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Signal, Information and data Processing (ICSIDP), IEEE International Conference on

作者： Xinlong Pan Xueqi Cheng Libo Yao Zhenqiu Zhu Biao Ding Tiantian Tang Minbo Li Chuanhui Liu CAS Key Laboratory of Network Data Science & Technology Inistitute of Computing Technology Beijing China Institute of Information Fusion Naval Aviation University Yantai China School of Software Fudan University Shanghai China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems.

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Match2: A Matching over Matching Model for Similar Question Identification

arXiv

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arXiv 2020年

作者： Wang, Zizhen Fan, Yixing Guo, Jiafeng Yang, Liu Zhang, Ruqing Lan, Yanyan Cheng, Xueqi Jiang, Hui Wang, Xiaozhao 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 University of Massachusetts Amherst MA United States Alibaba Group Beijing China

Community Question Answering (CQA) has become a primary means for people to acquire knowledge, where people are free to ask questions or submit answers. To enhance the efficiency of the service, similar question identification becomes a core task in CQA which aims to find a similar question from the archived repository whenever a new question is asked. However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i.e., there could be different ways to ask a same question or different questions sharing similar expressions. To alleviate this problem, it is natural to involve the existing answers for the enrichment of the archived questions. Traditional methods typically take a one-side usage, which leverages the answer as some expanded representation of the corresponding question. Unfortunately, this may introduce unexpected noises into the similarity computation since answers are often long and diverse, leading to inferior performance. In this work, we propose a two-side usage, which leverages the answer as a bridge of the two questions. The key idea is based on our observation that similar questions could be addressed by similar parts of the answer while different questions may not. In other words, we can compare the matching patterns of the two questions over the same answer to measure their similarity. In this way, we propose a novel matching over matching model, namely Match2, which compares the matching patterns between two question-answer pairs for similar question identification. Empirical experiments on two benchmark datasets demonstrate that our model can significantly outperform previous state-of-the-art methods on the similar question identification *** CONCEPTS •Information systems → Question answering;Copyright © 2020, The Authors. All rights reserved.

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Provably Robust Node Classification via Low-Pass Message Passing

Provably Robust Node Classification via Low-Pass Message Pas...

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IEEE International Conference on data Mining (ICDM)

作者： Yiwei Wang Shenghua Liu Minji Yoon Hemank Lamba Wei Wang Christos Faloutsos Bryan Hooi School of Computing National University of Singapore Singapore Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science & Technology CAS and University of Chinese Academy of Sciences Beijing China Carnegie Mellon University United States

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Graph Convolutional networks (GCNs) have achieved state-of-the-art performance on node classification. However, recent works have shown that GCNs are vulnerable to adversarial attacks, such as additions or deletions of adversarially-chosen edges in the graph, in order to mislead the node classification algorithms. How can we design robust GCNs that are resistant to such adversarial attacks? More challengingly, how can we do this in a way that is provably robust? We propose a robust node classification approach based on a low-pass `message passing' mechanism, that (a) reduces the effectiveness of adversarial attacks in experiments, and (b) provides theoretical guarantees against adversarial attacks. Our approach can be embedded into the existing GCN architectures to enhance their robustness. Empirical results show that our loss-pass method effectively improves the performance of multiple GCNs under miscellaneous perturbations and helps them to achieve superior performance on various graphs.

关键词： Resistance Perturbation methods Message passing Reliability engineering Robustness Task analysis Optimization

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Type-Representation Enhanced Event Detection via Structure Encoder

Type-Representation Enhanced Event Detection via Structure E...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

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UAV-Assisted Covert data Collection: When Remote Monitoring Meets Covert Communication

UAV-Assisted Covert Data Collection: When Remote Monitoring ...

