Personality identification plays important roles in understanding user behavior and offering foresight ability for downstream applications. The key challenge is how to address the scarcity of labeled personality data. Recently, some studies have adopted data augmentation and prompt learning to perform personality identification. However, they still heavily require a large amount of labeled data to learn an appropriate distance strategy, which limits the generalization and flexibility of the model. This study proposes a knowledge-enhanced hierarchical heterogeneous graph model, which adopts a global multi-view graph node encoding to acquire comprehensive personality features and their inherent associations, where three types of knowledge including part-of-speech (POS) tag, entity, and Linguistic Inquiry and Word Count (LIWC) are introduced. Then, a hierarchical heterogeneous graph with a “post-word-diverse knowledge” structure is constructed for each post to obtain enhanced representation. Finally, a relation guided representation optimization that considers intra-user relationships and inter-label relationships is further developed to learn more discriminative semantic representation. Experimental results on three widely used datasets demonstrate that the model outperforms state-of-the-art methods when training with only 100 samples (approximately 1% of the total data set).

Sarcastic remarks often appear in social media and e-commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users’ complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant’s product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model-based approach equipped with a novel hybrid multihead fusion-attention mechanism that combines self-attention, target-attention, and a feed-forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain-specific dialogue context representation, and bidirectionally enhanced sarcasm-cause pair representations are generated for detecting sarcasm spark. Experimental results on real-world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the National Natural Science Foundation of China [Grants 72293575, 62071467, and 62141608] and the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants 11500322 and 11500421]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0285 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0285 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

ABSTRACT Stylized animation strives for innovation and bold visual creativity. Integrating the inherent strong visual impact and color contrast of woodcut style into such animations is both appealing and challenging, especially during the design phase. Traditional woodcut methods, hand‐drawing, and previous computer‐aided techniques face challenges such as dwindling design inspiration, lengthy production times, and complex adjustment procedures. To address these issues, we propose a novel network framework, the Woodcut‐style Design Assistant Network (WDANet). Our research is the first to use diffusion models to streamline the woodcut‐style design process. We curate the Woodcut‐62 dataset, which features works from 62 renowned historical artists, to train WDANet in capturing and learning the aesthetic nuances of woodcut prints. WDANet, based on the denoising U‐Net, effectively decouples content and style features. It allows users to input or slightly modify a text description to quickly generate accurate, high‐quality woodcut‐style designs, saving time and offering flexibility. Quantitative and qualitative analyses, along with user studies, confirm that WDANet outperforms current state‐of‐the‐art methods in generating woodcut‐style images, demonstrating its value as a design aid.

The rapid development of agricultural cyber-physical systems sheds new light on facilitating agricultural production. The grazing behavior recognition of herded sheep is a paramount issue in animal husbandry. Triaxial accelerometers of agricultural cyber-physical systems provide fine-grained observations of herded sheep but also generate temporal-spatial correlated acceleration data with inherently large-scale dimensions and massive volumes. These inherent characteristics of the data constrain the direct application of existing recognition algorithms. Motivated by the unique features of triaxial accelerometers of agricultural cyber-physical systems, we design a hybrid temporal-spatial fuzzy deep neural network (TSFDNN) approach for predicting the grazing behaviors of herded sheep. We first extract temporal-spatial features and reduce data dimensionality using bidirectional long short-term memory network (Bi-LSTM) and convolutional neural network (CNN) in parallel, then control feature dimensions through principal component analysis (PCA), and finally use fuzzy neural network (FNN) to achieve feature enhancement and category mapping. The superiority of the designed TSFDNN is demonstrated through its empirical comparison with other state-of-the-art machine learning algorithms by using two datasets from sheep pastures. Furthermore, we analyze the rationale of each component in the designed TSFDNN by performing several ablation studies. We also conduct robustness experiments with heterogeneous dimension reduction and optimization algorithms to explore the generalization capabilities of TSFDNN. The managerial implications of precisely identifying herded sheep behaviors for production decision-making, agricultural management, animal welfare, and ecological protection are discussed.

Accurately predicting hotspot reprint paragraphs can timely provide valuable clues for topic selection, thereby improving the influence of the disseminated content. Most existing works in media reprint analysis focus on mining reprint relationships and reprint patterns. Meanwhile, few works predict the hotspot reprint paragraph from a fine-grained level. The writing style reflects the structure and semantic logic of the article to some extent. Thus, the challenge is to determine how to effectively incorporate writing style features into the semantic analysis while also reasoning deeply about the semantic relevance between sections of the article. This paper proposes a multi-perspective relevance collaborative modeling method called MPRCM-TS. It integrates writing styles of titles into the semantic representations and deeply mines the multi-perspective semantic relevance between the title and paragraphs on the basis of the attention mechanism. Simultaneously, multiple loss functions collaborate to enhance the parameter optimization ability. We evaluate the performance of the proposed model on a real-world dataset, and the experimental results demonstrate the efficacy.

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for enhancing agricultural production, where evapotranspiration estimation is a crucial issue in crop irrigation systems. However, tremendous and unsynchronized data from agricultural cyber-physical systems bring large computational costs as well as complicate performing conventional machine learning methods. To precisely estimate evapotranspiration with acceptable computational costs under the background of IoT, we combine time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO) to propose a hybrid machine learning approach. In the combination, a fuzzy granulation method and a time calibration technique are introduced to break voluminous and unsynchronized data into small-scale and synchronized granules with high representativeness. Subsequently, GBDT is implemented to predict evapotranspiration, and BO is utilized to find the optimal hyperparameter values from the reduced granules. IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.

This study proposes and evaluates the effect of “mixed” comparative reviews on review value and compares the results with “separate” comparative and regular reviews. A total of 201 subjects have participated in the experiment conducted in this study. Results indicate that mixed comparative reviews in text format are perceived as less valuable than separate comparative reviews in text format. However, mixed comparative reviews in tabular format have more review value than those in text format and are perceived as more valuable than regular reviews of one product in either format. Unsurprisingly, the positive reviews of the target product lead to higher product attitude than negative reviews. However, this effect is weak in mixed (vs. separate) comparative reviews.

Online reviews are playing an increasingly important role in understanding and predicting users’ rating behavior, which brings great opportunities for users and organizations to make better decisio...