An approximate approach of querying between heterogeneous ontology-basedinformation systems based on an association matrix is proposed. First, the association matrix isdefined to describe relations between concepts in...An approximate approach of querying between heterogeneous ontology-basedinformation systems based on an association matrix is proposed. First, the association matrix isdefined to describe relations between concepts in two ontologies. Then, a methodof rewriting queriesbased on the association matrix is presented to solve the ontology heterogeneity problem. Itrewrites the queries in one ontology to approximate queries in another ontology based on thesubsumption relations between concepts. The method also uses vectors to represent queries, and thencomputes the vectors with the association matrix; the disjoint relations between concepts can beconsidered by the results. It can get better approximations than the methods currently in use, whichdonot consider disjoint relations. The method can be processed by machines automatically. It issimple to implement and expected to run quite fast.展开更多
Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results...Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named "compressed global tree guide" (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMinerTID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.展开更多
Multidimensional data query has been gaining much interest in database research communities in recent years, yet many of the existing studies focus mainly on ten tralized systems. A solution to querying in Peer-to-Pee...Multidimensional data query has been gaining much interest in database research communities in recent years, yet many of the existing studies focus mainly on ten tralized systems. A solution to querying in Peer-to-Peer（P2P） environment was proposed to achieve both low processing cost in terms of the number of peers accessed and search messages and balanced query loads among peers. The system is based on a balanced tree structured P2P network. By partitioning the query space intelligently, the amount of query forwarding is effectively controlled, and the number of peers involved and search messages are also limited. Dynamic load balancing can be achieved during space partitioning and query resolving. Extensive experiments confirm the effectiveness and scalability of our algorithms on P2P networks.展开更多
This work aims to reduce queries on big data to computations on small data,and hence make querying big data possible under bounded resources.A query Q is boundedly evaluable when posed on any big dataset D,there exist...This work aims to reduce queries on big data to computations on small data,and hence make querying big data possible under bounded resources.A query Q is boundedly evaluable when posed on any big dataset D,there exists a fraction DQ of D such that Q(D)=Q(DQ),and the cost of identifying DQ is independent of the size of D.It has been shown that with an auxiliary structure known as access schema,many queries in relational algebra(RA)are boundedly evaluable under the set semantics of RA.This paper extends the theory of bounded evaluation to RAaggr,i.e.,RA extended with aggregation,under the bag semantics.(1)We extend access schema to bag access schema,to help us identify DQ for RAaggr queries Q.(2)While it is undecidable to determine whether an RAaggr query is boundedly evaluable under a bag access schema,we identify special cases that are decidable and practical.(3)In addition,we develop an effective syntax for bounded RAaggr queries,i.e.,a core subclass of boundedly evaluable RAaggr queries without sacrificing their expressive power.(4)Based on the effective syntax,we provide efficient algorithms to check the bounded evaluability of RAaggr queries and to generate query plans for bounded RAaggr queries.(5)As proof of concept,we extend PostgreSQL to support bounded evaluation.We experimentally verify that the extended system improves performance by orders of magnitude.展开更多
Description logics （DLs） play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web...Description logics （DLs） play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web recently. To acquire a tractable DL for query answering, DL-Lite is proposed. Due to the large amount of imprecision and uncertainty in the real world, it is essential to extend DLs to deal with these vague and imprecise information. We thus propose a new fuzzy DL f-DLR-Lite.n, which allows for the presence of n-ary relations and the occurrence of concept conjunction on the left land of inclusion axioms. We also suggest an improved fuzzy query language, which supports the presence of thresholds and user defined weights. We also show that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. DL f-DLR-Lite,n can make up for the disadvantages of knowledge representation and reasoning of classic DLs, and the enhanced query language expresses user intentions more precisely and reasonably.展开更多
