Miracle-dz/UniFAR-FTUs huggingface dataset:
The institutionalisation of commercialism in the audit profession

Purpose Previous literature on the commercialisation of the audit profession has focused on the coercive force of macro-institutional structures. The purpose of this paper is to focus on the agency of individual auditors and examine their active construction of the commercial self in a Big Four audit firm in China. Design/methodology/approach This paper applied a qualitative research approach to data collection and analysis. A total of 17 interviews were conducted with senior auditors, managers, directors and partners to generate a rich narrative. Findings This study analyses prevalent discourses identified in the Chinese organisational setting and finds that, within the “clan-like” structure of the audit engagement team, three recurring discourses (i.e. the client relationship, adding value and career) were powerful scripts in constructing individual subjectivity wherein the “professionalism” ideal was re-enacted to rationalise the incorporation of more commercialistic elements. Research limitations/implications This study collected interviews representing various perspectives within a Big Four audit firm in China. Nonetheless, the scope of this study was limited to certain types of audit firms at certain times. Originality/value This study demonstrates that the scripts are not just a matter of self-presentation, but important sources of self-formation and self-definition. Rather than being imposed externally, “commercial selves” are actively constructed by individual auditors, leading to the institutionalisation of commercialism in the audit profession at the micro level.
Tax Avoidance as a Sustainability Problem

equitable. The authors demonstrate that intermediaries and their clients do not share any IT and physical assets (e.g. open book accounting) but performance data is provided in the form of face-toface interactions/reports. However, they would be willing to do so with a ‘loyal’ supplier, such as the example they cite of a target cost contract between the UK firm and two very long-term specialist suppliers. Even though ‘fully integrated’ information systems are absent, a large array of performance measures are mobilised with the intermediaries (covering the internal processes such as customer profitability, delivery to customers, quality, etc.). Those measures are used to maintain the very slight margins and a positive cash flow on the long term. The intermediaries predominantly play the role of risk buffer (particularly related to relational risk), as they are placed between the consumer and the supplier (i.e. to reach service level supermarkets agreements in terms of specifications/price offered). However, the authors also mentioned the frustration of intermediaries in their attempts to negotiate with customers using financial information. By mobilising theories of justice, this study enriches our fledgling knowledge in the accounting literature (theoretical and empirical) about fair commercial relationships and particularly, how the participation of accounting and control practices can justify, support or may play a useful role in term of (un)fairness in the food supply chain. Future research could complement this debate by doing a comparative study of two sectors where the supply chain is highly integrated (such as the automotive sector or the aeronautics sector) or in supply chains that advocate more equity between the different actors. Finally, even if this research enriches the debate of the ‘role of corporations in fair societies’, further research could deep dive into the contribution of accounting practices and/or management control within global supply chains, notably to participate in the current debates around economic, social and environmental equity that have emerged with the 2030 Agenda of Sustainable Development Goals (SDG 1, 2 and 8 notably).
A selective overview of feature screening methods with applications to neuroimaging data

In neuroimaging studies, regression models are frequently used to identify the association of the imaging features and clinical outcome, where the number of imaging features (e.g., hundreds of thousands of voxel‐level predictors) much outweighs the number of subjects in the studies. Classical best subset selection or penalized variable selection methods that perform well for low‐ or moderate‐dimensional data do not scale to ultrahigh‐dimensional neuroimaging data. To reduce the dimensionality, variable screening has emerged as a powerful tool for feature selection in neuroimaging studies. We present a selective review of the recent developments in ultrahigh‐dimensional variable screening, with a focus on their practical performance on the analysis of neuroimaging data with complex spatial correlation structures and high‐dimensionality. We conduct extensive simulation studies to compare the performance on selection accuracy and computational costs between the different methods. We present analyses of resting‐state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange study.
Disadoption of conservation practices: cases of injecting manure and soil testing

Promoting the continuous use of conservation practices by farmers is an important environmental policy issue to minimize the impact of agriculture on environmental quality. The effectiveness of environmental policies relies on farmers’ continuous use of conservation practices. Using duration analysis, this study analyzes the disadoption of injecting manure into soil and soil testing. The results show that over time farmers can become more likely to disadopt conservation practices due to changing economic conditions and learning-by-doing. Therefore, programs and policies that focus on one-time adoption can be limited in promoting the continuous use of conservation practices.
Informational Benefits of Managerial Myopia

