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arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Distributed and Parallel Software Lab Huawei Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Hypertext systems

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Improved Bounds for Sampling Solutions of Random CNF Formulas

arXiv

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arXiv 2022年

作者： He, Kun Wu, Kewen Yang, Kuan The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China China University of California at Berkeley United States John Hopcroft Center for Computer Science Shanghai Jiao Tong University China

Let Φ be a random k-CNF formula on n variables and m clauses, where each clause is a disjunction of k literals chosen independently and uniformly. Our goal is to sample an approximately uniform solution of Φ (or equivalently, approximate the partition function of Φ). Let α = m/n be the density. The previous best algorithm runs in time npoly(k,α) for any α 2k/300 [Galanis, Goldberg, Guo, and Yang, SIAM J. Comput.'21]. Our result significantly improves both bounds by providing an almost-linear time sampler for any α 2k/3. The density α captures the average degree in the random formula. In the worst-case model with bounded maximum degree, current best efficient sampler works up to degree bound 2k/5 [He, Wang, and Yin, FOCS'22 and SODA'23], which is, for the first time, superseded by its average-case counterpart due to our 2k/3 bound. Our result is the first progress towards establishing the intuition that the solvability of the average-case model (random k-CNF formula with bounded average degree) is better than the worst-case model (standard k-CNF formula with bounded maximal degree) in terms of sampling solutions. Copyright © 2022, The Authors. All rights reserved.

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Finding Top-k Min-Cost Connected Trees in databases

Finding Top-k Min-Cost Connected Trees in Databases

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International Conference on data engineering

作者： Bolin Ding Jeffrey Xu Yu Shan Wang Lu Qin Xiao Zhang Xuemin Lin Chinese University of Hong Kong China Key Laboratory of Data Engineering and Knowledge Engineering MOE of China Renmin University of China China University of New South Wales Australia

ISBN: (纸本)1424408024

It is widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p 1 , p 1 , ..., p l , against a relational database which can be modeled as a weighted graph, G(V, E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let V i ⊆ V be a set of nodes that contain the keyword p i . We study finding top-k minimum cost connected trees that contain at least one node in every subset V i , and denote our problem as GST-k When k = 1, it is known as a minimum cost group Steiner tree problem which is NP-complete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3 l n + 2 l ((l + logn)n + m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. Our GST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding.

关键词： Tree graphs Relational databases Costs laboratories data engineering knowledge engineering Australia Information retrieval

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Energy Efficient Multicast Routing for Discrete Power Levels in Ad Hoc Sensor Networks

Energy Efficient Multicast Routing for Discrete Power Levels...

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The 4th International Conference on Wireless Communications, Networking and Mobile Computing(第四届IEEE无线通信、网络技术及移动计算国际会议)

作者： Deying Li Qinghua Zhu Zheng Li Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) MOE Scho School of Information Renmin University of China Beijing 100872 P.R.China

In this paper, we discuss the energy efficient multicast problem for discrete power levels in ad hoc sensor wireless networks. The problem of our concern is: given n nodes and each node v has l(v) transmission power levels and a multicast request (s, D), how to find a multicast tree rooted at s and spanning all destinations in D such that the total energy cost of the multicast tree is minimized. This problem is NP-hard. We propose a NWM_DST algorithm which has a theoretical guaranteed approximation performance ratio, and two efficient heuristics MNJT and g-D-MIP for multicast tree problem. Simulation results have shown efficiency of our proposed algorithms.

关键词： wireless sensor network energy efficiency approzimation algorithm multicast routing

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SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection

SAFL-Net: Semantic-Agnostic Feature Learning Network with Au...

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International Conference on Computer Vision (ICCV)

作者： Zhihao Sun Haoran Jiang Danding Wang Xirong Li Juan Cao Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Mathematics Science University of Chinese Academy of Sciences MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China

Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features. In this paper we propose SAFL-Net, which constrains a feature extractor to learn semantic-agnostic features by designing specific modules with corresponding auxiliary tasks. Applying constraints directly to the features extracted by the encoder helps it learn semantic-agnostic manipulation trace features, which prevents the biases related to semantic information within the limited training data and improves generalization capabilities. The consistency of auxiliary boundary prediction task and original region prediction task is guaranteed by a feature transformation structure. Experiments on various public datasets and comparisons in multiple dimensions demonstrate that SAFL-Net is effective for image manipulation detection.

