Due to the increase in life expectancy in advanced societies leading to an increase in population age, data-driven systems are receiving more attention to support the older people by monitoring their health. Intelligent sensor networks provide the ability to monitor their activities without interfering with routine life. Data collected from smart homes can be used in a variety of data-driven analyses, including behavior prediction. Due to privacy concerns and the cost and time required to collect data, synthetic data generation methods have been considered seriously by the research community. In this article, we introduce a new Generative Adversarial Network (GAN) algorithm, namely, BehavGAN , that applies GAN to the problem of behavior sequence generation. This is achieved by learning the features of a target dataset and utilizing a new application for GANs in the simulation of older people’s behaviors. We also propose an effective reward function for GAN back-propagation by incorporating n-gram-based similarity measures in the reinforcement mechanism. We evaluate our proposed algorithm by generating a dataset of human behavior sequences. Our results show that BehavGAN is more effective in generating behavior sequences compared to MLE, LeakGAN, and the original SeqGAN algorithms in terms of both similarity and diversity of generated data. Our proposed algorithm outperforms current state-of-the-art methods when it comes to generating behavior sequences consisting of limited-space sequence tokens.

Purpose Effective information security management is a strategic issue for organizations to safeguard their information resources. Strategic value alignment is a proactive approach to manage value conflict in information security management. Applying a critical success factor (CSF) analysis approach, this paper aims to propose a CSF model based on a strategic alignment approach and test a model of the main factors that contributes to the success of information security management. Design/methodology/approach A theoretical model was proposed and empirically tested with data collected from a survey of managers who were involved in decision-making regarding their companies’ information security (N = 219). The research model was validated using partial least squares structural equation modeling approach. Findings Overall, the model was successful in capturing the main antecedents of information security management performance. The results suggest that with business alignment, top management support and organizational awareness of security risks and controls, effective information security controls can be developed, resulting in successful information security management. Originality/value Findings from this study provide several important contributions to both theory and practice. The theoretical model identifies and verifies key factors that impact the success of information security management at the organizational level from a strategic management perspective. It provides practical guidelines for organizations to make more effective information security management.

The objective of this study is to propose and test a theoretical model on pre-adoption views of Electronic Medical Record (EMR) systems by Canadian medical practitioners. An empirical investigation was conducted with a sample of Canadian practitioners not currently using EMRs to identify relevant factors favouring or deterring adoption. The results show that positive performance and effort expectancies help while perceived psychological risk hinders adoption in the view of Canadian medical practitioners who are not yet using EMRs. Overall, this study contributes to understanding practitioners’ views on the potential adoption of EMRs with the purpose of increasing the success of this novel eHealth tool.

Demands for better health services have resulted in the enhancement of electronic health (eHealth) systems with newer concepts such as being patient-centric, easy to use, and having smarter interactions and more accurate decisions. While eHealth services aim to provide continuous medical and health services for both consumers and medical practitioners, access to allied healthcare experts such as nurses is still a challenge. In this paper, we propose a new framework, Virtual Remote Nursing (VRN) that provides a virtual nurse agent installed on the client's personal computer or smart phone to help manage the client's health condition continuously. With this approach, medical practitioners can assign different tasks to a virtual nurse using a generic task definition mechanism, where a task is defined as a combination of medical workflow, operational guidelines, and associated data. VRN is controlled by practitioners who decide on the patient's treatment. This allows a VRN to act as a personalized full-time nurse for its client, by performing practitioner support tasks, using data collected on the client's health condition. Such patient information could be obtained from an electronic Personal Health Record (ePHR) system such as Google Health or Microsoft Health Vault. We have developed a prototype system that enables traditional client applications and healthcare provider systems to collaborate using a VRN system. Finally, through a case study, we demonstrate how diabetic patients can take advantage of this system.

