We investigate how digital management innovation influences external knowledge search so as to survive the digital environment. Through 201 firms that apply big data technology in China, we find that management innovation positively contributes to both science-oriented and market-oriented external knowledge search. Specifically, digital orientation accentuates the positive relationship between management innovation and both external knowledge searches. The theoretical contribution extends the related research of knowledge management theory, integrating the institutional perspective and rational perspective of management innovation for explaining how an organization establishes new standardized and institutional routines with digital tools, ensuring efficient external knowledge search. Practical contributions indicate that firms should attach importance to management innovation for establishing governance systems that are beneficial to searching outside knowledge and keeping competitive advantage.

The great popularity of cloud services, together with the increasingly important aim of providing Internet context-aware services, has spurred interest in developing diverse agriculture applications. This paper presents a cloud-based service built by incrementally integrating state-of-the-art models of deep learning, photography, object recognition and the multi-functionalities of cloud services. This study consists of an object detection phase with a convolutional neural network (CNN) model, which involves enabling simultaneous image data gathered from drones. The experimental results show 97% accurate watermelon recognition. Our results also include a two-model comparison in the cloud-based service, with the main findings demonstrating the feasibility of developing accurate object recognition using a CNN model without the need for additional hardware. Finally, this study adopted a confusion matrix to validate the result with RetinaNet for recognizing images taken on the watermelon farm with an average precision in recognizing watermelon quantity of up to 98.8%.

Culture is the social norm that often dictates a person's thoughts, decision-making, and social behaviors during interaction at an individual level. In this study, we present a computational framework that automatically assesses an individual culture attribute of power distance (PDI), i.e., the measure to describe one's acceptance of social status, power and authority in organizations through multimodal modeling of a participant's expressive prosodic structures and brain connectivity using a social condition-enhanced network. In specific, we propose a joint learning approach of center-loss embedding network architecture that learns to "centerize" the embedding space given a particular social interaction condition to enhance the PDI discriminability of the representation. Our proposed method achieves 88.5% and 73.1% in binary classification task of recognizing low versus high power distance on prosodic and fMRI modality separately. After performing multimodal fusion, it improves to 96.2% of 2-class recognition rate (7.7% relative improvement). Further analyses reveal that average and standard deviation of speech energy are significantly correlated with power distance index; the right middle cingulate cortex (MCC) of brain region achieves the best recognition accuracy demonstrating its role in processing a person's belief about power distance.

Human adapt their behaviors by continuously monitoring one another to function socially in our society. The ability to process face identity from memory is a crucial basic capability. In this work, we propose an event-contrastive connectome network (E-cCN) in representing brain's functional connectivity with contrastive loss to handle layers of fMRI data variabilities exists under different controlled stimuli events to achieve improved automatic assessing of an individual's face processing and memory ability. Our proposed connectome network achieves an overall recognition accuracy of 80.20% and 82.05% in binary classification of separating high versus low scoring subjects on tasks of Taiwanese Face Memory Test (TFMT) and component inverse efficiency score (cIE) respectively. Further, our network embedding representation demonstrate distinct connectivity patterns in key face processing brain regions (ROIs) when comparing between high and low face processing and memory ability.

Understanding neuro-perceptual mechanism of vocal emotion perception continues to be an important research direction not only in advancing scientific knowledge but also in inspiring more robust affective computing technologies. The large variabilities in the manifested fMRI signals among subjects has been shown to be due to the effect of individual difference, i.e., inter-subject variability. However, relatively few works have developed modeling techniques in task of automatic neuro-perceptual decoding to handle such idiosyncrasies. In our work, we propose a novel computation method of deep voting fusion neural network architecture by learning an adjusted weight matrix applied at the fusion layer. The framework achieves an unweighted average recall of 53.10% in a four-class vocal emotion states decoding task, i.e., a relative improvement of 8.9% over a two-stage SVM decision-level fusion. Our framework demonstrates its effectiveness in handling individual differences. Further analysis is conducted to study the properties of the learned adjusted weight matrix as a function of emotion classification accuracy.

Studies have shown that measures of personal physiology, e.g., blood pressure (BP) variation and heart rate variability (HRV), is closely related to a subject's psychological states and are being used regularly to track patients' health conditions in medical settings. The conventional method of monitoring physiology requires wearing specialized sensors or utilizing medical instruments, which hinders the ability of scalable and just-in-time monitoring of patients. In this study, we propose a triplet-loss embedded deep regressor network to predict changes of BP using expressive prosodic features for on-boarding emergency room patients between pre- and post-triage sessions. The framework achieves correlations of 0.419 and 0.386 in predicting changes in SBP (systolic blood pressure) and DBP (diastolic blood pressure) respectively, which is 26.1% and 17.3% relative improvement compared to DNN-regressors without triplet-loss embedding. Further correlation analyses on the relationship between prosodic features and BP changes are presented.

In order to effectively allocate healthcare resource, a proper triage classification system plays an important role in assessing the severity of on-boarding patients at the emergency department. One of the major items in the current triage system is to assess the level of pain intensity, which relies solely on patients self-report numerical-rating scale (NRS) at the moment. The nature of self-report on pain level poses a challenge in maintaining the validity and consistency of the triage classification outcome. While there has been algorithms developed to automatically detect pain from expressive behaviors, most of them concentrate only on facial or body gestural expressions within the context of physical exercises. In this work, we propose to utilize stacked bottleneck acoustic representations in a long-short term memory neural networks (LSTMs) architecture as features for pain severity classification in a database consists of patients during real triage sessions. Our proposed framework achieves accuracy of 72.3% and 54.2% in binary and three-class pain intensity classification tasks. Our results further demonstrate that the severity of pain can largely be captured in the patients prosodic characteristics.