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As a key decision-making process in compensation and benefits (C&B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as "cold start" issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management. Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability. [ABSTRACT FROM AUTHOR]

Career planning consists of a series of decisions that will significantly impact one's life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studies have further revealed that our SSRL career path recommendation system would encourage people to gradually improve their career paths to maximize long-term benefits. Moreover, we have shown that the initial state (i.e., the first job) can have a significant impact, positively or negatively, on one's career, while in the long-term view, a carefully planned career path following our recommendation system may mitigate the negative impact of a lackluster beginning in one's career life. [ABSTRACT FROM AUTHOR]

The article focuses on rHDP, a hierarchical topic model for multi-domain corpora, addressing challenges in identifying inter-topic correlations and aspect-sharing patterns. Topics include the enhancement of nested Chinese restaurant processes for aspect-sharing pattern extraction, the development of a hierarchical Gibbs sampling scheme for model inference, and the validation of the model's efficacy through experiments on public and financial document datasets.

The article addresses document-level event extraction challenges, proposing a novel framework with a token-event-role data structure and a multi-channel prediction module. Topics include the significance of document-level event extraction, challenges in multi-event detection and argument identification, and previous methods' limitations.

In this article, we address the multiple-choice machine comprehension (MC) problem in natural language processing. Existing approaches for MC are usually designed for general cases; however, we specially develop a novel method for solving the multiple-choice MC problem. We take the inspiration generative adversarial networks (GANs) and first propose an adversarial framework for multiple-choice oriented MC, named McGAN. Specifically, our approach is designed as a GAN-based method that unifies both generative and discriminative MC models. Working together, the generative model focuses on predicting relevant answer given a passage (text) and a question; the discriminative model focuses on predicting their relevancy given an answer-passage-question set. Based on the competition via adversarial training in a minimize-maximize game, the proposed method takes advantages from both models. To evaluate the performance, we test our McGAN model on three well-known datasets for multiple-choice MC. Our results show that McGAN can achieve a significant increase in accuracy compared to existing models based on all three datasets, and it consistently outperforms all tested baselines, including state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Highlights • We define market shocks as the innovation of ARMA-GARCH. • We develop a methodology ARMA-GARCH-NN for market shock prediction. • A nearest-K cross-validation method is proposed and applied. • The predictability of market shocks is confirmed by experiments on the S&P 500 data. • The prediction result is used as a new signal for trading strategy development. Abstract Discovering hidden patterns under unexpected market shocks is a significant and challenging problem, which continually attracts attention from research communities of mathematics, economics, and data science. Classic financial pricing models present unsatisfactory prediction accuracy when applied to real-world data due to limited capacity in depicting complex market movements. In this paper, we develop a machine learning approach, called ARMA-GARCH-NN, to capture intra-day patterns for stock market shock forecasting. Specifically, we integrate classical financial pricing models with artificial neural networks, with explicitly designed feature selection and cross-validation methods. We conduct empirical studies on high-frequency data of the U.S. stock market for evaluation. Our results provide initial evidence of the predictability of market shocks. Additionally, we confirm the effectiveness of ARMA-GARCH-NN by recognizing patterns in massive stock data without strong assumptions on distribution. Our method can serve as a portable methodology that integrates the advantages of traditional financial models and data-driven methods to reveal hidden patterns in large-scale financial data. [ABSTRACT FROM AUTHOR]

