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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Wan, Shengxian Lan, Yanyan Xu, Jun Guo, Jiafeng Pang, Liang Xueqi, Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.

关键词： Dynamic programming

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Robust and Passive Motion Detection with COTS WiFi Devices

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Tsinghua science and technology 2017年第4期22卷 345-359页

作者： Hai Zhu Fu Xiao Lijuan Sun Xiaohui Xie Panlong Yang Ruchuan Wang the College of Computer Nanjing University of Posts and Telecommunications the Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education and College of Computer Nanjing University of Posts and Telecommunications the College of Computer Science and Technology University of Science and Technology of China

Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

关键词： device-free passive detection Received Signal Strength（RSS） channel state information MIMO

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ICTNET at Trec 2019 Decision Track 28

ICTNET at Trec 2019 Decision Track

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28th Text REtrieval Conference, TREC 2019

作者： Cui, Wanqing Jiang, Yan Tao, Shuchang Guo, Hanzhang CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

In this paper we report on our participation in the Trec 2019 Decision Track[1] which aims to provide a venue for research on retrieval methods that promote better decision making with search engines and develop new online and offline evaluation methods to predict the decision making quality induced by search results. We convert this task into a standard information retrieval task and use traditional IR model. Finally we give a summary for the solution of our work. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Decision making

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Predict anchor links across social networks via an embedding approach 25

Predict anchor links across social networks via an embedding...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Man, Tong Shen, Huawei Liu, Shenghua Jin, Xiaolong Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent we can address the anchor link prediction problem, if only structural information of networks is available. Most existing methods, unsupervised or supervised, directly work on networks themselves rather than on their intrinsic structural regularities, and thus their effectiveness is sensitive to the high dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods.

关键词： network embeddings

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Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

Controlling Risk of Retrieval-augmented Generation: A Counte...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Lu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761681

Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. However, existing studies on RAG seldom address the issue of predictive uncertainty, i.e., how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. In this work, we emphasize the importance of risk control, ensuring that RAG models proactively refuse to answer questions with low confidence. Our research identifies two critical latent factors affecting RAG's confidence in its predictions: the quality of the retrieved results and the manner in which these results are utilized. To guide RAG models in assessing their own confidence based on these two latent factors, we develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers. We also introduce a benchmarking procedure to collect answers with the option to abstain, facilitating a series of experiments. For evaluation, we introduce several risk-related metrics and the experimental results demonstrate the effectiveness of our approach. Our code and benchmark dataset are available at https://***/ictbigdatalab/RC-RAG. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Analysis and Measurement of the Displacement Sensor Based on an Up-tapered Mach-Zehnder Interferometer

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Chinese Physics Letters 2014年第3期31卷 65-68页

作者： WEN Xiao-Dong NING Ti-Gang YOU Hai-Dong KANG Ze-Xin LI Jing LI Chao FENG Ting YU Shao-Wei JIAN Wei Key Lab of All Optical Network and Advanced Telecommunication Network of Ministry of Education Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 Science and Information College Qingdao Agricultural University Qingdao 266109

A displacement sensor based on an up-tapered Mach–Zehnder interferometer （MZI） is proposed and demonstrated experimentally. For this purpose, a fiber MZI is fabricated by using a commercial fusion splicer. Then the transmission spectra of the sensors with different middle fiber lengths are measured by bending the MZIs with different movements of the moving stage. The maximum sensitivity of 2.457 nm/mm is achieved while the shifting of the moving stage changes from 3 mm to 3.5 mm. Note that this kind of up-taper configuration is strong in strength, easy to fabricate and low in cost.

关键词： INTERFEROMETERS RESEARCH DETECTORS OPTICAL fibers INTERFEROMETRY PHYSICS research

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Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor networks

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Computer Modeling in Engineering & sciences 2023年第10期137卷 555-575页

作者： Yang Zhao Jingmin An Hao Li Saru Kumari Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Department of Mathematics Chaudhary Charan Singh UniversityMeerut250004India

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-timedetection and operation control of vehicles and real-time transmission of data and information. In the environmentofVSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users,so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, inthis paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTASET).The scheme combines fault-tolerant and aggregate signcryption,whichnot onlymakes up for the deficiency oflow security of aggregate signature, but alsomakes up for the deficiency that aggregate signcryption cannot tolerateinvalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supportsunbounded aggregation when the total number of signcryptions grows dynamically. In addition, this schemesupports heterogeneous equality test, and realizes the access control of private data in different cryptographicenvironments, so as to achieve flexibility in the application of our scheme and realize the function of quick searchof plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, weconduct strict and standardized experimental operation and performance evaluation, which shows that the schemehas better performance.

