Miracle-dz/UniFAR-FTUs huggingface dataset:
The people vs. democracy: why our freedom is in danger and how to save it

Liberal Democracy, Yascha Mounk argues, is in the midst of an existential crisis. Whereas it had long appeared that liberal democracy was the ‘only game in town’ (Linz and Stepan 1996), the foundations of liberal democracy now seem shaky even in those states where until recently they had seemed most unshakeable. Mounk’s latest book, The people versus democracy: why our freedom is in danger and how to save it, is a comprehensive multi-disciplinary study of populism in Europe and the United States. In it, Mounk examines the populist moment much of the world seems to undergoing, explains the causes of the populists’ success, and proposes a course of action to reinvigorate liberal democracy.
Performances of injustice: The politics of truth, justice and reconciliation in Kenya

for instance, by the Austrian People’s party with the far right FPÖ. This is a major question that still needs to be addressed. A second crucial aspect of the book is linked to the role of the crises that Levitsky and Ziblatt consider as critical junctures for the authoritarian turn, in particular security crises such as terrorist attacks and political violence. This issue deserves a thorough examination, because a crisis could also lead to the reinforcement of democracy, as a reaction. Under what conditions, then, does a crisis become a catalyst for autocratisation or democratic renewal? It seems that the configuration of other agency-related and structural conditions should be assessed in order to have a more comprehensive picture. In conclusion, despite appearances, this is not a book about Donald Trump. On the contrary, the most interesting part of the work deals with the long-term and general evolution of US democracy, rather than the short-term, recent political events. Before (and beyond) Trump, the real threat to American democracy lies in the heavy polarisation of society as a consequence of the rise of inequalities. The authors write that ‘parties become wedded to incompatible worldviews (.) especially when their members are so socially segregated that they rarely interact’ (p. 116). So, look at the wood (of inequalities) and forget the (orange) tree. This is a powerful message that echoes the words of Charles Tilly (2007) on de-democratisation, when he warned about the risk that differences in ethnicity, gender, race, wealth, or religion ‘translate directly into categorical differences in political rights and obligations’ (2007, p. 75), paving the way to autocracy. Yet, the most worrying message of the book still is that authoritarian leaders always have and always will be there. The solution then lies in weakening their political and economic breeding ground, by any political means possible.
Get Your Project Funded: Using Biometric Data to Understand What Makes People Trust and Support Crowdfunding Campaigns

Creating a good crowdfunding campaign is difficult. By understanding why people contribute to crowdfunding campaigns we can make campaigns better and raise more money. Crowdfunding websites allow entrepreneurs to make a pitch, which is watched by potential funders. This article describes a pilot of an experiment that measures how people react to both successful and unsuccessful pitches. In particular we are interested in emotional reactions and trust reactions. Unexpectedly, failed campaigns were watched more and were judged to have higher integrity. Perceived ability seemed to be the best predictor of a campaign’s success. We hope to explore this subject in more depth with further experiments.
A Network Analysis of the Intersectoral Linkages Between Manufacturing and Other Industries in China and Italy

In recent years, the structural changes in global manufacturing and the economic crisis have sparked renewed interest in the role of manufacturing sector in driving economic development. Based on data from the World Input–Output Database (WIOD), this article uses network analysis (NA) to describe and compare inter-sectoral linkages between manufacturing and other sectors in two national economies, Italy and China, characterized by different stages of industrialization. The main metrics of the networks identified (at both node and network level) are discussed from an economic perspective. Besides confirming that manufacturing still has a fundamental role as the main source of demand for other economic activities in both countries, results highlight some fundamental differences in the structural characteristics of inter-sectoral linkages between China and Italy. Our exploratory analysis provides policymakers a complementary framework to illustrate backward and forward linkage effects, as well as potential knowledge and technology flows between industries.
Memory-Aware Scheduling for Fixed Priority Hard Real-Time Computing Systems

OF THE DISSERTATION MEMORY-AWARE SCHEDULING FOR FIXED PRIORITY HARD REAL-TIME COMPUTING SYSTEMS by Gustavo A. Chaparro-Baquero Florida International University, 2018 Miami, Florida Professor Gang Quan, Major Professor As a major component of a computing system, memory has been a key performance and power consumption bottleneck in computer system design. While processor speeds have been kept rising dramatically, the overall computing performance improvement of the entire system is limited by how fast the memory can feed instructions/data to processing units (i.e. so-called memory wall problem). The increasing transistor density and surging access demands from a rapidly growing number of processing cores also signi cantly elevate the power consumption of memory systems. In addition, the interference of memory accesses from different applications and processing cores signi cantly degrades the computation predictability, which is essential to ensure timing speci cations in realtime system design. The recent IC technologies (such as 3D-IC technology) and emerging data-intensive real-time applications (such as Virtual Reality/Augmented Reality, Arti cial Intelligence, Internet of Things) further amplify these challenges. We believe that it is not simply desirable but necessary to adopt a joint CPU/Memory resource management framework to deal with these grave challenges. In this dissertation, we focus on studying how to schedule xed-priority hard real-time tasks with memory impacts taken into considerations. We target on the xed-priority realtime scheduling scheme since this is one of the most commonly used strategies for practical real-time applications. Speci cally, we rst develop an approach that takes into con-
Building relationships through accountability: An expanded idea of accountability

