The future of corporate reporting: a review article

Significant changes in the corporate external reporting environment have led to proposals for fundamental changes in corporate reporting practices. Recent influential reports by major organisations have suggested that a variety of new information types be reported, in particular forward-looking, non-financial and soft information. This paper presents a review and synthesis of these reports and provides a framework for classifying and describing suggested information types. The existence of academic antecedents for certain current proposals are identified and the ambiguous relationship between research and practice is explored. The implications for future academic research are discussed and a research agenda is introduced

View from the virtual pocket: using virtual simulation and video game technology to assess the situation awareness and decision making of NCAA quarterbacks

View from the Virtual Pocket is a proof of concept study in which a theoretical proposition about situation awareness in time constrained decision making is wedded to the affordances of a computer based simulation to ascertain if the real world decision making in the pocket of an NCAA quarterback can be modeled successfully for simulation based learning. The researcher used the Situation Awareness Global Assessment Technique (SAGAT) for the purposes of (a) analyzing the situation awareness requirements for expert decision making and (b) to empirically assess the viability of using a computer based football simulator as a SAGAT simulation tool. The highlight of this study is a Goal Directed Task Analysis (GDTA) developed in conjunction with some of the most recognized names in professional and collegiate football. The results of the (GDTA), a form of cognitive task analysis, defined the information requirements for expert quarterbacking and shed light on the enormous cognitive demands placed on the quarterback. The researcher was able to create, categorize and program SAGAT queries from the Goal Directed Task Analysis into an innovative virtual reality simulator called the PlayAction Simulator PC. Once the queries were programmed and the plays were published, the Researcher evaluated the simulator\u27s ability to (a) stop a simulated repetition at random points to ask probing questions aimed at evaluating a quarterback\u27s SA and (b) create the ecological validity required to extapolate the informating needed to measure situation awareness in the domain of the quarterback. The results of this inquiry (a) identified the goals of the quarterback, the decisions the quarterback has to make to achieve those goals and the information the quarterback needs to know in order to make accurate decisions, (b) validated the ability of the interactive virtual simulator to be used as a SAGAT Simulation tool in the assessment of the quarterback\u27s situation awareness. Additionally, the Goal Directed Task Analysis led to the creation of the Decision Making Model 4 QB\u27s. The model, a hybrid of the Endsley (2000a; 2000b) SA Model and the Klein (1998) RPD Model, represents a viable and testable description of the situation assessment process that quarterbacks use to formulate an aerial hypothesis. Inherent in this new model is a proposition about the role of unconscious competence in the optimization of serially generated options

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments. This study aims to overview these main perspectives of trustworthy reinforcement learning considering its intrinsic vulnerabilities on robustness, safety, and generalizability. In particular, we give rigorous formulations, categorize corresponding methodologies, and discuss benchmarks for each perspective. Moreover, we provide an outlook section to spur promising future directions with a brief discussion on extrinsic vulnerabilities considering human feedback. We hope this survey could bring together separate threads of studies together in a unified framework and promote the trustworthiness of reinforcement learning.Comment: 36 pages, 5 figure

Zero-shot learning and its applications from autonomous vehicles to COVID-19 diagnosis: A review

The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection / recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few / one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection / recognition systems using ZSL

A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision

Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, especially in the research field of computer vision. Accordingly, there have been many significant advancements in the theory and application of GAN models, which are notoriously hard to train, but produce good results if trained well. There have been many a surveys on GANs, organizing the vast GAN literature from various focus and perspectives. However, none of the surveys brings out the important chronological aspect: how the multiple challenges of employing GAN models were solved one-by-one over time, across multiple landmark research works. This survey intends to bridge that gap and present some of the landmark research works on the theory and application of GANs, in chronological order