Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of People's Reasoning for Sharing Real and Fake News

The problem of the increase in the volume of fake news and its widespread over social media has gained massive attention as most of the population seeks social media for daily news diet. Humans are equally responsible for the surge of fake news spread. Thus, it is imperative to understand people’s behavior when they decide to share real and fake news items on social media. In an attempt to do so, we performed an analysis on data collected through a survey where participants (n= 363 ) were asked whether they were willing to share the given news item on their social media and explain the reasoning for their decision. The re-sults show that the analysis presents several commonalities with previous studies. Moreover, we also addressed the problem of predicting whether a person will share a given news item or not. For this, we used intrinsic features from participants’ open-ended responses and demographics attributes. We found that the perceived emotions triggered by the news item show a strong inﬂuence on the user’s decision to share news items on social media.
Improving Sampling-Based Motion Planning Using Library of Trajectories

Motion planning is one of the fundamental problems in robotics. This thesis combines the advances in motion planning and shape matching to improve planning speeds in static environments. The first part of this thesis covers current methods used in object similarity evaluation and motion planning. The middle part describes how these methods are used together to improve planning speeds by utilizing prior knowledge about the environment, along with additional modifications. In the last part, the proposed methods are tested against other state-of-the-art planners in an independent benchmarking facility. The proposed algorithms are shown to be faster than other planners in many cases, often finding paths in environments where the other planners are unable to.
The Importance of Authentic

Context: This article is based on a pragmatic theoretical perspective on education, in which theoretical and practical competences are developed through experiences and participation in realworld teaching contexts. Previous research points to a lack of culture for authentic workplacebased assessment in vocational and professional education in many countries. Prior to this study, professors/authors and student-teachers in a vocational teacher education program in Norway experienced that student-teachers were unable to demonstrate comprehensive teaching competence, as examinations and assessments assess theoretical knowledge separately from practice. Research questions: 1) How can an authentic workplace-based exam during placement give student-teachers an opportunity to showcase their comprehensive teacher competence? 2) What factors are important to emphasize in such an exam? 3) How do the student-teachers demonstrate and develop comprehensive teacher competence through an authentic exam? Methods: Using action research, professors/authors carried out sequential actions to develop a practical-theoretical exam in an authentic professional setting. This included demonstrating elements of practical and theoretical competence conducted during teaching practice. The exam involved planning in line with a guidance document and practical teaching in the classroom in VET-schools, followed by a piece of reflective writing based on teaching experiences. Supervisors and professors/authors observed the student-teachers teaching as part of multiple qualitative methods. *Corresponding author: sylte@oslomet.no 303 Dahlback, Berg Olstad, Sylte, Wolden Findings: The empirical results show how student-teachers demonstrate and develop comprehensive teaching competence. Both the student-teacher and the supervisors in VETschools experienced the authentic exam as realistic and professionally based. The biggest challenge involved logistics: Compensating the professors’/authors’ time and financial frameworks related to the observation of the student-teachers. However, this kind of authentic assessment leads to stronger coherence between both theory and practice, and between the vocational teacher education at the university and the VET in upper secondary school. It also supports the job-relevant learning process towards comprehensive teacher competence. Conclusions: This kind of authentic assessment i.e. an authentic exam requires an understanding of the complex role of teachers within their professional context in VET. Therefore, the professors/authors see the need of a broader, more comprehensive teacher competence in VET to meet the work life needs for competence.
Sexual Differentiation and Primordial Germ Cell Distribution in the Early Horse Fetus