Previous literature has focused on how external forces impose accountability on individuals (i.e., holding individuals to account), but has not considered the possibility of internal, personal accountability. We explain how an internalized sense of accountability, which we term internally assumed accountability, can enrich our understanding of why some organizational members might assume ownership for organizational problems, even ones that they did not actually cause. We offer a typology of accountability in organizations based on contrasting relationship norms and personal orientations. Our article concludes with a discussion on connections between different kinds of accountability and stakeholder relationships, suggesting a number of avenues for further investigation and practice.
Acculturation of Syrian Refugees in Germany

Since 2011, there has been an ongoing civil war in Syria between various militant groups, ISIS, and the Syrian government, in response to the oppressive regime of the Syrian government of President Bashar al-Assad. As a result, the largest migration that the world has seen since the Second World War has transpired. Approximately 13 million Syrians have been forcefully displaced from their homes, making this one of the largest humanitarian crises of our time. Many Syrians have sought refuge in neighbouring countries, as well as in Europe, the United States, and Canada. There is notably little research on refugee adaptation in Europe, which is the focus of this study. Using aspects of the Multidimensional Individual Differences Acculturation (MIDA) model, this study looked to examine the sociocultural and psychophysical adaptation of Syrian refugees in Germany. Measures that were excluded from the current version of the MIDA model were Ingroup Contact and Outgroup Contact. Researchers at Ludwig Maximilians University Munich administered paper and pencil surveys to 265 participants in Nuremberg, Germany who were attending vocational and language schools. Results displayed a significant relationship between Psychosocial Resources and Integration, and Psychophysical Distress; Co-National Connectedness and Integration; and Hassles and Psychophysical Distress. This study looks to inform host country government policies about positive integration strategies for refugee adaptation.
Human attention simulation on nature scenes in computer vision

Human Attention Simulation is a long-standing problem in computer vision area. Researchers either attempt to locate the most interesting objects for human beings in images or predict where people will look at in nature scenes. Accurate and reliable human attention detection can benefit numerous tasks ranging from tracking and recognition in vision to image manipulation in graphics. For example, successful saliency detection algorithms facilitate automated image segmentation, more reliable object detection, effective image thumbnailing and retargeting. However, state-of-the-art techniques neglect some essential problems that limit saliency models from precisely simulating human attention mechanism. On the input front, images used for saliency detection tasks always fail to preserve the high dimensional information of the scene; on the task front, it is inevitable to observe inconsistency among ground truth provided by different persons, resulting in uncertainty on prediction performances. Regarding the input data, existing saliency detection approaches using images as inputs are sensitive to foreground/background similarities, complex background textures, and occlusions. I explore the problem of utilizing light fields as input for saliency detection. The proposed technique is enabled by the availability of commercial plenoptic cameras that capture the light field of a scene in a single shot. I show that the unique refocusing capability of light fields provides useful focusness, depths, and objectness cues. I further develop a new saliency detection algorithm tailored for light fields. To validate the approach, I acquire a light field database of a range of indoor and outdoor scenes and generate the ground truth saliency map. Experiments show that the saliency detection scheme can robustly handle challenging scenarios such as similar foreground and background, cluttered background, complex occlusions, etc., and achieve high accuracy and robustness.
Impact of improved maize variety adoption on smallholder farmers’ welfare in Benin

ABSTRACT This paper aims to analyze the impact of improved maize adoption on farmers’ welfare in Benin, using a random sample of 356 maize farmers. We provide a rigorous estimation by addressing selection bias and endogeneity issues. First, we applied a double-hurdle model to identify factors that inform farmers’ decision to adopt improved maize. Second, we used the instrumental variable model to assess the impact of improved maize on farmers’ income, poverty status, and poverty gap. For this, we used the predicted area under improved maize from the double-hurdle model as an instrument for the observed area under improved maize. We found that farm size, extension services, training on improved seed, and farmers’ location are key variables that affect both farmers’ decision and the amount of land under improved maize. We also found evidence that adoption of improved maize is positively associated with yield, income, and poverty reduction. Finally, we found no heterogeneous impacts of improved maize among poor and non-poor farmers. Adoption of improved maize favors small landholding farmers but did not have a significant impact on income for those who own large farms size. Overall, our results suggest that improved maize variety is important for rural development.
The Price and Income Elasticity of Demand for Education in Ghana: Empirical Evidence from Household Level Data