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Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network 20

Search Result Diversification Framework Based on Dual Star-s...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Qin, Xubo Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong School of Information Renmin University Gaoling School of Artificial Intelligence Renmin University of China Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Research shows that most queries issued by users are short queries. Those queries may be ambiguous or broad for specifying users' actual information need. The same query issued by different users may lead to different information needs. In order to satisfy different users' needs, the search engines need to rank the search result document diversely. This leads to the search result diversification technology. For those previous diversification approaches based on global document interactions, fully-connected self-attention networks are used for capturing the interactions among all the candidate documents, leading to well performance. However, those models only take the document interactions into consideration, ignoring the relevance signals of candidate documents. As the annotated datasets are not enough for model training, the effectiveness of the model will be limited. In this paper we prove a new implicit diversification framework based on self-attention network in the shape of dual-star. A star-shaped topological structure is deployed with an external query node, in order to effectively extract the global interaction signals relevant to the query. Using self-attention to learn the similarity between each document, this model can be trained easily, and perform significantly better than the state-of-the-art implicit approach indicating the potential of implicit methods. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Search engines

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LotusX: A Position-Aware XML Graphical Search System with Auto-Completion

LotusX: A Position-Aware XML Graphical Search System with Au...

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International Conference on data engineering

作者： Chunbin Lin Jiaheng Lu Tok Wang Ling Bogdan Cautis Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Computing National University of Singapore Singapore Télécom Paris Technology France

ISBN: (纸本)9781467300421

The existing query languages for XML (e.g., XQuery) require professional programming skills to be formulated, however, such complex query languages burden the query processing. In addition, when issuing an XML query, users are required to be familiar with the content (including the structural and textual information) of the hierarchical XML, which is diffcult for common users. The need for designing user friendly interfaces to reduce the burden of query formulation is fundamental to the spreading of XML community. We present a twig-based XML graphical search system, called LotusX, that provides a graphical interface to simplify the query processing without the need of learning query language and data schemas and the knowledge of the content of the XML document. The basic idea is that LotusX proposes "position-aware" and "auto-completion" features to help users to create tree-modeled queries (twig pattern) by providing the possible candidates on-the-fly. In addition, complex twig queries (including order sensitive queries) are supported in LotusX. Furthermore, a new ranking strategy and a query rewriting solution are implemented to rank and rewrite the query effectively. We provide an online demo for LotusX system: http://***:8080/LotusX.

关键词： XML database languages Indexes Books Query processing Programming keyword search

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Adaptive gradient methods with dynamic bound of learning rate 7

Adaptive gradient methods with dynamic bound of learning rat...

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7th International Conference on Learning Representations, ICLR 2019

作者： Luo, Liangchen Xiong, Yuanhao Liu, Yan Sun, Xu MOE Key Lab of Computational Linguistics School of EECS Peking University China College of Information Science and Electronic Engineering Zhejiang University China Department of Computer Science University of Southern California United States Center for Data Science Beijing Institute of Big Data Research Peking University China

Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGRAD to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of ADAM and AMSGRAD, called ADABOUND and AMSBOUND respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://***/Luolc/AdaBound. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Gradient methods

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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Deep Representation of Hierarchical Semantic Attributes for Zero-shot Learning

Deep Representation of Hierarchical Semantic Attributes for ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhaocheng Zhang Gang Yang School of Information Renmin University of China Beijing China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

On account of a large scale of dataset need to be annotated to fit for specific tasks, Zero-Shot Learning(ZSL) has invoked so much attention and got significant progress in recent research due to the prevalence of deep neural networks. At present, ZSL is mainly solved through the utilization of auxiliary information, such as semantic attributes and text descriptions. And then, we can employ the mapping method to bridge the gap between visual and semantic space. However, due to the lack of effective use of auxiliary information, this problem has not been solved well. Inspired by previous work, we consider that visual space can be used as the embedding space to get a stronger ability to express the precise characteristics of semantic information. Meanwhile, we take into account that there are some noise attributes in the annotated information of public datasets that need to be processed. Based on these considerations, we propose an end-to-end method with convolutional architecture, instead of conventionally linear projection, to provide a deep representation for semantic information to solve ZSL. Semantic features would express more detailed and precise information after being feed into our method. Besides, we use word embedding to generate some superclasses for original classes and propose a new loss function for these superclasses to assist in training. Experiments show that our method can get decent improvements for ZSL and Generalized Zero-Shot Learning(GZSL) on several public datasets.

关键词： Semantics Visualization Training Testing Task analysis Neural networks Probabilistic logic

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