关键词： Aggregate signcryption fault-tolerant heterogeneous equality test vehicular sensor network

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SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data

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Computerized Medical Imaging and Graphics 2025年 124卷 102558页

作者： Yudan Ren Zihan Ma Zhenqing Ding Ruonan Yang Xiao Li Xiaowei He Tianming Liu School of Information Science & Technology Northwest University Xi’an China School of Network and Data Center Northwest University Xi’an China Cortical Architecture Imaging and Discovery Lab School of Computing University of Georgia United States

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people’s social communication and daily routine. Most existing imaging studies on ASD use single site resting-state functional magnetic resonance imaging (rs-fMRI) data, which may suffer from limited samples and geographic bias. Improving the generalization ability of the diagnostic models often necessitates a large-scale dataset from multiple imaging sites. However, centralizing multi-site data generally faces inherent challenges related to privacy, security, and storage burden. Federated learning (FL) can address these issues by enabling collaborative model training without centralizing data. Nevertheless, multi-site rs-fMRI data introduces site variations, causing unfavorable data heterogeneity. This negatively impacts biomarker identification and diagnostic decision. Moreover, previous FL approaches for fMRI analysis often ignore site-specific demographic information, such as age, gender, and full intelligence quotient (FIQ), providing useful information as non-imaging features. On the other hand, Graph Neural networks (GNNs) are gaining popularity in fMRI representation learning due to their powerful graph representation capabilities. However, existing methods often focus on extracting subject-specific connectivity patterns and overlook inter-subject relationships in brain functional topology. In this study, we propose a specificity-preserving federated population graph contrastive learning (SFPGCL) framework for rs-fMRI analysis and multi-site ASD identification, including a server and multiple clients/sites for federated model aggregation. At each client, our model consists of a shared branch and a personalized branch, where parameters of the shared branch are sent to the sever, while those of the personalized branch remain local. This setup facilitates invariant knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-te

关键词： Autism spectrum disorder Functional magnetic resonance imaging Model-contrastive learning Multi-site federated learning Population graph

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Efficient selective planar emitters for thermophotovoltaic systems at 1473 K

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Applied Thermal Engineering 2025年 276卷

作者： Jian Wang Shouhao Zhang Xiaoyu Sun Chong Wang Zunqian Tang Fangyuan Yu Zirui Zhao Jun Mao Qian Zhang Feng Cao School of Science and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen 518055 China School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen 518055 China

The development of efficient thermophotovoltaic (TPV) energy conversion systems can significantly address global energy crises and environmental pollution. Selective emitters in TPV systems play a crucial role in converting broad-spectra thermal radiation into narrowband radiation, thus effectively enhancing the energy conversion efficiency of TPV systems. However, achieving selective emitters with superior thermal stability, particularly at temperature above 1273 K, remains a significant challenge. The selective emitter exhibits an exceptional performance with a high in-band emittance of 0.92 under normal incidence, an outstanding emittance over 0.83 at an oblique incidence angle of 60°, as well as excellent thermal stability at 1473 K. The superior performance of the emitter is predominantly attributed to innovative optical design, including the introduction of co-sputtered ultra-high-temperature ceramics composite ceramic layers, thermal barrier layers and surface encapsulation layers. The TPV system combined with a 0.72 eV-GaSb photovoltaic cell, a filter, and the designed emitter, is predicted to achieve an energy conversion efficiency of approximately 27.4 % and a power output of 1.15 W/cm 2 at 1473 K. These findings highlight the potential of the multilayer selective thermal emitters as promising candidates for improving the performance of TPV systems.

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A Caching Strategy Based on Many-to-Many Matching Game in D2D networks

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Tsinghua science and technology 2021年第6期26卷 857-868页

作者： Kaihang Yu Zhonggui Ma Runyu Ni Tao Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Key Lab of Information Network Security Ministry of Public SecurityShanghai 201204China

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device(D2D) networks. We model the D2D User Equipments(DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy.(1) It can cache other contents besides high popularity contents.(2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used(LRU), Equal Probability Random Cache(EPRC), and the Most Popular Content Cache(MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

关键词： caching strategy Device-to-Device(D2D)networks Gauss determinantal point process many-to-many matching game

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