Miracle-dz/UniFAR-FTUs huggingface dataset:
Cora timucua (Hygrophoraceae), a new and potentially extinct, previously misidentified basidiolichen of Florida inland scrub documented from historical collections

Abstract. The known collections of the genus Cora in continental North America north of Mexico, all restricted to Florida, are shown to belong to a single species, representing a previously unrecognized taxon formally described herein as C. timucua. Based on data of the fungal ITS barcoding marker, obtained through Sanger and Illumina sequencing from two historical collections, the new species is phylogenetically most closely related to C. casanarensis from Colombia and C. itabaiana from Brazil, although it is morphologically most similar to the only distantly related C. hymenocarpa from Costa Rica. Based on data from the Consortium of North American Lichen Herbaria (CNALH) and from the Global Biodiversity Information Facility (GBIF), most of the collections of C. timucua originate from around the turn of the 19th century, while a few were made in the second half of the 20th century, all between 1968 and 1985. Almost all collections originate from Florida sand pine scrub, apparently the preferred habitat of this taxon. Neither modern collections nor extant localities are known. Based on these findings and the substantial degree of land use change in Florida in the past decades, we assessed the conservation status of C. timucua using the IUCN Red List criteria and found that it should be classified as critically endangered (CR), in line with the status of another Florida endemic, Cladonia perforata, which was the first federally red-listed lichen in the United States. The most likely location where C. timucua may still be extant is Ocala National Forest in the north-central portion of the Florida peninsula, although recent macrolichen surveys in that area did not encounter this species.
Hand-Crafting Neural Networks for Art-Making

A growing number of visual artists use neural networks in their practice. While these networks show promise as an art form, the lack of interpretability limits control to high level decisions based on observations. As an alternative, this research investigates the hand-crafting of network weights coupled with explanatory visualizations as a form of creative control over the internal and lower level processes. Two experimental tools were developed: one for parametrically generating ﬁrst layer kernels and the second for editing multiple layers. These tools attempt to transform the hand-crafting of features into “crafting” in a richer sense by bringing network weights and visual materials into a tight feed-back loop. The ﬁrst author extensively engaged with these tools and these case studies serve to examine the affordances of internal interaction for art-making. The ﬁndings suggest that direct manipulation can be used intentionally and can yield insights into network representations, but that hand-crafting networks of greater sophistication would likely require a hybrid approach integrating data-driven methods.
Mobile and Delay Tolerant Network for LoRa at Sea

Near-real-time (nRT) ocean observations are critical for the sustainable management of coastal as well as offshore regions, including implementation of early warning systems, mitigation of pollution, response to extreme events and natural hazards. This can be achieved via the implementation of the so-called Ocean of Things, which consist of distributed networks of low-cost sensors enabling the acquisition of real-time or near-real-time data in support of the sustainable management of aquatic environments. In this project, we propose a LoRa-based system for sensor data collection at sea through mobile LoRa gateways mounted in ships. The sensor nodes will keep looking for mobile gateways, while trying to conserve energy, and will transmit their data as soon as a gateway ship approaches. The system aims at extending the LoRa network from the cities to the shore and finally to the open ocean.
Solution dynamics in aqueous monohydric alcohol systems

The associative behavior of aqueous methanol, ethanol, and tert-butyl alcohol solutions at mole fractions ranging from 0 to 1 at 273, 283, and 298 K was examined using PGSE NMR measurements of the self-diffusion coefficients of the alkyl group, water and, depending on the exchange rate, hydroxyl protons. The results show that tert-butyl alcohol has the greatest ability to stabilize water through hydrophobic hydration than methanol or ethanol due to the more ideal fit of the tert-butyl group to the structure of water. However, at higher concentrations tert-butyl alcohol is the least able to cohesively interact with water through hydrogen bonding. The results provide compelling evidence for alcohol self-association (methanol < ethanol < tert-butyl alcohol) in very dilute solution. The alcohol molecules can be likened to very short lipid molecules undergoing complicated solution interactions due to their amphiphilic nature.
Understanding Purchase Intention in O2O E-Commerce: The Effects of Trust Transfer and Online Contents

