Miracle-dz/UniFAR-FTUs huggingface dataset:
Ageing Entrepreneurs and Business Transfer Challenges in Croatia

Abstract Business transfer as a research topic awakened the interest of researchers in the past few decades. Business transfer is defined as a change of ownership of any firm to another person or legal entity assuring the continuous existence and commercial activity of the enterprise, and it encompasses different kinds of transfers both to family and non-family members. Ageing and consequently retirement are often mentioned as the reasons for entrepreneurs’ exits from the companies and intention to initiate the business transfer process. A successful business transfer process is one of the key prerequisites for long-term sustainability of small and medium-sized enterprises. There is a significant number of ageing business owners in Croatia who will exit their companies in the coming years. Considering the number of these entrepreneurs and the influence their exits can have on company stakeholders and national economy in general, it is of great importance to better understand the factors that can influence the choice of their exit strategies. The aim of the paper is to provide an understanding of the dilemmas that the ageing entrepreneurs face when considering different exit modes. The data for the study was collected through in-depth, semi-structured interviews conducted with six ageing business owners. The analysis of the interviews enabled the identification of the challenges that ageing entrepreneurs face in the business transfer process: emotional attachment to the firm, strong preference for family succession and concerns about financial security after retirement. Some of these challenges are a result of the undeveloped business transfer ecosystem, strong tradition and cultural values that imply family succession as the only acceptable exit strategy for retiring company owners in Croatia.
IDD – A Platform Enabling Differential Debugging

Abstract Debugging is a very time consuming task which is not well supported by existing tools. The existing methods do not provide tools enabling optimal developers’ productivity when debugging regressions in complex systems. In this paper we describe a possible solution aiding differential debugging. The differential debugging technique performs analysis of the regressed system and identifying the cause of the unexpected behavior by comparing to a previous version of the same system. The prototype, idd, inspects two versions of the executable – a baseline and a regressed version. The interactive debugging session runs side by side both executables and allows to examine and to compare various internal states. The architecture can work with multiple information sources comparing data from different tools. We also show how idd can detect performance regressions using information from third-party performance facilities. We illustrate how in practice we can quickly discover regressions in large systems such as the clang compiler.
Teachers' perceptions of creating supportive school environments for children from same-sex parented families

The purpose of this study was to explore foundation phase teachers’ perceptions of the way in which supportive school environments are being created for children from same-sex parented families. It focused specifically on how foundation phase teachers perceive their role in accommodating, including, and positively representing the same-sex parented family in their classroom practice. Current literature highlights the negative experiences of homophobia and heteronormativity in schools, together with the need to create more supportive school environments for children from samesex parented families. The increasing prevalence of same-sex parented families in South Africa has created the need for extended research in this regard, and yet there is a gap in national literature on the school experiences of children from this nontraditional minority family form. Foundation phase teachers play a central role in teaching their young learners to accept and celebrate diversity. However, no research has been done in South Africa to explore foundation phase teachers’ perspectives on their role in interrupting heteronormativity in their schools and classrooms. This study was approached from an interpretive paradigm and qualitative methods were employed to collect and analyse the data. Individual interviews were conducted with four foundation phase teachers, and interpretive thematic data analysis techniques were used to analyse the data. Culturally responsive pedagogy was used as a framework to explore barriers to inclusion, and to recommend ways in which foundation phase teachers in South African schools can be supported in creating safe, positive and counter-heteronormative school environments for children from same-sex parented families.
Distribuion of Leading Digits of Numbers II

Abstract In this paper, we study the sequence (f (pn))n≥1,where pn is the nth prime number and f is a function of a class of slowly increasing functions including f (x)=logb xr and f (x)=logb(x log x)r,where b ≥ 2 is an integer and r> 0 is a real number. We give upper bounds of the discrepancy DNi*(f(pn),g) D_{{N_i}}^*\left( {f\left( {{p_n}} \right),g} \right) for a distribution function g and a sub-sequence (Ni)i≥1 of the natural numbers. Especially for f (x)= logb xr, we obtain the effective results for an upper bound of D Ni*(f(pn)g) D_{{N_i}}^*\left( {f\left( {{p_n}} \right),g} \right) .
Expressing the quantity of urinary analytes: a discussion of some issues arising from the monitoring of the menstrual cycle

