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arXiv 2021年

作者： Guo, Jiafeng Cai, Yinqiong Fan, Yixing Sun, Fei Zhang, Ruqing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences NO. 6 Kexueyuan South Road Haidian District Beijing100190 China Alibaba Group Beijing100102 China

Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts during past decades, the first-stage retrieval has long been dominated by classical term-based models. Unfortunately, these models suffer from the vocabulary mismatch problem, which may block re-ranking stages from relevant documents at the very beginning. Therefore, it has been a long-term desire to build semantic models for the first-stage retrieval that can achieve high recall efficiently. Recently, we have witnessed an explosive growth of research interests on the first-stage semantic retrieval models. We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development. In this paper, we describe the current landscape of the first-stage retrieval models under a unified framework to clarify the connection between classical term-based retrieval methods, early semantic retrieval methods and neural semantic retrieval methods. Moreover, we identify some open challenges and envision some future directions, with the hope of inspiring more researches on these important yet less investigated topics. © 2021, CC BY-NC-ND.

关键词： Surveys

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ICTNET at TREC 2019 Deep Learning Track 28

ICTNET at TREC 2019 Deep Learning Track

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28th Text REtrieval Conference, TREC 2019

作者： Chen, Jiangui Cai, Yinqiong Jiang, Haoquan University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

We participated in the Deep Learning Track at TREC 2019. We built a ranking system which combines a search component based on BM25 and a semantic matching component using pretraining knowledge. Our best run achieves NDCG@10 of 0.6650, MAP of 0.2035. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Deep learning

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ICTNET at Trec 2019 Incident Streams Track 28

ICTNET at Trec 2019 Incident Streams Track

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28th Text REtrieval Conference, TREC 2019

作者： Guangsheng, Kuang Kun, Zhang Jiabao, Zhang Xin, Zheng University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

Social medial become our public ways to share our information in our lives. Crisis management via social medial is becoming indispensable for its tremendous information. While deep learning shows surprising outcome in many tasks. So in this paper, we cope this learning task with neural network in the view of classification problem. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Deep learning

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ICTNET at TREC 2019 News Track 28

ICTNET at TREC 2019 News Track

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28th Text REtrieval Conference, TREC 2019

作者： Ding, Yuyang Lian, Xiaoying Zhou, Houquan Liu, Zhaoge Ding, Hanxing Hou, Zhongni University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

This paper describes our work in the background linking task and entity ranking task in TREC 2018 News Track. We explore four methods in background linking task and two methods in entity ranking task. All of our methods largely rely on off-the-shell open-source components(e.g., Solr for indexing the documents), and follow the basic thoughts-BM25 and relevance feedback. These methods differ in how they analyze the given input to obtain a query and to what extent the returned results are re-ranked taking meta data of the document(e.g., publish dates) into account. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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Novel node classification framework

arXiv

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arXiv 2019年

作者： Cen, Keting Shen, Huawei Gao, Jinhua Cao, Qi Xu, Bingbing Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology

GCN is a recent effective algorithm which effectively learns a function incorporate both graph structure and node features for semisupervised graph based node classification. Although GCN exceeds other state-of-the-art methods, the number of parameters that need to be learned are still significantly more than the number of used samples. Because only labeled nodes and their 2-hop neighbors are used for learning parameters in GCN. As a consequence the accuracy of node classification declines sharply when decreasing the size of train data. In order to reducing parameters and making better use of unlabeled nodes, we proposes to use unsupervised representation algorithm such as autoencoder to pretrain a embedding for each node, which captures effective information from input features and reduce parameters in graph convolution layers. Experiments show that our model has increased by 0.87%, 1.96%, 1.87% compared to baseline on Cora, Citeseer and Pubmed data respectively. The performance of our model declines slower than GCN when decreasing the train samples. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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ICTNET at TREC 2019 Complex Answer Retrieval Track 28

ICTNET at TREC 2019 Complex Answer Retrieval Track

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28th Text REtrieval Conference, TREC 2019

作者： Ren, Hongfei Xiong, Ruibin Zeng, Yutao Chen, Jiangui Cai, Yinqiong Jiang, Haoquan University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

We participate in the Complex Answer Retrieval(CAR) track at TREC 2019. We applied several useful models in this work. In the rough ranking, we applied doc2query model to predict queries and retrieve using BM25. In the re-ranking, we submitted 5 different runs which use 5 different models, include BM25, DRMMTKS, Bert-DRMMTKS, Bert-ConvKNRM and Bert-ConvKNRM-50, to try to get the best result. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless networks

arXiv

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arXiv 2022年

作者： Gong, Shimin Cui, Leiyang Gu, Bo Lyu, Bin Hoang, Dinh Thai Niyato, Dusit The School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518055 China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China The School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. blueWe aim to minimize the average age-of-information (AoI) by adapting the nodes’ transmission scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming bluevector and the IRS’s phase shifting bluematrix. blueConsidering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to bluefor AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the blueinner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes. © 2022, CC BY.

