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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Ye, Zonxin Li, Wenyu Qiao, Peng Dou, Yong National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing School of Computer Changsha China

ISBN: (纸本)9798350331417

Neural Radiance Field (NeRF) has received widespread attention for its photo-realistic novel view synthesis quality. Current methods mainly represent the scene based on point sampling of ray casting, ignoring the influence of the observed area changing with distance. In addition, The current sampling strategies are all focused on the distribution of sampling points on the ray, without paying attention to the sampling of the ray. We found that the current ray sampling strategy for scenes with the camera moving forward severely reduces the convergence speed. In this work, we extend the point representation to area representation by using relative positional encoding, and propose a ray sampling strategy that is suitable for camera trajectory moving forward. We validated the effectiveness of our method on multiple public datasets. © 2023 IEEE.

关键词： NeRF neural radiance field neural rendering novel view synthesis

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Unsupervised feature learning for human activity recognition using smartphone sensors 2

Unsupervised feature learning for human activity recognition...

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2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

作者： Li, Yongmou Shi, Dianxi Ding, B. Liu, Dongbo National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783319138169

Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from nonadaptability to the particular dataset. To alleviate the problems of handcrafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and *** evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods. The sparse auto-encoder achieves better results than other two techniques within our framework. © Springer International Publishing Switzerland 2014.

关键词： Signal encoding

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Data dependence-based optimistic data consistency maintenance method

Data dependence-based optimistic data consistency maintenanc...

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6th IEEE International Conference on Computer and Information Technology, CIT 2006

作者： Zhou, Jing Wang, Yijie Li, Sikun National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology China

ISBN: (纸本)076952687X

Data replication introduces well-known consistency issues. This paper puts forward the question about data dependence in data consistency, which embodies pseudo-conflict updates and update dependency. According to that, an optimistic data consistency method is proposed. In our method, data object is partitioned into data blocks by fixed size, as the basic unit of data management. Updates are compressed by Bloom filter technique and propagated in double-path. Negotiation algorithms detect and reconcile update conflicts, and dynamic data management algorithms accommodate dynamic data processing. The results of the performance evaluation show that our method is an efficient method to achieve consistency, good dynamic property, and strong robustness. © 2006 IEEE.

关键词： Information technology

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CANSE: A churn adaptive approach to network size estimation

CANSE: A churn adaptive approach to network size estimation

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16th IEEE International Conference on parallel and distributed Systems, ICPADS 2010

作者： Ma, Xing-Kong Wang, Yi-Jie Zheng, Zhong National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9780769543079

Network size is one of the fundamental information of distributed applications. The approach to estimate network size must feature both high accuracy and robustness in order to adapt to the dynamic environment in different topologies. However, existing approaches fail to guarantee accuracy and robustness simultaneously in dynamic topologies due to the randomness of nodes sampled. In this paper, we propose a churn adaptive approach to network size estimation - CANSE, which collects closest nodes in identification to each node's identification by sampling nodes periodically. Each node collects closest identifications by two schemes. One scheme is sampling random nodes from random walks along the topology. The other one is exchanging the closest identifications with other nodes. Finally, each node calculates the average spacing of the closest identifications collected to estimate network size. Compared with existing approaches, extensive experiments show that CANSE provides accurate estimation values quickly in various dynamic topologies. © 2010 IEEE.

关键词： Topology

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Local-Adaptive Transformer for Multivariate Time Series Anomaly Detection and Diagnosis

Local-Adaptive Transformer for Multivariate Time Series Anom...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Xiaohui Wang, Yijie Xu, Hongzuo Liu, Mingyu Zhang, Ruyi College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350337020

Time series data are pervasive in varied real-world applications, and accurately identifying anomalies in time series is of great importance. Many current methods are insufficient to model long-term dependence, whereas some anomalies can be only identified through long temporal contextual information. This may finally lead to disastrous outcomes due to false negatives of these anomalies. Prior arts employ Transformers (i.e., a neural network architecture that has powerful capability in modeling long-term dependence and global association) to alleviate this problem;however, Transformers are insensitive in sensing local context, which may neglect subtle anomalies. Therefore, in this paper, we propose a local-adaptive Transformer based on cross-correlation for time series anomaly detection, which unifies both global and local information to capture comprehensive time series patterns. Specifically, we devise a cross-correlation mechanism by employing causal convolution to adaptively capture local pattern variation, offering diverse local information into the long-term temporal learning process. Furthermore, a novel optimization objective is utilized to jointly optimize reconstruction of the entire time series and matrix derived from cross-correlation mechanism, which prevents the cross-correlation from becoming trivial in the training phase. The generated cross-correlation matrix reveals underlying interactions between dimensions of multivariate time series, which provides valuable insights into anomaly diagnosis. Extensive experiments on six real-world datasets demonstrate that our model outperforms state-of-the-art competing methods and achieves 6.8%-27.5% F1 score improvement. Our method also has good anomaly interpretability and is effective for anomaly diagnosis. © 2023 IEEE.

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Media access control protocol based on hybrid antennas in ad hoc networks

Media access control protocol based on hybrid antennas in ad...