Abstract Practical domestic monitoring of the menstrual cycle requires measurements of urinary metabolites of reproductive hormones: oestrone glucuronide (E1G) and pregnanediol glucuronide (PdG). Data reported in the literature are expressed as (i) concentration, without or with either creatinine- or specific gravity correction, or (ii) excretion rates. This variation in such a fundamental issue prompts consideration of the relationships between the four measures. Because the menstrual cycle kinetics of E1G and PdG are complex, we consider measurements of urinary creatinine, urea, galactose, xylose and inulin which tend to be more stable. We show that uncorrected concentration measurements of these urinary analytes can be positively correlated, negatively correlated or uncorrelated with the serum concentration. Based on measurements of urinary creatinine concentrations, urinary specific gravity and creatinine excretion rates, we conclude that urinary analyte concentration are likely to be more reliable when creatinine-corrected rather than corrected using specific gravity, but that both are less reliable than measurements of the excretion rate. This has implications for the quantitation of any urinary analyte, but especially for the monitoring of the menstrual cycle in which changes in E1G and PdG from one day to the next can be physiologically significant for a woman monitoring her fertility.
Effect of different white-spot lesion treatment on orthodontic shear strength and enamel morphology: In vitro study

Aim: White-spot lesion treatment is required before fixed appliance insertion as caries prevalence increases during orthodontic treatment. The aim of this study was to analyze the effect of fluoride varnish, casein phosphopeptide amorphous calcium phosphate fluoride (CPP-ACPF) varnish, and resin infiltration as white-spot lesion treatments on orthodontic shear strength and enamel morphology. Materials and Methods: This is an observational analysis study with posttest-only control group design and blind total sampling methods. Sixty bovine mandible incisors were cleaned and divided into five groups (n = 12). No treatment was applied on Group 1 (control), whereas the four other groups were immersed in demineralization solution for 96h before being stored in artificial saliva (Group 2), treated with fluoride varnish (Group 3), CPP-ACPF varnish (Group 4), and resin infiltration (Group 5). After bracket bonding with resin-modified glass ionomer on all samples, shear bond strength was evaluated. Adhesive remnant index (ARI) was observed by using a digital photo. Enamel morphology was evaluated using a scanning electron microscopy (SEM) before bracket bonding and after polishing. The Kruskal–Wallis test (P > 0.05) followed by Mann–Whitney U test was carried out (P < 0.05). Results: The shear strength in Groups 2 and 5 was slightly increased as compared with control group (P > 0.05), whereas it was significantly decreased in Groups 3 and 4 (P < 0.05). The ARI score was found to be highest after treatment with resin infiltration. Conclusion: Pretreatment with resin infiltration is recommended to be performed before bracket bonding on white-spot lesion; however, it may need to be reapplied after debonding and polishing to conceal enamel morphology damage formed after polishing.
The Logic and Normative Force of Dual-Character Generics: Towards a Theoretical Model for the Study of Normatively Shifted Predications

Abstract This paper investigates the relationship between generic statements and the expression, transmission and persistence of social norms. The author presents the concept of normativity and its importance in the decision-making process in the context of social reality and social norms that comprise it (Bicchieri, 2006, 2016; Bicchieri et al., 2018). The paper analyses the idea of “what is normal” (Haslanger, 2014) to show how social norms are triggered by particular generic constructions relating to “social kinds”, represented by noun phrases denoting “dual character concepts” (Knobe et al., 2013; Prasada et al., 2013; Leslie, 2015). DCCs are shown as effectively serving their persuasive and explanatory function due to their polysemous nature (Leslie, 2015) rather than to different pragmatics (Leslie, forthcoming). Special focus is placed on gender terms as particularly salient social kinds; this salience can be explained by a culturally pivotal role of social constructs of manhood and womanhood and by linguistic potential of generics in the development of social beliefs and legitimizing norm-driven behaviours.
Developing Models for Managing Drones in the Transportation System in Smart Cities

Abstract Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.
Pediatric Toxicoepidemiology of Tramadol Intoxication in Iran: A 5-year Cross-Sectional Study

