Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining system success factors with trust to explain e-government adoption using fsQCA

The study examines how quality and trust, critical factors of IS success, combine and interact with each other to explain e-government adoption. Quality is decomposed in information quality, system quality, and service quality, and trust into trust of service and trust of the government. E-government success and adoption is measured as citizens perceived net benefits. Drawing from complexity and configuration theories a conceptual model is developed that depicts the possible combinations between quality and trust. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to identify these combinations (or configurations) on 502 users of e-government services. Findings identify 5 alternative solutions that increase net benefits and 5 solutions that hinder it. None of the factors is a necessary or sufficient condition for increased net benefits, but it is their combinations that can lead to high net benefits. We stress the need to have either high perceptions on quality or trust for high net benefits, as there is no solution on which any of the quality and trust factors are both at low levels. The study contributes to theory and practice (1) by offering new insights into the interrelationships among predictors of egovernment adoption, and (2) by advancing the theoretical and methodological foundation of how quality and trust combine to lead to high and low/medium net benefits when using e-government services.
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition

Violence is an epidemic in Brazil and a problem on the rise world-wide. Mobile devices provide communication technologies which can be used to monitor and alert about violent situations. However, current solutions, like panic buttons or safe words, might increase the loss of life in violent situations. We propose an embedded artificial intelligence solution, using natural language and speech processing technology, to silently alert someone who can help in this situation. The corpus used contains 400 positive phrases and 800 negative phrases, totaling 1,200 sentences which are classified using two well-known extraction methods for natural language processing tasks: bag-of-words and word embeddings and classified with a support vector machine. We describe the proof-of-concept product in development with promising results, indicating a path towards a commercial product. More importantly we show that model improvements via word embeddings and data augmentation techniques provide an intrinsically robust model. The final embedded solution also has a small footprint of less than 10 MB.
Ultra Rapid Data Assimilation Based on Ensemble Filters

The goal of this work is to analyse and study an ultra-rapid data assimilation (URDA) method for adapting a given ensemble forecast for some particular variable of a dynamical system to given observation data which become available after the standard data assimilation and forecasting steps. Initial ideas have been suggested and tested by Etherthon 2006 and Madaus and Hakim 2015 in the framework of numerical weather prediction. The methods are, however, much more universally applicable to general non-linear dynamical systems as they arise in neuroscience, biology and medicine as well as numerical weather prediction. Here we provide a full analysis in the linear case, we formulate and analyse an ultra-rapid ensemble smoother and test the ideas on the Lorentz 63 dynamical system. In particular, we study the assimilation and preemptive forecasting step of an ultra-rapid data assimilation in comparison to a full ensemble data assimilation step as calculated by an ensemble Kalman square root filter. We show that for linear systems and observation operators, the ultra-rapid assimilation and forecasting is equivalent to a full ensemble Kalman filter step. For nonlinear systems this is no longer the case. However, we show that we obtain good results even when rather strong nonlinearities are part of the time interval $[t_0, t_n]$ under consideration. Then, an ultra-rapid ensemble Kalman smoother is formulated and numerically tested. We show that when the numerical model under consideration is different from the true model, used to generate the nature run and observations, errors in the correlations will also lead to errors in the smoother analysis. The numerical study is based on the popular Lorenz 1963 model system used in geophysics and life sciences. We investigate both the situation where the full system forecast is calculated and the situation important to practical applications where we study reduced data, when only one or two variables are known to the URDA scheme.
Exploring Correlations in Multiple Facial Attributes through Graph Attention Network

Estimating multiple attributes from a single facial image gives comprehensive descriptions on the high level semantics of the face. It is naturally regarded as a multi-task supervised learning problem with a single deep CNN, in which lower layers are shared, and higher ones are task-dependent with the multi-branch structure. Within the traditional deep multi-task learning (DMTL) framework, this paper intends to fully exploit the correlations among different attributes by constructing a graph. The node in graph represents the feature vector from a particular branch for a given attribute, and the edge can be defined by either the prior knowledge or the similarity between two nodes in the embedding with a fully data-driven manner. We analyze that the attention mechanism actually takes effect in the latter case, and utilize the Graph Attention Layer (GAL) for exploring on the most relevant attribute feature and refining the task-dependant feature by considering other attributes. Experiments show that by mining the correlations among attributes, our method can improve the recognition accuracy on CelebA and LFWA dataset. And it also achieves competitive performance.
What Attracts the Best Information Technology Talents? Employer Branding in Transition Economies