Abstract We show that managerial myopia has an informational benefit that has been overlooked in the prior research. Compared with managers who care sufficiently about the long-term, a moderately myopic manager incentivizes the proponent of a risky long-term project to produce more information about the project, leading to more informed decision making and higher firm value.
The relationship between the Australian Olympic committee and the Australian sports commission

Since the 2016 Rio Olympics, the Australian Olympic Committee (AOC) and Australian Sports Commission (ASC) have had several very public disagreements regarding the funding and direction of high performance sport. In 1980, the Fraser Government requested the then Australian Olympic Federation to boycott the Moscow Olympics. This led to the AOC not accepting any direct funding from the Australian Government for the preparation of Summer and Winter Olympics teams since 1992. However, the AOC particularly through its President John Coates used its influence to ensure that the Australian Government adequately funded Olympic sports through the ASC's expenditure distributions to the Australian Institute of Sport (AIS) and national sports organisations. Tensions will continue to arise due to the different aims of both organisations. The AOC is primarily focused on Olympic sport representation and achievements; the ASC on highperformance sport including the Olympics, organisational governance and sport participation.
Spuren der Satellitengeodäsie in Deutschland

Künstliche Erdsatelliten ermöglichen Messungen in einem globalen geodätischen Bezugsrahmen zwischen weit entfernten Punkten über Länderund tektonischen Plattengrenzen hinweg. In einer kurzen Zeitspanne kann eine hohe Anzahl von Beobachtungen gesammelt werden. Dies bietet die Möglichkeit für eine Analyse der Veränderungen und Reaktionen, welche aus den Wechselwirkungen zwischen der inneren Dynamik, der äußeren Atmosphäre und der planetaren Umgebung der Erde entstehen. Dieser Beitrag befasst sich mit der Entwicklung der Satellitengeodäsie und stellt ihre Anwendungen im Bereich der mittels Satellitenbeobachtungen bestimmten geodätischen Netze in Deutschland vor, die für die nationalen und internationalen Dienste zur präzisen Positionierung, Echtzeit-Anwendung, Bodenbewegung, Wettervorhersage, Klimaänderung und für die Steuerung autonomer Systeme die Grundlage bilden.
Fuzzy Black Hole Entropy Based Clustering

In this paper, a clustering algorithm based on Fuzzy Black-hole Entropy is purposed. A Fuzzy Black-hole Entropy based on fuzzy similarity is introduced. The algorithm automatically computes the number of clusters and their initial positions. It assigns cluster center to a point in the database with minimum entropy. A cluster is formed using above cluster center by considering all points having similarity with center. This process continues till all points are clustered. Since cluster centers are formed sequentially, there is possibility of misclassifications. To further improve the clustering, data points are reconfigured to cluster centers using similarity measure. In this way, we get better clusters. Cluster centers are again aligned to mean of each clusters by reassigned it to mean point of the cluster. In this method, there is no need to revised entropy value for each data point after cluster center is determined. Comparison of the proposed algorithm with existing algorithms, shows that it performs better than many such algorithms.
The Storage Structure of Convolutional Neural Network Reconfigurable Accelerator Based on ASIC

Abstract. With the development of deep convolutional neural networks (CNNs), it can be achieved higher accuracy in many aspects, including computer vision, speech and natural language processing. Performance efficiency of CNN at the hardware level requires overcoming the large calculation-related problems, so memory bandwidth and power budgets, should be in economical limits. CNNs models also adopts different kernel sizes, depends on the application nature, therefore it is important for designed architecture to be reconfigurable. In this work, we propose a new highperformance multi-precision reconfigurable architecture (MPRA) and optimize it for recent CNNs using 3×3/5×5/7×7 convolution such as AlexNet, GoogLeNet and ResNet with 16-bit fixed and 8-bit fixed precision. The architecture synthesized on 65 nm CMOS technologies achieves average performance (GOPS) of 276.5 in 16bit×16bit and 1105.9 in 8bit×8bit mode, running at 640 MHz and 1 V with a power dissipation of 599 mW respectively. Compared to state-of-the-art designs, the proposed architecture achieves 2.36× energy efficiency, 2.4× to 6.8× area efficiency, and 16.3% to 27.4% higher computational efficiency for AlexNet benchmarked reference.
Does geographical location matter for ethnic wage gaps?

