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发布时间:2023-12-27 21:27:16    浏览次数:



期刊:Human Resource Management Journal

期刊介绍:ABS 4*

论文题目:How green human resource management affects employee voluntary workplace green behaviour: An integrated model

作者:袁艺玮(本院教师); Ren, S.; Tang, G.; Ji, H.; Cooke, F. L.; & Wang, Z.


Green human resource management (GHRM), a set of HRM practices targeted at environmental goals, has been proposed as the key to achieving organisational sustainable development. However, the mechanisms through which GHRM influences employee green behaviour are not yet well understood. Drawing on conservation of resources theory, this study presents an integrated model revealing the mixed effects of GHRM on employees' voluntary workplace green behaviour (VWGB). Path analysis based on two studies undertaken in China largely supported our hypotheses. Specifically, GHRM was found to positively influence employees' VWGB through environmental commitment, while simultaneously decreasing their VWGB through emotional exhaustion. Meanwhile, supervisory support for environmental behaviour mitigated the impact of GHRM on emotional exhaustion as well as the relationship between GHRM and employee VWGB via emotional exhaustion. This study contributes to the GHRM literature in particular and organisational environmental management literature in general.


期刊:IEEE Transactions on Knowledge and Data Engineering

期刊介绍:CCF A

论文题目:Heterogeneous Latent Topic Discovery for Semantic Text Mining.

作者:Yawen Li(李雅文);Di JiangRongzhong LianXueyang WuConghui TanYi XuZhiyang Su.摘要:

In order to mine latent semantics from text data, word embedding and topic modeling are two major methodologies in the industry. From a pragmatic perspective, each of these two lines of semantic models faces increasing challenges from real-life applications. Topic modeling view documents as bags of words and is unable to capture the sequential relationship between words. On the other hand, word embedding models the co-occurrence of neighboring words but lacks the global view of the document. Therefore, they can only discover homogenous semantics from a single aspect. However, modern text mining tasks typically require a panoramic view of the latent semantics. Hence, discovering heterogeneous semantics (e.g., heterogeneous types of latent topics) is critical for the performance of these tasks, and it is necessary to design a model that meets this demand. Furthermore, with the arrival of the big data era and the increasing awareness of data privacy, it is necessary to study mining heterogeneous semantics with high efficiency while avoiding compromising data privacy. In this work, we develop a novel method called Heterogeneous Latent Topic Discovery (HLTD) which seamlessly integrates topic modeling with word embedding to discover heterogeneous latent topics. By coupling parameter-server architecture with new private sampling algorithms, HLTD can be efficiently trained to protect underlying data privacy. We evaluate HLTD through a wide range of qualitative and quantitative metrics in the industry. Extensive experiments demonstrate the superiority of HLTD over the state-of-the-arts.


期刊:Information Processing & Management


论文题目:Coarse-grained privileged learning for classification

作者:付赛际(本院教师); Xiaoxiao wang, Yingjie Tian, Tianyi Dong, Jingjing Tang, Jicai Li


Privileged information, a form of prior knowledge, can significantly enhance traditional machine learning performance through a novel paradigm known as learning using privileged information (LUPI). Although effective, current studies on LUPI require a distinct piece of privileged information per input, and these fine-grained priors are difficult to collect in practice. To this end, this paper proposes a brand new problem of learning with class-wise privileged information, where instances within the same class share identical privileged information. As far as we know, this problem has not yet been explored. We build a support vector machine with coarse-grained class-wise priors (CGSVM+) and put forward a novel and reliable augmenting strategy to solve it. In addition, two datasets are collected from nature reserves in Xinjiang, China, along with their class-wise privileged information annotated by professionals. Extensive experiments demonstrate the effectiveness of CGSVM+, with the best average accuracy of 80.16% (94.70%) and the best average F-score of 79.87% (94.57%) on the plant (animal) datasets.


期刊:Pattern Recognition


论文题目:Skeleton estimation of directed acyclic graphs using partial least squares from correlated data

作者:王晓康(本院教师);Shan Lu Rui ZhouHuiwen Wang

摘要:Directed acyclic graphs (DAGs) are directed graphical models that are well known for discovering causal relationships between variables in a high-dimensional setting. When the DAG is not identifiable due to the lack of interventional data, the skeleton can be estimated using observational data, which is formed by removing the direction of the edges in a DAG. In real data analyses, variables are often highly correlated due to some form of clustered sampling, and ignoring this correlation will inflate the standard errors of the parameter estimates in the regression-based DAG structure learning framework. In this work, we propose a two-stage DAG skeleton estimation approach for highly correlated data. First, we propose a novel neighborhood selection method based on sparse partial least squares (PLS) regression, and a cluster -weighted adaptive penalty is imposed on the PLS weight vectors to exploit the local information. In the second stage, the DAG skeleton is estimated by evaluating a set of conditional independence hypotheses. Simulation studies are presented to demonstrate the effectiveness of the proposed method. The algorithm is also tested on publicly available datasets, and we show that our algorithm obtains higher sensitivity with comparable false discovery rates for high-dimensional data under different network structures.(c) 2023 Elsevier Ltd. All rights reserved.