Simple Summary In horses, gonadal development and sexual differentiation occur during early fetal life. This is accompanied by primordial germ cell differentiation and migration to the gonad site. However, little is known about the time when these processes take place and the mechanisms behind them. Additionally, no information is available regarding number and distribution of primordial germ cells in the equine gonad. During development and differentiation, gonads can be affected by stressors causing infertility, but this condition can only be diagnosed in post-pubertal animals. Herein we show that equine gonads develop asynchronously between male and female fetuses, and that the number and distribution of primordial germ cells is influenced by fetal sex. By day 45 of pregnancy, equine gonads were clearly differentiated, while migration of primordial germ cells still occurred at this stage. No sign of genital duct regression was seen until day 60 of pregnancy. Abstract It was the aim of this study to characterize the development of the gonads and genital ducts in the equine fetus around the time of sexual differentiation. This included the identification and localization of the primordial germ cell population. Equine fetuses between 45 and 60 days of gestation were evaluated using a combination of micro-computed tomography scanning, immunohistochemistry, and multiplex immunofluorescence. Fetal gonads increased in size 23-fold from 45 to 60 days of gestation, and an even greater increase was observed in the metanephros volume. Signs of mesonephros atrophy were detected during this time. Tubular structures of the fetal testes were present from day 50 onwards, whereas cell clusters dominated in the fetal ovary. The genital ducts were well-differentiated and presented a lumen in all samples. No sign of mesonephric or paramesonephric duct degeneration was detected. Expression of AMH was strong in the fetal testes but absent in ovaries. Irrespective of sex, primordial germ cells selectively expressed LIN28. Migration of primordial germ cells from the mesonephros to the gonad was detected at 45 days, but not at 60 days of development. Their number and distribution within the gonad were influenced (p < 0.05) by fetal sex. Most primordial germ cells (86.8 ± 3.2% in females and 84.6 ± 4.7% in males) were characterized as pluripotent according to co-localization with CD117. However, only a very small percentage of primordial germ cells were proliferating (7.5 ± 1.7% in females and 3.2 ± 1.2% in males) based on co-localization with Ki67. It can be concluded that gonadal sexual differentiation in the horse occurs asynchronously with regard to sex but already before 45 days of gestation.
THE RELATIONSHIP BETWEEN FOREIGN DIRECT INVESTMENT AND CURRENT ACCOUNT THROUGH PROFIT OUTFLOWS: THE TURKISH CASE

The developing world invites Foreign Direct Investment (FDI) to their countries nearly unconditionally for the developmental benefits. However, in literature there are concerns regarding ‘unfettered FDI’ in terms of several factors including FDI outflows which contribute to Current Account Deficits. This paper investigates the relationship between FDI and profit outflows on the case of Turkey via economic causality analysis through Granger causality test using a method developed by Toda and Yamamoto (1995) and the impulse-response analysis. The results indicate that although there are short-term positive effects of FDI inflows in terms of current account financing, the causality results point to the long-term adverse effects of FDI inflows on profit remittance leading to current account deficit, which is an issue that policy makers should consider when trying to attract FDI.
Physics-Informed Machine

Traditional lithium-ion (Li-ion) battery state of health (SOH) estimation methodologies that focused on estimating present cell capacity do not provide sufficient information to determine the cell’s lifecycle stage or value in second-life use. Quantifying the underlying degradation modes that cause capacity fade can give further insight into the electrochemical state of the cell and provide more detailed health information such as the remaining active materials and lithium inventory. However, current physics-based methods for degradation diagnostics require long-term cycling data and are computationally expensive to deploy locally on a device. To improve upon current methods, we propose and extensively test two light-weight physics-informed machine learning methods for online estimating the capacity of a battery cell and diagnosing its primary degradation modes using only limited early-life experimental degradation data. To enable late-life prediction (e.g. > 1.5 years) without the use of late-life experimental data, each of the methods is trained using simulation data from a physics-based half-cell model and early-life (e.g. < 3 months) degradation data obtained from cycling tests. The proposed methods are comprehensively evaluated using data from a long-term (3.5 years) cycling experiment of 16 implantable-grade Li-ion cells cycled under two temperatures and C-rates. Results from a four-fold cross-validation study show that the proposed physics-informed machine learning models are capable of improving the estimation accuracy of cell capacity and the state of three primary degradation modes by over 50% compared to a purely data-driven approach. Additionally, this work provides insights into the role of temperature and C-rate in cell degradation
Poverty Reduction Effect of New-Type Agricultural Cooperatives: An Empirical Analysis Using Propensity Score Matching and Endogenous Switching Regression Models

Agricultural cooperatives have been found to effectively alleviate poverty in developing countries because of their specific socioeconomic functions that allow poor households to overcome marketing and production constraints. However, cooperative evaluations are inevitably influenced by other poverty alleviation measures and rarely consider the characteristics of specific ethnic groups. Using cross-sectional surveys in Southwest China and employing propensity score matching (PSM) and endogenous switching regression (ESR) models, this paper analyzed the participation of poor households in New-type Agricultural Cooperatives (NACs) in ethnic areas and assessed the income impacts of NAC membership by eliminating unobserved biases and group heterogeneity. This study detected heterogeneous policy perceptions and behavioral differences between the member and nonmember groups, and the PSM and ESR model results indicated that, overall, participation in the NACs had a positive effect on household income. The ESR model was found to be more plausible as it was able to reveal the significant income gaps under a counterfactual inference framework. Local policymakers need to focus on the policy perception and behavioral and earning capability differences between groups and increase the balanced policy implementation.
Supplementary Material: Model-Contrastive Federated Learning