Objective: We aimed to find the toxicoepidemiological indicators of tramadol poisoning in children and also the relationship of these indicators (such as demographic characteristics, and referral time) with the final therapeutic outcome. Methods: In this cross-sectional study with retrospective data collection, we included the records for all the patients under 18 that have been admitted due to tramadol poisoning between 2010 and 2015 to Noor and Ali-Asghar (PBUH) University hospital which serves as the referral medical center for acute poisonings management in the central part of Iran and is located in Isfahan. Demographic characteristics, ingested dose, dosage forms, clinical manifestations, coingested drugs, and the outcome of treatment for all pediatric patients were documented and descriptively analyzed. Findings: Demographic and clinical data of a total of 189 patients including 101 male (53.4%) with a mean age of 16.66 ± 2.64 years were abstracted and included in this study. The average time between tramadol ingestion and hospital admission was 3.39 ± 3.23 h. Mean duration of hospitalization was 12.3 ± 10.7 h. In all cases, the route of drug exposure was oral, and the most common form of drug dosage form was 100 mg tablets (n = 122) proceeded by 200 mg tablets (n = 32). The mean estimated dose of ingested tramadol was 1126 ± 1061 mg (median, 900 range, 50–7000 mg). 43.9% of the poisoned patients were high school students, and 23.3% had a high school diploma. Intentional intoxications were reported in 93.1% cases and 42.9% had coingestions. Activated charcoal (87.3%), gastric lavage (59.3%), oxygen therapy with mask (46.6%), naloxone (11.6%), anticonvulsants (13.2%), and intubation and ventilation (5.3%) were done as first-line therapeutic measures. Conclusion: Our results suggest that the trend of acute tramadol poisoning among children is decreasing, mostly accidental in adolescents and commonly intentional among young children. Proper education to improve emotional intelligence for young adults and to keep drugs out of reach of the children and safer packaging is recommended to reduce tramadol poisoning incidence in the pediatric population.
Data-Driven Location Annotation for Fleet Mobility Modeling

The large availability of mobility data allows studying human behavior and human activities. However, this massive and raw amount of data generally lacks any detailed semantics or useful categorization. Annotations of the locations where the users stop may be helpful in a number of contexts, including user modeling and profiling, urban planning, activity recommendations, and can even lead to a deeper understanding of the mobility evolution of an urban area. In this paper, we foster the expressive power of individual mobility networks, a data model describing users’ behavior, by defining a data-driven procedure for locations annotation. The procedure considers individual, collective, and contextual features for turning locations into annotated ones. The annotated locations own a high expressiveness that allows generalizing individual mobility networks, and that makes them comparable across different users. The results of our study on a dataset of trucks moving in Greece show that the annotated individual mobility networks can enable detailed analysis of urban areas and the planning of advanced mobility applications.
A novel spatio-temporal clustering technique to study the bike sharing system in Lyon

In the last decades cities have been changing at an incredible rate growing the needs of efficient urban transportation to avoid traffic jams and high environmental pollution. In this context, bike sharing systems (BSSs) have been widely adopted by major regions like Europe, North America and Asia-Pacific becoming a common feature of all metropolitan areas. Its fast growing has increased the need of new monitoring and forecasting tools to take fast decisions and provide an efficient mobility management. In this context we focus on the BSS of Lyon in France, called Vélo’v. In particular we analyse a dataset containing the loading profiles of 345 bike stations over one week, treating the data as continuous functional observations over a period of one day. The aim of this work is to identify spatio-temporal patterns on the usage of bike sharing stations, identifying groups of stations and days with similar behaviour, with the purpose of providing useful information to the fleet managers. To this scope, we develop a novel bi-clustering algorithm able to deal with functional data, extending a nonparametric algorithm developed for multivariate data. This new algorithm is able to find simultaneously subsets of rows and columns with similar behaviour when the elements of the dataset are functional objects. Obtained results show that through this analysis it is possible to identify different usage patterns according to the area of the city and the day of the week.
Futility of a Right to Explanation

In the last few years, interpretability of classification models has been a very active area of research. Recently the concept of interpretability was given a more specific legal context. In 2018 EU introduced General Data Protection Regulation with a Right to Explanation for people subjected to automated decision making. The Regulation itself is very brief on what such a right might imply. In this paper, we attempt to explain what the Right to Explanation may involve. We then argue that this right would be very difficult to implement due to technical challenges. We also maintain that the Right to Explanation may not be needed and sometimes may even be harmful. We propose instead an external evaluation of classification models with respect to their correctness and fairness.
Forecasting Corporate Financial Time Series using Multi-phase Attention Recurrent Neural Networks

