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PGGAN: Improve Password Cover Rate Using the Controller

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Journal of Physics: Conference Series 2021年第1期1856卷

作者： Xiaozhou Guo Yi Liu Kaijun Tan Min Jin Huaxiang Lu Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China University of Chinese Academy of Sciences Beijing 100089 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing 200031 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing 100083 China

Password generation model based on generative adversarial network usually has the problem of high duplicate rate, which further leads to low cover rate. In this regard, we propose PGGAN model. It sets up an additional controller network which is similar to the discriminator in the aspect of structure and function. The discriminator and the controller respectively learn the measure between the distribution of generated password with the real password distribution and the uniform distribution, and then use two measures to teach generator meanwhile. By changing the activation function and loss function of the controller, different measure functions can be selected. The experimental results show that compared with GAN, our PGGAN performs better both in cover rate and duplicate rate. Moreover, Wasserstein distance usually has a better effect to the other measure in model. Specifically, PGGAN with Wasserstein distance can increase the cover rate by 3.57% and reduce the duplicate rate by 30.85% on rockyou dataset.

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An application of insurance mechanism in risk management for energy internet

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High technology Letters 2018年第2期24卷 196-202页

作者： Yang Yunxue Zhang Qin Zhang Libo CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 P. R. China College of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Business College Yantai Nanshan University Yantai 265713 P. R. China

As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the *** paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.

关键词： energy internet insurance risk management optimal control fuzzy theory

MQGrad: Reinforcement learning of gradient quantization in parameter server

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arXiv 2018年

作者： Cui, Guoxin Xu, Jun Zeng, Wei Lan, Yanyan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of ComputingTechnology Chinese Academy of Sciences Beijing100190 China

One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the workers and servers during the training iterations. Gradient quantization has been proposed as an effective approach to reducing the communication volume. One key issue in gradient quantization is setting the number of bits for quantizing the gradients. Small number of bits can significantly reduce the communication overhead while hurts the gradient accuracies, and vise versa. An ideal quantization method would dynamically balance the communication overhead and model accuracy, through adjusting the number bits according to the knowledge learned from the immediate past training iterations. Existing methods, however, quantize the gradients either with fixed number of bits, or with predefined heuristic rules. In this paper we propose a novel adaptive quantization method within the framework of reinforcement learning. The method, referred to as MQGrad, formalizes the selection of quantization bits as actions in a Markov decision process (MDP) where the MDP states records the information collected from the past optimization iterations (e.g., the sequence of the loss function values). During the training iterations of a machine learning algorithm, MQGrad continuously updates the MDP state according to the changes of the loss function. Based on the information, MDP learns to select the optimal actions (number of bits) to quantize the gradients. Experimental results based on a benchmark dataset showed that MQGrad can accelerate the learning of a large scale deep neural network while keeping its prediction accuracies. Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

Dynamic-K recommendation with personalized decision boundary 23rd

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Dynamic-K recommendation with personalized decision boundary

23rd China conference on Information Retrieval, CCIR 2017

作者： Gao, Yan Guo, Jiafeng Lan, Yanyan Liao, Huaming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319686981

In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of items (e.g., webpages, products). The top-N results are then provided to users as recommendations, where the N is usually a fixed number pre-defined by the system according to some heuristic criteria (e.g., page size, screen size). There is one major assumption underlying this fixed-number recommendation scheme, i.e., there are always sufficient relevant items to users’ preferences. Unfortunately, this assumption may not always hold in real-world scenarios. In some applications, there might be very limited candidate items to recommend, and some users may have very high relevance requirement in recommendation. In this way, even the top-1 ranked item may not be relevant to a user’s preference. Therefore, we argue that it is critical to provide a dynamic-K recommendation, where the K should be different with respect to the candidate item set and the target user. We formulate this dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. The ranking objective is the same as existing methods, i.e., to create a ranking list of items according to users’ interests. The classification objective is unique in this work, which aims to learn a personalized decision boundary to differentiate the relevant items from irrelevant items. Based on these ideas, we extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions, namely DK-BPRMF and DK-HRM. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods. © Springer International Publishing AG 2017.