We use a projection head to map the representation like [1]. Here we study the effect of the projection head. We remove the projection head and conduct experiments on CIFAR-10 and CIFAR-100 (Note that the network architecture changes for all approaches). The results are shown in Table 2. We can observe that MOON can benefit a lot from the projection head. The accuracy of MOON can be improved by about 2% on average with a projection head.
FS-Net Supplementary Material

As stated in Section 3.5 of the paper, the 3D deformation mechanism is box-cage based and the deformations are applied in a canonical space. In the canonical coordinate system, every box edge is parallel to an axis (shown in Figure A). This property makes the 3D deformation calculation easier. For example, when we need to elongate/shrink the mug along Y axis by n times. We enlarge the distance between surface S1,2,3,4 and surface S5,6,7,8 by n times. Since these two surfaces are parallel to the XZ-plane, the x and z coordinates are unchanged. Then points coordinates are changed from [x, y, z] to [x, ny, z]. The calculations are similar when we need to elongate/shrink the mug along X or Z axis by n times:
Catalytic Isolation and Physicochemical Properties of Nanocrystalline Cellulose (NCC) using HCl-FeCl3 System Combined with Ultrasonication

This research emphasizes the isolation of nanocrystalline cellulose (NCC) from palm tree cellulose (PTC) and α-cellulose (AC), using acidic FeCl3-assisted catalytic pretreatment coupled with ultrasonication. The cavitation effect of ultrasonication affects the microstructure of the fibers, ultimately enhancing the crystallinity index of the prepared nanocrystalline cellulose (NCC) sample. In this research, Fourier transform infrared spectroscopy (FTIR) was used to identify the specific functional groups on both types of NCC sample. X-ray diffraction (XRD) analysis demonstrated that the isolated NCC from PTC and AC showed a higher crystallinity index of 73.51% and 89.03%, with diameters of 20 to 70 nm and 15 to 50 nm, respectively. The change in surface morphological features was observed by scanning electron microscopy (SEM), atomic force microscopy (AFM), and transmission electron microscopic (TEM) analysis. It was observed that PTC-based NCC had higher thermal stability than the starting cellulosic sample, whereas NCC isolated from AC showed an opposite trend of reduced thermal stability relative to the raw sample. The results indicated that catalytic acid hydrolysis with ultrasonication was able to yield up to 80.88% and 81.20% of NCC from PTC and AC, respectively, which is comparatively high enough for economic viability of the process.
Herbicides doses in the defoliation of common bean to anticipate mechanized harvesting

Abstract Background The use of desiccant herbicides can allow the anticipation of common bean harvesting by providing a reduction in the humidity of plants and grains. Objective Determine the effect of doses of desiccant herbicides on the moisture content of the plants (stems, leaves and grains), in the 100 grains mass, grain yield and physiological quality of the seeds of two contrasting common bean cultivars. Methods The experimental design for each cultivar (BRS FC 104, super early cycle, 60 days [. ]
Mathematical Models for FDG Kinetics in Cancer: A Review

Compartmental analysis is the mathematical framework for the modelling of tracer kinetics in dynamical Positron Emission Tomography. This paper provides a review of how compartmental models are constructed and numerically optimized. Specific focus is given on the identifiability and sensitivity issues and on the impact of complex physiological conditions on the mathematical properties of the models.
Development Methodologies for Safety Critical Machine Learning Applications in the Automotive Domain: A Survey

Enabled by recent advances in the field of machine learning, the automotive industry pushes towards automated driving. The development of traditional safety-critical automotive software is subject to rigorous processes, ensuring its dependability while decreasing the probability of failures. However, the development and training of machine learning applications substantially differs from traditional software development. The processes and methodologies traditionally prescribed are unfit to account for specifics like, e.g., the importance of datasets for a development. We perform a systematic mapping study surveying methodologies proposed for the development of machine learning applications in the automotive domain. We map the identified primary publications to a general machine learning–based development process and preliminary assess their maturity. The reviews’s goal is providing a holistic view of current and previous research contributing to ML-aware development processes and identifying challenges that need more attention. Additionally, we list methods, network architectures, and datasets used within these publications. Our meta-study identifies that model training and model V&V received by far the most research attention accompanied by the most mature evaluations. The remaining development phases, concerning domain specification, data management, and model integration, appear underrepresented and in need of more thorough research. Additionally, we identify and aggregate typically methods applied when developing automated driving applications like models, datasets and simulators showing the state of practice in this field.
1 PSYCHOLOGICAL OWNERSHIP OF ( BORROWED ) MONEY

