241 research outputs found
Organizational Informal Structure Influence On Project Success: Social Capital Approach
Knowledge sharing and organizational citizenship behavior (OCB) among project team members are crucial for project success. The IS project team is a temporary organization and has to produce outcomes in a limited time. we investigate how internal and external social capital (SoC) influence knowledge sharing and OCB within a team and how OCB and knowledge sharing affect project success. We also analyze the relationships between the three social capital dimensions. Our investigation will be analyzed using multi-level approach, which can make up for shortcomings of single-level analysis. This research adds to the current body of knowledge by examining the facilitation of knowledge sharing in the organization through informal interaction and citizenship. A statistical testing has not been complete. We will explore both HLM6 and MPLUS for multiple structural equation modeling and introduce a comparative analysis of each set of results. We expect the results of the research can provide project managers with insights on how to encourage project team members to share their knowledge and build teamwork more efficiently
The Effect of Paradoxical Tensions Between Confucian Culture and Organizational Culture on Fear and Knowledge Sharing Intention
Individuals often encounter challenges balancing collaboration and competition in organizational life. Although paradoxes exist in all organizations, there is minimal empirical research investigating paradoxical tensions at the micro level. Furthermore, previous organizational studies have overlooked employees emotionally driven acts. To fill these research gap, this study examined the paradoxical relationships between espoused cultural values, perceived organizational culture, negative emotions (fear of social exclusion), and knowledge sharing in South Korean organizations. The results show that paradoxical tensions between espoused Confucian culture and knowledge sharing supportive culture result in fear of social exclusion. Subsequently, fear of social exclusion has a negative association with knowledge sharing intention. This study contributes to micro-level research of paradoxes by examining the paradoxes of belonging and of performance at the individual level and their influence on employees’ knowledge-sharing behavior
Observation-Guided Diffusion Probabilistic Models
We propose a novel diffusion model called observation-guided diffusion
probabilistic model (OGDM), which effectively addresses the trade-off between
quality control and fast sampling. Our approach reestablishes the training
objective by integrating the guidance of the observation process with the
Markov chain in a principled way. This is achieved by introducing an additional
loss term derived from the observation based on the conditional discriminator
on noise level, which employs Bernoulli distribution indicating whether its
input lies on the (noisy) real manifold or not. This strategy allows us to
optimize the more accurate negative log-likelihood induced in the inference
stage especially when the number of function evaluations is limited. The
proposed training method is also advantageous even when incorporated only into
the fine-tuning process, and it is compatible with various fast inference
strategies since our method yields better denoising networks using the exactly
same inference procedure without incurring extra computational cost. We
demonstrate the effectiveness of the proposed training algorithm using diverse
inference methods on strong diffusion model baselines
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
As influencers play considerable roles in social media marketing, companies
increase the budget for influencer marketing. Hiring effective influencers is
crucial in social influencer marketing, but it is challenging to find the right
influencers among hundreds of millions of social media users. In this paper, we
propose InfluencerRank that ranks influencers by their effectiveness based on
their posting behaviors and social relations over time. To represent the
posting behaviors and social relations, the graph convolutional neural networks
are applied to model influencers with heterogeneous networks during different
historical periods. By learning the network structure with the embedded node
features, InfluencerRank can derive informative representations for influencers
at each period. An attentive recurrent neural network finally distinguishes
highly effective influencers from other influencers by capturing the knowledge
of the dynamics of influencer representations over time. Extensive experiments
have been conducted on an Instagram dataset that consists of 18,397 influencers
with their 2,952,075 posts published within 12 months. The experimental results
demonstrate that InfluencerRank outperforms existing baseline methods. An
in-depth analysis further reveals that all of our proposed features and model
components are beneficial to discover effective influencers.Comment: ICWSM 202
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Bipolar disorder (BD) is closely associated with an increased risk of
suicide. However, while the prior work has revealed valuable insight into
understanding the behavior of BD patients on social media, little attention has
been paid to developing a model that can predict the future suicidality of a BD
patient. Therefore, this study proposes a multi-task learning model for
predicting the future suicidality of BD patients by jointly learning current
symptoms. We build a novel BD dataset clinically validated by psychiatrists,
including 14 years of posts on bipolar-related subreddits written by 818 BD
patients, along with the annotations of future suicidality and BD symptoms. We
also suggest a temporal symptom-aware attention mechanism to determine which
symptoms are the most influential for predicting future suicidality over time
through a sequence of BD posts. Our experiments demonstrate that the proposed
model outperforms the state-of-the-art models in both BD symptom identification
and future suicidality prediction tasks. In addition, the proposed temporal
symptom-aware attention provides interpretable attention weights, helping
clinicians to apprehend BD patients more comprehensively and to provide timely
intervention by tracking mental state progression.Comment: KDD 2023 accepte
Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output
Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices-Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)-were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05 degrees)
Classification and mapping of paddy rice by combining Landsat and SAR time series data
Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach
Between comments and repeat visit: capturing repeat visitors with a hybrid approach
Purpose
Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.
Design/methodology/approach
The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).
Findings
The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.
Originality/value
The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services
Auto-Guiding System for CQUEAN (Camera for QUasars in EArly uNiverse)
To perform imaging observation of optically red objects such as high redshift
quasars and brown dwarfs, the Center for the Exploration of the Origin of the
Universe (CEOU) recently developed an optical CCD camera, Camera for QUasars in
EArly uNiverse(CQUEAN), which is sensitive at 0.7-1.1 um. To enable
observations with long exposures, we developed an auto-guiding system for
CQUEAN. This system consists of an off-axis mirror, a baffle, a CCD camera, a
motor and a differential decelerator. To increase the number of available
guiding stars, we designed a rotating mechanism for the off-axis guiding
camera. The guiding field can be scanned along the 10 acrmin ring offset from
the optical axis of the telescope. Combined with the auto-guiding software of
the McDonald Observatory, we confirmed that a stable image can be obtained with
an exposure time as long as 1200 seconds.Comment: Accepted for publication in Journal of Korean Astronomical Society
(JKAS
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