The purpose of this research is to examine the effects of trust transfer and online contents on purchase intention in the online-to-offline e-commerce. A partial least squares based structural equation model was utilized to test the framework of causality and a series of multivariate analyses of variance were conducted to test the effects of online content size. The partial least squares results indicate that trust transfer from online-to-offline platforms to offline merchants, perceived effectiveness of online product reviews as well as perceived effectiveness of numerical ratings positively influence trust in merchants directly and purchase intention indirectly, while perceived effectiveness of online production descriptions shows no significant effect. The multivariate analyses of variance results confirm the positive effects of review number and picture number on customer trust toward merchants and purchase intention. The multivariate analyses of variance results also support the positive effects of review length when a merchant has a high review balance. This research contributes to e-commerce and behavior literature by investigating how trust transfer and online contents engage customers and induce their purchase behaviors in online-to-offline commerce. It also assists leading a better online-to-offline outcome for online-to-offline platforms and offline merchants in the highly competitive online-to-offline markets.
Aesthetic Preferences of Neural Style Transfer-Generated Portrait Images: An Exploratory Study with the Two-Alternative-Forced-Choice Task

Neural style transfer is a popular deep learning algorithm to generate images to mimic human artistry. This work applies the psychological method of the two-alternative forced choice (2afc) task to measure aesthetic preferences for neural style generated images. Portrait photos of three popular celebrities were generated by varying three parameters of neural style transfer in five configuration levels. Participants had to choose the image they preferred aesthetically from all pairwise combinations of configurations per style. The rate of being chosen was calculated for each neural style transfer configuration level. The findings show a differentiated picture of aesthetic preferences. On the one side, they indicate that people prefer images rendered with 500 iterations and a learning rate of 2e1, i.e. configurations that allow them to recognize the structure of the portrait image despite the stylization. On the other side, aesthetic preferences peak for two distinctly different content-to-style weight ratios. Whereas the medium-high configuration (100:100) may be favored by people who like abstract arts, the high configuration (300:100) may be chosen by people who prefer realistic art. These results indicate that aesthetic preferences for neural style transfer-generated images can be characterized by unique patterns, and their optimal configuration levels can be captured by the 2afc task.
Understanding and Strengthening the Computational Creativity Community: A Report From The Computational Creativity Task Force

The ﬁeld of computational creativity is rightfully home to a great diversity of perspectives and disciplines. As members of such a diverse community we need to pay special attention to how we can sustain a common identity, and how we communicate, support, and evaluate our work. Here, we introduce the ﬁrst report from the Computational Creativity Task Force, which was established to support the collective advancement of the computational creativity research community. As a ﬁrst step towards that goal, we present an exploration of who we are as a community: the authors and program committee members of the International Conference on Computational Creativity. We welcome a discussion of both this data and the mission of the task force going forwards.
Determinants of the Intention to Adopt Mobile Augmented Reality Apps in Shopping Malls among University Students

Augmented Reality (AR) technology adoption has been growing worldwide in recent years. The potential of AR to blend digital information into the physical world has been a challenge for both academia and industry, who attempt to realize and anticipate its impact on users’ perceptions, adoption intention and use. The present paper is an empirical study aimed at making substantial suggestions and investigating an integrative theoretical paradigm which attempts to establish the significance of specific factors which allow using mobile augmented reality apps in shopping malls. The study employs information from the extant literature with a view to extending the Unified Theory on Acceptance and Use of Technology (UTAUT). The results show that performance expectancy, enjoyment and reward are direct determining factors of adopting the specific technology in shopping malls, whereas facilitating conditions, social influence, innovativeness and trust exert an indirect effect on behavioral intention adoption. The research findings have far-reaching theoretical and practical implications for the development, marketing and application of mobile AR apps in the context of the specific contemporary form of shopping.
Want it, Rent it: Exploring Attributes Leading to Conversion for Online Furniture Rental Platforms

Online furniture rental platforms are expanding choice and convenience, allowing customers to rent from a wide array of providers with the click of a button or tap of a finger. The business of online furniture rental is undergoing rapid change as new online platforms race to capture markets and customers across most of the metropolitan cities in India. The paper aims to investigate attributes for online furniture rental platforms by proposing and empirically testing platform attributes-conversion model, examine how platform characteristics influence the renting decision of a consumer and how it subsequently lead to conversion. A mix method design was adopted for the study and a pilot study comprising of 341 respondents was carried out. The study focuses on six key attributes occupational mobility, psychological ownership, complementary services, social gratification, perceived value, and customization, while identifying the most important attributes for renting furniture online.
Bisociative Literature-Based Discovery: Lessons Learned and New Prospects

