Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Intelligence and User-generated Data are Transforming how Firms come to Understand Customer Needs

The rapid growth of social media and online platforms provides users and customers abundant opportunities to express their opinions, thoughts, and feelings about companies and products. Digital footprints provide extremely valuable data with which to understand and manage customers' needs. While the explosion of such unstructured data being generated by customers can be daunting for traditional methods, the advancement in artificial intelligence (AI) and machine learning (ML) algorithms enables firms to process such data rapidly, efficiently, and effectively. In this chapter, we provide an overview of how artificial intelligence is transforming the ways that firms identify customer needs, structure customer needs for insight, and prioritize customer needs. This practice has come to be called the voice of customers (VOC). We aim to contribute to the marketing literature in the following ways. First, we summarize how VOC helps firms to gain insights on using user-generated data. Second, we discuss the types of usergenerated data and how the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms’ goals and the structure of the data, that are used to analyze VOC. Fourth, and most importantly, we map the methods used to relevant applications, providing guidance on how to select the appropriate method of analysis to address the desired research questions.
Visor: A System Solution for Trustworthy and Eﬀective Reports for Social VR

Social VR apps, which enable users to interact in virtual spaces, are increasingly popular. Unfortunately, a large number of harassment incidents have taken place in these apps. To mitigate this issue, VR platforms and apps ask victims to report the incidents to analyze the reports, and if needed, punish the harassers. However, existing reporting solutions are ad hoc and cannot provide strong guarantees for integrity and authenticity of reports. Also, reports cannot always identify the harassers. To address these problems, we introduce Visor, a system solution for providing trustworthy and eﬀective reports for social VR. To enable trustworthy reports, Visor incorporates a secure recorder on VR headsets to capture tamper-proof and irrefutable snapshots of visual content shown on the headset’s display. To enable eﬀective reports, Visor introduces mandatory recording, a novel paradigm, which, alongside a search engine in an auditing server, facilitates identifying the harasser in a report. Our security analysis and evaluation with a VR headset prototype show that Visor is secure and adds small performance overhead. Moreover, using a 12-hour 99-user social VR experiment with automated users, we demonstrate that the mandatory recording paradigm helps an auditor ﬁnd a harasser with 4 minutes of manual eﬀort, on average.
Pre-training and Data Augmentation for Dialogue State Tracking

Dialogue State Tracking (DST) is an important component of task-oriented dialogue systems, which involves tracking of user’s goal and slot values based on dialogue context. While large-scale pre-trained language models like BERT and T5 have proven to achieve state-of-the-art performance for DST, these models are pre-trained on generic text datasets from the web and require huge amounts of training data to adapt to the task of dialogue understanding. In this work, we address these challenges by exploring dialogue-focused pre-training strategies for different transformer architectures. Additionally, we augment the training data with several rule-based as well as deep learning networks to enable the robustness of these models. Our detailed experiments show that baseline models benefit immensely when pre-trained with our span-level objectives in multiple phases. Our experiments also suggest that the use of data augmentation techniques such as paraphrasing also improve the performance on DST.
Feline Infectious Peritonitis as a Systemic Inflammatory Disease: Contribution of Liver and Heart to the Pathogenesis

Feline infectious peritonitis (FIP) is a fatal immune-mediated disease of cats, induced by feline coronavirus (FCoV). A combination of as yet poorly understood host and viral factors combine to cause a minority of FCoV-infected cats to develop FIP. Clinicopathological features include fever, vasculitis, and serositis, with or without effusions; all of which indicate a pro-inflammatory state with cytokine release. As a result, primary immune organs, as well as circulating leukocytes, have thus far been of most interest in previous studies to determine the likely sources of these cytokines. Results have suggested that these tissues alone may not be sufficient to induce the observed inflammation. The current study therefore focussed on the liver and heart, organs with a demonstrated ability to produce cytokines and therefore with huge potential to exacerbate inflammatory processes. The IL-12:IL-10 ratio, a marker of the immune system’s inflammatory balance, was skewed towards the pro-inflammatory IL-12 in the liver of cats with FIP. Both organs were found to upregulate mRNA expression of the inflammatory triad of cytokines IL-1β, IL-6, and TNF-α in FIP. This amplifying step may be one of the missing links in the pathogenesis of this enigmatic disease.
Meta-Learning for Question Answering on SQuAD 2.0

