Miracle-dz/UniFAR-FTUs huggingface dataset:
Lenders' Perceived Risk in Online P2P Lending: the Elaboration Likelihood Model Perspective

Online peer-to-peer (P2P) lending platforms become a critical channel for financing. However, lenders suffer from economic lose due to dishonest behaviors of platforms and borrowers. By identifying risk correctly, lenders can make appropriate investment decisions to survive from risk and gain profits. Yet the existing empirical literature remains unclear about which factors influence lenders' risk perceptions and investment decision. Therefore, we draw on elaboration likelihood model to examine how lenders make their investment decisions away from problematic platforms and risky bids. Specifically, we identify argument frame as a central route of perceived risk, and platform reputation and source credibility as peripheral cues. We also examine roles of lender personal factors, i.e., cognitive capability and risk preference. We plan to conduct a laboratory experiment to collect data and test hypotheses. This study contributes to the cognition research in online financial products and provides insights for risk control and website design.
Psychological Contract and Knowledge Sharing: Evidence from Enterprise System Post-Implementation

This paper discusses the relationship between psychological contract and knowledge sharing behavior in enterprise system post-implementation stage. The fulfillment and obligation of psychological contract are proposed as antecedents of knowledge sharing behavior performed by client firms. Additionally, the strategic concern is incorporated by addressing the moderating role of entrepreneurial orientation. Based on survey data collected from 132 client firms, psychological contract fulfillment is negatively related to knowledge sharing; whereas the positive role of psychological contract obligation is supported. The moderating effects of entrepreneurial orientation is significant as proposed by hypotheses. Discussion and implication are also provided.
Water Depth Estimation from Worldview-2 Image with Back Propagation Neural Network in Coastal Area

Bathymetry provides important information for marine environment with various hydrological applications. Traditional acoustic echo-sounding techniques have been developed for several decades which can measure water depth more than hundreds of meters. However, they have their limitation in the shallow area when it is difficult to access by ships. Recent airborne bathymetry LIDAR systems can access coastal area without restriction and can measure water depth up to tens of meters in clear water, but the operational expanse is high. The economic approach for bathymetry estimation in coastal area is optical satellite images. They can survey a large area with single or multiple satellite images and the penetration of visible light in water merely reaches 30 meters. In this study, a three-layer back propagation neural network is proposed to estimate bathymetry with limited number of training samples. The experiments show the mean square errors are less than 5 meters.
Travel and Online Review Behavior

Understanding the generation of online reviews is a fundamental issue for firms to gain benefits from online reviews. Our study tries to investigate the antecedents of online review characteristics by figuring out the following two research questions: (1) Will travel influence consumers’ post-consumption behavior (i.e., consumers’ review behavior)? And if so, (2) will consumers’ social capital moderate the influence of travel on consumers’ review behavior? The results show that consumers on travel tend to give higher review ratings and are more possible to post pictures while writing online reviews; consumers’ social capital level exacerbates the positive influence of travel on review ratings and mitigates the positive impact of travel on review richness.
Validation of Global Land Surface Satellite Phase-2 Surface Broadband Albedo Product

Surface broadband albedo is a critical parameter in surface radiation budget (SRB), which controls the input, separation, and allocation of the Earth's surface energy. Recently, a Global LAnd Surface Satellite (GLASS) phase-2 surface broadband albedo product has been generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) data. In this study, we present a preliminary validation of the GLASS phase-2 surface broadband albedo with the FLUXNET 2015 dataset. The results show that the GLASS phase-2 surface broadband albedo product has a reasonable good accuracy compared with the currently widely used surface albedo dataset, and can provides more reliable estimations of surface albedo at the seasonal snowfall and snowmelt regions. In addition, the GLASS phase- 2 surface albedo product is along-term, global covered (including land and ocean surfaces) dataset, which can be applied in various scenarios of SRB and climate change studies.
Weak Ties and Super Bowl Advertisements: Relating Network Density with Performance and Information Diffusion

The presence of hashtags in social media conversations is now a norm and represents the public’s perceptions of associations between entities in reference to events, emotions, personal tastes, and preferences. Much is yet unknown about relationships among hashtags and how they impact performance and information diffusion in relation to associated entities, brands, and personalities. This study examines the relationship between strength of network ties with performance and information diffusion. We adhere to the strength of ties theory of Granovetter (1973) which implies that weak and strong ties between entities have different implications to performance and information diffusion. Using Twitter data from the 2018 Super Bowl advertisements, we found that advertisements with weak ties (low network density) tend to be associated with higher performance while those with strong ties (high network density) are more likely to relate to higher information diffusion. Contributions to both research and practice are discussed.
Percutaneous Vertebral Augmentation Techniques in Osteoporotic and Traumatic Fractures