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International Conference on Computer and Communications (ICCC)

作者： Huasheng Su Xiaodong Xu Jing Zhao Jiaxi Zhou Key Lab. of WOC-CAS University of Science and Technology of China Hefei Anhui China Institute of Space Integrated Ground Network Hefei Anhui China

Unmanned aerial vehicle (UAV) assisted remote monitoring and data collection are prevailing applications thanks to its deployment flexibility and strong mobility. To meet security and privacy requirements, this work investigates a UAV-assisted covert data collection scenario, where the collection scheduling, three-dimensional trajectory and transmit power of the involved UAV are jointly optimized while communicating with another UAV receiver in the presence of adversaries. Taking into account the channel uncertainty at the transmitter, the optimization problem is first formulated to maximize the average covert communication rate for the worst case and the average case, respectively. Then, due to the mixed-integer non-convex nature of the optimization problem, the penalty successive convex approximation (P-SCA) scheme is introduced as a solver in association with proper mathematical approximation of some constraints. The numerical results are finally provided to validate the covertness performance of the proposed scheme, which outperforms the baseline who has no information of channel mismatch.

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Uncertainty calibration for ensemble-based debiasing methods 21

Uncertainty calibration for ensemble-based debiasing methods

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ruibin Xiong Yimeng Chen Liang Pang Xueqi Cheng Zhiming Ma Yanyan Lan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and Baidu Inc. University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences and Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Institute for AI Industry Research Tsinghua University

ISBN: (纸本)9781713845393

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy.

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Proposal of a free-space-to-chip pipeline for transporting single atoms

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Physical Review A 2023年第3期108卷 033108-033108页

作者： Aiping Liu Jiawei Liu Zhanfei Kang Guang-Jie Chen Xin-Biao Xu Xifeng Ren Guang-Can Guo Qin Wang Chang-Ling Zou Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing 210003 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 China

A free-space-to-chip pipeline is proposed for the efficient transportation of single atoms from a magneto-optical trap to an on-chip evanescent-field trap. Due to the reflection of the dipole laser on the chip surface, the conventional conveyor-belt approach can transport only atoms in close proximity to the chip surface, with a distance of about one wavelength. This limitation hinders the efficient interaction between the atom and the on-chip waveguide devices. Here, a two-layer photonic chip architecture is proposed to realize a free-space-to-chip atom pipeline. By employing a diffraction beam of the integrated grating with an incident angle of the Brewster angle, the reflection of the dipole laser is suppressed, allowing the atoms to be brought to the chip surface with a distance of only 100nm. The pipeline is verified numerically and provides a reliable atom source for a hybrid photonic-atom chip.

关键词： Atom & ion trapping & guiding Integrated optics Interference & diffraction of light Light propagation, transmission & absorption Nanophotonics Polarization of light Atoms Trapped atoms Two-level models

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Visual Transformation Telling

arXiv

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arXiv 2023年

作者： Cui, Wanqing Hong, Xin Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Data Intelligence System Research Center Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

Humans can naturally reason from superficial state differences (e.g. ground wetness) to transformations descriptions (e.g. raining) according to their life experience. In this paper, we propose a new visual reasoning task to test this transformation reasoning ability in real-world scenarios, called Visual Transformation Telling (VTT). Given a series of states (i.e. images), VTT requires to describe the transformation occurring between every two adjacent states. Different from existing visual reasoning tasks that focus on surface state reasoning, the advantage of VTT is that it captures the underlying causes, e.g. actions or events, behind the differences among states. We collect a novel dataset to support the study of transformation reasoning from two existing instructional video datasets, CrossTask and COIN, comprising 13,547 samples. Each sample involves the key state images along with their transformation descriptions. Our dataset covers diverse real-world activities, providing a rich resource for training and evaluation. To construct an initial benchmark for VTT, we test several models, including traditional visual storytelling methods (CST, GLACNet, Densecap) and advanced multimodal large language models (LLaVA v1.5-7B, Qwen-VL-chat, Gemini Pro Vision, GPT-4o, and GPT-4). Experimental results reveal that even state-of-the-art models still face challenges in VTT, highlighting substantial areas for improvement. © 2023, CC BY-NC-SA.

关键词： Visual languages

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SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Yinnan Zhou Hao Zhou Luowei Li Siming Chen Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn't be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions.

关键词： Location awareness Performance evaluation Accuracy Line-of-sight propagation Switches Mobile handsets Sensors Indoor environment Wireless fidelity Antennas

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A quantum-inspired classical algorithm for separable non-negative matrix factorization

arXiv

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arXiv 2019年

作者： Chen, Zhihuai Li, Yinan Sun, Xiaoming Yuan, Pei Zhang, Jialin CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Centrum Wiskunde & Informatica and QuSoft Science Park 123 Amsterdam1098XG Netherlands

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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