The current research introduces the concept of psychological ownership of money, the notion that consumers perceive money in differing degrees as their own. We suggest that this concept is particularly important in the realm of consumer debt, where consumers use borrowed money. We show that individuals naturally vary in the extent to which they experience psychological ownership of money, and variation on this dimension predicts willingness to borrow for discretionary purchases. Moreover, this construct is distinct from other individual-level factors such as debt aversion, financial literacy, income, intertemporal discounting, materialism, propensity to plan, and self-control, and it predicts willingness to borrow above and beyond these factors. We further show that psychological ownership of borrowed money is shaped by context. Specifically, we document systematic differences in psychological ownership across debt types that can be used for discretionary purchases, and show that these differences explain consumers’ interest in borrowing. Finally, we find that differences in psychological ownership manifest in online search behavior, and can be leveraged to predict patterns of online search behavior for different debt types.
DO UNIVERSITIES AND STUDENTS NEED TO BE ENTREPRENEURIALLY ORIENTED? A LITERATURE REVIEW*

Entrepreneurial orientation is a theoretical concept defined by three dimensions: innovativeness, proactivity and risk-taking. Entrepreneurial orientation researchers suggest that entrepreneurial orientation is a concept that pervades through all organizational levels of the company (Wiklund and Shepherd, 2005; Wales, Monsen and McKelvie, 2011). Although focus of early research on the concept has been oriented on the company level and its influence on profitability, cotemporary research has put much accent both on research of individual entrepreneurial orientation, as well as on research of entrepreneurial orientation of non-profit companies. Having on mind the fact that contemporary university should be an incubator of practical and applicable knowledge, but also being aware of many practical dimensions related to university management, in this paper we will focus on investigating whether there is a rationale for incentives for universities and students to be more entrepreneurially oriented by systematizing the findings of research papers whose focus has been on measuring individual entrepreneurial orientation of students and on researching different dimensions of entrepreneurial orientation of universities.
Improvement of Power System Small-Signal Stability by Artificial Neural Network Based on Feedback Error Learning

Electrical power systems usually suffer from instabilities because of some disturbances occurring due to environmental conditions, system failures, and loading conditions. The most frequently encountered problem is the loss of synchronization between the rotor angle and the stator magnetic angle for synchronous generators. The contribution of this study is that a nonlinear adaptive control approach called feedback error learning (FEL) is utilized to improve the small-signal stabilities of an electric power system. The power system under study is composed of a synchronous machine connected to infinite bus. Many advantages of FEL control approach makes it capable to robustly adapting with all possible operating conditions rather than using optimization algorithms for tuning the conventional power system stabilizer (CPSS) that is still unsatisfactory especially at some critical operating points. The performances of two controllers, namely the proposed FEL scheme and the conventional controller CPSS, are tested by Matlab simulations. It is found that the FEL controller can be effectively used as an alternative stabilizer for improving small-signal stabilities of the power system.
Exploring the Impact of Persuasive System Features on User Sentiments in Health and Fitness Apps

Although behavioural change support systems have proven to be effective in changing user’s behaviour, the need to design effective persuasive systems that optimize persuasive experiences of users continue to remain a challenge. This study seeks to contribute to existing literature that aims at addressing this challenge. Using stratified random sampling technique, 23 health and fitness iOS and Android apps were selected. User reviews of each app were downloaded and compared with the corresponding persuasive systems features using cluster analysis. The findings demonstrated that more system features do not produce higher positive sentiment. It was also observed that apps with more social support features were associated with higher frequencies of fear, sadness and anger re-lated sentiments.
Verification Multiple Choice

The following sections cover more details regarding the results of our experiments and benchmark generation process. First, we separate each model’s performance on binary questions that have two possible answers versus on questions that have open answers. Next, we detail how we create high quality questions, including how we augment the spatio-temporal scene graphs, ignore unchallenging and ambiguous questions, and balance answer and question structure distributions. We then explain which compositions were held out when generating the novel composition training/test split. Finally, we describe the two human studies that validate the correctness of our generation process, analyze the source of errors, and provide recommendations for future work. Although all our experiments were conducted on the balanced version of the dataset, we will also release two additional versions of the benchmark: a full unbalanced version and a smaller subsampled unbalanced version. Since the full unbalanced AGQA is 192M questions, training models with data at this scale might be prohibitive for smaller research groups. The smaller unbalanced set can be used to serve as a benchmark under resource constraints.