The Internet of Things(IoT)and cloud technologies have encouraged massive data storage at central repositories.Software-defined networks(SDN)support the processing of data and restrict the transmission of duplicate va...The Internet of Things(IoT)and cloud technologies have encouraged massive data storage at central repositories.Software-defined networks(SDN)support the processing of data and restrict the transmission of duplicate values.It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead.Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows.This paper presents an efficient file-level de-duplication scheme(EFDS)where the cost of creating tags is reduced by employing a hash table with key-value pair for each block of the file.Further,an algorithm for hash table-based duplicate block identification and storage(HDBIS)is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index.Hash tables normally have a consistent time complexity for lookup,generating,and deleting stored data regardless of the input size.The experiential results show that the proposed EFDS scheme performs better compared to its counterparts.展开更多
As a solution for data storage and information sharing for peer-to-peer(P2P)networks,a novel distributed hash table(DHT)structure called PChord is presented in this paper.PChord adopts a bi-directional searching mecha...As a solution for data storage and information sharing for peer-to-peer(P2P)networks,a novel distributed hash table(DHT)structure called PChord is presented in this paper.PChord adopts a bi-directional searching mechanism superior to Chord and enhances the structure of the finger table.Based on Hilbert space filling curve,PChord realizes the mapping mechanism for multikeyword approximate searching.Compared with the Chord and Kademlia protocols,PChord evidently increases speed on resource searching and message spreading via theoretic proof and simulation results,while maintaining satisfactory load balance.展开更多
In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requi...In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.展开更多
Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.A...Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.Another problem is that privacy-preserving was not taken into account in these algorithms.In this paper,we propose a new data structure named Interval Hash Table(IHTable)to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table(ADIHT).The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast.Furthermore,to make our proposed algorithm fit for anomaly detection under multiple participation,we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption.Compared with existing anomaly detection schemes with privacy-preserving,OP-ADIHT needs less communication cost and calculation cost.Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants.Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC,and ADIHT needs the least time.展开更多
基金supported in part by Hankuk University of Foreign Studies’Research Fund for 2023 and in part by the National Research Foundation of Korea(NRF)grant funded by the Ministry of Science and ICT Korea No.2021R1F1A1045933.
文摘The Internet of Things(IoT)and cloud technologies have encouraged massive data storage at central repositories.Software-defined networks(SDN)support the processing of data and restrict the transmission of duplicate values.It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead.Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows.This paper presents an efficient file-level de-duplication scheme(EFDS)where the cost of creating tags is reduced by employing a hash table with key-value pair for each block of the file.Further,an algorithm for hash table-based duplicate block identification and storage(HDBIS)is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index.Hash tables normally have a consistent time complexity for lookup,generating,and deleting stored data regardless of the input size.The experiential results show that the proposed EFDS scheme performs better compared to its counterparts.
基金supported by the National Natural Science Foundation of China (Grant No.60773041)the Natural Science Foundation of Jiangsu Province,China (No.BK2008451)+6 种基金the National High Technology Research and Development Program of China (No.2006AA01Z219)the High Technology Research and Development Program of Nanjing City (Nos.2007RZ106,2007RZ127)Foundation of National Laboratory for Modern Communications (No.9140C1105040805)Project supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.07KJB520083)Special Fund for Software Technology of Jiangsu Province,Post-doctoral Foundation of Jiangsu Province (No.0801019C)Science&Technology Innovation Fund for Higher Education Institutions of Jiangsu Province (Nos.CX08B-085Z,CX08B-086Z)the six kinds of Top Talent of Jiangsu Province.
文摘As a solution for data storage and information sharing for peer-to-peer(P2P)networks,a novel distributed hash table(DHT)structure called PChord is presented in this paper.PChord adopts a bi-directional searching mechanism superior to Chord and enhances the structure of the finger table.Based on Hilbert space filling curve,PChord realizes the mapping mechanism for multikeyword approximate searching.Compared with the Chord and Kademlia protocols,PChord evidently increases speed on resource searching and message spreading via theoretic proof and simulation results,while maintaining satisfactory load balance.
基金supported by the National Natural Science Foundation of China(61473202)。
文摘In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.
基金supported by Natural Science Foundation of Guangdong Province,China(Grant No.2020A1515010970)Shenzhen Research Council(Grant No.JCYJ20200109113427092,GJHZ20180928155209705).
文摘Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.Another problem is that privacy-preserving was not taken into account in these algorithms.In this paper,we propose a new data structure named Interval Hash Table(IHTable)to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table(ADIHT).The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast.Furthermore,to make our proposed algorithm fit for anomaly detection under multiple participation,we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption.Compared with existing anomaly detection schemes with privacy-preserving,OP-ADIHT needs less communication cost and calculation cost.Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants.Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC,and ADIHT needs the least time.