For small devices like the PDAs and mobile phones, formulation of relational database queries is not as simple as using conventional devices such as the personal computers and laptops. Due to the restricted size and r...For small devices like the PDAs and mobile phones, formulation of relational database queries is not as simple as using conventional devices such as the personal computers and laptops. Due to the restricted size and resources of these smaller devices, current works mostly limit the queries that can be posed by users by having them predetermined by the developers. This limits the capability of these devices in supporting robust queries. Hence, this paper proposes a universal relation based database querying language which is targeted for small devices. The language allows formulation of relational database queries that uses minimal query terms. The formulation of the language and its structure will be described and usability test results will be presented to support the effectiveness of the language.展开更多
This study examined users' querying behaviors based on a sample of 30 Chinese college students from Peking University. The authors designed 5 search tasks and each participant conducted two randomly selected searc...This study examined users' querying behaviors based on a sample of 30 Chinese college students from Peking University. The authors designed 5 search tasks and each participant conducted two randomly selected search tasks during the experiment. The results show that when searching for pre-designed search tasks, users often have relatively clear goals and strategies before searching. When formulating their queries, users often select words from tasks, use concrete concepts directly, or extract 'central words' or keywords. When reformulating queries, seven query reformulation types were identified from users' behaviors, i.e. broadening, narrowing, issuing new query, paralleling, changing search tools, reformulating syntax terms, and clicking on suggested queries. The results reveal that the search results and/or the contexts can also influence users' querying behaviors.展开更多
Online social networks(OSNs)offer people the opportunity to join communities where they share a common interest or objective.This kind of community is useful for studying the human behavior,diffusion of information,an...Online social networks(OSNs)offer people the opportunity to join communities where they share a common interest or objective.This kind of community is useful for studying the human behavior,diffusion of information,and dynamics of groups.As the members of a community are always changing,an efficient solution is needed to query information in real time.This paper introduces the Follow Model to present the basic relationship between users in OSNs,and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying.Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system.Based on 75 GB message data and 26 GB relation network data from Twitter,a case study was realized using two dynamic discussion communities:#musicmonday and#beatcancer.The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.展开更多
Big data introduces challenges to query answering, from theory to practice. A number of questions arise. What queries are ＂tractable＂ on big data？ How can we make big data ＂small＂ so that it is feasible to find e...Big data introduces challenges to query answering, from theory to practice. A number of questions arise. What queries are ＂tractable＂ on big data？ How can we make big data ＂small＂ so that it is feasible to find exact query answers？When exact answers are beyond reach in practice, what approximation theory can help us strike a balance between the quality of approximate query answers and the costs of computing such answers？ To get sensible query answers in big data,what else do we necessarily do in addition to coping with the size of the data？ This position paper aims to provide an overview of recent advances in the study of querying big data. We propose approaches to tackling these challenging issues,and identify open problems for future research.展开更多
Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),ha...Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.展开更多
ChatGPT,a popular large language model developed by OpenAI,has the potential to transform the management of diabetes mellitus.It is a conversational artificial intelligence model trained on extensive datasets,although...ChatGPT,a popular large language model developed by OpenAI,has the potential to transform the management of diabetes mellitus.It is a conversational artificial intelligence model trained on extensive datasets,although not specifically health-related.The development and core components of ChatGPT include neural networks and machine learning.Since the current model is not yet developed on diabetes-related datasets,it has limitations such as the risk of inaccuracies and the need for human supervision.Nevertheless,it has the potential to aid in patient engagement,medical education,and clinical decision support.In diabetes management,it can contribute to patient education,personalized dietary guidelines,and providing emotional support.Specifically,it is being tested in clinical scenarios such as assessment of obesity,screening for diabetic retinopathy,and provision of guidelines for the management of diabetic ketoacidosis.Ethical and legal considerations are essential before ChatGPT can be integrated into healthcare.Potential concerns relate to data privacy,accuracy of responses,and maintenance of the patient-doctor relationship.Ultimately,while ChatGPT and large language models hold immense potential to revolutionize diabetes care,one needs to weigh their limitations,ethical implications,and the need for human supervision.The integration promises a future of proactive,personalized,and patient-centric care in diabetes management.展开更多
Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was use...Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was used for validation.It was split into training and testing sets in an 8:2 ratio.Classical You Only Look Once(YOLO)models of three sizes(n,m,x)were trained on the training set.Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer(DETR)with Improved DeNoising AnchOr Boxes(DINO),i.e.,Grounding DINO and Open-Vocabulary DINO(OV-DINO),as well as our proposed few-shot learning model,across a range of different shot sizes.Finally,all trained models were compared on the test set.Results After training on LUNA16,the precision,recall,and mean average precision(mAP)of the different-sized YOLO models showed no significant differences,with peak values of 82.8%,73.1%,and 77.4%,respectively.OV-DINO’s recall was significantly higher than YOLO’s,but it did not show clear advantages in precision or mAP.Using only one-fifth of the training samples and one-tenth of the training epochs,our proposed model outperformed both YOLO and OV-DINO,achieving improvements of 6.6%,9.3%,and 6.9%in precision,recall,and mAP,respectively,with final values of 89.4%,96.2%,and 87.7%.Conclusion The proposed few-shot learning model demonstrates stronger scene transfer capabilities,requiring fewer samples and training epochs,and can effectively improve the accuracy of lung nodule detection.展开更多
Time-series databases(TSDBs)are essential for managing large-scale time-series data in fields like finance,IoT,and agriculture.However,traditional query optimization methods,such as dynamic programming,struggle with h...Time-series databases(TSDBs)are essential for managing large-scale time-series data in fields like finance,IoT,and agriculture.However,traditional query optimization methods,such as dynamic programming,struggle with high computational complexity and inaccurate cost estimates.This paper proposes a novel query optimization module for TSDBs using reinforcement learning(RL),specifically Deep Q-Networks(DQN)and Double Deep Q-Networks(DDQN).These algorithms dynamically learn optimal join orders based on query workloads and connection costs.Experiments show that RL-based methods achieve better optimization performance and stability compared to traditional heuristics,especially under complex cost models.This work highlights the potential of RL in improving query optimization for TSDBs.展开更多
Cardinality estimation is crucial for query optimization,but traditional methods struggle with complex queries.We propose LW-CQ,a lightweight machine learning-based algorithm that improves cardinality estimation for c...Cardinality estimation is crucial for query optimization,but traditional methods struggle with complex queries.We propose LW-CQ,a lightweight machine learning-based algorithm that improves cardinality estimation for complex queries by enhancing the LW-XGB method.LW-CQ introduces four feature-level improvements and extends support for disjunctive queries and LIKE predicates.Experimental results show that LW-CQ achieves competitive accuracy while significantly reducing training and inference time,making it a promising solution for real-world database applications.展开更多
聚焦于中小型企业,深入探讨借助Excel Power Query工具批量生成记账凭证的方法。通过分析中小型企业记账凭证处理的现状,对比手工录入的会计电算化记账方式(以下简称手工录账)与借助Excel Power Query批量生成记账凭证的模式,阐述Excel ...聚焦于中小型企业,深入探讨借助Excel Power Query工具批量生成记账凭证的方法。通过分析中小型企业记账凭证处理的现状,对比手工录入的会计电算化记账方式(以下简称手工录账)与借助Excel Power Query批量生成记账凭证的模式,阐述Excel Power Query在数据处理各环节的应用优势,详细介绍应用该工具批量生成记账凭证的具体步骤,并结合实际案例展示其应用效果。展开更多
In order to protect the privacy of the query user and database,some QKD-based quantum private query(QPQ)protocols were proposed.One example is the protocol proposed by Zhou et al,in which the user makes initial quantu...In order to protect the privacy of the query user and database,some QKD-based quantum private query(QPQ)protocols were proposed.One example is the protocol proposed by Zhou et al,in which the user makes initial quantum states and derives the key bit by comparing the initial quantum state and the outcome state returned from the database by ctrl or shift mode,instead of announcing two non-orthogonal qubits as others which may leak part secret information.To some extent,the security of the database and the privacy of the user are strengthened.Unfortunately,we find that in this protocol,the dishonest user could be obtained,utilizing unambiguous state discrimination,much more database information than that is analyzed in Zhou et al's original research.To strengthen the database security,we improved the mentioned protocol by modifying the information returned by the database in various ways.The analysis indicates that the security of the improved protocols is greatly enhanced.展开更多
文摘An approximate approach of querying between heterogeneous ontology-basedinformation systems based on an association matrix is proposed. First, the association matrix isdefined to describe relations between concepts in two ontologies. Then, a methodof rewriting queriesbased on the association matrix is presented to solve the ontology heterogeneity problem. Itrewrites the queries in one ontology to approximate queries in another ontology based on thesubsumption relations between concepts. The method also uses vectors to represent queries, and thencomputes the vectors with the association matrix; the disjoint relations between concepts can beconsidered by the results. It can get better approximations than the methods currently in use, whichdonot consider disjoint relations. The method can be processed by machines automatically. It issimple to implement and expected to run quite fast.
