检索结果-内蒙古大学图书馆

Deep residual correction network for partial domain adaptation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Li, Shuang Liu, Chi Harold Lin, Qiuxia Wen, Qi Su, Limin Huang, Gao Ding, Zhengming School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Automation Tsinghua University Beijing China Department of Computer Information and Technology Indiana University-Purdue University Indianapolis United States

—Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain. Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in many real-world applications. With the emergence of big data, there is a more practical scenario called partial domain adaptation, where we are always accessible to a more large-scale source domain while working on a relative small-scale target domain. In this case, the conventional domain adaptation assumption should be relaxed, and the target label space tends to be a subset of the source label space. Intuitively, reinforcing the positive effects of the most relevant source subclasses and reducing the negative impacts of irrelevant source subclasses are of vital importance to address partial domain adaptation challenge. This paper proposes an efficiently-implemented Deep Residual Correction Network (DRCN) by plugging one residual block into the source network along with the task-specific feature layer, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes. Specifically, the plugged residual block, which consists of several fully-connected layers, could deepen basic network and boost its feature representation capability correspondingly. Moreover, we design a weighted class-wise domain alignment loss to couple two domains by matching the feature distributions of shared classes between source and target. Comprehensive experiments on partial, traditional and fine-grained cross-domain visual recognition demonstrate that DRCN is superior to the competitive deep domain adaptation approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Differentially Private Machine Learning Model against Model Extraction Attack 13

学校读者我要写书评

暂无评论

Differentially Private Machine Learning Model against Model ...

2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020

作者： Cheng, Zelei Li, Zuotian Zhang, Jiwei Zhang, Shuhan Purdue University Department of Computer and Information Technology West Lafayette United States Integrated Innovation Institute Carnegie Mellon University Mountain View United States School of Computer Science National Pilot Software Engineering School Beijing Univ of Posts and Telecommunications Beijing China International School Beijing Univ of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728176475

Machine learning model is vulnerable to model extraction attacks since the attackers can send plenty of queries to infer the hyperparameters of the machine learning model thus stealing confidential information of the learning models. Therefore, there is a urgent need to defend against such an attack. Differential privacy is a promising technique to protect the valuable information. We propose a differential privacy-based method applied in the linear neural network to obfuscate the output of the machine learning model. The security and utility issue of injecting a noise layer to the linear neural network is mathematically analyzed. The experiment results show that our proposed method can lower the attacker's extraction rate while keeping high utility. © 2020 IEEE.

关键词： Extraction

Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

学校读者我要写书评

暂无评论

Model-Based Reinforcement Learning for Quantized Federated L...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

Sparsely-labeled source assisted domain adaptation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wang, Wei Wang, Zhihui Xiang, Yuankai Sun, Jing Li, Haojie Sun, Fuming Ding, Zhengming DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian116000 China School of Information & Communication Dalian Minzu University Dalian116600 China Department of Computer Information and Technology Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis IndianapolisIN46202 United States

Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. Specifically, due to the label scarcity problem, the projected clustering is first conducted on both the source and target domains, so that the discriminative structures of data could be leveraged elegantly. Then the label propagation is adopted to propagate the labels from those limited labeled source samples to the whole unlabeled data progressively, so that the cluster labels are revealed correctly. Finally, we jointly align the marginal and conditional distributions to mitigate the cross-domain mismatch problem, and optimize those three procedures iteratively. However, it is nontrivial to incorporate those three procedures into a unified optimization framework seamlessly since some variables to be optimized are implicitly involved in their formulas, thus they could not promote to each other. Remarkably, we prove that the projected clustering and conditional distribution alignment could be reformulated as different expressions, thus the implicit variables are revealed in different optimization steps. As such, the variables related to those three quantities could be optimized in a unified optimization framework and facilitate to each other, to improve the recognition performance obviously. Extensive experiments have verified that our approach could deal with the challenge in SLSA-DA setting, and best performances could be achieve

关键词： Machine learning

Recent Advances in Cognitive Informatics and Cognitive Computing towards Autonomous Systems (Plenary Panel Report-II of IEEE ICCI*CC'22)

学校读者我要写书评

暂无评论

Recent Advances in Cognitive Informatics and Cognitive Compu...

IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Okyay Kaynak Haibin Zhu Ming Hou Julia Rayz Marina Gavrilova Guoyin Wang George Baciu Jun Peng Christine W. Chan Runhe Huang Bowen Du Dept of ECE Univ. of Manitoba Winnipeg Canada Bogazici University Bebek Istanbul Turkey Nipissing University ON Canada DRDC Toronto Canada Dept. of Computer Science Purdue Univ. USA CPSC Department University of Calgary Chongqing Univ. of Posts and Telecommunications China Dept. of Computing Hong Kong Polytechnic Univ. Hong Kong School of AI Chongqing Univ. of Science and Technology Chongqing China Univ. of Regina Canada Faculty of Computer and Information Sciences Hosei University Tokyo Japan School of Computer Science and Engineer Beihang University Beijing China

Basic research in Cognitive Informatics (CI) and Cognitive Computing (CC) provides fundamental theories of intelligence science for Autonomous AI (AAI) and cognitive systems. The field of CI and CC has led to general AI technologies triggered by the transdisciplinary advances in brain, intelligence, computer, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary report of the plenary panel (Part II) on “Recent Advances in Cognitive Informatics and Cognitive Computing towards Autonomous Systems” in the 21 st IEEE International CI/CC Conference (ICCI*CC'22). Strategic CI/CC applications are presented in cognitive systems, AAI, cognitive robots, intelligent vehicles, AI knowledge learning, autonomous intelligence generation, cognitive digital twins, and safety-and-mission-critical systems.

关键词： Knowledge engineering Autonomous systems Intelligent vehicles Digital twins Cognitive informatics Cognitive robotics Cognitive systems

Evaluation of sampling methods for robotic sediment sampling systems

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Bae, Jun Han Jo, Wonse Park, Jee Hwan Voyles, Richard M. McMillan, Sara K. Min, Byung-Cheol SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States SMART Lab Department of Computer and Information Technology School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Collaborative Robotics Lab School of Engineering Technology Purdue University West LafayetteIN47907 United States Department of Agricultural & Biological Engineering Purdue University West LafayetteIN47907 United States

Analysis of sediments from rivers, lakes, reservoirs, wetlands and other constructed surface water impoundments is an important tool to characterize the function and health of these systems, but is generally carried out manually. This is costly and can be hazardous and difficult for humans due to inaccessibility, contamination, or availability of required equipment. Robotic sampling systems can ease these burdens, but little work has examined the efficiency of such sampling means and no prior work has investigated the quality of the resulting samples. This paper presents an experimental study that evaluates and optimizes sediment sampling patterns applied to a robot sediment sampling system that allows collection of minimally-disturbed sediment cores from natural and man-made water bodies for various sediment types. To meet this need, we developed and tested a robotic sampling platform in the laboratory to test functionality under a range of sediment types and operating conditions. Specifically, we focused on three patterns by which a cylindrical coring device was driven into the sediment (linear, helical, and zig-zag) for three sediment types (coarse sand, medium sand, and silt). The results show that the optimal sampling pattern varies depending on the type of sediment and can be optimized based on the sampling objective. We examined two sampling objectives: maximizing the mass of minimally disturbed sediment and minimizing the power per mass of sample. This study provides valuable data to aid in the selection of optimal sediment coring methods for various applications and builds a solid foundation for future field testing under a range of environmental conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Sediments

Hybrid classical-quantum communication networks

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Lukens, Joseph M. Peters, Nicholas A. Qi, Bing Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute West LafayetteIN47907 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States New York University Shanghai NYU-ECNU Institute of Physics NYU Shanghai 567 West Yangsi Road Shanghai200126 China State Key Laboratory of Precision Spectroscopy School of Physical and Material Sciences East China Normal University Shanghai200062 China

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum-classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications. © 2025, CC BY.

