Cyber attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems (IDSs). To address this issue, we introduce EvolCostDeep, a hybrid model of stacked autoencoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning (DL) on Big data hinders the scalability of IIoT IDSs. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-level mechanisms. After aggregating detection outputs from worker nodes to the master, the result is passed to the cloud platform for mitigating attacks. A series of experiments is conducted on the ToN-IoT and UNSW-NB15 data sets to evaluate the performance of EvolCostDeep and DeepIDSFog. The results show that our frameworks can effectively handle both class imbalance problem and scalability of big IIoT traffic data compared with the other models. The averaged values of the EvolCostDeep for recall, precision, and $F1$ -score on the data sets are of 93.3%, 97.6%, and 95.2%, respectively, which are higher than the compared methods. Also, the DeepIDSFog provides an average speedup of $38.7\times $ over other comparing models.

Analysis of online user-generated content is receiving attention for its wide applications from both academic researchers and industry stakeholders. In this pilot study, we address common Big Data problems of time constraints and memory costs involved with using standard single-machine hardware and software. A novel Big Data processing framework is proposed to investigate a niche subset of user-generated popular culture content on Douban, a well-known Chinese-language online social network. Huge data samples are harvested via an asynchronous scraping crawler. We also discuss how to manipulate heterogeneous features from raw samples to facilitate analysis of various film details, review comments, and user profiles on Douban with specific regard to a wave of South Korean films (2003–2014), which have increased in popularity among Chinese film fans. In addition, an improved Apriori algorithm based on MapReduce is proposed for content-mining functions. An exploratory simulation of results demonstrates the flexibility and applicability of the proposed framework for extracting relevant information from complex social media data, knowledge which can in turn be extended beyond this niche dataset and used to inform producers and distributors of films, television shows, and other digital media content.

This article uses a case study of the reception of popular South Korean films in China to investigate aspects of proactive participation in the Chinese social mediasphere. Based on the big data collected from the popular Douban online review platform, the authors provide an in-depth investigation of the characteristics of online opinion leaders and leadership. First, we statistically analyze user-generated content-related features for both leader and non-leader groups. In addition, we employ a Bayesian optimization-based imbalanced learning algorithm to identify characteristics of the minority opinion leaders. The efficiency of the proposed algorithm is evaluated based on 17,786 reviews collected from 35 popular films covering a variety of genres, and the proposed algorithm is shown to perform accurately by identifying opinion leaders, compared to other state-of-the-art approaches. Our study also reveals some major features associated with opinion leaders, such as vocabulary, viewing habits, and friendship networks. Finally, we show how these elements are contributing to new understandings of the rapidly transforming digital domain of Chinese social media and the roles of such platforms.

To obtain indications of the influence of electronic health records (EHR) in managing risks and meeting information system accreditation standard in Australian residential aged care (RAC) homes. The hypothesis to be tested is that the RAC homes using EHR have better performance in meeting information system standards in aged care accreditation than their counterparts only using paper records for information management. Content analysis of aged care accreditation reports from the Aged Care Standards and Accreditation Agency produced between April 2011 and December 2013. Items identified included types of information systems, compliance with accreditation standards, and indicators of failure to meet an expected outcome for information systems. The Chi-square test was used to identify difference between the RAC homes that used EHR systems and those that used paper records in not meeting aged care accreditation standards. 1,031 (37.4%) of 2,754 RAC homes had adopted EHR systems. Although the proportion of homes that met all accreditation standards was significantly higher for those with EHR than for homes with paper records, only 13 RAC homes did not meet one or more expected outcomes. 12 used paper records and nine of these failed the expected outcome for information systems. The overall contribution of EHR to meeting aged care accreditation standard in Australia was very small. Risk indicators for not meeting information system standard were no access to accurate and appropriate information, failure in monitoring mechanisms, not reporting clinical incidents, insufficient recording of residents’ clinical changes, not providing accurate care plans, and communication processes failure. The study has provided indications that use of EHR provides small, yet significant advantages for RAC homes in Australia in managing risks for information management and in meeting accreditation requirements. The implication of the study for introducing technology innovation in RAC in Australia is discussed.