The objective of this paper is to identify the most important issues in employer branding that make companies attractive to information technology talents, specifically in transition economies, viz. economies or countries that are in the long process of moving from a planned economy to a market-driven economy. We report on a survey in Poland, and based on 127 responses and exploratory factor analysis, we show what seem to be the important issues in employer branding in this transition economy. We also compare our findings with previously published finding from studies conducted in mature economies. Furthermore, we propose signaling theory as a possible explanatory context for employer branding in transition economies.
CNN inference acceleration using dictionary of centroids

It is well known that multiplication operations in convolutional layers of common CNNs consume a lot of time during inference stage. In this article we present a flexible method to decrease both computational complexity of convolutional layers in inference as well as amount of space to store them. The method is based on centroid filter quantization and outperforms approaches based on tensor decomposition by a large margin. We performed comparative analysis of the proposed method and series of CP tensor decomposition on ImageNet benchmark and found that our method provide almost 2.9 times better computational gain. Despite the simplicity of our method it cannot be applied directly in inference stage in modern frameworks, but could be useful for cases calculation flow could be changed, e.g. for CNN-chip designers.
Supporting Diffusion of IoT Solutions Exemplified by the ChainPORT Initiative

Within the ChainPORT community of port authorities (PAs) around the world, many different Internet of Things (IoT) based solutions have been piloted over the last years, successfully demonstrating how interconnected sensors and devices can improve business efficiency. The current challenge of how participating PAs can realize benefits by adopting other PAs solutions is related to the innovation diffusion process and enterprise architecture (EA) management. In cooperation with the members of the ChainPORT community, we explored their understanding of the term solution in this specific context and analyzed 24 solutions from 8 port authorities. We present (1) a conceptual definition of the solution term in the context of Internet of Things innovation and enterprise architecture management, formalized by an EA meta-model and (2) EA extensions to allow more structured and uniform communication of IoT innovation among a collaborating group of enterprises.
Technological Advancement in Marketing: Co-creation of Value with Customers

Social commerce is a new concept in consumer activity which has emerged through the phenomenon of social media. Social commerce platform tends to create an environment where consumers can interact with users and marketer. Also, it has been portrayed as a means of managing brands for businesses. This research explores the relationships among social interaction, social support, relationship quality, and consumers' intention to co-create brand value with applying social support and relationship marketing theories. Empirical data were collected from a social networking website in China. The results demonstrate that social commerce interactions, specifically, user perspective and marketer perspective, positively affect social support and in turn, intention to co-create brand value. Social support positively affects relationship quality. This study provides new insights by proposing a model of co-create brand through social commerce interactions.
Catching Loosely Synchronized Behavior in Face of Camouflage

Fraud has severely detrimental impacts on the business of social networks and other online applications. A user can become a fake celebrity by purchasing "zombie followers" on Twitter. A merchant can boost his reputation through fake reviews on Amazon. This phenomenon also conspicuously exists on Facebook, Yelp and TripAdvisor, etc. In all the cases, fraudsters try to manipulate the platform's ranking mechanism by faking interactions between the fake accounts they control and the target customers.
Basis of Shoulder Nerve Entrapment Syndrome: An Ultrasonographic Study Exploring Factors Influencing Cross-Sectional Area of the Suprascapular Nerve