基金the National Natural Science Foundation of China (No. 60603044)the National Key Technologies Supporting Program of China during the 11th Five-Year Plan Period (No. 2006BAH02A03)the Program for Changjiang Scholars and Innovative Research Team in University of China (No. IRT0652)
文摘Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named "compressed global tree guide" (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMinerTID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.
基金Supported by the Natural Science Foundation ofJiangsu Province(BG2004034)
文摘Multidimensional data query has been gaining much interest in database research communities in recent years, yet many of the existing studies focus mainly on ten tralized systems. A solution to querying in Peer-to-Peer（P2P） environment was proposed to achieve both low processing cost in terms of the number of peers accessed and search messages and balanced query loads among peers. The system is based on a balanced tree structured P2P network. By partitioning the query space intelligently, the amount of query forwarding is effectively controlled, and the number of peers involved and search messages are also limited. Dynamic load balancing can be achieved during space partitioning and query resolving. Extensive experiments confirm the effectiveness and scalability of our algorithms on P2P networks.
基金supported in part by Royal Society YVolfson Research Merit Award WRM/R1/180014,ERC 652976,EPSRC EP/M025268/1,Shenzhen Institute of Computing Sciences,and Beijing Advanced Innovation Center for Big Data and Brain Computing.
文摘This work aims to reduce queries on big data to computations on small data,and hence make querying big data possible under bounded resources.A query Q is boundedly evaluable when posed on any big dataset D,there exists a fraction DQ of D such that Q(D)=Q(DQ),and the cost of identifying DQ is independent of the size of D.It has been shown that with an auxiliary structure known as access schema,many queries in relational algebra(RA)are boundedly evaluable under the set semantics of RA.This paper extends the theory of bounded evaluation to RAaggr,i.e.,RA extended with aggregation,under the bag semantics.(1)We extend access schema to bag access schema,to help us identify DQ for RAaggr queries Q.(2)While it is undecidable to determine whether an RAaggr query is boundedly evaluable under a bag access schema,we identify special cases that are decidable and practical.(3)In addition,we develop an effective syntax for bounded RAaggr queries,i.e.,a core subclass of boundedly evaluable RAaggr queries without sacrificing their expressive power.(4)Based on the effective syntax,we provide efficient algorithms to check the bounded evaluability of RAaggr queries and to generate query plans for bounded RAaggr queries.(5)As proof of concept,we extend PostgreSQL to support bounded evaluation.We experimentally verify that the extended system improves performance by orders of magnitude.
基金the Program for New Century Excellent Talents in University (NCET-05-0288)the Specialized Research Fund for the Doctoral Program of Higher Education of China (20050145024)
文摘Description logics （DLs） play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web recently. To acquire a tractable DL for query answering, DL-Lite is proposed. Due to the large amount of imprecision and uncertainty in the real world, it is essential to extend DLs to deal with these vague and imprecise information. We thus propose a new fuzzy DL f-DLR-Lite.n, which allows for the presence of n-ary relations and the occurrence of concept conjunction on the left land of inclusion axioms. We also suggest an improved fuzzy query language, which supports the presence of thresholds and user defined weights. We also show that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. DL f-DLR-Lite,n can make up for the disadvantages of knowledge representation and reasoning of classic DLs, and the enhanced query language expresses user intentions more precisely and reasonably.
