Miracle-dz/UniFAR-FTUs huggingface dataset:
Does a Smile Matter if the Person Is Not Real?: The Effect of a Smile and Stock Photos on Persona Perceptions

ABSTRACT We analyze the effect of using smiling/non-smiling and stock photo/non-stock photo pictures in persona profiles on four key persona perceptions, including credibility, likability, similarity, and willingness to use. For this, we collect data from an experiment with 2,400 participants using a 16-item survey instrument and multiple persona profile treatments of which half have a smiling photo/stock photo and half do not. The results from structural equation modeling, supplemented by a qualitative analysis, show that a smile enhances the perceived similarity with the persona, similar personas are more liked, and that likability increases the willingness to use a persona. In contrast, the use of stock photos decreases the perceived similarity with the persona as well as persona credibility, both of which are significant predictors to a willingness to use a persona. These professionally crafted stock-photos seem to diminish the sense of identification with the persona. The above effects are consistent across the tested ages, genders, and races of the persona picture, although the effect sizes tend to be small. The results suggest that persona creators should use smiling pictures of real people to evoke positive perceptions toward the personas. In addition to presenting quantitative evidence on the predictors of willingness to use a persona, our research has implications for the design of persona profiles, showing that the picture choice influences individuals’ persona perceptions even when the other persona information is identical.
DEAD WOOD DETECTION BASED ON SEMANTIC SEGMENTATION OF VHR AERIAL CIR IMAGERY USING OPTIMIZED FCN-DENSENET

Abstract. The assessment of the forests’ health conditions is an important task for biodiversity, forest management, global environment monitoring, and carbon dynamics. Several research works were proposed to evaluate the state condition of a forest based on remote sensing technology. Concerning existing technologies, employing traditional machine learning approaches to detect the dead wood in aerial colour-infrared (CIR) imagery is one of the major trends due to its spectral capability to explicitly capturing vegetation health conditions. However, the complicated scene with background noise restricted the accuracy of existing approaches as those detectors normally utilized hand-crafted features. Currently, deep neural networks are widely used in computer vision tasks and prove that features learnt by the model itself perform much better than the hand-crafted features. The semantic image segmentation is a pixel-level classification task, which is best suitable to dead wood detection in very high resolution (VHR) mode because it enables the model to identify and classify very dense and detailed components on the tree objects. In this paper, an optimized FCN-DenseNet is proposed to detect dead wood (i.e. standing dead tree and fallen tree) in a complicated temperate forest environment. Since the appearance of dead trees generally occupies greatly different scales and sizes; several pooling procedures are employed to extract multi-scale features and dense connection is employed to enhance the inline connection among the scales. Our proposed deep neural network is evaluated over VHR CIR imagery (GSD-10cm) captured in a natural temperate forest in Bavarian national forest park, Germany, which has undergone on-site bark beetle attack. The results show that the boundary of dead trees can be accurately segmented, and the classification are performed with a high accuracy, even though only one labelled image with moderate size is used for training the deep neural network.
SEMANTIC URBAN MESH ENHANCEMENT UTILIZING A HYBRID MODEL

Abstract. We propose a feature-based approach for semantic mesh segmentation in an urban scenario using real-world training data. There are only few works that deal with semantic interpretation of urban triangle meshes so far. Most 3D classifications operate on point clouds. However, we claim that point clouds are an intermediate product in the photogrammetric pipeline. For this reason, we explore the capabilities of a Convolutional Neural Network (CNN) based approach to semantically enrich textured urban triangle meshes as generated from LiDAR or Multi-View Stereo (MVS). For each face within a mesh, a feature vector is computed and fed into a multi-branch 1D CNN. Ordinarily, CNNs are an end-to-end learning approach operating on regularly structured input data. Meshes, however, are not regularly structured. By calculating feature vectors, we enable the CNN to process mesh data. By these means, we combine explicit feature calculation and feature learning (hybrid model). Our model achieves close to 80% Overall Accuracy (OA) on dedicated test meshes. Additionally, we compare our results with a default Random Forest (RF) classifier that performs slightly worse. In addition to slightly better performance, the 1D CNN trains faster and is faster at inference.
Selection of Cocoa Tree (Theobroma cacao Linn) Endophytic Bacteria Solubilizing Tri-Calcium Phosphate, Isolated from Seedlings Grown on Soils of Six Producing Regions of Côte d’Ivoire