This paper analyzes ethnic wage gaps in Great Britain by comparing minorities to majority workers in the same local labor market and focuses on the variation of wage gaps across areas. As wage gaps vary across areas, using one single national measure may be misleading. Higher wage gaps across groups are associated with higher occupational segregation and ethnic diversity, while higher wage gaps within groups are associated with higher regional specialization and proportion of co‐ethnics. Policies could help by improving job location and selection into occupations across groups.
Gamified Learning: a Rationale for Ludo-pedagogy and Gamified Classrooms

This paper examines the value of ludo-pedagogy and gamified teaching and learning in a contemporary context and introduces strategies that can be adopted to enable a gamified classroom. In particular, this paper outlines the framework for creating a workshop-style ‘escape room’ which foregrounds core elements of ludo-pedagogy and gamified classrooms and moves beyond documented cases that feature more simplistic methodologies. Gamified teaching and learning has been proven to help students achieve higher order learning outcomes and increase student self-perception, and the primary aims of this paper are to outline potential approaches and to encourage further development.
La Universidad Nacional de Rosario durante la última dictadura militar en Argentina (1976-1983). Un acercamiento a los conflictos al interior de la gestión interventora

El objetivo de este articulo es abordar algunos aspectos referentes a la intervencion de la Universidad Nacional de Rosario durante la ultima dictadura militar en Argentina (1976-1983), gestion encabezada por Humberto Riccomi. En ese contexto se analiza las estrategias de control y disciplinamiento establecidas planteando que aun cuando lograron en parte su objetivo, esto es modificar estructuralmente la vida universitaria, su implementacion genero consecuencias no previstas. Entre ellas se advierten las tensiones al interior del grupo de funcionarios y academicos colaboradores con la gestion. Esos conflictos si bien no desestabilizaron la accion interventora, marcaron las particularidades de la Universidad Nacional de Rosario.
The unbalancing act : proxy preservation for censorship resistance systems

Internet censorship is a form of digital authoritarianism in certain countries that restrict access to the internet. Internet freedom, to a degree, is possible even in these countries by means of proxies maintained outside of the censor’s boundaries. These proxies can be compromised by censors who pose as legitimate users to discover proxies. Censors are powerful adversaries and may block access to any proxy once they know about it. We propose a novel technique to address the proxy distribution problem in this thesis. We introduce the needle algorithm that preserves proxies by limiting their distribution. We show that it is a useful mechanism for both preserving proxies and maintaining client service under a censorship threat model. We examine characteristics of the needle algorithm in a simulation. Three measures are important under the censorship threat model; the enumeration or discovery of all proxies, load balancing guarantees, and the collateral damage of innocent bystanders. We compare the results of these experiments with two well-known algorithms, uniform random and power of 2 choices, as well as Tor’s bridgedb proxy assignment mechanism.
Forming Rate Options for Various Types of Consumers in the Retail Electricity Market by Solving the Adverse Selection Problem

ABSTRACT This paper is concerned with the game theoretic mechanisms of interaction between a load serving entity and several types of power consumers (with elastic and inelastic demand). The main focus is on the bounded rational and fully rational consumers, and respective utility functions are defined for them. The demand-side management (DSM) principles are used to analyze the load at different times of the day for different types of consumers. The proposed statement allows the formulation of coordinated prices in rates for different types of consumers, and optimization of demand: peak load decreases relative to a daily average one. The problem is reduced to a convex optimization problem that has a unique solution in the presented form. The obtained result is in the form of a separating equilibrium (when different consumers choose different rates), which brings the retail market equilibrium closer to the maximum public welfare.
How will climate change shape climate opinion?

As climate change intensifies, global publics will experience more unusual weather and extreme weather events. How will individual experiences with these weather trends shape climate change beliefs, attitudes, and behaviors? In this article, we review 73 papers that have studied the relationship between climate change experiences and public opinion. Overall, we find mixed evidence that weather shapes climate opinions. Although there is some support for a weak effect of local temperature and extreme weather events on climate opinion, the heterogeneity of independent variables, dependent variables, study populations, and research designs complicate systematic comparison. To advance research on this critical topic, we suggest that future studies pay careful attention to differences between self-reported and objective weather data, causal identification, and the presence of spatial autocorrelation in weather and climate data. Refining research designs and methods in future studies will help us understand the discrepancies in results, and allow better detection of effects, which have important practical implications for climate communication. As the global population increasingly experiences weather conditions outside the range of historical experience, researchers, communicators, and policymakers need to understand how these experiences shape-and are shaped by-public opinions and behaviors.
The IPO Corporate Social Responsibility Information Disclosure: Does the Stock Market Care?

