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Although traditional studies concerning the diffusion effect of board interlocks stress the influence of mimetic pressures, extant studies increasingly emphasize board interlocks' communication role. From the perspective of corporate social responsibility (CSR) reporting, this paper seeks to investigate the validity of the two views by introducing market development as a moderating variable. Based on samples of Chinese listed companies between 2008 and 2015, we find that board interlocks positively affect firms' CSR reporting. Consistent with the communication mechanism view, the impact of board interlocks on CSR reporting is relatively weak in developed regions. With this study, we contribute to the literature concerning the diffusion effect of board interlocks and the institutional literature related to CSR reporting. K E Y W O R D S board interlocks, corporate social responsibility (CSR) reporting, diffusion effect, market development

Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

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In recent years, sensor-based human activity recognition (HAR) has gained tremendous attention around the world with a range of applications. Instead of using body sensor network-based recognition systems which are intrusive and increase equipment cost, we focus on the development of efficient HAR approach based on a single triaxial accelerometer. In order to improve the recognition accuracy of the system, a novel recognition approach based on kernel discriminant analysis (KDA) and quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) is proposed. KDA is utilized to extract more meaningful features and enhance the discrimination between different activities. To verify the effectiveness of KDA, three kinds of features including original features, linear discriminant analysis (LDA) features and KDA features are extracted and compared for activity recognition. In addition, QPSO-KELM is compared with two existing classification methods: support vector machine (SVM) and extreme learning machine (ELM), which are commonly utilized in activity recognition. Meanwhile, two comparative optimization methods for KELM are also discussed in the experiment. The experimental results demonstrate the superiority of the proposed approach. INDEX TERMS Human activity recognition, kernel Fisher discriminant analysis, kernel extreme learning machine, quantum-behaved particle swarm optimization, wearable sensor.