Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through project...
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ISBN:
(数字)9781728148038
ISBN:
(纸本)9781728148045
Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.
Code review is an important process to reduce code defects and improve software quality. However, in social coding communities using the pull-based model, everyone can submit code changes, which increases the required...
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In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming,...
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In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives(PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0 X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-theart GPU-based framework.
Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected o...
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The widespread use of pull-requests boosts the development and evolution for many open source software projects. However, due to the parallel and uncoordinated nature of development process in GitHub, duplicate pull-r...
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Emerging blockchain systems have been widely adopted in sharing economy, such as e-commerce, to allow mutually distrustful parties to transact fairly without trusted parties. Most blockchain systems, however, lack tra...
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Emerging blockchain systems have been widely adopted in sharing economy, such as e-commerce, to allow mutually distrustful parties to transact fairly without trusted parties. Most blockchain systems, however, lack transactional privacy protection. All transactions, including trading relationship between pseudonyms and content transacted, are exposed on the blockchain. Although many existing privacy protection methods on the blockchain have been proposed, it is difficult to find a trade-off between keeping speed and protecting privacy of transactions. To address this limitation, we propose a novel privacy-preserving method RZKPB that does not store financial transactions in clear on the blockchain, thus retaining transactional privacy from the public's view. Meanwhile, these transactions are as proofs to solve disputes between trading partners. RZKPB ensures fairness and privacy of transactions between participants without adding a new trusted party and breaking the verifying protocol on the blockchain. We take the e-commerce as an example in sharing economy to introduce RZKPB in our paper. Our experimental results show that compared with existing privacy-preserving methods based on the blockchain, RZKPB is more efficient under different settings.
Blockchain is a distributed system with efficient transaction recording and has been widely adopted in sharing economy. Although many existing privacy-preserving methods on the blockchain have been proposed, finding a...
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Blockchain is a distributed system with efficient transaction recording and has been widely adopted in sharing economy. Although many existing privacy-preserving methods on the blockchain have been proposed, finding a trade-off between keeping speed and preserving privacy of transactions remain challenging. To address this limitation, we propose a novel Fast and Privacy-preserving method based on the Permissioned Blockchain (FPPB) for fair transactions in sharing economy. Without breaking the verifying protocol and bringing additional off-blockchain interactive communication, FPPB protects the privacy and fairness of transactions. Additionally, experiments are implemented in EthereumJ (a Java implementation of the Ethereum protocol) to measure the performance of FPPB. Compared with normal transactions without cryptographic primitives, FPPB only slows down transactions slightly.
Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linea...
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Open-domain question answering (QA) is an important problem in natural language processing. A QA system usually consists of an information retrieve (IR) model for finding the relevant passages that may have answers an...
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Open-domain question answering (QA) is an important problem in natural language processing. A QA system usually consists of an information retrieve (IR) model for finding the relevant passages that may have answers and a read comprehension (RC) model for generating answers from the selected passages. The state-of-the-art RC model uses all the passages for feature extraction when matching semantic information of text pairs. In this paper, however, we argue that the texts of passages often have different importance for specific questions and some even provide negative information that might affect the correctness of final prediction. To address this problem, we design dynamic semantic discard reader (DSDR), a deep model that tries to drop negative information irrelevant to the questions. The result of our experiment shows that DSDR outperforms existing methods in the exact-match and F1 scores on open-domain QA datasets.
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