We speed up the solution of the mobile sequential recommendation (MSR) problem that requires searching optimal routes for empty taxi cabs through mining massive taxi GPS data. We develop new methods that combine parallel computing and the simulated annealing with novel global and local searches. While existing approaches usually involve costly offline algorithms and methodical pruning of the search space, our new methods provide direct real-time search for the optimal route without the offline preprocessing. Our methods significantly reduce computational time for the high dimensional MSR problems from days to seconds based on the real-world data as well as the synthetic ones. We efficiently provide solutions to MSR problems with thousands of pick-up points without offline training, compared to the published record of 25 pick-up points. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic optimization problem focusing on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel computing strategy, coined the weighted aggregating stochastic gradient descent (WASGD). Following a theoretical analysis on the characteristics of the new objective function, WASGD introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically gauges the importance of local workers and accepts them by their contributions. Furthermore, we have developed an enhanced version of the method, WASGD+, by (1) implementing a designed sample order and (2) upgrading the weight evaluation function. To validate the new method, we benchmark our pipeline against several popular algorithms including the state-of-the-art deep neural network classifier training techniques (e.g., elastic averaging SGD). Comprehensive validation studies have been conducted on four classic datasets: CIFAR-100, CIFAR-10, Fashion-MNIST, and MNIST. Subsequent results have firmly validated the superiority of the WASGD scheme in accelerating the training of deep architecture. Better still, the enhanced version, WASGD+, is shown to be a significant improvement over its prototype. [ABSTRACT FROM AUTHOR]

Abstract: Given its importance, the problem of classification in imbalanced data has attracted great attention in recent years. However, few efforts have been made to develop feature selection techniques for the classification of imbalanced data. This paper thus fills this critical void by introducing two approaches for the feature selection of high-dimensional imbalanced data. To this end, after introducing three traditional methods, we study and illustrate the challenges of feature selection in imbalanced data with Bayesian learning. Indeed, we reveal that the samples in the larger classes have a dominant influence on these feature selection methods. However, the samples in rare classes are essential for the learning performances of rare classes. Based on these observations, we provide a new feature selection approach based on class decomposition. Specifically, we partition the large classes into relatively smaller pseudo-subclasses and generate the pseudo-class labels accordingly. Feature selection is then performed on the new decomposed data for computing the goodness measurement of features. In addition, we also introduce a Hellinger distance-based method for feature selection. Hellinger distance is a measure of distribution divergence, which is strongly skew insensitive as the class prior information is not involved for computing the distance. Finally, we theoretically show the effectiveness of these two approaches with Bayesian learning on synthetic data. We also test and compare the performances of the proposed feature-selection methods on some real-world data sets. The experimental results show that both decomposition-based and Hellinger distance-based methods can outperform existing feature-selection methods with a clear margin on imbalanced data. [Copyright &y& Elsevier]

Given a large transactional database, correlation computing/association analysis aims at efficiently finding strongly correlated items. For traditional association analysis, relationships among variables are usually measured at a global level. In this study, we investigate confounding factors that can help to capture abnormal correlation behaviors at a local level. Indeed, many real-world phenomena are localized to specific markets or subpopulations. Such local relationships may not be visible or may be miscalculated when collectively analyzing the entire data. In particular, confounding effects that change the direction of correlation are a most severe problem because the global correlations alone leads to errant conclusions. To this end, we propose CONFOUND, an efficient algorithm to identify paradoxical correlation patterns (i.e., where controlling for a third item changes the direction of association for strongly correlated pairs) using effective pruning strategies. Moreover, we also provide an enhanced version of this algorithm, called CONFOUND+, which substantially speeds up the confounder search step. Finally, experimental results showed that our proposed CONFOUND and CONFOUND+ algorithms can effectively identify confounders and the computational performance is orders of magnitude faster than benchmark methods. [ABSTRACT FROM AUTHOR]

Advances in real-time location systems have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and generating useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm to model workflows through knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by integrated analysis of indoor location traces in the hospital environment. In particular, we develop a workflow modeling framework that automatically constructs the workflow states and estimates the parameters describing the workflow transition patterns. More specifically, we propose effective and efficient regularizations for modeling the indoor location traces as stochastic processes. First, to improve the interpretability of the workflow states, we use the geography relationship between the indoor rooms to define a prior of the workflow state distribution. This prior encourages each workflow state to be a contiguous region in the building. Second, to further improve the modeling performance, we show how to use the correlation between related types of medical devices to reinforce the parameter estimation for multiple workflow models. In comparison with our preliminary work [11] , we not only develop an integrated workflow modeling framework applicable to general indoor environments, but also improve the modeling accuracy significantly. We reduce the average log-loss by up to 11 percent. [ABSTRACT FROM PUBLISHER]