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Instance-Specific Algorithm Selection via Multi-Output Learning

Tsinghua science and technology 2017年第2期22卷 210-217页

作者： Kai Chen Yong Dou Qi Lv Zhengfa Liang the National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410037China the College of Computer National University of Defense TechnologyChangsha 410037China

Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more *** kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.

关键词： algorithm selection multi-output learning extremely randomized trees performance prediction constraint satisfaction

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Measuring success for a future vision: Defining impact in science gateways/virtual research environments

Measuring success for a future vision: Defining impact in sc...

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作者： Calyam, Prasad Wilkins-Diehr, Nancy Miller, Mark Brookes, Emre H. Arora, Ritu Chourasia, Amit Jennewein, Douglas M. Nandigam, Viswanath Drew LaMar, M. Cleveland, Sean B. Newman, Greg Wang, Shaowen Zaslavsky, Ilya Cianfrocco, Michael A. Ellett, Kevin Tarboton, David Jeffery, Keith G. Zhao, Zhiming González-Aranda, Juan Perri, Mark J. Tucker, Greg Candela, Leonardo Kiss, Tamas Gesing, Sandra Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States San Diego Supercomputer Center University of California San DiegoCA United States Department of Biochemistry University of Texas Health Science Center HoustonTX United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Information Technology Services University of South Dakota VermillionSD United States Biology Department College of William and Mary WilliamsburgVA United States Information Technology Services–Cyberinfrastructure University of Hawaii HonoluluHI United States Natural Resource Ecology Laboratory Colorado State University Fort CollinsCO United States Department of Geography and Geographic Information Science University of Illinois UrbanaIL United States Department of Biological Chemistry and Life Sciences Institute University of Michigan Ann ArborMI United States Pervasive Technology Institute Indiana University BloomingtonIN United States Civil and Environmental Engineering Utah State University LoganUT United States Keith G. Jeffery Consultants Faringdon United Kingdom Multiscale Networked Systems Group University of Amsterdam Amsterdam Netherlands Chief Technology Office LifeWatch ERIC Seville Spain Chemistry Department Sonoma State University Rohnert ParkCA United States Department of Geological Sciences CIRES University of Colorado Boulder BoulderCO United States National Research Council of Italy Pisa Italy Research Center for Parallel Computing University of Westminster London United Kingdom Center for Research Computing University of Notre Dame Notre DameIN United States

Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations. © 2020 John Wiley & Sons, Ltd.

关键词： Web services

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TEMPORAL PYRAMID RELATION NETWORK FOR VIDEO-BASED GESTURE RECOGNITION

TEMPORAL PYRAMID RELATION NETWORK FOR VIDEO-BASED GESTURE RE...

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IEEE International Conference on Image Processing

作者： Ke Yang Rongchun Li Peng Qiao Qiang Wang Dongsheng Li Yong Dou National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Gesture recognition in video is an important application of computer vision. However, there are few works talked about the temporal order or relation of the frames in video, which is important for model gestures. In this paper, we propose Temporal Pyramid Relation Network (TPRN) which can model the temporal relation of video frames effectively and efficiently. First, we use Temporal Pyramid Pooling (TPP) layer to get temporal feature sequences of multiple scale pyramids. Then, a Temporal Relation Network (TRN) is stacked on the feature sequence of each scale respectively to model the temporal relations of video frames at multiple scales. At last, representations of all scales are aggregated to get the final prediction. TPRN can take video clips of various length as input and is scalable for video length. We evaluate TPRN on a recently released very large video-based gesture recognition dataset - 20BN-Jester dataset v1, and TPRN achieves competitive performance.

关键词： Feature extraction Gesture recognition Solid modeling Hidden Markov models Three-dimensional displays Computational modeling Training

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RZKPB: A Privacy-Preserving Blockchain-Based Fair Transaction Method for Sharing Economy

RZKPB: A Privacy-Preserving Blockchain-Based Fair Transactio...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bin Li Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Hunan Changsha P.R. China

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.

关键词： Privacy Protocols Encryption Distortion

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FPPB: A Fast and Privacy-Preserving Method Based on the Permissioned Blockchain for Fair Transactions in Sharing Economy

FPPB: A Fast and Privacy-Preserving Method Based on the Perm...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bin Li Yijie Wang Peichang Shi Huan Chen Li Cheng Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China

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.

关键词： Privacy Cloud computing Encryption Registers Protocols

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Deep Discriminative Clustering Network

Deep Discriminative Clustering Network

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International Joint Conference on Neural Networks

作者： Xuying Shao Keshi Ge Huayou Su Lei Luo Baoyun Peng Dongsheng Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions. In this paper, we present a deep discriminative clustering network for clustering the real-world images. We use a convolutional auto-encoder stacked with a softmax layer to predict clustering assignments. To learn a discriminative representations, the proposed approach adds discriminative loss as embedded regularization with relative entropy minimization. With the discriminative loss, the network can not only produce clustering assignments, but also learn discriminative features by reducing intra-cluster distance and increasing inter-cluster distance. We evaluate the proposed method on three datasets: MNIST-full, YTF and FRGC-v2.0. We outperform state-of-the-art results on MNIST-full and FRGC-v2.0 and achieve competitive result on YTF. The source code has been made publicly available at .

关键词： Machine learning Clustering algorithms Image reconstruction Linear programming Clustering methods Neural networks Task analysis

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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

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IEEE International Conference on Image Processing

作者： Zheng Qin Zhaoning Zhang Xiaotao Chen Changjian Wang Yuxing Peng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast down-sampling strategy to MobileNet framework. In FD-MobileNet, we perform 32× downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing Mobile-Net by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11× inference speedup over MobileNet and 1.82× over ShuffleNet under the same complexity.

关键词： computer architecture Complexity theory Neural networks Training Hardware Computational modeling Standards

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Multi-Owner Keyword Search over Shared Data without Secure Channels in the Cloud

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China Communications 2017年第5期14卷 124-133页

作者： Yilun Wu Xicheng Lu Jinshu Su Peixin Chen Xiaofeng Wang Bofeng Zhang College of Computer National University of Defense TechnologyChangsha 410073China National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyCbangsba 410073China

Searchable encryption allows cloud users to outsource the massive encrypted data to the remote cloud and to search over the data without revealing the sensitive information. Many schemes have been proposed to support the keyword search in a public cloud. However,they have some potential limitations. First,most of the existing schemes only consider the scenario with the single data owner. Second,they need secure channels to guarantee the secure transmission of secret keys from the data owner to data users. Third,in some schemes,the data owner should be online to help data users when data users intend to perform the search,which is *** this paper,we propose a novel searchable scheme which supports the multi-owner keyword search without secure channels. More than that,our scheme is a non-interactive solution,in which all the users only need to communicate with the cloud server. Furthermore,the analysis proves that our scheme can guarantee the security even without secure channels. Unlike most existing public key encryption based searchable schemes,we evaluate the performance of our scheme,which shows that our scheme is practical.

关键词： keyword search cloud security secure channels proxy re-encryption

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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International Joint Conference on Neural Networks

作者： Mingyang Geng Yiying Li Bo Ding Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learning-based approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot vision-based trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the single-robot system.

关键词： Task analysis Robot kinematics Machine learning Multi-robot systems Quality of service Cloud computing

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Learning environmental sounds with multi-scale convolutional neural network

arXiv

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arXiv 2018年

作者： Zhu, Boqing Wang, Changjian Liu, Feng Lei, Jin Lu, Zengquan Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology College of Meteorology and Oceanology National University of Defense Technology Changsha China

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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