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International Conference on Autonomic and Trusted Computing

作者： Ding, Yanrong Wang, Xiaodong Shen, Hu Guo, Pengyu National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769542720

Nested Circular Directional MAC, a modified medium access control protocol of DMAC protocol, is proposed in this paper to support both directional antennas and omni-directional antennas simultaneously in one Ad Hoc network. Omni-directional antennas follow the RTS/CTS mechanism which is multi-hop and directional antennas follow the DRTS/DCTS mechanism, ensuring the asymmetric communication effectively. In order to resolve new deafness problem and hidden terminal problem, the directional antennas also use circular DRTS and nested antenna circumrotation. Simulation results show that NCDMAC supports hybrid antennas effectively and is better than MMAC and Circular-MAC in terms of increasing throughput, but it also increases the communication delay. © 2010 IEEE.

关键词： Ad hoc networks

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False-positive probability and compression optimization for tree-structured bloom filters

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ACM Transactions on Modeling and Performance Evaluation of Computing Systems 2016年第4期1卷 1–39页

作者： Fu, Yongquan Biersack, Ernst National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Sanyi Road Changsha Hunan Province410073 China CAIPY Valbonne06560 France

Bloom filters are frequently used to to check the membership of an item in a set. However, Bloom filters face a dilemma: the transmission bandwidth and the accuracy cannot be optimized simultaneously. This dilemma is particularly severe for transmitting Bloom filters to remote nodes when the network bandwidth is limited. We propose a novel Bloom filter called BloomTree that consists of a tree-structured organization of smaller Bloom filters, each using a set of independent hash functions. BloomTree spreads items across levels that are compressed to reduce the transmission bandwidth need. We show how to find optimal configurations for BloomTree and investigate in detail by how much BloomTree outperforms the standard Bloom filter or the compressed Bloom filter. Finally, we use the intersection of BloomTrees to predict the set intersection, decreasing the false-positive probabilities by several orders of magnitude compared to both the compressed Bloom filter and the standard Bloom filter. © 2016 ACM 2376-3639/2016/09-ART19 $15.00

关键词： Genetic algorithms

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Continuous probabilistic reverse skyline monitoring over uncertain data streams

Continuous probabilistic reverse skyline monitoring over unc...

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2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013

作者： Yang, Yong Tao Wang, Yi Jie Guo, Min Li, Xiao Yong National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9783037858202

Reverse skyline is useful for supporting many applications, such as marketing decision, environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a need for processing probabilistic reverse skyline queries. In this paper, we study the problem of efficiently processing these queries on uncertain data streams. We first show the formal definitions of reverse skyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRS to maintain the most recent N uncertain data elements and to process continuous queries on them. CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structure named Characteristic Rectangle, are incorporated into it to handling the extra computing complexity arising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniques are very efficient in handling uncertain data streams. © (2013) Trans Tech Publications, Switzerland.

关键词： Decision trees

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A technical capability evaluation model based concept and prerequisite relation in computer education 33

A technical capability evaluation model based concept and pr...

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33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021

作者： Luo, Jiwen Wang, Tao Chang, Junsheng Guo, Xiaoting National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)1891706527

Effectively assessing the results of users' online learning and enhancing social recognition has become a major development direction for online education platforms. For computer education, this article constructs a technical capability assessment model. This model integrates professional concepts in the field of computer science and extracts knowledge concepts from educational resources. The model first extracts candidate concepts, then uses a graph propagation algorithm to quantify candidate concepts and obtains concepts from them, and finally uses prerequisite relationships to further quantify the concepts mastered by students. The model combines the prerequisite relationship among concepts to quantify the skills that students have mastered. It can not only effectively evaluate the user's skill mastery but also lays a foundation for subsequent course recommendations and career recommendations for users. The model is tested in the real learning environment of 250 students. This model has been proved to own certain practicability and reliability by Kendall rank correlation coefficient, which is used as an evaluation index. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Students

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Property-oriented testing of real-time systems

Property-oriented testing of real-time systems

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Proceedings - 11th Asia-Pacific Software Engineering Conference, APSEC 2004

作者： Li, Shuhao Wang, Ji Dong, Wei Qi, Zhi-Chang National Laboratory for Parallel and Distributed Processing Changsha China State Key Laboratory for Software Engineering Wuhan University China

ISBN: (纸本)0769522459

Although Statecharts has gained widespread use as a formalism for modeling reactive real-time systems, testing these systems still confronts some difficulties, of which a major one is the existence of numerous and complex system behaviors. It is extremely difficult to conduct comprehensive and in-depth testing of such real-time systems. This paper presents an approach to property-oriented real-time testing. Necessary real-time extensions are proposed such that the time-enriched Statecharts can describe non-trivial timing constraints. The properties to be tested are characterized by a restricted real-time logic. Then the targeted test sequences are derived from the real-time models according to the user-specified properties. Using this approach, testing efforts can be focused on particular properties of the real-time systems and usually only a small portion of the total behaviors needs to be tested. © 2004 IEEE.

关键词： Real time systems

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