Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Imprinting: The Role of History in Digital Strategising at a Predigital Organisation

Advances in digital technologies are rapidly transforming organisations in multiple industries, including traditional firms. When adopting technologies to compete with digital players, predigital organisations face unique challenges because of their history and traditions. However, little is known about the role of organisational history on digital strategising. We conducted an in-depth historical case study of a traditional hardware store that is currently implementing a digital strategy. Grounded in a qualitative interpretative analysis, we find that organisational traditions were rewritten over time and continue to affect digital strategising practices. Adopting imprinting theory as a lens, we plan to develop a process model of digital imprinting that explains how traditions inform the emergence of digital imprints and their role in the making of digital strategy in predigital organisations.
Purchase Intentions on Social Media as Predictors of Consumer Spending

The paper addresses the problem of forecasting consumer expenditure from social media data. Previous research of the topic exploited the intuition that search engine traffic reflects purchase intentions and constructed predictive models of consumer behaviour from search query volumes. In contrast, we derive predictors from explicit expressions of purchase intentions found in social media posts. Two types of predictors created from these expressions are explored: those based on word embeddings and those based on topical word clusters. We introduce a new clustering method, which takes into account temporal co-occurrence of words, in addition to their semantic similarity, in order to create predictors relevant to the forecasting problem. The predictors are evaluated against baselines that use only macroeconomic variables, and against models trained on search traffic data. Conducting experiments with three different regression methods on Facebook and Twitter data, we find that both word embeddings and word clusters help to reduce forecasting errors in comparison to purely macroeconomic models. In most experimental settings, the error reduction is statistically significant, and is comparable to error reduction achieved with search traffic variables.
3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.
Assessing the Capability of Knowledge Integration in Virtual Smart Grid Projects

Globalization not only drives the convergence of international markets, but also creates an ever stronger network of organizations and people, with the result that increasing cooperation takes place across national borders. This is also evident in the energy sector, where Smart Grid projects serve to build up the future energy system. New technical possibilities show that these cooperations increasingly take place on a virtual level and that data and information are therefore exchanged via the Internet. Although project work on a virtual level could bring many advantages, such as a fast and easy accessibility, it can also lead to the risk that the success of the project will be negatively influenced by the fact that little trust is built up in the joint communication. As today’s literature mainly focuses on the tools, methods and opportunities through virtual teams, hardly any research considers the actual influence of virtuality on knowledge integration. Using a qualitative empirical approach, this paper aims to find out how spatially distributed knowledge can be integrated in virtual Smart Grid projects in order to develop new innovations. The research is of particular relevance as virtual teams play an increasingly important organizational role in today’s Information Systems.
Improve Image Classication by Convolutional Network on Cambricon

Cambricon provides us with a complete intelligent application system, how to use this system for deep learning algorithms development is a challenging issue. In this paper, we exploit, evaluate and validate the performance of the ResNet101 image classiﬁcation network on Cambricon with Cambricon Caﬀe framework, demonstrating the availability and ease of use of this system. Experiments with various operational modes and the processes of model inference show, the optimal running time of a common ResNet101 network that classiﬁes the CIFAR-10 dataset on Cambricon is 1715ms. We hope that this work will provide a simple baseline for further exploration of the performance of convolutional neural network on Cambricon.
Towards Using Word Embedding Vector Space for Better Cohort Analysis

On websites like Reddit, users join communities where they discuss specific topics which cluster them into possible cohorts. The authors within these cohorts have the opportunity to post more openly under the blanket of anonymity, and such openness provides a more accurate signal on the real issues individuals are facing. Some communities contain discussions about mental health struggles such as depression and suicidal ideation. To better understand and analyse these individuals, we propose to exploit properties of word embeddings that group related concepts close to each other in the embeddings space. For the posts from each topically situated sub-community, we build a word embeddings model and use handcrafted lexicons to identify emotions, values and psycholinguistically relevant concepts. We then extract insights into ways users perceive these concepts by measuring distances between them and references made by users either to themselves, others or other things around them. We show how our proposed approach can extract meaningful signals that go beyond the kinds of analyses performed at the individual word level.
Distant Reading of Religious Online Communities: A Case Study for Three Religious Forums on Reddit

We present results of a project examining the application of computational text analysis and distant reading in the context of comparative religious studies, sociology, and online communication. As a source for our corpus, we use the popular platform Reddit and three of the largest religious subreddits: the subreddit Christianity, Islam and Occult . We have acquired all posts along with metadata for an entire year resulting in over 700,000 comments and around 50 million tokens. We explore the corpus and compare the different online communities via measures like word frequencies, bigrams, collocations and sentiment and emotion analysis to analyze if there are differences in the language used, the topics that are talked about and the sentiments and emotions expressed. Furthermore, we explore approaches to diachronic analysis and visualization. We conclude with a discussion about the limitations but also the benefits of distant reading methods in religious studies.
Emotion Preservation in Translation: Evaluating Datasets for Annotation Projection