We create textual information indices using corporate social responsibility (CSR) information extracted from IPO prospectuses in China. We use the indices to measure the issuers’ corporate social performance (CSP) and corporate environmental performance (CEP) and assess how the stock market reacts. We find that CSP disclosure is significantly related to the post‐market performance of the firm. Specifically, better CSP disclosure is correlated with higher post‐IPO listing holding period returns among firms that do not disclose donations or environmental expenditures, although the association does not hold for firms that make donations and environmental expenditures. In addition, institutional investors seem to care more about the CEP information for a firm than the CSP information.
Effects of the Minimum Wage on Infant Health

The minimum wage has increased in multiple states over the past three decades. Research has focused on effects on labor supply, but very little is known about how the minimum wage affects health, including children's health. We address this knowledge gap and provide an investigation focused on examining the impact of the effective state minimum wage rate on infant health. Using data on the entire universe of births in the US over 25 years, we find that an increase in the minimum wage is associated with an increase in birth weight driven by increased gestational length and fetal growth rate. The effect size is meaningful and plausible. We also find evidence of an increase in prenatal care use and a decline in smoking during pregnancy, which are some channels through which minimum wage can affect infant health. Labor market policies that enhance wages can thus affect wellbeing in broader ways, and such health effects should enter into any cost-benefit calculus of such policies.
The Role of Interregional Trade in Virtual Water on the Blue Water Footprint and the Water Exploitation Index in Brazil

Brazil is known for its abundant water supply. However, an uneven spatial distribution of water and strong concentration of economic activities has caused some regions to face water restrictions. The objective of this research is to identify the main water users in Brazil, in terms of virtual blue water, and the impacts of the water use pattern on the regional Water Exploitation Index. Among the main results, the hydrographic basin Tiet\^{e} was identified as the largest responsible basin for virtual blue water demand, while the hydrographic basin Litoral AL PE PB was an important virtual water supplier. Virtual blue water flows are largely interregional and a majority of the flows (66 percent) were exports from basins where the water balance indicates potential water restrictions. These results suggest that interregional trade in virtual blue water affects water availability for some Brazilian hydrographic basins, potentially undermining water security.
Architectures and Tools for Efficient Reconfigurable Computing

Architectures and Tools for Efficient Reconfigurable Computing Stephen Alexander Chin Doctor of Philosophy The Edward S. Rogers Sr. Department of Electrical & Computer Engineering University of Toronto 2018 Recent decades have seen large growth in the silicon industry with transistor scaling and transistor count approximately doubling every two years. With the continued growth of transistors-per-chip and increasing power density, dark silicon challenges have risen. Reconfigurable computing poses a possible solution to some of the challenges through improving performance and energy efficiency by tailoring the hardware to the application. Field-Programmable Gate Arrays (FPGAs) are a platform for realizing reconfigurable computing and have had traction for over a decade. Their recent introduction into mainstream data-centres bodes well for the field. Another type of reconfigurable architecture, Coarse Grained Reconfigurable Arrays (CGRAs), poses one other platform for computing. Being more specialized than FPGAs, CGRAs’ main selling point is increased efficiency versus FPGAs, at the cost of platform flexibility. This dissertation looks at efficient computing first from the perspective of FPGAs, developing new architectures and new CAD tools, making for a more efficient FPGA. The proposed FPGA architecture consists of a hybrid multiplexer / look-up-table logic block that has reduced area with respect to traditional architectures. Then, with the prospect that CGRA architectures hold, we develop an open-source framework, CGRA-ME, for the modelling and exploration of CGRAs. This unifying software framework incorporates, architecture description through a custom language, architecture modelling, application mapping, and RTL generation, and allows further development of CGRA architectures and related CAD tools throughout the research community. Within the CGRA-ME framework, a new architecture-agnostic application mapper formulated in an integer linear program was also developed for generic CGRAs. Through this dissertation, we have made headway towards more efficient reconfigurable architectures through architecture design and related CAD and are optimistic that these contributions will have positive impact on further research and industrial application of reconfigurable architectures.
SECURITY FOR IoT SYSTEMS USING MACHINE LEARNING

IoT(Internet of things) represents the systems which are connected by number of devices, having sensors and actuators by wired or wireless network. More than 30 million of devices will be connected together by 2025, IoT is one of the technology, which is rapidly growing in the last decade. One of the weakest part in IoT is the “security”. IoT systems lacks the security for the data they collect, store & share over the network. Providing the security has become a major challenge. Securities such as preventing the unauthorized access of data, handling overloading of requests for access by authorized user. To approach these challenges for implementing secured IoT environment, we propose a centralized system for allowing only authorized user to access the IoT systems, using cryptography and analyse and maintain logs of the user activity with centralized system and IoT system environment using machine learning.