The ﬁeld of bisociative literature-based discovery aims at exploring scientiﬁc literature to reveal new discoveries based on yet uncovered relations between knowledge from different, relatively isolated ﬁelds of specialization. This paper outlines selected outlier-based literature mining approaches, which focus on ﬁnding out-lier documents as means for ﬁnding unexpected links crossing different contexts. Selected approaches to bridging term detection through outlier document exploration are brieﬂy outlined, together with the lessons learned from recent applications in medical and biological literature-based knowledge discovery. Finally, the paper addresses new prospects in bisociative literature-based discovery, emphasizing the use of advanced embeddings technology for cross-domain literature mining.
Deep Learning Classification of two-class Motor Imagery EEG signals using Transfer Learning

Motor imagery (MI) based Brain-Computer Interface (BCI) system uses Electroencephalography (EEG) signals recorded over the scalp during imagination of motor movements to control a computer or mobility device. Such systems require a method to classify the acquired MI EEG signals into commands. In this study, three pre-trained Convolutional Neural Networks (CNN) models- AlexNet, ResNet50 and InceptionV3 are studied for the classification of Left-hand and Right-hand MI EEG signals. BCI Competition IV dataset 2a and acquired MI EEG dataset of nine healthy subjects are used to study the classification performance. Classification results show that transfer learning using InceptionV3 model produces the highest classification accuracy of $82.78 \pm 4.87$% for the BCI competition dataset and $83.79 \pm 3.49$% for the acquired dataset compared to AlexNet and ResNet50. Hence, InceptionV3 CNN can be used to efficiently classify MI signals in BCI systems to aide people suffering from neuromuscular disorders by replacing or restoring motor functions.
Structural and catalytic investigations on vanadium oxide nanoparticles supported on silica films grown on a Mo(112) substrate

The widespread use of model systems for understanding the heterogeneous catalytic processes is based on bridging the structural complexity gap between present generation of supported metal and metal oxide technical catalysts and crystalline metal and planar metal/oxide systems, which are utilized to investigate structure-reactivity relationships by a large variety of surface science techniques. In this thesis, we focused on a concept of socalled 'monolayer' vanadium oxide catalysts, which have been introduced particularly for methanol oxidation reactions. Following a bottom-up approach, silica supported vanadium oxide model catalysts were investigated. Combining a number of experimental techniques, the surface of Mo(112) used as a substrate for the silica films was characterized in detail and the atomic structure of the silica film was determined. Adsorption of water and growth of vanadium oxide nanoparticles on the silica films, and finally the reactivity of vanadium oxide/silica systems towards methanol were studied. In contrast to the previously suggested models, an oxygen induced p(2×3) superstructure formed on a Mo(112) surface should be considered as one dimensional surface oxide where Mo=O groups are formed preferentially along the 1] 1 1 [ direction of the Mo(112) surface. Monolayer silica films grown on Mo(112) surfaces are composed of two-dimensional network of SiO4 tetrahedra. Depending on the film preparation conditions, the structure can be altered by additional oxygen atoms adsorbed on the Mo substrate. The defect structure includes antiphase domain boundaries which form by a half-lattice shift along the 10] 1 [ direction and a low density of point defects, most probably silicon vacancies. Water does not dissociate on the monolayer silica film. An ordered structure of water with respect to silica film was observed at 140 K owing to good lattice matching between the silica film and hexagonal ice. Amorphous solid water layers homogenously covering the surface at 100 K were used as reactive layers for vanadium oxide particles in order to mimic 'wet chemistry' used in preparation of technical catalysts. The results revealed that ice layer assisted the formation of hydrated vanadium oxide nanoparticles partially terminated by V=O and V-OH groups. The dehydration takes place above 500 K, thus exposing V-terminated surface. Methanol dissociates on dehydrated vanadium oxide particles and methoxy species are stable on the surface up to 500 K only in the presence of vanadium terminated surface sites. Formaldehyde production which takes place at ~550 K is strongly affected by the surface structure of the vanadium oxide particles and exhibits a maximum at specific ratio between Vand V=O sites on the surface. The results presented may have a strong impact on our understanding of the catalytic reactions at the molecular level.
Dynamic Task Allocation for Cost-Efficient Edge Cloud Computing