II ACKNOWLEDGEMENT . III DEDICATION. IV TABLE OF CONTENT . V LIST OF TABLES . IX LIST OF FIGURES . XI LIST OF ABBREVIATIONS . XII CHAPTER ONE . 1 1.
Integrated quality environmental management implementation in food processing SMEs

Purpose The purpose of this paper is to identify the critical success factors (CSFs) of integrated quality environmental management (IQEM) and analyze their impact on operational performance (OP) and environmental performance (EP) in food processing Small and medium-sized enterprises (SMEs) in Pakistan. Design/methodology/approach The study is based on collecting data using a survey questionnaire through snowball sampling technique. A total of 302 food processing SMEs operating in Punjab, Pakistan, responded to the survey. SPSS version-23 and SmartPLS-3 were used for data analysis. Findings The literature review identified leadership (LS), employee management (EM), strategic planning (SP), information management (IM), process management (PM), supplier management (SM) and customer focus (CF) as CSFs of IQEM. The results of this study found a significant relationship of all identified CSFs with operational performance in food processing SMEs whereas EM, IM, PM and SM were insignificant with the EP in the food processing SMEs. Research limitations/implications Although this study has collected data from one province, the Punjab province, it still relevant in identifying the CSFs for IQEM implementation within food processing SMEs to improve performance. Originality/value Despite the wide spread of integrated systems practices in the developed countries, little attention has been placed to implement and assess the IQEM initiatives by organizations in the developing countries. Thus, this study identified CSFs of IQEM based on empirical studies and analyzed their impact on OP and EP of food processing SMEs.
Corporate governance, board ethnicity and financial statement fraud: evidence from Malaysia

Purpose This study aims to examine the relationship between the presence of a Malay director on the board and financial statement fraud in Malaysia. Further, the authors investigate whether financial statement fraud firms improve their governance mechanisms compared to non-fraud firms subsequent to the fraud year. Design/methodology/approach The authors use hand-collected data comprising 76 financial statement fraud and 76 non-fraud firms over a period of eight years from 2001 to 2008. Findings Using a univariate and logistic regression model, the results demonstrate a significant positive relationship between the proportion of Malay directors on the board and the financial statement fraud. The authors also find that fraud firms significantly increase the proportion of independent directors on their boards, increase the frequency of board and audit committee meetings and reduce duality subsequent to the detection of financial statement fraud compared to the non-fraud firms. Originality/value The findings of the study are useful to policy-makers, regulators, firms and investors.
Job Mobility Among Millennials: Do They Stay or Do They Go?

Millennials are a substantial segment of the workforce; they are perceived to be driven by higher pay, quick to be dissatisfied and leave a job, and committed to volunteering. This article examines how these perceptions translate to job mobility in terms of job switching within and across sectors, without drawing cross-generation comparisons. Using data from the National Longitudinal Survey of Youth 1997 cohort (NLSY97) from 2008 to 2013, we notice a trend among millennials of frequent job switching within rather than across sectors. Job dissatisfaction is the strongest predictor of public-sector employees switching jobs within the sector. For sector switching, we find some variation: Low pay corresponds with exiting the nonprofit sector, whereas job dissatisfaction is the strongest predictor of leaving the public sector. Millennials working in the public and nonprofit sectors are less likely to switch sectors if they volunteer. Implications for theory and practice are discussed.
DEaf-MOPS/D: An Improved Differential Evolution Algorithm for Solving Complex Multi-objective Portfolio Selection Problems Based on Decomposition

With the high-speed economic development and economic diversification of the world, it is very necessary to develop an effective and efficient portfolio selection method with high precision and robustness. In this study, we first introduce an enhanced multi-objective cardinality constrained mean-variance (CCMV) model, in which the transaction costs and price-earning (P/E) ratio are appended in the model, then an improved differential evolution algorithm with adaptive fine-tune is proposed to solve multi-objective portfolio selection problems based on decomposition (DEaf-MOPS/D). Finally, five simulation experiments on five benchmark datasets (HangSeng, DAX 100, FTSE 100, S&P 100, and Nikkei 225) are employed to investigate the performance of our method. The experimental results indicate that the performance of DEaf-MOPS/D is superior to other compared algorithms, and its runtime is much less than other algorithms, which demonstrate that our method is efficient in solving high-dimension portfolio selection problems.
Citizens’ motivated reasoning about public performance: experimental findings from the US and Denmark

Abstract Recent studies find motivated reasoning in citizens’ processing of information about public performance. Using experiments in the US and Denmark, we examine effects on an accuracy-based task of two forms of motivated reasoning: partisan identity-based reasoning and reasoning from ideology-based governance preferences (favoring either the public or the private sector). The experiments incorporate a political prime, a health care needs prime (to reduce politicization), and a neutral, no-prime, condition. We find that priming citizens to think politically accentuates the influence of partisan identities and governance preferences on reasoning. In contrast, priming about the need for a service reduces these biases. These findings extend knowledge of motivated reasoning in an accuracy-based task and priming with a no-prime benchmark, and confirm some findings of previous studies. Reducing the salience of partisan identities or governance preferences in the presentation of information may help stimulate more accuracy-based reasoning about public performance.
Dual Pursuit for Subspace Learning

In general, low-rank representation (LRR) aims to find the lowest rank representation with respect to a dictionary. In fact, the dictionary is a key aspect of low-rank representation. However, a lot of low-rank representation methods usually use the data itself as a dictionary (i.e., a fixed dictionary), which may degrade their performances due to the lack of clustering ability of a fixed dictionary. To this end, we propose learning a locality-preserving dictionary instead of the fixed dictionary for low-rank representation, where the locality-preserving dictionary is constructed by using a graph regularization technique to capture the intrinsic geometric structure of the dictionary and, hence, the locality-preserving dictionary has an underlying clustering ability. In this way, the obtained low-rank representation via the locality-preserving dictionary has a better grouping-effect representation. Inversely, a better grouping-effect representation can help to learn a good dictionary. The locality-preserving dictionary and the grouping-effect representation interact with each other, where dual pursuit is called. The proposed method, namely, Dual Pursuit for Subspace Learning, provides us with a robust method for clustering and classification simultaneously, and compares favorably with the other state-of-the-art methods.
DTB Net: A text detector in a football match scene

The text detection of football match scenes is an important step in text recognition. Due to the complex background of the football scenes, the arbitrary orientation of the target position, the different aspect ratios of the texts, small targets and so on. Therefore, text detection in football scenes is also a challenging problem. The text detection network TextBoxes has been improved to produce a text detector in the football scene, named DTB Net, which can detect texts with different aspect ratios and small target texts in the football scenes. Through experimental comparison, DTB Net has higher precision and recall rate in the text detection of football matches, which lays a foundation for the recognition of texts in football scenes.
Unconventional Monetary Policy in US: Empirical Evidence from Estimated Shadow Rate DSGE Model

Recent empirical research on macro-finance has proved that the shadow rate can be used as an accurate measure to represent the stance of unconventional monetary policy in the zero lower bound (ZLB) environment. We use the shadow rate to estimate a dynamic stochastic general equilibrium (DSGE) model of US economy and conduct counterfactual simulation to quantify the macroeconomic effects of unconventional monetary policy implemented by the Fed. Compared with the estimation result of pre-ZLB sub-sample, the structural parameters estimated from full-samples with shadow rate have reasonable posterior distributions that are consistent with most of related DSGE literatures. This finding validates the applicability of shadow rate in the estimation of DSGE models without using any nonlinear techniques. Counterfactual simulation shows that without the unconventional monetary policy conducted by the Fed, macroeconomic variables of US economy would have worse performance than their actual realisations.
Techno-economic evaluation of five-level nested neutral point clamped converter topology for transformer-less connection of high-power wind energy conversion systems

Developers and operators are interested in improving the reliability and reducing the associated costs of wind power plants (WPPs) because of the continuous increase in the power capacity of wind energy conversion systems (WECSs) and the increasing development of WPPs. The electrical subsystem of the WPP experiences the highest failure rate and constitutes a significant proportion of its total cost. Reliability of the WECS can be increased and its cost reduced by eliminating the wind turbine transformer from the electrical subsystem. This study gives a techno-economic evaluation of a five-level nested neutral point clamped (NNPC) converter topology for transformer-less connection of high- power WECSs. The approach entailed the calculation of reliability of five-level NNPC converter topology deployed in the grid-side of a WECSs. This method presents a mathematical formula for deriving the reliability of a five-level NNPC converter topology by using the reliability block diagram and reliability estimation-based models in the military handbook (MIL-HDBK-217F). The cost analysis model shows that the total cost of the five-level diode clamped converter topology was higher than the five-level NNPC converter topology. The study could be extended by carrying out accurate modelling of the mission profile of the presented converter by using multi-domain simulation technique.