In a general Question Answering (QA) system training, we teach the system to answer a question from understanding the associated paragraph. Often time, we’d encounter the challenge in learning new tasks from different domains with limited samples. In this study, we built a QA system that is robust to domain shifts with an implementation of the meta-learning (MAML) framework for few-shot supervised learning [1]. We explored training (1) MAML models from scratch and (2) MAML after baseline model pre-trained and fine-tuned. We found that training MAML after fine-tuning baseline outperformed the baseline occasionally, and the best performing model on the out-of-domain validation set was a 10-task 20-shot MAML that scored EM: 40.436 and F1: 50.49 on the out-of-domain test set.
Cyber-rumor and internalizing symptoms in adolescence: mediating effect of resilience

Digital media have acquired a key role in the social dynamics among adolescents, increasing the prevalence of risk behaviors such as cyberbullying. Although its study has increased in recent decades, there are still few studies focused on the effects of a specific type of cybervictimization, such as cyber-rumor. The aims of this study were first to examine whether victims of cyber-rumor have higher levels of internalizing symptoms and second to explore whether resilience mediates this association, controlling for the sex effect. A total of 558 students (54.3% girls) aged between 13 and 17 years old (M = 14.65;SD = 1.19) participated in the study. Data processing followed a mediation model through PROCESS. The results evidenced that both girls and cyber-rumor victims presented higher levels of depression, anxiety and stress. The mediation models showed that resilience only mediated the effect that being victim of cyber-rumor had on levels of depression and anxiety, but not on stress levels. Sex was not found to moderate such effects. In conclusion, these results underscore the importance of attending to cyberaggression phenomena given their effects on social and emotional well-being identified in this study. This suggests the need to design prevention programs that include among their strategies the promotion of skills for coping with cyber-rumor. © 2022. Psy, Soc & Educ.
Leader’s Intellectual Stimulation Training and Church Leadership Performance

- This study sought to investigate the influence of intellectual stimulation training on graduate’s performance focused on the Pan Africa Christian University (PAC) Transformational Church Leadership (TCL) diploma program graduates of 2016 and 2017, in selected churches in Kenya. The study sought to establish the extent to which TCL trained graduates/church leaders in intellectual stimulation influence the church leader’s performance. Survey and interviews were used to collect quantitative, and qualitative data. The study found leader’s intellectual stimulation to have a strong positive and significant correlation with church leader’s performance among graduates, with a Pearson correlation of 0.570, while in church members/congregation had a Pearson correlation of 0.69. The study revealed that church leader’s intellectual stimulation made them more creative and innovative in decision-making and conflict resolution which significantly influenced church growth. The study concluded that intellectual stimulation positively and significantly increased performance in church leaders in Kenya.
Perfect non-specular reflection through spatially dispersive boundary conditions

– This work addresses the practical realization of perfect anomalous reflection through a metasurface consisting of a metallic patterning printed over a grounded slab. An exact solution for the homogenized penetrable impedance representing the metallic cladding is found, and the connection of this solution with the one obtained for a nondispersive impenetrable impedance boundary condition is discussed. It is shown that the two solutions can be related though an equivalent transmission network where transmission lines with different wavenumbers are associated to the incident and reflected waves.
Robust Question-Answering using Adversarial Learning