文摘For small devices like the PDAs and mobile phones, formulation of relational database queries is not as simple as using conventional devices such as the personal computers and laptops. Due to the restricted size and resources of these smaller devices, current works mostly limit the queries that can be posed by users by having them predetermined by the developers. This limits the capability of these devices in supporting robust queries. Hence, this paper proposes a universal relation based database querying language which is targeted for small devices. The language allows formulation of relational database queries that uses minimal query terms. The formulation of the language and its structure will be described and usability test results will be presented to support the effectiveness of the language.
基金partially supported by China Scholarship Council(Grant No.:2009601175)
文摘This study examined users' querying behaviors based on a sample of 30 Chinese college students from Peking University. The authors designed 5 search tasks and each participant conducted two randomly selected search tasks during the experiment. The results show that when searching for pre-designed search tasks, users often have relatively clear goals and strategies before searching. When formulating their queries, users often select words from tasks, use concrete concepts directly, or extract 'central words' or keywords. When reformulating queries, seven query reformulation types were identified from users' behaviors, i.e. broadening, narrowing, issuing new query, paralleling, changing search tools, reformulating syntax terms, and clicking on suggested queries. The results reveal that the search results and/or the contexts can also influence users' querying behaviors.
基金Project supported by the Brazilian National Council for Scientific and Technological Development(CNPq)(No.304058/2010-6)
文摘Online social networks(OSNs)offer people the opportunity to join communities where they share a common interest or objective.This kind of community is useful for studying the human behavior,diffusion of information,and dynamics of groups.As the members of a community are always changing,an efficient solution is needed to query information in real time.This paper introduces the Follow Model to present the basic relationship between users in OSNs,and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying.Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system.Based on 75 GB message data and 26 GB relation network data from Twitter,a case study was realized using two dynamic discussion communities:#musicmonday and#beatcancer.The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.
基金supported in part by the National Basic Research 973 Program of China under Grant No.2014CB340302Fan is also supported in part by the National Natural Science Foundation of China under Grant No.61133002+3 种基金the Guangdong Innovative Research Team Program under Grant No.2011D005Shenzhen Peacock Program under Grant No.1105100030834361the Engineering and Physical Sciences Research Council of UK under Grant No.EP/J015377/1the National Science Foundation of USA under Grant No.III-1302212
文摘Big data introduces challenges to query answering, from theory to practice. A number of questions arise. What queries are ＂tractable＂ on big data？ How can we make big data ＂small＂ so that it is feasible to find exact query answers？When exact answers are beyond reach in practice, what approximation theory can help us strike a balance between the quality of approximate query answers and the costs of computing such answers？ To get sensible query answers in big data,what else do we necessarily do in addition to coping with the size of the data？ This position paper aims to provide an overview of recent advances in the study of querying big data. We propose approaches to tackling these challenging issues,and identify open problems for future research.
基金supported by the Special Project of Henan Provincial Key Research,Development and Promotion(Key Science and Technology Program)under Grant 252102210154in part by the National Natural Science Foundation of China under Grant 62403437.
文摘Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.
文摘ChatGPT,a popular large language model developed by OpenAI,has the potential to transform the management of diabetes mellitus.It is a conversational artificial intelligence model trained on extensive datasets,although not specifically health-related.The development and core components of ChatGPT include neural networks and machine learning.Since the current model is not yet developed on diabetes-related datasets,it has limitations such as the risk of inaccuracies and the need for human supervision.Nevertheless,it has the potential to aid in patient engagement,medical education,and clinical decision support.In diabetes management,it can contribute to patient education,personalized dietary guidelines,and providing emotional support.Specifically,it is being tested in clinical scenarios such as assessment of obesity,screening for diabetic retinopathy,and provision of guidelines for the management of diabetic ketoacidosis.Ethical and legal considerations are essential before ChatGPT can be integrated into healthcare.Potential concerns relate to data privacy,accuracy of responses,and maintenance of the patient-doctor relationship.Ultimately,while ChatGPT and large language models hold immense potential to revolutionize diabetes care,one needs to weigh their limitations,ethical implications,and the need for human supervision.The integration promises a future of proactive,personalized,and patient-centric care in diabetes management.