Abstract Percutaneous vertebral augmentation/consolidation techniques are varied. These are vertebroplasty, kyphoplasty, and several methods with percutaneous introduction of an implant (associated or not with cement injection). They are proposed in painful osteoporotic vertebral fractures and traumatic fractures. The objectives are to consolidate the fracture and, if possible, to restore the height of the vertebral body to reduce vertebral and regional kyphosis. Stabilization of the fracture leads to a reduction in pain and thus restores the spinal support function as quickly as possible, which is particularly important in the elderly. The effectiveness of these interventions on fracture pain was challenged once by two randomized trials comparing vertebroplasty to a sham intervention. Since then, many other randomized studies in support of vertebroplasty efficacy have been published. International recommendations reserve vertebroplasty for medical treatment failures on pain, but earlier positioning may be debatable if the objective is to limit kyphotic deformity or even reexpand the vertebral body. Recent data suggest that in osteoporotic fracture, the degree of kyphosis reduction achieved by kyphoplasty and percutaneous implant techniques, compared with vertebroplasty, is not sufficient to justify the additional cost and the use of a somewhat longer and traumatic procedure. In young patients with acute traumatic fractures and a significant kyphotic angle, kyphoplasty and percutaneous implant techniques are preferred to vertebroplasty, as in these cases a deformity reduction has a significant positive impact on the clinical outcome.
AIS Electronic Library (AISeL)

Recent years have witnessed an increasing amount of research efforts and applications on keyword bid in search engine advertising, giving rise to a focal point of research for the question of how to generate more relevant and novel keywords in targeting users’ intents. This paper introduces the notion of keywords with indirect relevance based on query logs, and then proposes a new semantic transfer keyword suggestion method (namely, STK) to generate the candidate keywords with links to the seed keyword in light of semantic transfer paths that are formulated with multi-step relevance. To reduce computational consumption in generating the links, a pruning strategy is developed based upon certain properties of semantic transfer paths, so that those unnecessary links are cut in the algorithmic process. Furthermore, the STK method is tested via data experiments and illustrative examples, demonstrating the overall superiority of STK in comparison with other major keyword suggestion methods of concern for effectiveness measures.
Environmental Monitoring Using Drone Images and Convolutional Neural Networks

Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: “water”, “deforesting area”, “forest”, and “buildings”. The results are analyzed by experts in the field and considered pretty much reasonable.
What Motivates Crowdsourcing Contributors? A Cross-Platform Comparative Analysis

The crowdsourcing business model has gained increasing popularity over the last years. When requesters (companies, institutions or individuals) crowdsource tasks, they place them on a platform where contributors can view and complete them. This study assesses what factors motivate contributors to complete crowdsourcing tasks and whether and to what degree these factors differ across crowdsourcing platforms. This study compares the results of quantitative surveys of contributors on two crowdsourcing platforms: RapidWorkers and Amazon Mechanical Turk. The results show that contributors on both platforms are motivated by payment and by indirect feedback from the job. However, contributors on RapidWorkers are also motivated by enjoyment-based factors, whereas contributors at Mechanical Turk are also motivated by community-based factors and delayed payoffs. Thus, the contributors at RapidWorkers tend to be more extrinsically motivated, while the contributors at Mechanical Turk tend to be more intrinsically motivated.
Long Term Elevation Change Monitoring of Antarctic Ice Sheet by Combining ICESat, Envisat and CryoSat-2 Data

A long term assessment of the Antarctic ice sheet elevation change trend from 2003 to 2016 has been carried out using a combination of ICESat (2003–2008), Envisat (2003–2008) and CryoSat-2 (2010–2016). The contemporaneous ICESat and Envisat results showed a consistency in the estimated elevation change rate of Antarctica during 2003 to 2008: $0.1\pm 0.1\ \text{cm}\ \mathrm{a}^{-1}$ to $0.3\pm 0.6\ \text{cm}\ \mathrm{a}^{-1}$, respectively, which were then combined as the overall elevation change of Antarctica during 2003 to 2008. The recent CryoSat-2 result suggested an overall elevation change rate of Antarctic ice sheet of from 2010 to 2016. Furthermore, the elevation change of East Antarctica is relatively small, while West Antarctica appears to have an elevation decrease trend. The above is based on our preliminary data processing and analysis results. The high uncertainties in Basin 15 and Antarctic Peninsula need to be further investigated. We will report our improved results at the conference.
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning

The ability to perform offline A/B-testing and off-policy learning using logged contextual bandit feedback is highly desirable in a broad range of applications, including recommender systems, search engines, ad placement, and personalized health care. Both offline A/B-testing and off-policy learning require a counterfactual estimator that evaluates how some new policy would have performed, if it had been used instead of the logging policy. In this paper, we identify a family of counterfactual estimators which subsumes most such estimators proposed to date. Our analysis of this family identifies a new estimator - called Continuous Adaptive Blending (CAB) - which enjoys many advantageous theoretical and practical properties. In particular, it can be substantially less biased than clipped Inverse Propensity Score (IPS) weighting and the Direct Method, and it can have less variance than Doubly Robust and IPS estimators. In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator. Experimental results show that CAB provides excellent evaluation accuracy and outperforms other counterfactual estimators in terms of learning performance.
Shipnet for Semantic Segmentation on VHR Maritime Imagery

For VHR maritime images, sematic segmentation is a new research hotspot and plays an important role in coastline navigation, resource management and territory protection. Without enough labeled training data, it is a challenge to separate small objects on a large scale while segment the big area clearly. To deal with it, we propose a novel ShipNet and design a weighted loss function for simultaneous sea-land segmentation and ship detection. To prove the proposed method, we also built and opened a new dataset to the community which contains VHR multiscale maritime images. Compared with the FCN and ResNet, the proposed method got much better F1 scores 85.90% for ship class and 97.54% overall accuracy. Compared with multiscale FCN, the ShipNet could obtain details results like sharp edges. Even for images with bad quality, the ShipNet could also keep robust and get good results.
Estimation of Wildfire Spread Rate from Geostationary Satellite Data

Fire Spread Rate (FSR) is one of the key factors for fire rescue and prevention. Remote sensing images have an advantage of acquiring intuitive information timely. To achieve extracting real-time FSR from remote sensing data, a method based on the movement rate of burned area centroid is presented in this study. The FSR extracted from geostationary Himawari-8 (H-8) data in two bushfires outbroke in Esperance, Western Australia in 2015. And the FSR estimated from CSIRO (Commonwealth Scientific and Industrial Research Organization) Grassland Fire Spread Model (CGFSM) were set as the benchmark to assess the presented approach. The results illustrated that the proposed method yield a promising accuracy by comparing with the FSR from CGFSM in that two fires, with the coefficient of determination (R2) reaches to 0.76 and root-mean-square error (RMSE) is 0.50 m·s−1. Furthermore, this study provides a potential application of geostationary satellite in extracting real-time wildfire behavior.
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians

Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common and specific knowledge among different views into consideration. To enhance the robustness of model, the complexity, non-consistency and similarity of noise in multi-view data should be fully taken into consideration. Most current MSL methods only assume a simple Gaussian or Laplacian distribution for the noise while neglect the complex noise configurations in each view and noise correlations among different views of practical data. To this issue, this work initiates a MSL method by encoding the multi-view-shared and single-view-specific noise knowledge in data. Specifically, we model data noise in each view as a separated Mixture of Gaussians (MoG), which can fit a wider range of complex noise types than conventional Gaussian/Laplacian. Furthermore, we link all single-view-noise as a whole by regularizing them by a common MoG component, encoding the shared noise knowledge among them. Such regularization component can be formulated as a concise KL-divergence regularization term under a MAP framework, leading to good interpretation of our model and simple EM-based solving strategy to the problem. Experimental results substantiate the superiority of our method.
Estimation of Complex Permittivity for Solids and Liquids at sub-THz Frequency Range

In this paper, two different extraction techniques are presented to estimate the complex permittivity of materials with high level accuracy. The performances of presented models are discussed to illustrate their potential for obtaining the dielectric properties and composing a database which will is used to identify the unknown materials easily. Furthermore, the composed data base can be used to classify the liquids as safe or unsafe. Moreover, the relationship of between S-parameters and permittivity are showed using estimating methods. The proposed model gives better results as well as analytical and numerical extraction techniques.
A Novel Ant Colony Optimization Based Training Subset Selection Algorithm for Hyperspectral Image Classification

