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Shiqiang Yang

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6 papers
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6

JBHI Journal 2026 Journal Article

A Novel Grasping Robot Control Method Using Motion Execution BCI Combining Knowledge Reasoning

  • Rui Li
  • Jing Liu
  • Jinli Liu
  • Shiqiang Yang
  • Weiping Liu
  • Ke Deng
  • Wen Wang

Recently, with the growing number of disabled people, brain-controlled technology offers a novel way to help patients restore their daily abilities. However, the conventional brain-controlled system based on the motion related task lacks intelligence in real-world environments. To address above problem, this study proposed a share-controlled system combining a precise hand movement (PHM)-based brain computer interface (BCI) system and knowledge-driven reasoning method. Six types of precise hand movements were selected to design novel motion execution paradigm for BCI system. A feature intermediate fusion convolutional neural network was employed to accurately decode electroencephalogram. Furthermore, a shared control grasping technology based on knowledge-based reasoning combined PHM-based BCI system was designed for grasping robot, which enhancing the system's intelligence and versatility in selecting objects. To verify the improvement of proposed method, experiments were conducted with 15 healthy subjects and 2 patients. The proposed method achieved an average accuracy of 82. 80 ± 6. 08%, with the highest accuracy reaching 94. 27%. All the experimental results demonstrate the effectiveness of the proposed shared control method.

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IJCAI Conference 2018 Conference Paper

Power-law Distribution Aware Trust Prediction

  • Xiao Wang
  • Ziwei Zhang
  • Jing Wang
  • Peng Cui
  • Shiqiang Yang

Trust prediction, aiming to predict the trust relations between users in a social network, is a key to helping users discover the reliable information. Many trust prediction methods are proposed based on the low-rank assumption of a trust network. However, one typical property of the trust network is that the trust relations follow the power-law distribution, i. e. , few users are trusted by many other users, while most tail users have few trustors. Due to these tail users, the fundamental low-rank assumption made by existing methods is seriously violated and becomes unrealistic. In this paper, we propose a simple yet effective method to address the problem of the violated low-rank assumption. Instead of discovering the low-rank component of the trust network alone, we learn a sparse component of the trust network to describe the tail users simultaneously. With both of the learned low-rank and sparse components, the trust relations in the whole network can be better captured. Moreover, the transitive closure structure of the trust relations is also integrated into our model. We then derive an effective iterative algorithm to infer the parameters of our model, along with the proof of correctness. Extensive experimental results on real-world trust networks demonstrate the superior performance of our proposed method over the state-of-the-arts.

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AAAI Conference 2017 Conference Paper

Community Preserving Network Embedding

  • Xiao Wang
  • Peng Cui
  • Jing Wang
  • Jian Pei
  • Wenwu Zhu
  • Shiqiang Yang

Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.

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AAAI Conference 2017 Conference Paper

Treatment Effect Estimation with Data-Driven Variable Decomposition

  • Kun Kuang
  • Peng Cui
  • Bo Li
  • Meng Jiang
  • Shiqiang Yang
  • Fei Wang

One fundamental problem in causal inference is the treatment effect estimation in observational studies when variables are confounded. Control for confounding effect is generally handled by propensity score. But it treats all observed variables as confounders and ignores the adjustment variables, which have no influence on treatment but are predictive of the outcome. Recently, it has been demonstrated that the adjustment variables are effective in reducing the variance of estimated treatment effect. However, how to automatically separate the confounders and adjustment variables in observational studies is still an open problem, especially in the scenarios of high dimensional variables, which are common in big data era. In this paper, we propose a Data-Driven Variable Decomposition (D2 VD) algorithm, which can 1) automatically separate confounders and adjustment variables with a data driven approach, and 2) simultaneously estimate treatment effect in observational studies with high dimensional variables. Under standard assumptions, we show experimentally that our D2 VD algorithm can automatically separate the variables precisely, and estimate treatment effect more accurately and with tighter confidence intervals than the state-of-the-art methods on both synthetic data and real online advertising dataset.

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AAAI Conference 2016 Conference Paper

Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds

  • Meng Jiang
  • Peng Cui
  • Nicholas Jing Yuan
  • Xing Xie
  • Shiqiang Yang

People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fullyoverlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially overlapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPTRANS. To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user’s behaviors in different platforms. Extensive experiments across two real social networks show that XPTRANS significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPTRANS can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms.

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AAAI Conference 2011 Conference Paper

Item-Level Social Influence Prediction with Probabilistic Hybrid Factor Matrix Factorization

  • Peng Cui
  • Fei Wang
  • Shiqiang Yang
  • Lifeng Sun

Social influence has become the essential factor which drives the dynamic evolution process of social network structure and user behaviors. Previous research often focus on social in- fluence analysis in network-level or topic-level. In this paper, we concentrate on predicting item-level social influence to reveal the users’ influences in a more fine-grained level. We formulate the social influence prediction problem as the estimation of a user-post matrix, where each entry in the matrix represents the social influence strength the corresponding user has given the corresponding web post. To deal with the sparsity and complex factor challenges in the research, we model the problem by extending the probabilistic matrix factorization method to incorporate rich prior knowledge on both user dimension and web post dimension, and propose the Probabilistic Hybrid Factor Matrix Factorization (PHF- MF) approach. Intensive experiments are conducted on a real world online social network to demonstrate the advantages and characteristics of the proposed method.

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