Question Answering (QA) is a critical task for NLP applications such as conversational agents and search engines in which generalization to new domains is highly desirable. Despite outperforming human benchmarks, state-of-the-art QA models often fail to generalize to new domains without significant fine-tuning. To address this challenge, Lee et al. [1] couples a pre-trained language model such as BERT [2] and a discriminator model (which predicts domain labels) so that the language model learns to predict features that are indistinguishable within the in-domain datasets. We aim to build a robust question-answering model by applying above-mentioned adversarial learning approach with pre-trained distilBERT [3] generator with a simple 3-layer discriminator. Our best model outperforms baseline and attains F-1 score of 58.63 and EM score of 40.14 on test leaderboard of RobustQA track. Alongside, we performed extensive experiments to determine impact of hyper-parameters on F-1 score and EM metrics which can be seen in results section.
Improved QANet on SQuAD 2.0

In this project, we built reading comprehension (a.k.a question answering) systems for the Stanford Question Answering Dataset (SQuAD) 2.0 focusing on two models (BiDAF[1] and QANet[2]) and their variations for the best performance. We have three major contributions. Firstly, we improved the baseline BiDAF model by introducing learnable character embeddings and a co-attention fusion function. Secondly, we implemented QANet from scratch and searched for the best model size. Finally, we explored QANet variations and showed that using an input embedding refine layer and a condition output layer can further improve original QANet performance. More precisely, input embedding refine layer better refines the word and character embeddings and condition output layer allows the answer’s ending position prediction to be based on the starting position. Our best QANet single model achieved 64.82 / 67.81 EM and F1 score on non-PCE leaderboard testing sets.
Comparison of biosynthetic versus synthetic mesh in clean and contaminated ventral hernia repairs

Abdominal hernias are an increasingly common presentation due to obesity, ageing and prevalence of prior abdominal surgery. Mesh repair is the mainstay of treatment; however, mesh selection remains largely subjective. There are little data available to assess the performance of biosynthetic meshes against synthetic meshes across all wound types. This study assessed the 6‐month outcomes of a single surgeon's cohort of ventral hernia repairs performed with either GORE BIO‐A (BioA) or Parietex ProGrip (ProGrip).
The Right to Remain Plain: Summarization and Simplification of Legal Documents

Legal documents are a critical tool for contractual agreements and protections, but legal jargon and document length can present a barrier to comprehension. Having a tool to summarize and simplify these texts can greatly improve understanding, which is key to ensuring fairness. While previous work has developed summarization and simplification models independently, this work seeks to build on these existing techniques to fine-tune a model for legal domain-specific tasks; understand how fine-tuning on one dataset generalizes to other datasets within the legal domain; and examine the impact of simplification as a pre- or post-processing step in summarization. We show that a large language model fine-tuned on legal data outperforms the model with no fine-tuning and other simple, unsupervised baseline methods, and we also demonstrate that it is possible to generalize across different sub-domains within law, training on a sub-domain dataset and applying the model to other sub-domains. Further, initial results show that simplification as a post-processing step preserves meaning and increases readability, and is promising for building a summarization and simplification pipeline. We conclude with a call for higher-quality legal datasets to improve large language models for the law domain.
Convergence properties of an Objective-Function-Free Optimization regularization algorithm, including an O ( (cid:15) − 3 / 2 ) complexity bound

An adaptive regularization algorithm for unconstrained nonconvex optimization is presented in which the objective function is never evaluated, but only derivatives are used. This algorithm belongs to the class of adaptive regularization methods, for which optimal worst-case complexity results are known for the standard framework where the objective function is evaluated. It is shown in this paper that these excellent complexity bounds are also valid for the new algorithm, despite the fact that signiﬁcantly less information is used. In particular, it is shown that, if derivatives of degree one to p are used, the algorithm will ﬁnd a (cid:15) 1 -approximate ﬁrst-order minimizer in at most O ( (cid:15) − ( p +1) /p 1 ) iterations, and an ( (cid:15) 1 , (cid:15) 2 )-approximate second-order minimizer in at most O (max[ (cid:15) − ( p +1) /p , (cid:15) − ( p +1) / ( p − 1) 2 ]) iterations. As a special case, the new algorithm using ﬁrst and second derivatives, when applied to functions with Lipschitz continuous Hessian, will ﬁnd an iterate x k at which the gradient’s norm is less than (cid:15) 1 in at most O ( (cid:15) − 3 / 2 1 ) iterations.
All for One or One for All: Ensemble of Diverse Augmentation for Self-Attention

