Conversational agents (CAs) offer new functionality and convenience. While their sales have been soaring, they have also rapidly become victims of verbal abuse by their users. Without proper handling ...of abusive usage, abusers' actions can be reinforced and transferred to real life. This study investigates whether alternative response styles of empathy orientation and emotional expressivity of voice-activated virtual assistants influence users' moral emotions found to reduce verbal aggression as well as whether they affect user perceptions of the agent's capability. Ninety-eight participants were assigned to one of the three emotional expressivity conditions (no-facial expression, fixed-facial expression, varied-facial expression) and interacted with two alternative empathy orientation conditions (other-oriented, self-oriented) of agents. The experimental results show that, regardless of the emotional expressivity types, the agent's empathy orientation has a significant effect on the moral emotions and agent capability perceptions. Overall, an agent that employed other-oriented empathy style elicited most positive responses from the users. However, the preference was not across the board, as about one-third of the participants showed preference to the self-oriented CA. Users valued agents' verbal contents and vocal characteristics above their facial expressions. Based on the study findings, we draw several design guidelines and suggest avenues for future research.
Hedonic pricing method (HPM), which is commonly used for estimating real estate property values, considers the property’s internal and external characteristics for its valuation. Despite its ...popularity, however, the method lacks the mechanism that directly reflects the target property’s price fluctuation and the real estate market’s volatility over time. To overcome these limitations, we propose Pseudo Self Comparison Method (PSCM), which reduces the real estate valuation problem to finding a pseudo self, which is defined as a housing property that can most closely approximate the characteristics of the target housing property, and adjusting its previous transaction price to be in sync with the real estate market change. The proposed PSCM is tested for two scenarios in which the volatility of the real estate market varies greatly, using the transaction data compiled from Seoul, the capital of South Korea, and its surrounding region, Gyeonggi. The study results show almost five times lower estimation errors when predicting housing transaction prices using the PSCM compared to the HPM in both scenarios and in both areas. The proposed method is particularly useful for mass valuation of apartments or densely located housing units.
Fueled by the power of AI, chatbots are becoming more personal. Prior research showed that a chatbot has great potential to elicit its user's self-disclosure because it does not judge the user. ...However, the chatbot's features beyond the conversational characteristics in eliciting a user's self-disclosure are not as well researched. In this study, we have developed a chatbot and implemented two non-conversation features: (1) co-activity (COA), conducting an activity together, and (2) conversation atmosphere visualization (CAV), visually displaying the emotional feelings conveyed in the conversation, to examine their effects on self-disclosure and user experience. We conducted a field study involving 87 participants who were randomly assigned to one of the four experimental conditions (control, COA only, CAV only, CAV + COA) and asked to use the assigned chatbot for 10 days in their natural life setting. Our results from this field study show that both the COA and CAV features have significant effects on a user's self-disclosure. In addition, interaction effects between COA and CAV have been found to affect a user's intention to use. Based on the findings, we provide design implications for a user's self-disclosure and trusting relationship development with a chatbot.
This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). ...Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II errors. To overcome this critical problem, this paper presents a simple yet effective method for noisy patch classification. The proposed method, validated using stomach cancer images, provides a significant improvement compared to other existing methods in patch-based cancer classification, with accuracies of 98.81%, 97.30% and 89.47% for binary, ternary, and quaternary classes, respectively. Moreover, we conduct several experiments at different noise levels using a publicly available dataset to further demonstrate the robustness of the proposed method. Given the high cost of producing explicit annotations for whole-slide images and the unavoidable error-prone nature of the human annotation of medical images, the proposed method has practical implications for whole-slide image annotation and automated cancer diagnosis.
The swine industry is one of the industries that progressively incorporates smart livestock farming (SLF) to monitor the grouped-housed pigs’ welfare. In recent years, pigs’ positive welfare has ...gained much attention. One of the evident behavioral indicators of positive welfare is playing behaviors. However, playing behavior is spontaneous and temporary, which makes the detection of playing behaviors difficult. The most direct method to monitor the pigs’ behaviors is a video surveillance system, for which no comprehensive classification framework exists. In this work, we develop a comprehensive pig playing behavior classification framework and build a new video-based classification model of pig playing behaviors using deep learning. We base our deep learning framework on an end-to-end trainable CNN-LSTM network, with ResNet34 as the CNN backbone model. With its high classification accuracy of over 92% and superior performances over the existing models, our proposed model highlights the importance of applying the global maximum pooling method on the CNN final layer’s feature map and leveraging a temporal attention layer as an input to the fully connected layer for final prediction. Our work has direct implications on advancing the welfare assessment of group-housed pigs and the current practice of SLF.
In this paper, we share our experience in augmenting a focused crawler of our vertical search engine designed to work with academic slides. The goal of the
focused
crawler was to collect Microsoft ...PowerPoint files from academic institutions. A previous approach based on a
general
web crawler can fail to collect a sufficient number of files mainly because of the robots exclusion protocol and missing hyperlinks. As a remedy to these problems, we propose a combinatory approach in which the indexing information maintained by a general web search engine such as Google is utilized for target URL list generation through our query generator, further then complemented by our URL extractor and file downloader. Because Google has already crawled billions of web pages, it will be more cost-efficient and potentially effective to systematically retrieve the desired information from Google than to redo crawling from scratch by ourselves. Our focused crawler, which we call
SlideCrawler
, has been used for our vertical search engine
CourseShare
since the fall of 2011. The capability of SlideCrawler was verified for the top-500 world wide universities. SlideCrawler collected about one million files from the top-500 universities. Further, the study results show that SlideCrawler outperforms Nutch, collecting 3.7 times more slide files.
In recent years, the use of deep learning techniques to forecast the weather has increased significantly; however, existing machine learning methods based on observed data are only suitable for very ...short-term forecasting. Numerical models are more stable for short- and medium-term forecasting, but the results may deviate from the observed data. This study proposes a deep learning method to improve the performance of numerical weather prediction models. In this method, the transformation relationship between the output of the numerical model and the observed data is learned by a generative adversarial network, which is then used to correct the forecasts of the numerical model. Experiments on 9 months of paired numerical model data and observed radar data demonstrate that correction of the forecast data using this method improves prediction performance, especially of heavy rainfall events. The proposed method provides a practical approach to combining conventional numerical weather prediction with data-driven deep learning models.
Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. ...Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists.
Our whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7-10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17-30 times the number of potential human errors were detected within an average of 1.2 days.
The AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.
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