This paper is a pilot study that aims to explore the viability of annotation projection from one language to another as well as to evaluate the multilingual data set we have created for emotion analysis. We study different language pairs based on parallel corpora for sentiment and emotion annotations and explore annotator agreement. We show that the data source is a possible one for reliable L1 data to be used in annotation projection from high-resource languages, such as English, into low-resource languages and that this is a reliable way of creating data sets for fine-grained sentiment analysis and emotion detection.
The effect of fluorescent pigments on butterfly copulation

Avoiding inadvertent interference with natural processes of mate selection is critical to responsible investigations of patterns of mating success. Several recent field studies investigating the population dynamics of Euphydryas butterflies have employed fluorescent dye as a tool t o determine the relative proportion of copulations between males and females in different experimental classes (Wheye & Ehrlich, 1985; Ehrlich & Wheye, 1986; Baughman, 1991). Use of the dye does not, however, enable one t o assess the evolutionary value of copulation attempts as it does not address the likelihood of sperm transfer or the viability of sperm transferred when copulation does occur. The technique requires that the external portions of the genitalia of male butterflies be dipped in powdered dye which fluoresces when exposed to ultraviolet light. Traces of the dye are transferred to the female external genitalia during copulation and subsequently can be detected either with the naked eye or with the aid of an ultraviolet light source that makes the dye fluoresce more strongly in the visible range of the spectrum. Judicious use of the fluorescent dye technique to determine relative frequency of copulations requires two assumptions: first, that the effect of each pigment being used is the same, and second, that so marking male butterflies does not influence those males’ ability to obtain copulations with females. While equivalent behavioural modifications might not limit the utility or invalidate the results of experiments employing the dye technique, such studies might still disrupt mating patterns, a factor of particular concern in small, possibly genetically constrained populations. However, uniformity of effect and behavioural neutrality have been assumed rather than tested experimentally prior to the studies described here. These assumptions could well be false because the com-
Supervised OCR Post-Correction of Historical Swedish Texts: What Role Does the OCR System Play?

Current approaches for post-correction of OCR errors offer solutions that are tailored to a speciﬁc OCR system. This can be problematic if the post-correction method was trained on a speciﬁc OCR system but have to be applied on the result of another system. Whereas OCR post-correction of historical text has received much attention lately, the question of what role does the OCR sys-tem play for the post-correction method has not been addressed. In this study we explore a dataset of 400 documents of historical Swedish text which has been OCR processed by three state-of-the-art OCR systems: Abbyy Finereader, Tesseract and Ocropus. We examine the OCR results of each system and present a supervised machine learning post-correction method that tries to approach the challenges exhibited by each system. We study the performance of our method by using three evaluation tools: PrimA, Spr˚akbanken evaluation tool and Fron-tiers Toolkit. Based on the evaluation analysis we discuss the impact each of the OCR systems has on the results of the post-correction method. We report on quantitative and qualitative results showing varying degrees of OCR post-processing complexity that are important to consider when developing an OCR post-correction method.
Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.
A Targeted “Next-Generation” Sequencing-Informatic Approach to Define Genetic Diversity in Theileria orientalis Populations within Individual Cattle: Proof-of-Principle

Oriental theileriosis is an economically important tickborne disease of bovines, caused by some members of the Theileria orientalis complex. Currently, 11 distinct operational taxonomic units (OTUs), or genotypes, are recognized based on their major piroplasm surface protein (MPSP) gene sequences. Two of these genotypes (i.e., chitose and ikeda) are recognized as pathogenic in cattle, causing significant disease in countries of the Asia-Pacific region. However, the true extent of genetic variation and associated virulence/pathogenicity within this complex is unknown. Here, we undertook a proof-of-principle study of a small panel of genomic DNAs (n = 13) from blood samples originating from individual cattle known to harbor T. orientalis, in order to assess the performance of a targeted “next-generation” sequencing-informatic approach to identify genotypes. Five genotypes (chitose, ikeda, buffeli, type 4, and type 5) were defined; multiple genotypes were found within individual samples, with dominant and minor sequence types representing most genotypes. This study indicates that this sequencing-informatic workflow could be useful to assess the nature and extent of genetic variation within and among populations of T. orientalis on a large scale, and to potentially employ panels of distinct gene markers for expanded molecular epidemiological investigations of socioeconomically important protistan pathogens more generally.
Gut Microbiota Abrogates Anti-α-Gal IgA Response in Lungs and Protects against Experimental Aspergillus Infection in Poultry