Phosphorus (P) is an essential macronutrient for the growth and development of cocoa tree (Theobroma cacao Linn). Most of the soils used for cocoa farming in Cote d’Ivoire are low in phosphorus. But cocoa farmers generally have a widespread reluctance to invest in chemicals fertilizers due to high costs and environmental associated risk. Phosphate Solubilizing Bacteria (PSB) are kwon to play an important role in supply of phosphorous to plants in a sustainable manner in P deficient soils. The aim of this research was to screen the endophytic bacteria of cocoa nurseries able to solubilize tri-calcium phosphate. Seedlings of two varieties of cocoa (P7 and NA32) and seedlings of an all-comer, were grown on eighteen (18) samples soils collected in six producing regions of Cote d’Ivoire. A total of 218 endophytic bacteria were isolated and tested on the Pikovskaya’s agar medium, containing Ca3 (PO4) 2. The colonies with clear zone around the microbial growth were suspected as phosphate solubilizing bacteria. Out of 218 bacteria, 90 (41.28%) showed a clear zone around colonies after 7 days of incubation. The Phosphate Solubilization Index (PSI) ranged from 20% to 200%. Bacterial isolated from the soils of Duekoue locality showed the highest mean index of 137.67%. Five PSB (CEBSP5, CEBSP6, CEBSP7, CEBSP8, and CEBSP9) from Duekoue soils and two PSB (CEBSP12 and CEBSP13) from Soubre soils have a PSI ranged from 150% to 200%. Further study in greenhouse and in field condition will confirm the use of these PSB as biofertilizer to increase the available P content in soils, reduce environmental pollution and promote sustainable agriculture.
A Morphable Model For The Synthesis Of 3D Faces

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.
Social Support for Female Entrepreneurs: A Study of South Korea and the United States

An entrepreneur is a person who organizes, builds, owns, and runs a business; however, what else is involved in starting a new business venture? The United States cultivates a society of individualist outlooks, people that place their needs and the needs of their immediate family before others, which is a sharp contradiction to South Korea’s traditionally collectivist society, where individuals put the needs of the community, family, and their country before their personal needs. With such differing outlooks, which culture is better at supporting entrepreneurs? This paper will explore possible differences between the cultures of the United States and South Korea in relation to social support for female entrepreneurs. ARTICLE HISTORY Received 13 July 2019 Revised 2 September 2019 Accepted 15 September 2019
Core Metadata Element Recommendations for Institutional Repositories at Texas A&M University Libraries

Abstract In 2017, a working group was formed at Texas A&M University Libraries to propose policies that would ensure the quality and consistency of descriptive metadata in the Libraries’ new DAME (Digital Asset Management Ecosystem), which consists of multiple repositories. This article describes the process that it used to identify a “core” set of metadata elements for all resources that would be stored within the DAME, and their recommendations for a three-tiered approach to address different levels of completeness. In addition, this article describes the issues that were encountered when recommending controlled vocabularies and standards for inputting certain metadata elements.
Stage-GAN with Semantic Maps for Large-scale Image Super-resolution

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ×8, our method performs favorably against other methods in terms of gradients similarity.
Neural Text Generation from Structured and Unstructured Data

A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several natural language processing tasks. In this thesis, we show that this is also the case for text generation from structured and unstructured data. Specifically, we consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data respectively. Table-to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models. Experiments demonstrate that a basic attention-based sequence-to-sequence model trained with exponential moving average technique achieves state of the art in both tasks. We further investigate using reinforcement learning with different reward functions to refine our pre-trained model for both tasks.
HR3DHG version 1: modelling the spatio-temporal dynamics of mercury in the Augusta Bay (southern Italy)