Accurate question and answering systems are crucial to web search engines that can tailor specifically towards user needs and information. Our project strives to produce a question answering system that works well specifically on the SQuAD 2.0 dataset. We extend the provided baseline BiDAF architecture through a character embedding layer and an implementation of self-attention inspired by QANet and R-NET. We further experiment with the data augmentation technique of backtrans-lation using a variety of languages, and seek to answer the question: is ensembling many small models, each augmented by a unique language, better than training a single large model on the full augmented dataset? Our final model, an ensemble of our self-attention model and a BiDAF model trained augmented data, achieves an EM score of 62.93 and an F1 score of 65.78 on the held out test set, and significantly improves upon the baseline BiDAF model on the development set.
Investigating Views for Contrastive Learning of Language Representations

Contrastive learning is a self-supervised learning technique that trains models to learn representations of sentences where similar sentences are close together in vector space and dissimilar sentences are far apart. This project builds upon existing contrastive learning methods applied to transformers in the DeCLUTR paper [1] to analyze whether the sentence embeddings produced by contrastive learning can be comparable or better than standard Masked Language Models. In this project, I apply the SimCLR objective function with slice views, neighbor views, and neighboring slice views to a roberta-longformer model trained on a Wikipedia dataset and compare its performance to a roberta-longformer MLM pretrained model on transfer tasks such as sentiment analysis and topic prediction. I demonstrate that models trained with SimCLR objective function performs better on topic prediction tasks than MLM.
Digital Commons@Georgia Southern Assessing Habitat Suitability of Ribbed Mussels (Geukensia Demissa) in Georgia Salt Marshes by Examining Predicted Mussel Densities and Mussel Population Parameters

The salt marshes of coastal Georgia are ecologically and economically important ecosystems that are threatened by erosion as a result of sea level rise. Naturally these marshes are stabilized by the mutualistic relationship between Spartina alterniflora (cordgrass) and Geukensia demissa (ribbed mussel). Together these two organisms hold sediment in place through the intertwined connections of mussel byssal threads and cordgrass’ roots. In addition, the nitrogenous waste of mussel has been found to increase the growth of cordgrass. Due to these interactions, mussels have the potential to contribute to the success of salt marsh restoration. However, little is known about the drivers of the spatial distribution of mussels in Georgia. Understanding the factors that contribute to the distribution of mussels in Georgia can help inform managers on choosing locations to optimize the survivorship of mussels in restoration projects. This study sought to model mussel densities across the coast of Georgia and to compare predicted mussel densities in local marshes with mussel population parameters as means to gauge habitat suitability. Mussel densities were collected through a series of field surveys across a range of salt marshes along the coast of Georgia. These data were then overlaid with spatial data such as distance to creek heads (the ends of intertidal creeks which flood the marsh platform), elevation, and slope. Highest predicted mussel densities occurred at an elevation of 0.7m relative to NAVD 88, close to creek heads and far from subtidal creeks. Using the predicted mussel densities from the model, low, medium and high density mussel sites were selected at two geographic locations, Cannon’s Point Preserve, St. Simons, Georgia and Dean Creak, Sapelo Island, Georgia, to conduct mussel growth, predation, and recruitment experiments. In areas with higher predicted mussel densities, mussel recruitment and growth rates were the highest. Despite being statistically significant, differences in growth rates may not be biologically meaningful. While not statistically significant, predation risk was lowest in areas of high predicted mussel densities and increased with decreasing density. This indicates that in areas of low predicted mussel densities, recruitment and predation risk are likely the limiting factor to mussel densities.
Improved Image Super-Resolution Using Frequency Channel Attention and Residual Dense Networks