As changes in nerves' shape and size are common ultrasonographic findings of entrapment neuropathy, measurement of the nerve cross-sectional area (CSA) becomes the mostly used indicator to differentiate normality from pathology. Recently, more US research has been conducted to measure the shape of the suprascapular notch and the diameter of the suprascapular nerve. Because the suprascapular nerve is paramount for various shoulder disorders, the present study aims to establish normal values of suprascapular nerve sizes at different levels as well as to investigate potential influence of participants' characteristics on the CSA measurements. The present study used a cross-sectional design investigating the CSA values of the suprascapular nerve from the supraclavicular region to spinoglenoid notch. We employed the inside-epineurium and outside-epineurium methods to quantify CSA of cervical roots (C5 and C6) and the suprascapular nerve on US imaging. Univariate comparisons of nerve sizes among different age and gender groups were carried out. Multivariate analysis was performed to analyze the impact of participants' characteristics on nerve CSA. Repeated measurement analysis of variance was conducted to examine segmental variations of CSA of the suprascapular nerve from its origin to infraspinatus fossa. Our study included 60 healthy adults with 120 shoulders and had three major findings: (1) the inside-epineurium method was more reliable than the outside-epineurium approach for CSA measurements due to higher intra- and inter-rater reliability, (2) women had smaller sizes for cervical nerve roots and for the most proximal segment of the suprascapular nerves, and (3) using the outside-epineurium method, the suprascapular nerve CSA was larger in its distal division than the portion proximal to the mid-clavicular line. In conclusion, the inside-epineurium method has better reliability for nerve CSA assessment but the outside-epineurium method is needed for quantifying the size of distal suprascapular nerve. Gender difference in CSA values should be considered during evaluation of the most proximal nerve segment. Using the outside-epineurium method, the distal suprascapular nerve would be estimated larger than its proximal portion and the segmental discrepancy should be not misinterpreted as pathology.
Epigenetics of Delirium and Aging: Potential Role of DNA Methylation Change on Cytokine Genes in Glia and Blood Along With Aging

Background: Delirium in elderly patients is common and dangerous. Major risk factors include aging and exogenous insults, such as infection or surgery. In animal models, aging enhances pro-inflammatory cytokine release from microglia in response to exogenous insults. The epigenetic mechanism DNA methylation (DNAm) regulates gene expression and changes with age. Older individuals may have methylation changes that influence the increased cytokine upon insult, but the degree to which aging affects DNAm of cytokine genes is not fully understood. Methods: The relationship between DNAm and aging of pro-inflammatory cytokine genes (TNF-alpha, IL1-beta, IL-6) was investigated using methylation array data in two cohorts. Brain and blood samples were collected from a neurosurgery cohort (NSG) of 21 subjects who underwent brain resection. A second cohort, the Grady Trauma Project (GTP), included blood samples from 265 subjects. Results: In the NSG cohort, a significant negative correlation between age and DNAm in brain was found at a CpG in IL-6. With the GTP dataset, significant negative correlations between age and DNAm were seen at most of the CpGs in TNF-alpha. Also, TNF-Alpha expression increases with age. These GTP DNAm correlations were also nominally significant in NSG blood samples. In neuronal negative NSG brain tissue, a similar negative trend was observed. Conclusions: With aging, a decrease in DNAm of cytokines gene CpGs in glia and blood was seen. As this can affect their expression, additional research is needed to fully elucidate the role of DNAm in aging and how it may influence the pathogenesis of delirium.
How can deep learning advance computational modeling of sensory information processing?

Deep learning, computational neuroscience, and cognitive science have overlapping goals related to understanding intelligence such that perception and behaviour can be simulated in computational systems. In neuroimaging, machine learning methods have been used to test computational models of sensory information processing. Recently, these model comparison techniques have been used to evaluate deep neural networks (DNNs) as models of sensory information processing. However, the interpretation of such model evaluations is muddied by imprecise statistical conclusions. Here, we make explicit the types of conclusions that can be drawn from these existing model comparison techniques and how these conclusions change when the model in question is a DNN. We discuss how DNNs are amenable to new model comparison techniques that allow for stronger conclusions to be made about the computational mechanisms underlying sensory information processing.
Expression Patterns, Genomic Conservation and Input Into Developmental Regulation of the GGDEF/EAL/HD-GYP Domain Proteins in Streptomyces

To proliferate, antibiotic-producing Streptomyces undergo a complex developmental transition from vegetative growth to the production of aerial hyphae and spores. This morphological switch is controlled by the signaling molecule cyclic bis-(3′,5′) di-guanosine-mono-phosphate (c-di-GMP) that binds to the master developmental regulator, BldD, leading to repression of key sporulation genes during vegetative growth. However, a systematical analysis of all the GGDEF/EAL/HD-GYP proteins that control c-di-GMP levels in Streptomyces is still lacking. Here, we have FLAG-tagged all 10 c-di-GMP turnover proteins in Streptomyces venezuelae and characterized their expression patterns throughout the life cycle, revealing that the diguanylate cyclase (DGC) CdgB and the phosphodiesterase (PDE) RmdB are the most abundant GGDEF/EAL proteins. Moreover, we have deleted all the genes coding for c-di-GMP turnover enzymes individually and analyzed morphogenesis of the mutants in macrocolonies. We show that the composite GGDEF-EAL protein CdgC is an active DGC and that deletion of the DGCs cdgB and cdgC enhance sporulation whereas deletion of the PDEs rmdA and rmdB delay development in S. venezuelae. By comparing the pan genome of 93 fully sequenced Streptomyces species we show that the DGCs CdgA, CdgB, and CdgC, and the PDE RmdB represent the most conserved c-di-GMP-signaling proteins in the genus Streptomyces.
Local Mammary Glucose Supply Regulates Availability and Intracellular Metabolic Pathways of Glucose in the Mammary Gland of Lactating Dairy Goats Under Malnutrition of Energy