Abstract. The biogeochemical dynamics of Hg, and specifically of its three species Hg0, HgII, and MeHg (elemental, inorganic, and organic, respectively) in the marine coastal area of Augusta Bay (southern Italy) have been explored by the high resolution 3D Hg (HR3DHG) model, namely an advection-diffusion-reaction model for the dissolved mercury in the seawater compartment coupled with i) a diffusion-reaction model for dissolved mercury in the pore water of sediments and ii) a sorption/de-sorption model for total mercury in the sediments. The spatio-temporal variability of dissolved and total mercury concentration both in seawater ([HgD] and [HgT]) first layers of bottom sediments ([HgsedD] and [HgsedT]), and the Hg fluxes at the boundaries of the 3D model domain have been theoretically reproduced, showing an excellent agreement with the experimental data, collected in multiple field observations during six different oceanographic cruises. The mass-balance of the different Hg species in seawater has been calculated for the Augusta Harbor, improving previous estimations. The HR3DHG model includes modules that can be implemented for specific and detailed exploration of the effects of climate change on the spatio-temporal distribution of Hg in highly contaminated coastal-marine areas.
Activities of Irish psychiatric units and hospitals 2010. HRB Statistics Series 15.

This report presents data on all admissions to, discharges from, and deaths in, Irish psychiatric units and hospitals as recorded on the National Psychiatric In-patient Reporting System (NPIRS) in 2010. Data are presented nationally, regionally, by HSE area, and locally, by county, by hospital type, and by individual psychiatric units and hospitals. Rates reported are per 100,000 total population.
Connected Vehicles at Signalized Intersections: Traffic Signal Timing Estimation and Optimization

Summary: While traffic signals ensure safety of conflicting movements at intersections, they also cause much delay, wasted fuel, and tailpipe emissions. Frequent stops and goes induced by a series of traffic lights often frustrates passengers. However, the connectivity provided by connected vehicles applications can improve this situation. A uni-directional traffic signal to vehicle communication can be used to guide the connected vehicles to arrive at green which increases their energy efficiency; and in the first part of the dissertation, we propose a traffic signal phase and timing estimator as a complementary solution in situations where timing information is not available directly from traffic signals or a city’s Traffic Management Center. Another approach for improving the intersection flow is optimizing the timing of traditional traffic signals informed by uni-directional communication from connected vehicles. Nevertheless, one can expect further increase in energy efficiency and intersection flow with bi-directional vehicle-signal communication where signals adjust their timings and vehicles their speeds. Autonomous vehicles can further benefit from traffic signal information because they not only process the incoming information rather effortlessly but also can precisely control their speed and arrival time at a green light. The situation can get even better with 100% penetration of autonomous vehicles since a physical traffic light is not needed anymore. However, the optimal scheduling of the autonomous vehicle arrivals at such intersections remains an open problem. The second part of the dissertation attempts to address the scheduling problem formulation and to show its benefits in microsimulation as well as experiments. Intellectual Merit: In the first part of this research, we study the statistical patterns hidden in the connected vehicle historical data stream in order to estimate a signal’s phase and timing (SPaT). The estimated SPaT data communicated in real-time to connected vehicles can help drivers plan over time the best vehicle velocity profile and route of travel. We use low-frequency probe data streams to show what the minimum achievable is in estimating SPaT. We use a public feed of bus location and velocity data in the city of San Francisco as an example data source. We show it is possible to estimate, fairly accurately, cycle times and duration of reds for pre-timed traffic lights traversed by buses using a few days worth of aggregated bus data. Furthermore, we also estimate the start of greens in real-time by monitoring movement of buses across intersections. The results are encouraging, given that each bus sends an update only sporadically (≈ every 200 meters) and that bus passages are infrequent (every 5-10 minutes). The accuracy of the SPaT estimations are ensured even in
A no-reference framework for evaluating video quality streamed through wireless network