In our real life, due to the various influences, low-resolution images exist widely. The resolution of the image represents the amount of information carried by the image and the quality of the image. Image super-resolution means reconstructing low-resolution images, and it can help us improve the image quality to get more information. This paper attempts to improve the existing super-resolution reconstruction model based on deep learning. We use nested residual dense connection to prompt the model to focus on the recovery of detailed textures and accelerate convergence. Meanwhile, we use frequency channel attention mechanism to weight channels. By comparing the experimental results with other methods including FSRCNN, VDSR and MemNet, our proposed method has achieved better results and visual improvements. Especially on the Urban100 test dataset, the increases of PSNR and SSIM reach higher.
A two-stage modular blind denoising algorithm based on real scene

The use of deep learning methods has developed rapidly in image denoising. Now deep learning methods have a good improvement in denoising effects compared to traditional methods, but there are also some problems. On the one hand, these denoisers are more used to remove the noise of a specific distribution, while the image noise distribution in real life is not fixed, so it is more difficult and more practical to denoise the real image. On the other hand, more and more complex network structures and deeper and deeper network models seem to have become a necessary condition for better denoising effects, but its improvement is not linear, and deeper networks are likely to be removed. The improvement in noise effect is minimal. Because of this, we designed a relatively simple network structure to remove real image noise, which includes the noise level estimation stage using the channel attention mechanism, and the non-blind noise reduction stage using the micro-branch structure we designed. Experiments show that our method has good visual perception quality compared with other methods on commonly used image denoising data sets.
Research on Image Defogging Algorithms Based on Color Attenuation Prior

As for the defogging algorithm of color attenuation prior, an optimization algorithm based on color attenuation prior is proposed to solve the problem of image blurring in high frequency area of close-up view. Firstly, by improving the scene depth estimation information, the nonlinear model about scene depth is re-established to find the appropriate scene depth. Second, binary tree search algorithm is used to find the optimal atmospheric light value, and finally, a certain range of parameters are adjusted to achieve model optimization. The experimental results show that the algorithm solves the problem of blurring the close-up area and improves the speed of the algorithm.
Helium bubble formation and evolution in NiMo-Y2O3 alloy under He ion irradiation

We report helium ion irradiation experiments for a new type of dispersion-strengthened NiMo-Y2O3 alloy with three different irradiation doses and varying irradiation dose rates at 750 °C to evaluate its helium-induced damage behavior. Transmission electron microscopy was used to reveal the evolution of helium bubbles after irradiation. The experimental results show that with increasing ion dose, the number density of helium bubbles increases continuously. However, the mean size of helium bubbles first increases and then decreases, mainly due to the varied ion dose rates. The volume fractions of helium bubbles in the three investigated samples after irradiation are 0.15%, 0.32%, and 0.27%, which are lower than that of the Hastelloy N alloy (0.58%) after similar irradiation conditions. This indicates that the NiMo-Y2O3 alloy exhibits better helium-induced-swelling resistance than the Hastelloy N alloy, highlighting its potential applicability to MSRs, from the perspective of irradiation performance.
Loop Closure Detection for SLAM Based on Siamese Convolutional Neural Network

Loop closure detection is an indispensable part of the SLAM system, which can reduce the cumulative error of mobile robots in positioning and mapping. This paper proposes a supervised learning method, which combines the Siamese network and the VGG16 network in deep learning to handle loop closure detection. The Siamese network method and the traditional method are contrasted in the New College and City Centre data sets to prove the method’s usefulness in this research. The experiment’s findings reveal that the method of this paper ensures precision and recall rate while reducing the time necessary to assess similarities compared to the traditional method. It has a better chance of achieving real-time SLAM.