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Query understanding via intent description generation

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arXiv 2020年

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

Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users’ search queries, e.g., query classification or query clustering. However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. We discuss the potential usage of such Q2ID technique through an example application. Copyright © 2020, The Authors. All rights reserved.

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Adversarial Immunization for Certifiable Robustness on Graphs

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arXiv 2020年

作者： Tao, Shuchang Shen, Huawei Cao, Qi Hou, Liang Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models. Existing researches focus on developing either robust GNN models or attack detection methods against adversarial attacks on graphs. However, little research attention is paid to the potential and practice of immunization to adversarial attacks on graphs. In this paper, we propose and formulate the graph adversarial immunization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of graph against any admissible adversarial attack. We further propose an effective algorithm, called AdvImmune, which optimizes with meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem. Experiments are conducted on two citation networks and one social network. Experimental results demonstrate that the proposed AdvImmune method remarkably improves the ratio of robust nodes by 12%, 42%, 65%, with an affordable immune budget of only 5% edges. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering 2nd

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell ...

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wu, Zheyu Wang, Yueyue Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9789819619061

Single-cell RNA sequencing provides high-throughput gene expression information and offers higher resolution of cell differences compared to bulk RNA sequencing, which helps identify cell types and explore cellular heterogeneity at the single-cell level. Although many single-cell clustering methods based on masked autoencoders have been developed recently, they rarely effectively utilize the correlations between cells, resulting in suboptimal clustering results. In this work, we propose a novel clustering method for scRNA-seq data based on a masked graph convolutional autoencoder, which incorporates a graph convolutional network onto the basis of a masked autoencoder to enhance the capture of the correlation between cells and genes by fusing the cell-cell interaction graph and the gene expression matrix. By increasing the relationships between cells, dimensionality reduction is guided, and a clustering-friendly representation is obtained. We conducted extensive comparative experiments using various clustering evaluation metrics on six scRNA-seq datasets from different sequencing platforms. The experimental results demonstrate that scMGCAE outperforms other methods on these datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

ICTNET at Trec 2019 Conversational Assistance Track 28

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ICTNET at Trec 2019 Conversational Assistance Track

28th Text REtrieval Conference, TREC 2019

作者： Hao, Changying Zhang, Yuanyuan Yang, Weifeng Zhao, Heng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper we report on our participation in the TREC 2019 Conversational Assistance Track which focuses on Conversational Information Seeking（CIS）namely understand the dialogue context and retrieve candidate response information from collections provided. We convert the CIS task into a standard information retrieval task and use both traditional IR model and neural IR model to rerank the baseline official evaluation results. We compare the results of models in two categories(four models in total), and give a summary for the solution of our work. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Information retrieval

Efficient Multi-Authority Attribute-based Signcryption with Constant-Size Ciphertext

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Efficient Multi-Authority Attribute-based Signcryption with ...

IEEE Conference on Dependable and Secure computing

作者： Yang Zhao Ankang Ruan Guohang Dan Jicheng Huang Yi Ding Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

Recently, efficient fine-grained access mechanism has been studied as a main concern in cloud storage area for several years. Attribute-based signcryption (ABSC) which is logical combination of attribute-based encryption(ABE) and attribute-based signature(ABS), can provide confidentiality, authenticity for sensitive data and anonymous authentication. At the same time it is more efficient than previous “encrypt-then-sign” and “sign-then-encrypt” patterns. However, most of the existing ABSC schemes fail to serve for real scenario of multiple authorities and have heavy communication overhead and computing overhead. Hence, we construct a novel ABSC scheme realizing multi-authority access control and constant-size ciphertext that does not depend on the number of attributes or authorities. Furthermore, our scheme provides public verifiability of the ciphertext and privacy protection for the signcryptor. Specially, it is proven to be secure in the standard model, including ciphertext indistinguishability under adaptive chosen ciphertext attacks and existential unforgeability under adaptive chosen message attack.

关键词： Privacy Cloud computing Adaptation models Outsourcing Encryption Servers Standards

Graph jigsaw learning for cartoon face recognition

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arXiv 2021年

作者： Li, Yong Lao, Lingjie Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. © 2021, CC BY-NC-SA.

关键词： Face recognition

Provably Robust Node Classification via Low-Pass Message Passing

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

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

Modelling universal order book dynamics in bitcoin market

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arXiv 2021年

作者： Shi, Fabin Aden, Nathan Huang, Shengda Johnson, Neil Sun, Xiaoqian Gao, Jinhua Xu, Li Shen, Huawei Cheng, Xueqi Song, Chaoming CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing100049 China Department of Physics University of Miami Coral GablesFL33142 United States Physics Department George Washington University WashingtonD.C.20052 United States

Understanding the emergence of universal features such as the stylized facts in markets is a longstanding challenge that has drawn much attention from economists and physicists. Most existing models, such as stochastic volatility models, focus mainly on price changes, neglecting the complex trading dynamics. Recently, there are increasing studies on order books, thanks to the availability of large-scale trading datasets, aiming to understand the underlying mechanisms governing the market dynamics. In this paper, we collect order-book datasets of Bitcoin platforms across three countries over millions of users and billions of daily turnovers. We find a 1+1D field theory, govern by a set of KPZ-like stochastic equations, predicts precisely the order book dynamics observed in empirical data. Despite the microscopic difference of markets, we argue the proposed effective field theory captures the correct universality class of market dynamics. We also show that the model agrees with the existing stochastic volatility models at the long-wavelength limit. © 2021, CC BY.

关键词： Bitcoin

FLDDoS: DDoS Attack Detection Model based on Federated Learning

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FLDDoS: DDoS Attack Detection Model based on Federated Learn...

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jiachao Zhang Peiran Yu Le Qi Song Liu Haiyu Zhang Jianzhong Zhang Institute of Systems and Networks Nankai University College of Computer Science Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Shijiazhuang Huayan Traffic Technology Co. Ltd.

ISBN: (纸本)9781665416597

Recently, DDoS attack has developed rapidly and become one of the most important threats to the Internet. Traditional machine learning and deep learning methods can-not train a satisfactory model based on the data of a single client. Moreover, in the real scenes, there are a large number of devices used for traffic collection, these devices often do not want to share data between each other depending on the research and analysis value of the attack traffic, which limits the accuracy of the model. Therefore, to solve these problems, we design a DDoS attack detection model based on federated learning named FLDDoS, so that the local model can learn the data of each client without sharing the data. In addition, considering that the distribution of attack detection datasets is extremely imbalanced and the proportion of attack samples is very small, we propose a hierarchical aggregation algorithm based on K-Means and a data resampling method based on SMOTEENN. The result shows that our model improves the accuracy by 4% compared with the traditional method, and reduces the number of communication rounds by 40%.

关键词： Deep learning Privacy data privacy Computational modeling Clustering algorithms Denial-of-service attack Collaborative work