基金supported by the Natural Science Foundation of Beijing Municipality(No.7222320)the Capital Health Research and Development Special Fund(No.2022–2–6081)+1 种基金the Scientific Research Fund of Aerospace Center Hospital(No.YN202301)Aerospace Medical Health Science and Technology Research Projects(No.2021YK09).
文摘Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was used for validation.It was split into training and testing sets in an 8:2 ratio.Classical You Only Look Once(YOLO)models of three sizes(n,m,x)were trained on the training set.Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer(DETR)with Improved DeNoising AnchOr Boxes(DINO),i.e.,Grounding DINO and Open-Vocabulary DINO(OV-DINO),as well as our proposed few-shot learning model,across a range of different shot sizes.Finally,all trained models were compared on the test set.Results After training on LUNA16,the precision,recall,and mean average precision(mAP)of the different-sized YOLO models showed no significant differences,with peak values of 82.8%,73.1%,and 77.4%,respectively.OV-DINO’s recall was significantly higher than YOLO’s,but it did not show clear advantages in precision or mAP.Using only one-fifth of the training samples and one-tenth of the training epochs,our proposed model outperformed both YOLO and OV-DINO,achieving improvements of 6.6%,9.3%,and 6.9%in precision,recall,and mAP,respectively,with final values of 89.4%,96.2%,and 87.7%.Conclusion The proposed few-shot learning model demonstrates stronger scene transfer capabilities,requiring fewer samples and training epochs,and can effectively improve the accuracy of lung nodule detection.
基金supported by Sichuan Science and Technology Program(2024YFHZ0161).
文摘Time-series databases(TSDBs)are essential for managing large-scale time-series data in fields like finance,IoT,and agriculture.However,traditional query optimization methods,such as dynamic programming,struggle with high computational complexity and inaccurate cost estimates.This paper proposes a novel query optimization module for TSDBs using reinforcement learning(RL),specifically Deep Q-Networks(DQN)and Double Deep Q-Networks(DDQN).These algorithms dynamically learn optimal join orders based on query workloads and connection costs.Experiments show that RL-based methods achieve better optimization performance and stability compared to traditional heuristics,especially under complex cost models.This work highlights the potential of RL in improving query optimization for TSDBs.
基金supported by Sichuan Science and Technology Program(2024YFHZ0161).
文摘Cardinality estimation is crucial for query optimization,but traditional methods struggle with complex queries.We propose LW-CQ,a lightweight machine learning-based algorithm that improves cardinality estimation for complex queries by enhancing the LW-XGB method.LW-CQ introduces four feature-level improvements and extends support for disjunctive queries and LIKE predicates.Experimental results show that LW-CQ achieves competitive accuracy while significantly reducing training and inference time,making it a promising solution for real-world database applications.
文摘聚焦于中小型企业,深入探讨借助Excel Power Query工具批量生成记账凭证的方法。通过分析中小型企业记账凭证处理的现状,对比手工录入的会计电算化记账方式(以下简称手工录账)与借助Excel Power Query批量生成记账凭证的模式,阐述Excel Power Query在数据处理各环节的应用优势,详细介绍应用该工具批量生成记账凭证的具体步骤,并结合实际案例展示其应用效果。
基金supported by the National Key R&D Program of China(Grant No.2022YFC3801700)the National Natural Science Foundation of China(Grant No.62472052)Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security(Grant No.CZ002702-3)。
文摘In order to protect the privacy of the query user and database,some QKD-based quantum private query(QPQ)protocols were proposed.One example is the protocol proposed by Zhou et al,in which the user makes initial quantum states and derives the key bit by comparing the initial quantum state and the outcome state returned from the database by ctrl or shift mode,instead of announcing two non-orthogonal qubits as others which may leak part secret information.To some extent,the security of the database and the privacy of the user are strengthened.Unfortunately,we find that in this protocol,the dishonest user could be obtained,utilizing unambiguous state discrimination,much more database information than that is analyzed in Zhou et al's original research.To strengthen the database security,we improved the mentioned protocol by modifying the information returned by the database in various ways.The analysis indicates that the security of the improved protocols is greatly enhanced.