期刊:Engineering Applications of Artificial Intelligence


论文题目:Detection of outlying patterns from sparse and irregularly sampled electronic

health records data

作者:王晓康(本院教师);Chengjian LiHao ShiCongshan WuChao Liu

摘要:Within the intensive care unit (ICU), vital signs such as arterial blood pressure (ABP) collected from electronic health records (EHRs) are typically recorded at different and uneven sampling frequencies and are often infrequently measured due to the nature of the medical treatment. Furthermore, from a temporal trajectory perspective, EHR data are likely to be corrupted by outlying patterns that deviate from normal samples in terms of the curves' magnitude and shape. In this work, we propose a two-stage outlier detection approach for sparse and irregularly sampled (SiS) temporal data using functional data analysis (FDA) tools. In the first stage, an outlier identification measure is defined by a max-min statistic and a clean subset that contains nonoutliers. In the second stage, a multiple hypothesis testing problem is formulated based on the asymptotic distribution of the proposed measure. The simulation-based framework shows that the proposed method is robust to different types of shape and magnitude outliers. The detection results are more accurate than the widely used functional depth methods, especially in extremely sparse settings where the proportion of the observed data points over the entire time series is approximately 10%. Extensive experiments are also conducted on the real-world MIMIC-II dataset, which demonstrate that the method effectively detects clinically meaningful outlying patterns.


期刊:European Journal of Operational Research

期刊介绍:ABS 4;中国科学院一区

论文题目:Robust regression under the general framework of bounded loss functions

作者:付赛际(本院教师); Yingjie Tian; Long Tang

摘要:Conventional regression methods often fail when encountering noise. The application of a bounded loss function is an effective means to enhance regressor robustness. However, most bounded loss functions exist in Ramp-style forms, losing some inherent properties of the original function due to hard truncation. Besides, there is currently no unified framework on how to design bounded loss functions. In response to the above two issues, this paper proposes a general framework that can smoothly and adaptively bound any non-negative function. It can not only degenerate to the original function, but also inherit its elegant properties, including symmetry, differentiability and smoothness. Under this framework, a robust regressor called bounded least squares support vector regression (BLSSVR) is proposed to mitigate the effects of noise and outliers by limiting the maximum loss. With appropriate parameters, the bounded least squares loss grows faster than its unbounded form in the initial stage, which facilitates BLSSVR to assign larger weights to non-outlier points. Meanwhile, the Nesterov accelerated gradient (NAG) algorithm is employed to optimize BLSSVR. Extensive experiments on synthetic and real-world datasets profoundly demonstrate the superiority of BLSSVR over benchmark methods.


期刊:European Journal of Operational Research


论文题目:Responsive strategic oscillation for solving the disjunctively constrained knapsack problem(北京运筹学会优秀青年论文)

作者:魏泽群(本院教师),Jin-kao HaoJintong Ren, Fred Glover

摘要:This paper presents a responsive strategic oscillation algorithm for the NP-hard disjunctively constrained knapsack problem, which has a variety of applications. The algorithm uses an effective feasible local search to find high-quality local optimal solutions and employs a strategic oscillation search with a responsive filtering strategy to seek still better solutions by searching along the boundary of feasible and infeasible regions. The algorithm additionally relies on a frequency-based perturbation to escape deep local optimal traps. Extensive evaluations on two sets of 6340 benchmark instances show that the algorithm is able to discover 39 new lower bounds and match all the remaining best-known results. Additional experiments are performed on 21 real-world instances of a daily photograph scheduling problem. The critical components of the algorithm are experimentally assessed.(c) 2023 Elsevier B.V. All rights reserved.


期刊:IEEE Transactions on Engineering Management

期刊介绍:ABS 3ESI全球Top 1%高被引论文(Economics & Business领域)

论文题目:How can government promote technology diffusion in manufacturing paradigm shift? Evidence from China

作者:许冠南(本院教师), Yuan Zhou, and Huanyong Ji

摘要:Traditional technology diffusion literature focuses on the diffusion of technologies within the extant manufacturing paradigm. By contrast, few studies have explored the determinants and mechanisms of technology diffusion when moving across manufacturing paradigms. In this article, therefore, we aim to explore the intrinsic and institutional factors, as well as the impact mechanism on technology diffusion in the context of manufacturing paradigm shift. Specifically, this article investigates the role of the government in this scenario. A firm-level survey is conducted to investigate the National Programme "Made in China 2025" and its first demonstration city Quanzhou. The data comes from multiple sources, including questionnaires, official statistics data, and patent databases. Logistical regression is used to analyze 236 valid observations. Results reveal that besides the intrinsic factors including the characteristics of general purpose technology (GPT) and economic expectation, GPT-oriented service platforms also have significant impacts on technology diffusion in a manufacturing paradigm shift. In addition, government interventions, especially indirect ones, have significant moderating effects on this influential mechanism. This study provides insight into how government can promote technology diffusion in a manufacturing paradigm shift. These results will be of interest to policy makers, industrialists, and academics.






























































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