关键词： Websites

On privacy protection of latent dirichlet allocation model training

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

Beyond precision: A study on recall of initial retrieval with neural representations

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arXiv 2018年

作者： Xiao, Yan Guo, Jiafeng Fan, Yixing Lan, Yanyan Xu, Jun Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: If the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

Neural or statistical: An empirical study on language models for Chinese input recommendation on mobile 23rd

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Neural or statistical: An empirical study on language models...

23rd China conference on Information Retrieval, CCIR 2017

作者： Zhang, Hainan Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

Academic access data analysis for literature recommendation 23rd

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Academic access data analysis for literature recommendation

23rd China conference on Information Retrieval, CCIR 2017

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Academic reading plays an important role in researchers’ daily life. To alleviate the burden of seeking relevant literature from rapidly growing academic repository, different kinds of recommender systems have been introduced in recent years. However, most existing work focused on adopting traditional recommendation techniques, like content-based filtering or collaborative filtering, in the literature recommendation scenario. Little work has yet been done on analyzing the academic reading behaviors to understand the reading patterns and information needs of real-world academic users, which would be a foundation for improving existing recommender systems or designing new ones. In this paper, we aim to tackle this problem by carrying out empirical analysis over large scale academic access data, which can be viewed as a proxy of academic reading behaviors. We conduct global, group-based and sequence-based analysis to address the following questions: (1) Are there any regularities in users’ academic reading behaviors? (2) Will users with different levels of activeness exhibit different information needs? (3) How to correlate one’s future demands with his/her historical behaviors? By answering these questions, we not only unveil useful patterns and strategies for literature recommendation, but also identify some challenging problems for future development. © Springer International Publishing AG 2017.

关键词： Recommender systems

Spectral Energy Distributions in Three Deep-Drilling Fields of the Vera C. Rubin Observatory Legacy Survey of Space and Time: Source Classification and Galaxy Properties

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arXiv 2022年

作者： Zou, Fan Brandt, W.N. Chen, Chien-Ting Leja, Joel Ni, Qingling Yan, Wei Yang, Guang Zhu, Shifu Luo, Bin Nyland, Kristina Vito, Fabio Xue, Yongquan Department of Astronomy and Astrophysics 525 Davey Lab The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics 104 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Marshall Space Flight Center HuntsvilleAL35811 United States Science and Technology Institute Universities Space Research Association HuntsvilleAL35805 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Department of Physics and Astronomy Texas A&M University College StationTX77843-4242 United States George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy Texas A&M University College StationTX77843-4242 United States School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Nanjing210093 China U.S. Naval Research Laboratory 4555 Overlook Ave SW WashingtonDC20375 United States Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa56126 Italy INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Gobetti 93/3 BolognaI-40129 Italy CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China School of Astronomy and Space Sciences University of Science and Technology of China Hefei230026 China

W-CDF-S, ELAIS-S1, and XMM-LSS will be three Deep-Drilling Fields (DDFs) of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), but their extensive multi-wavelength data have not been fully utilized as done in the COSMOS field, another LSST DDF. To prepare for future science, we fit source spectral energy distributions (SEDs) from X-ray to far-infrared in these three fields mainly to derive galaxy stellar masses and star-formation rates. We use CIGALE v2022.0, a code that has been regularly developed and evaluated, for the SED fitting. Our catalog includes 0.8 million sources covering 4.9 deg2 in W-CDF-S, 0.8 million sources covering 3.4 deg2 in ELAIS-S1, and 1.2 million sources covering 4.9 deg2 in XMM-LSS. Besides fitting normal galaxies, we also select candidates that may host active galactic nuclei (AGNs) or are experiencing recent star-formation variations and use models specifically designed for these sources to fit their SEDs;this increases the utility of our catalog for various projects in the future. We calibrate our measurements by comparison with those in well-studied smaller regions and briefly discuss the implications of our results. We also perform detailed tests of the completeness and purity of SED-selected AGNs. Our data can be retrieved from a public website. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys