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Guofei Jiang

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3 papers

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JAAMAS Journal 2026 Journal Article

Functional Validation in Grid Computing

Guofei Jiang
George Cybenko

Abstract The development of the World Wide Web has changed the way we think about information. Information on the web is distributed, updates are made asynchronously and resources come online and go offline without centralized control. Global networking will similarly change the way we think about and perform computation. Grid computing refers to computing in a distributed networked environment where computing and data resources are located throughout a network. In order to locate these resources dynamically in a grid computation, a broker or matchmaker uses keywords and ontologies to describe and specify grid services. However, we believe that keywords and ontologies can not always be defined or interpreted precisely enough to achieve deep semantic agreement in a truly distributed, heterogeneous computing environment. To this end, we introduce the concept of functional validation. Functional validation goes beyond the symbolic level of brokering and matchmaking, to the level of validating actual functional performance of grid services. In this paper, we present the functional validation concept in grid computing, analyze the possible validation situations and apply basic machine learning theory such as PAC learning and Chernoff bounds to explore the relationship between sample size and confidence in service semantics.

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IJCAI Conference 2017 Conference Paper

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

Yao Qin
Dongjin Song
Haifeng Chen
Wei Cheng
Guofei Jiang
Garrison W. Cottrell

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a. k. a. , input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.

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AAAI Conference 2014 Conference Paper

Improving Semi-Supervised Target Alignment via Label-Aware Base Kernels

Qiaojun Wang
Kai Zhang
Guofei Jiang
Ivan Maric

Semi-supervised kernel design is an essential step for obtaining good predictive performance in semi-supervised learning tasks. In the current literatures, a large family of algorithms builds the new kernel by using the weighted average of predefined base kernels. While optimal weighting schemes have been studied extensively, the choice of base kernels received much less attention. Many methods simply adopt the empirical kernel matrices or its eigenvectors. Such base kernels are computed irrespective of class labels and may not always reflect useful structures in the data. As a result, in case of poor base kernels, the generalization performance can be degraded however hard their weights are tuned. In this paper, we propose to construct high-quality base kernels with the help of label information to globally improve the final target alignment. In particular, we devise label-aware kernel eigenvectors under the framework of semi-supervised eigenfunction extrapolation, which span base kernels that are more useful for learning. Such base kernels are individually better aligned to the learning target, so their mixture will more likely generate a good classifier. Our approach is computationally efficient, and demonstrates encouraging performance in semisupervised classification and regression.

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