Hyperspectral images captured by airborne sensors provide huge information about earth cover. Human eye can see reflectance from limited wavelengths whereas hyperspectral images contain more than 200 bands captured in visible and infrared region of light. Classification is one of the methods used to analyse this information for decision making. Several techniques are available in the literature for supervised, unsupervised, and semi-supervised classification of hyperspectral images. Training samples for supervised classification are generated by ground sample collection from the actual site captured in the image. These training samples play an important role in classification and can greatly influence classification accuracy. In recent research, for selecting training samples ‘pixel purity’ based new methods were developed. However, in some cases, it might not be possible to gather ground truth of selected pixel. This paper advocate for a novel method inspired by Ant Colony System for selection of a subset from already developed training samples to achieve better classification results. It is interesting to see that a small subset of existing training samples achieve equivalent or in some cases better accuracy when compared to the use of all available training samples.
Clustering of Excess Body Weight-Related Behaviors in a Sample of Brazilian Adolescents

The aim of the study was to identify the existence of clusters in multiple lifestyle behaviors, including consumption of fruits/vegetables, sugary products/soft drinks, physical activity and sedentary behavior. The association between identified clusters and excess body weight in a sample of adolescents from Dourados, Brazil, was examined. This is a cross-sectional school-based study involving 578 participants aged 12–18 of both sexes. Anthropometric measurements were performed and a questionnaire was applied with structured questions to collect data. Excess body weight was identified through body mass index. Cluster analysis was performed to identify sex-specific clusters of multiple lifestyle behaviors. Analysis of covariance and logistic regression were used to analyze associations between clusters and excess body weight. Six clusters were identified in both sexes. Girls and boys in the cluster characterized by greater time spent in sedentary behavior were 53% (OR = 1.53 [1.06–2.26]) and 63% (OR = 1.63 [1.12–2.35]) more likely to present excess body weight compared to their peers in the reference cluster. In the case of adolescents in the cluster characterized by high consumption of sugary products/soft drinks, girls were 47% more likely to be overweight (OR = 1.47 [1.05–2.13]) and boys were 51% more likely (OR = 1.51 [1.05–2.16]). High consumption of fruits/vegetables, low consumption of sugary products/soft drinks and less sedentary behavior was considered the most effective combination for the maintenance of a healthy weight.
Implementation of Smart Pump Technology With Home Infusion Providers

While hospitals have adopted smart pump technology (SPT) featuring drug libraries and medication safety software, most home infusion providers (HIPs) continue to use traditional infusion pumps that don't offer drug libraries or medication safety software. As infusion delivery is moving from the hospital to the home, the purpose of this study was to determine whether SPT was a feasible alternative at both a hospital-based and a rural HIP. HIP personnel were trained on an ambulatory infusion pump. Patients requiring home infusion used the pump and recorded daily pump interactions for 5 to 7 days. After the creation of a drug library, clinicians felt comfortable programming pumps after 7 uses. Patients reported 100% overall satisfaction, and the majority of alarms were resolved without contacting the HIP. Ambulatory SPT can be implemented successfully by HIPs and can be used effectively by patients.
Semi-Supervised Deep Attribute Networks for Fine-Grained Ship Category Recognition

Classifying ships in satellite or aerial images is a challenging problem in remote sensing imagery. This task requires data-hungry learning algorithms (in particular deep models) that build discriminative representations which capture highly variable ship categories. As labeled training data are scarce and expensive, ship category recognition should also rely on abundant unlabeled data in order to enhance its effectiveness. In this paper, we introduce a novel representation learning algorithm based on semi-supervised attributes. Our method allows us to learn deep and discriminative image characteristics shared among different categories while taking into account both labeled and unlabeled data. We demonstrate the effectiveness of this method on the challenging ship category recognition problem and we show its out-performance with respect to related baselines, especially under the regime of scarce labeled data and abundant unlabeled ones.
Influence of Lidar Full-Waveform Density and Voxel Size on Forest Stand Estimates

The aim of this study was to analyze the variation on accuracy and errors of aboveground biomass (AGB) and canopy base height (CBH) estimates when modifying lidar full-waveform (FW) pulse density, voxel size and regression methods. We reduced randomly pulse density from 9 to 1 pulses.m−2 in steps of 0.5 pulses.m−2 in 36 plots. Afterwards, eight FW metrics were computed for each pulse density, voxel size (i.e. 0.25, 0.5 and 1 m) and plot. These metrics were used as explanatory variables, while AGB and CBH obtained from field work were used as response variables in linear, square-root-transformed, exponential and power regression models. Results on CBH showed that power regression reduces pulse density influence. AGB was however influenced by voxel size, showing that it was more affected by pulse density changes when using smaller voxel sizes. In addition, square-root-transformed and power regressions performed better for AGB and CBH, respectively.