Information and communication technologies: interfaces the nursing work process

ABSTRACT Objectives: to unveil the meanings that nurses attribute Information and Communication Technologies for the nursing work process Methods: qualitative research, theoretically and methodologically based on the Complexity Theory and on the Grounded Theory, respectively. Research with 19 participants, being 12 clinical nurses, and 7 resident nurses. Semi-structured interviews were used for data collection. Results: the results revealed the meanings that clinical nurses attribute to Information and Communication Technologies and, thus, the motivations and limitations for the use of these technologies, pointing out possibilities and strategies that impact the nursing work process, based on the interactions promoted by the official and non-official use of these resources. Final Considerations: the meanings that nurses attribute to Information and Communication Technologies are dependent on their ability to successfully employ those technologies and their importance to the work process developed by the professionals.

Optimal Decision-Making Model of Agricultural Product Information Based on Three-Way Decision Theory

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information.

Extending Proximity Measures to Attributed Networks for Community Detection

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user’s social-demographic features, the item’s content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model.

Multi-attribute interactive visualization of three-dimensional trajectory sets

Rapidly advancing location-awareness technologies and services have collected and stored massive amounts of moving object trajectory data with attribute information that involves various degrees of spatial scales, timescales, and levels of complexity. Unfortunately, interesting behaviors regarding combinations of attributes are scarcely extracted from datasets. Further, trajectories are typically dependent on the environment of three-dimensional space, and another issue of interest to us is to preserve spatial-location visualization while guaranteeing the description of temporal information. Therefore, we developed a novel analytics tool that combines visual and interactive components to enable a dynamic visualization of three-dimensional trajectory multi-attribute behaviors. Under the context of spatiotemporal analysis, this approach integrates multiple attributes into one view to efficiently explore the attribute visualization problem of multi-attribute combination without over-plotting. To assess the feasibility of our solution, we visualized and analyzed multi-attribute information of moving object trajectories using a real mining truck dataset as a case study.

Progressive Open-Domain Response Generation with Multiple Controllable Attributes

It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attributes. In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage. A vital characteristic of the CVAE is to separate the latent variables at each stage into two types: a global variable capturing the common semantic features and a specific variable absorbing the attribute information at that stage. PHED then couples the CVAE latent variables with the Transformer encoder and is trained by minimizing a newly derived ELBO and controlled losses to produce the next stage's input and produce responses as required. Finally, we conduct extensive evaluations to show that PHED significantly outperforms the state-of-the-art neural generation models and produces more diverse responses as expected.

Augmented reality as a product presentation tool: focusing on the role of product information and presence in AR

As augmented reality (AR) technology advances, marketers are eager to adopt the technology for communication to persuade consumers to develop favorable attitudes and behaviors toward their products and services. This study aims to investigate the effect of product information (utilitarian vs. hedonic attributes) and presence on consumers’ product evaluation in AR. Through a quasi experiment, this study demonstrates how product attribute information and presence in AR affect product evaluation by mediating imagery, information fulfillment, and psychological ownership. At the same time, this study identifies the moderating role of consumers’ technological innovativeness in the effect of presence on consumers’ imagery. This research offers new insights into the role of product information in AR, which previous studies lack, to explore and highlight the predictors of positive product experiences in AR. Innovative marketers are likely to benefit from this study in developing product presentation tactics with AR technology.