关键词： Quantum communication

Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network 9th

学校读者我要写书评

暂无评论

Efficient SDN-Based Traffic Monitoring in IoT Networks with ...

9th International Conference on Computational Data and Social Networks, CSoNet 2020

作者： Nguyen, Tri Gia Phan, Trung V. Hoang, Dinh Thai Nguyen, Tu N. So-In, Chakchai Faculty of Information Technology Duy Tan University Danang50206 Viet Nam Chair of Communication Networks Technische Universität Chemnitz Chemnitz09126 Germany School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Department of Computer Science Purdue University Fort Wayne Fort WayneIN46805 United States Department of Computer Science Faculty of Science Khon Kaen University Khon Kaen40002 Thailand

ISBN: (纸本)9783030660451

In an Internet of Things (IoT) environment, network traffic monitoring tasks are intractable to achieve due to various IoT traffic types. Recently, the development of Software-Defined Networking (SDN) enables outstanding flexibility and scalability abilities in network control and management, thereby providing a potential approach to mitigate challenges in monitoring the IoT traffic. In this paper, we propose an IoT traffic monitoring approach that implements deep reinforcement learning technique to maximize the fine-grained monitoring capability, i.e., level of traffic statistics details, for several IoT traffic groups. Specifically, we first study a flow-rule matching control system constrained by different expected levels of statistics details and by the flow-table limit of the SDN-based gateway device. We then formulate our control optimization problem by employing the Markov decision process (MDP). Afterwards, we develop Double Deep Q-Network (DDQN) algorithm to quickly obtain the optimal flow-rule matching control policy. Through the extensive experiments, the obtained results verify that the proposed approach yields outstanding improvements in terms of the ability to simultaneously provide different required degrees of statistics details while protecting the gateway devices from being overflowed in comparisons with those of the conventional Q-learning method and the typical SDN flow rule setting. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

Natural width of the superconducting transition in epitaxial TiN films

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Baeva, Elmira M. Kolbatova, Anna I. Titova, Nadezhda A. Saha, Soham Boltasseva, Alexandra Bogdanov, Simeon Shalaev, Vladimir M. Semenov, Alexander V. Goltsman, Gregory N. Khrapai, Vadim S. Moscow Pedagogical State University Moscow119435 Russia HSE University Moscow101000 Russia Birck Nanotechnology Center Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Holonyak Micro and Nanotechnology Lab University of Illinois at Urbana-Champaign UrbanaIL61801 United States Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We investigate the effect of various fluctuation mechanisms on the DC resistance in superconducting devices based on epitaxial titanium nitride (TiN) films. The samples we studied show a relatively steep resistive transition (RT), with a transition width ∆T/Tc ∼ 0.002 − 0.025, depending on the film thickness (20 nm, 9 nm, and 5 nm) and device dimensions. This value is significantly broader than expected due to conventional superconducting fluctuations (∆T/Tc 10−3). The shape and width of the RT can be perfectly described by the well-known effective medium theory, which allows us to understand the origin of the inhomogeneity in the superconducting properties of TiN films. We propose that this inhomogeneity can have both dynamic and static origins. The dynamic mechanism is associated with spontaneous fluctuations in electron temperature (T-fluctuations), while the static mechanism is due to a random spatial distribution of surface magnetic disorder (MD). Our analysis has revealed clear correlations between the transition width and material parameters as well as device size for both proposed mechanisms. While T-fluctuations may contribute significantly to the observed transition width, our findings suggest that the dominant contribution comes from the MD mechanism. Our results provide new insights into the microscopic origin of broadening of the superconducting transition and inhomogeneity in thin superconducting films. Copyright © 2022, The Authors. All rights reserved.

关键词： Titanium nitride