关键词： Reinforcement learning

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Exploring the boundary of quantum correlations with a time-domain optical processor

arXiv

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arXiv 2022年

作者： Liu, Zheng-Hao Meng, Yu Wu, Yu-Ze Hao, Ze-Yan Xu, Zhen-Peng Ai, Cheng-Jun Wei, Hai Wen, Kai Chen, Jing-Ling Ma, Jie Xu, Jin-Shi Li, Chuan-Feng Guo, Guang-Can CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China School of Mathematical Sciences University of Science and Technology of China Hefei230026 China CAS Centre for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Beijing QBoson Quantum Technology Co. Ltd. Beijing100015 China School of Physics and Optoelectronics Engineering Anhui University Hefei230601 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Walter-Flex-Straße 3 Siegen57068 Germany Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen518045 China Theoretical Physics Division Chern Institute of Mathematics Nankai University Tianjin300071 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China Anhui Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

Contextuality is a hallmark feature of the quantum theory that captures its incompatibility with any noncontextual hidden-variable model. The Greenberger–Horne–Zeilinger (GHZ)-type paradoxes are proofs of contextuality that reveal this incompatibility with deterministic logical arguments. However, the GHZ-type paradox whose events can be included in the fewest contexts and which brings the strongest nonclassicality remains elusive. Here, we derive a GHZ-type paradox with a context-cover number of three and show this number saturates the lower bound posed by quantum theory. We demonstrate the paradox with a time-domain fiber optical platform and recover the quantum prediction in a 37-dimensional setup based on high-speed modulation, convolution, and homodyne detection of time-multiplexed pulsed coherent light. By proposing and studying a strong form of contextuality in high-dimensional Hilbert space, our results pave the way for the exploration of exotic quantum correlations with time-multiplexed optical systems. © 2022, CC BY.

关键词： Optical correlation

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Attribute Propagation Enhanced Community Detection Model for Bitcoin De-anonymizing 3rd

Attribute Propagation Enhanced Community Detection Model for...

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Wang, Jiming Xie, Xueshuo Fang, Yaozheng Lu, Ye Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin300350 China College of Cyber Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030622220

Bitcoin is a kind of decentralized cryptocurrency on a peer-to-peer network. Anonymity makes Bitcoin widely used in online payment but it is a disadvantage for regulatory purposes. We aim to de-anonymize Bitcoin to assist regulation. Many previous studies have used heuristic clustering or machine learning to analyze historical transactions and identify user behaviors. However, the accuracy of user identification is not ideal. Heuristic clustering only uses the topological structure of the transaction graph and ignores many transaction information, and supervised machine learning methods are limited by the size of labeled datasets. To identify user behaviors, we propose a community detection model based on attribute propagation, combining the topological structure of the transaction graph and additional transaction information. We first parse the transaction data of public ledger and construct a bipartite graph to describe correlations between addresses and transactions. We also extract address attributes from historical transactions to construct an attributed graph with the previous bipartite graph. Then, we design an adaptive weighted attribute propagation algorithm named AWAP running on the attributed graph to classify bitcoin addresses, and further identify user behaviors. Extensive experiments highlight that the proposed detection model based on AWAP achieves 5% higher accuracy on average compared to state-of-the-art address classification methods in Bitcoin. AWAP also achieves 25% higher F-score on average compared to previous community detection algorithms on two datasets. © 2020, Springer Nature Switzerland AG.

关键词： Bitcoin

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引用

2020 International Conference on networking and network Applications, NaNA 2020

作者： Wang, Zhongmin Zhou, Rui Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an Shaanxi710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi710121 China

ISBN: (纸本)9781728189543

The information interaction when the brain processes emotional activities is intricate. Therefore, it is very necessary for us to explore the mechanisms of the functional coordination of various brain regions. Recent studies have demonstrated that there are hub regions in the brain that are highly connected and highly central, and are responsible for coordinating dynamic interactions between brain regions. This study aims to construct the dynamic brain networks related to emotions and examine the structure of the hubs of these networks that reflect functional connectivity. In this paper, we demonstrate that a combination of network hubs set a closely connected collective called 'rich-club',which can be used as the core network architecture for identifying different emotions. According to the phase synchronization relationship of the Electroencephalogram (EEG) in three time periods to construct the corresponding brain networks in different sub-states, we found that the dynamic connection patterns produced by different emotions are different, which is manifested in the different rich-club organization. The discovery of rich-club structure related to emotions in this study is of great significance for studying the brain mechanisms behind human emotional processes. © 2020 IEEE.

关键词： Electroencephalography

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