Naturally occurring human antibodies (Abs) of the isotypes IgM and IgG and reactive to the galactose-α-1,3-galactose (α-Gal) epitope are associated with protection against infectious diseases, caused by pathogens expressing the glycan. Gut microbiota bacteria expressing α-Gal regulate the immune response to this glycan in animals lacking endogenous α-Gal. Here, we asked whether the production of anti-α-Gal Abs in response to microbiota stimulation in birds, confers protection against infection by Aspergillus fumigatus, a major fungal pathogen that expresses α-Gal in its surface. We demonstrated that the oral administration of Escherichia coli O86:B7 strain, a bacterium with high α-Gal content, reduces the occurrence of granulomas in lungs and protects turkeys from developing acute aspergillosis. Surprisingly, the protective effect of E. coli O86:B7 was not associated with an increase in circulating anti-α-Gal IgY levels, but with a striking reduction of anti-α-Gal IgA in the lungs of infected turkeys. Subcutaneous immunization against α-Gal did not induce a significant reduction of lung anti-α-Gal IgA and failed to protect against an infectious challenge with A. fumigatus. Oral administration of E. coli O86:B7 was not associated with the upregulation of lung cytokines upon A. fumigatus infection. We concluded that the oral administration of bacteria expressing high levels of α-Gal decreases the levels of lung anti-α-Gal IgA, which are mediators of inflammation and lung damage during acute aspergillosis.
Bio-functional properties of probiotic Lactobacillus: current applications and research perspectives

Abstract Lactic acid bacteria as a starter culture are very important component in the fermentation process of dairy and food industry. Application of lactic acid bacteria as probiotic bacteria adds more functionality to the developed product. Gut colonizing bacteria have attractive benefits related to human health. Bio-functional properties such as antimicrobial activity, anti-inflammatory, ACE-inhibitory, antioxidant, antidiarrheal, antiviral, immunomodulatory, hypocholesterolemic, anti-diabetic and anti-cancer activities are the most applicable research areas of lactic acid bacteria. Different strains of Lactobacillus are generally consumed as probiotics and colonize the gastrointestinal tract. Sometimes these bacteria may possess antimicrobial activity and may positively influence the effect of antibiotics. Use of Lactobacillus spp. for the development of functional foods is one of the promising areas of current research and applications. Individual bacterial species have unique biological activity, which may vary from strains to strains and identification of this uniqueness could be helpful in the development of functional and therapeutic food products.
A DETAILED ANALYSIS ON THE USE OF GAMIFICATION IN CLOUD BASED LEARNING MANAGEMENT SYSTEMS

The concept of infusing game elements into non-game applications has been gaining momentum as of lately. In this paper we explore and identify the variables which affect learning experience and how knowing those can help in implementing game mechanics on a Learning Management System (LMS) which is flexible in nature in its operations so that it can be scaled as required using cloud infrastructure. This research tries to assess how, why and where gamification and cloud computing should be best utilized in the context of learning management systems by exploring both existing learning management systems using game mechanics and outlook of how gamification can be used to propel students’ overall learning experience.
Prevalence of Seasonal Influenza Vaccination in Chronic Obstructive Pulmonary Disease (COPD) Patients in the Balearic Islands (Spain) and Its Effect on COPD Exacerbations: A Population-Based Retrospective Cohort Study

To determine the prevalence of influenza vaccination in chronic obstructive pulmonary disease (COPD) patients and its effect on COPD exacerbations, we conducted a retrospective population-based cohort study analyzing real-life data. We included all registered COPD patients ≥40 years old using respiratory medication during the study period (2012–2013). Influenza vaccination during the 2012/2013 campaign was the parameter studied. Moderate and severe exacerbations during 2013 were the dependent outcome variables. Logistic regression adjusting for age, gender, concomitant asthma diagnosis, COPD severity, smoking status, number of moderate and severe exacerbations the previous year, and comorbidities was performed, and 59.6% of the patients received seasonal influenza vaccination. The percentage of patients with exacerbations was higher among those vaccinated. Influenza vaccination had a statistically significantly negative (non-protective) crude effect favoring the risk of severe exacerbations: OR: 1.20 (95% CI; 1.05–1.37). This association diminished and lost statistical significance after adjustment: aOR: 0.93 (95% CI; 0.74–1.18). The protective effect in the analysis restricted to the epidemic period was not significant: aOR: 0.82 (95% CI; 0.58–1.16). We concluded that prevalence of influenza vaccination was suboptimal. In contrast with most of the available evidence, our results did not support a protective effect of influenza vaccination on the risk of admission for COPD exacerbation.
The Effect of Front-of-Pack Nutritional Labels and Back-of-Pack Tables on Dietary Quality

A healthy diet is important to prevent lifestyle diseases. Food labels have been proposed as a policy tool to improve the healthiness of food choices, as they provide information about nutritional content and health attributes which may otherwise have been unknown to the consumer. This study investigates the effect of food labels with different formats on dietary quality by using home-scan panel data and difference-in-difference methods to compare the change in dietary quality over time for households that start to use food labels with households that do not use labels. I find that the use of front-of-pack (FOP) nutritional labels increases overall dietary quality, which is driven by reduced intake of added sugar and increased intake of fiber. The use of back-of-pack (BOP) nutritional tables does not influence dietary quality. There is no additional benefit to overall dietary quality by using both labels. However, the results indicate that there could be a benefit of using both labels on certain nutrients. The results imply that additional policies are needed to supplement food labels in order to improve dietary quality.