Electronic personal health record systems (PHRs) support patient centered healthcare by making medical records and other relevant information accessible to patients, thus assisting patients in health self-management. We reviewed the literature on PHRs including design, functionality, implementation, applications, outcomes, and benefits. We found that, because primary care physicians play a key role in patient health, PHRs are likely to be linked to physician electronic medical record systems, so PHR adoption is dependent on growth in electronic medical record adoption. Many PHR systems are physician-oriented, and do not include patient-oriented functionalities. These must be provided to support self-management and disease prevention if improvements in health outcomes are to be expected. Differences in patient motivation to use PHRs exist, but an overall low adoption rate is to be expected, except for the disabled, chronically ill, or caregivers for the elderly. Finally, trials of PHR effectiveness and sustainability for patient self-management are needed.

This paper explores the performance of Chinese small and medium sized enterprises (SMEs) on Business-to-Business (B2B) electronic marketplaces (EMs). Based on a content analysis of 155 cases of high performing online Chinese vendors, this paper explains the success of SME online B2B vendors within a Motivation-Capability framework. This first generation of SME B2B online vendors proved highly motivated to increase sales and developed a set of Internet leveraged organizational capabilities to compete online, including capabilities for online marketing, product innovation, eCommerce management, etc. This study differs from traditional wisdom that online marketplaces will render Guanxi (a Chinese cultural phenomenon defined as close and pervasive interpersonal relationships, Yang, 1994) irrelevant since online marketplaces are perceived to be impersonal. In fact, Guanxi still matters online, but it takes new forms. This research offers important managerial implications for B2B SME online vendors on how to leverage EMs for higher performance.

Ease of Doing Business (EDB) indicators are essential to overall understanding and evaluation of national business environment, and strategy formulation for business policy and regulations. The World Bank does an annual study of these indicators for over 170 nations, but there are many complications and uncertainties involved in the work. This paper proposes a new systematic approach that employs fuzzy set theory to generate composite EDB indicators for ranking and classification problems. We implemented this approach and illustrate its steps and procedures. A case study example for Canada is also presented in which EDB indicators are evaluated, linguistically identified, and ranked. This approach demonstrates the ease of using this fuzzy application, and its potential benefits for future research. We also compare ranking results, obtained from our proposed approach, with the World Bank's results.

A neural network based fuzzy set model is proposed to support organizational decision making under uncertainty. This model incorporates three theories and methodologies: classical decision-making theory under conflict, as suggested by Luce and Raiffa (1957), the fuzzy set theory of Zadeh (1965, 1984), and a modified version of the backpropagation (BP) neural network algorithm originated by Rumelhart et al. (1986). An algorithm that implements the model is described, and an application of the model to a real data example is used to demonstrate its use.

A human-computer interface is described, which was designed to study user preferences and the effectiveness of output modes and levels of information abstraction in a decision making environment. The interface was tested in an exploratory study of an apartment selection problem. It was observed that text plus voice was preferred over voice alone, but there was no significant difference in preferences between text and voice or between text and text plus voice. This indicates that adding text to voice output improves the perceived acceptability of voice, but adding voice to text does not alter the perceived acceptability of text. The text mode was most efficient in performing information search, followed by voice mode and text plus voice mode in that order. We observed inconsistencies between the users' perceived importance of information attributes and the actual usage of these attributes, and inconsistencies between the perceived importance of and the usage of abstraction levels. We did not observe significant differences between users with task domain experience and those which did not have domain experience, but cognitive style did affect task performance. Our findings suggest that a user interface should either provide flexible access at different abstraction levels, or should organize information based on its perceived importance to the user rather than its level of abstraction.

As a learning machine, a neural network using the backpropagation training algorithm is subject to learning bias. This results in unpredictability of boundary generation behavior in pattern recognition applications, especially in the case of small training sample size. It is suggested that in a large class of pattern recognition problems such as managerial and other problems possessing monotonicity properties, the effect of learning bias can be controlled by using multiarchitecture monotonic function neural networks.>

In neural network classification techniques, the uncertainty of a new observation belonging to a particular class is difficult to express in statistical terms. On the other hand, statistical classification techniques are also poor for supplying uncertainty information for new observations. The use of fuzzy sets is a promising approach to providing imprecise class membership information. The monotonic function neural network is a tool that can be used to develop fuzzy membership functions. This research suggests that a multiarchitecture monotonic function neural network can be used for fuzzy set representation of classification boundaries in monotonic pattern recognition.>