theorems which include search methods, coordinate-free methods, and formal logic methods. Some critical issues about these approaches are also discussed. Furthermore, the authors propose three further research directions for the readable machine proofs for geometry theorems, including geometry inequalities, intelligent geometry softwares and machine learning.
基金This work was partially supported by China 973 Project NKBRPC(Grant No.2004CB318003)the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KJCX2-YW-S02)
文摘By means of dimension-decreasing method and cell-decomposition,a practical algorithm is proposed to decide the positivity of a certain class of symmetric polynomials,the numbers of whose elements are variable.This is a class of mechanically decidable problems beyond Tarski model.To implement the algorithm,a program nprove written in maple is developed which can decide the positivity of these polynomials rapidly.