In this work, a no-reference framework is proposed for the video quality estimation streamed through the wireless network. The work presents a comprehensive survey of the existing full reference (FR), reduced reference (RR), and no-reference (NR) algorithms. A comparison has been made among existing algorithms, i.e. in terms of subjective correlation and feasibility to use these algorithms in wireless architecture, to describe the necessity of the proposed framework to overcome the limitations of the existing algorithms. A brief summary of our previously published algorithms, i.e. NR blockiness, NR blur, NR network, NR just noticeable distortion, and RR, has also been presented. These algorithms have also been used as function modules in the proposed framework. The proposed framework is able to measure the video quality by taking into account major spatial, temporal, network impairments, and human visual system effects for a comprehensive quality evaluation. The proposed framework is able to measure the video quality compressed by different codecs, i.e. MPEG x / H.264x, Motion JPEG/Motion, and JPEG2000, etc. The framework is able to work with two different kinds of received data, i.e. bit streams and decoded pixels. The framework is an integration of the RR and NR method, and can work in three different modes depending on the availability of the RR data, i.e. 1) only RR measurement, 2) hybrid of RR and NR measurement, and 3) only NR estimation. In addition, any individual function block, i.e. blurring, can also be used independently for particular specific distortion. A new subjective video quality database containing compressed and distorted videos (due to channel induced distortions) is also developed to test the proposed framework. The framework has also been tested on publicly available LIVE Video Quality Database. Overall test results show that our framework demonstrates a strong correlation with subjective evaluation of the two separate video databases as compared with other existing algorithms. The proposed framework also shows good results while working only in NR mode as compared with existing RR and FR algorithms. The proposed framework is more scalable and feasible to use in any kind of available network bandwidth as compared with other algorithms, as it can be used in different modes by using different function modules.
The Effectiveness of Performance Assessment in Project-Based Learning by Utilizing Local Potential to Increase the Science Literacy

Learning science requires students not only to remember the subject matter but also to develop science literacy. One of the ways to develop science literacy competencies is by applying performance assessment in project-based learning that utilizes local potential. This study aims to determine the effectiveness of performance assessment in local-potential-based learning on environmental pollution subject matter. The research was carried out using a quasi-experimental method with a pre- and post-test design. The sample for the research is 7th grade students in Pontianak, and the data collection tool is an observation sheet using a science literacy rubric test. The research results show that performance assessment of project-based learning utilizing local potential can increase students’ science literacy.
Machine Learning Techniques for Heterogeneous Data Sets

Jingxiang Chen: Machine Learning Techniques for Heterogeneous Data Sets (Under the direction of Yufeng Liu and Michael R. Kosorok) Over the past few decades, machine learning tools are under rapid development in various application fields to support statistical decision making. In this dissertation, we aim at investigating new supervised machine learning techniques which can contribute to analysis of complex datasets. First, we discuss a new learning method under Reproducing Kernel Hilbert Spaces (RKHS) to achieve variable selection and data extraction simultaneously. In particular, we propose a unified RKHS learning method, namely, DOuble Sparsity Kernel (DOSK) learning, to overcome this challenge. We prove that under certain conditions, our new method can asymptotically achieve variable selection consistency. Numerical study results demonstrate that DOSK is highly competitive among existing approaches for RKHS learning. Second, we study on how machine learning can be applied to heterogeneous data analysis by detecting an optimal individual treatment rule for the ordinal treatment case. One of the primary goals in precision medicine is to obtain an optimal individual treatment rule (ITR). Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for the ordinal treatment settings such as dose level finding, it is unclear how to use OWL. We propose a new technique for estimating ITR with ordinal treatments. Simulated examples and an application to a type-2 diabetes study demonstrate the highly competitive performance of the proposed method. Third, we also focus on analyzing the heterogeneous data but in a different point of view.