These days, attention-based Recurrent Neural Networks (RNNs) have been widely used for learning the hidden temporal struc-tureofrawtimeseriesdata.Morerecently,attention-basedRNNs havebeenfurtherenhancedtorepresentmultivariatetemporal or spatio-temporal structure underlying multivariate time series. This latest study achieved more effective prediction by employing attention structure that simultaneously capture the spatial relationships among multiple different time series and the temporal structure of those time series. That method assumes single time-series samples of multi-or uni-variate explanatory variables, and thus, no prediction method was designed for multiple time-series samples of multivariate explanatory variables. Moreover, such previous studies have not explored on financial time series incorporating macroeconomic time series, such as Gross Domestic Product (GDP) and stock market indexes, to our knowledge. Also, no neural network structure has been designed for focusing a specific industry. We aim in this paper to achieve effective forecasting of corporate financial time series from multiple time-series samples of multivariate explanatory variables. We propose a new industry specific model that appropriately captures corporate financial time series, incorporating the industry trends and macroeconomic time series as side information. We demonstrate the performance of our model through experiments with Japanese corporate financial time series in the task of predicting the return on assets (ROA) for each company.
Multi-view Clustering through Optimal Transport

Research on Multi-View Clustering (MVC) has become more and more attractive thanks to the richness of its application in several ﬁelds. In this paper, we present a novel framework for MVC, inspired by the Optimal Transport (OT) theory to learn the clusters in each view and compute an ensemble clustering of all the views in order to form a consensus cluster. We propose two algorithms for this purpose: a Consensus Projection Approach (CPA) and a Consensus with New Representation (CNR), both based on the entropy regularized Wasserstein distance and the Wasserstein barycenters between the data distributions in each view. To validate the approaches, extensive experiments were conducted on multiple data-sets.
Visualizing Dependencies during Incremental Discovery Processes

Functional dependencies (fds), and their extensions relaxed functional dependencies (rfds), represent an important semantic property of data. They have been widely used over the years for several advanced database operations. Thanks to the availability of discovery algorithms for inferring them from data, in the last years (relaxed) fds have been exploited in many new application contexts, including data cleansing and query relaxation. One of the main problems in this context is the possible “big” number of rfds that might hold on a given dataset, which might make it difficult for a user getting insights from them. On the other hand, one of the main challenges that has recently arisen is the possibility of monitoring how dependencies change during discovery processes run over data streams, also known as continuous discovery processes. To this end, in this paper we present a tool for visualizing the evolution of discovered rfds during continuous discovery processes. It permits to analyze detailed results for different types of rfds, and uses quantitative measures to monitor how discovery results evolve. Finally, in order to facilitate the analysis of results in long discovery processes, the tool enables the comparison among rfds holding in different time-slots. The effectiveness of the proposed tool has been evaluated in a case study focused on dependencies discovered from streams of data associated to the tweets posted over the Twitter social network.
Reliability assessment of complex networks using rules extracted from trained ANN and SVM models

This paper describes the application of hybrid intelligent system (HIS) to extract rules from two machine learning approaches: neural networks (NN) and support vector machine (SVM). The experimentation is based on the TREPAN algorithm. TREPAN, a well-known technique developed originally to extract linguistic rules from a trained artificial neural network, is modified to cope with SVM models. An example related to the reliability assessment of a 21-links network and its excellent performance results is presented.
Equine High-Intensity Exercise, Articular Biochemistry, and Nutritional Antioxidant Supplementation

EQUINE HIGH-INTENSITY EXERCISE, ARTICULAR BIOCHEMISTRY, AND NUTRITIONAL ANTIOXIDANT SUPPLEMENTATION Jennifer L. MacNicol Advisor: University of Guelph, 2017 Professor W. Pearson The objective of this thesis was to document the response in horses to high-intensity exercise and antioxidant supplementation. In a pilot study, horses underwent an exercise test. Plasma and synovial fluid (SF) samples were taken 24 hr prior to exercise (BL), 0.5, 1, 2, 4, 8, and 24 hr following exercise. High-intensity exercise in horses resulted in a transient systemic inflammatory state and articular inflammation and cartilage breakdown. In a subsequent study, horses were provided with a supplement comprised of Cucumis melo pulp extract to determine whether antioxidant supplementation impacted parameters of exercise-induced systemic or articular inflammation/oxidative stress. Based on the prior time course, samples were taken at BL, 1 and 24 hr following exercise. Supplementation resulted in post-exercise mitigation of articular oxidative stress and cartilage breakdown, increased antioxidant status, reduced blood lymphocyte levels, and 24 hr following high-intensity exercise supplemented horses displayed reduced inflammation and muscle breakdown.