As glucose is the regulator of both the milk yield and mammary oxidative status, glucose supply is considered to play important nutritional and physiological role on mammary gland (MG) metabolism. However, inconsistent results were observed from different infusion methods to evaluate the effect of glucose on MG glucose metabolism. Thus, precise method should be developed to learn how availability and intracellular metabolic pathways of glucose in the MG are altered by the direct mammary glucose supply. In addition, limited information is available on the role of mammary glucose supply in milk synthesis in lactating ruminants under an energy-deficient diet. Direct glucose supply to the MG was implemented in the current study through the external pudendal artery infusion under an energy-deficient diet. Six doses of glucose (0, 20, 40, 60, 80, and 100 g/d) were infused through the external pudendal arteries, which is the main artery to the MG, to six lactating goats fed with basal diet meeting 81% energy requirement in a 6 × 6 Latin square design. Milk and lactose yields were both quadratically increased with increased glucose infusion, whereas the milk yield changed inconsistently with the increased energy balance (EB), indicating local glucose supply, rather than EB, improved milk production. Glucose fluxes in the MG were significantly increased and correlated with mammary plasma flow. However, the ratio of lactose yield to glucose absorbed by the MG was significantly decreased. The increased glucose fluxes in the MG and changed glucose-related metabolites in milk indicated that the glucose availability and intracellular metabolic pathways was regulated by local mammary glucose. Acute glycolysis consumed the superfluous glucose and induced accumulation of oxygen radicals in the MG during over-supplied glucose conditions. The present study provided insight to optimal glucose supply to the MG during the lactation.
Real-time Neural-based Input Method

The input method is an essential service on every mobile and desktop devices that provides text suggestions. It converts sequential keyboard inputs to the characters in its target language, which is indispensable for Japanese and Chinese users. Due to critical resource constraints and limited network bandwidth of the target devices, applying neural models to input method is not well explored. In this work, we apply a LSTM-based language model to input method and evaluate its performance for both prediction and conversion tasks with Japanese BCCWJ corpus. We articulate the bottleneck to be the slow softmax computation during conversion. To solve the issue, we propose incremental softmax approximation approach, which computes softmax with a selected subset vocabulary and fix the stale probabilities when the vocabulary is updated in future steps. We refer to this method as incremental selective softmax. The results show a two order speedup for the softmax computation when converting Japanese input sequences with a large vocabulary, reaching real-time speed on commodity CPU. We also exploit the model compressing potential to achieve a 92% model size reduction without losing accuracy.
'If at First You Don't Succeed, Try, Try Again' Might Not Always Make Sense: On the Influence of Past Technology Category Satisfaction on Technology Usage

In this paper, we drew from the Expectation Confirmation Theory to postulate that Past Technology Category Satisfaction, which we define as the degree of a person’s past satisfaction with a technology category across manufacturers, indirectly influences the Behavioral Intention to Use a specific technological product of the same category today. After collecting 106 online questionnaires about voice assistants and applying a structural equation modeling approach, our findings indicate that people’s satisfaction with technological categories in the past does indirectly influence their usage behavior of current specific technologies through Perceived Usefulness and Perceived Enjoyment
The impact of product placements in YouTube tutorials on the buying decision

This study examines how product placements in YouTube tutorials influence the attitude towards the tutorial and a purchase intention. Using the Source Credibility Theory, Social Comparison Theory, and the Halo Effect Theory, this study proposes a model which asses the influence of expertness, trustworthiness and attractiveness of YouTube influencer on the attitude towards the tutorial and thereby onto the purchase intention. The hypothesized model is tested using structural equation modeling. The results support the proposed model with a major impact of the construct of attractiveness. Accordingly YouTube tutorials are identified as an innovation capability for the competitiveness of national and global market participants and managerial implications are given.