Edge cloud computing systems are widely used to supply various computation services in Internet of Things (IoT). An essential problem is how to efficiently allocate task requests to various edge and cloud servers given task requirements (e.g., response time and required memory space), in order to minimize various costs generated in edge cloud computing. Existing studies on task allocation usually consider the viewpoint of provider cost such as offloading cost, uploading cost and deployment cost. However, the viewpoint of user cost (e.g., server fee) is rarely considered which is becoming an important issue in the deployment of edge cloud computing systems, especially for cost sensitive users like venture companies. In this paper, we study a dynamic task allocation problem in edge cloud computing where both servers’ status and arriving tasks would change along with time; the goal is to search the task allocation policy that can minimize user cost. Specifically, we consider a parallel processing case where a task’s workload can be infinitely divided among the various servers; this causes a huge solution space and makes the problem hard to solve. Thus, we consider an approximate method from the perspective of server coalitions rather than a single server, and propose a dynamic coalition formation algorithm called coalitional R-learning (CR-learning) to guide several edge servers in forming a coalition dynamically. Simulations verify that our algorithm can significantly reduce user cost comparing with some other existing algorithms while shrinking the solution space.
Image Enhancement for Tuberculosis Detection Using Deep Learning

The latest World Health Organization’s (WHO) study on 2018 is showing that about 1.5 million people died and around 10 million people are infected with tuberculosis (TB) each year. Moreover, more than 4,000 people die every day from TB. A number of those deaths could have been stopped if the disease was identified sooner. In the recent literature, important work can be found on automating the diagnosis by applying techniques of deep learning (DL) to the medical images. While DL has yielded promising results in many areas, comprehensive TB diagnostic studies remain limited. DL requires a large number of high-quality training samples to yield better performance. Due to the low contrast of TB chest x-ray (CXR) images, they are often is in poor quality. This work assesses the effect of image enhancement on performance of DL technique to address this problem. The employed image enhancement algorithm was able to highlight the overall or local characteristics of the images, including some interesting features. Specifically, three image enhancement algorithms called Unsharp Masking (UM), High-Frequency Emphasis Filtering (HEF) and Contrast Limited Adaptive Histogram Equalization (CLAHE), were evaluated. The enhanced image samples were then fed to the pre-trained ResNet and EfficientNet models for transfer learning. In a TB image dataset, we achieved 89.92% and 94.8% of classification accuracy and AUC (Area Under Curve) scores, respectively. All the results are obtained using Shenzhen dataset, which are available in the public domain.
Association Rules and Machine Learning for Enhancing Undeclared Work Detection

Undeclared work is, by definition, a multi-faceted phenomenon that needs to be detected. In welfare states, undeclared work results in loss of public revenue and thus resources critical for welfare mechanisms’ funding, lack of worker protection and, last but not least, unfair competition for legitimate businesses. Yet, little to no studies have proposed the use of sophisticated machine learning methods in tackling this severe socioeconomic problem. In this study, we demonstrate the application of an advanced data analysis method, the association rule mining, which has significant advantages over rule-based systems, in classifying employers likely to engage in undeclared work. Indeed, the results of this pilot project proved divulging, even to the most experienced labour inspectors, offering insights in patterns of employers’ illegal behaviour, that were previously unidentified.
Zero-Forcing Beamforming for Active and Passive Eavesdropper Mitigation in Visible Light Communication Systems

This article proposes zero-forcing (ZF) beamforming strategies that can simultaneously deal with active and passive eavesdroppers in visible light communication (VLC) systems. First, we propose a ZF beamforming scheme that steers a transmission beam to the null space of active eavesdroppers’ (AEDs) channel, while simultaneously considering the SNRs for a legitimate user (UE) and passive eavesdroppers (PEDs) residing at unknown locations. To find an eigenmode related to the optimal beamforming vector, we adopt an inverse free preconditioned Krylov subspace projection method. For unfavorable VLC secrecy environments, the proposed ZF beamformer appears to be incapable of effectively coping with the PEDs due to the strict condition that the data transmission must be in the null space of the AEDs’ channel matrix. Hence, an alternative beamforming scheme is proposed by relaxing the constraint on the SNRs of the AEDs. The related optimization problem is formulated to reduce the secrecy outages caused by PEDs, while simultaneously satisfying the target constraints on the SNRs of the UE and the AEDs. To simplify the mathematical complexity of the approach, Lloyd’s algorithm is employed to sample the SNR field, which in turn discretizes the problem, thus making it tractable for practical implementation. The numerical results show that both the exact and relaxed ZF beamforming methods achieve superior performance in the sense of secrecy outage relative to a benchmark ZF scheme. Moreover, the proposed relaxed ZF beamforming method is shown to cope with PEDs better than the exact ZF beamforming approach for unfavorable VLC environments.