Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods

As the penetration of solar energy generation into power systems keeps rising, intra-hour solar forecasting (IHSF) is becoming increasingly important for the secure and economical operation of a power system. One major difficulty in providing very accurate IHSF emanates from rapid cloud changes in the sky. The ground-based sky image (GSI) provides the intuitive information of intra-hour cloud changes and has thus been widely utilized in studies on IHSF. This paper presents a systematic review of the state-of-the-art of ground-based sky image-based intra-hour solar forecasting (GSI-IHSF). To our knowledge, we first propose a generic framework of GSI-IHSF consisting of four modules, i.e., sky image acquisition, sky image preprocessing, cloud forecasting, and solar forecasting. Then, as for each module, this paper introduces its core function, shows the major challenges, briefly reviews several extensively used techniques, summarizing research trends. Finally, this paper offers a prospect of GSI-IHSF research, discusses recent advances that demonstrate the potential for a great improvement in forecast accuracy, pointing out some new requirements and challenges that should be further investigated in the future.     

Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan

New avenues of technological opportunities in agriculture are opening as we are further delving deeper into the 21st century, but at the same time, new challenges are emerging. One of these challenges is the growing quantity of food demand, which is highly vital for regional trade, food security, and meeting the nutritious requirements of the population. A timely prediction with accuracy about crop yield could be valuable for greater food production and maintainability of sustainable agricultural growth. This paper presents a predictive model of wheat production using machine learning. The northern areas of Pakistan which grow wheat are selected as a case study due to their importance in the country's agricultural sector. We collected data of five years and selected the best attribute subset related to crop production. We applied twelve (12) algorithms by dividing data samples into three sets. Experimental results helped to shortlist three algorithms for the final analysis i.e. Sequential Minimal Optimization Regression (SMOreg), Multilayer Processing (MLP) and Gaussian Process (GP). The Root Mean Square (RMSE) and Percentage Absolute Difference (PAD) metrics were used to validate the results. The SMOreg obtained the lowest PAD (0.0093) and RMSE (0.5552) values. MLP was a little closer with second-lowest PAD (0.0116) and RMSE (0.737) value. The performance of GP was found lowest due to higher PAD (0.2203) and RMSE (17.7423) values. Our findings confirm the predictive ability of machine learning algorithms on a crop dataset recorded in a localized environment, which could be replicated on other crops and regions.     

Social media networks,fake news,and polarization

We study how the structure of social media networks and the presence of fake news affects the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and exhibit bounded rationality. Key to the analysis is the presence of internet bots: agents in the network that spread fake news (e.g., a constant flow of biased information). We characterize how agents’ opinions evolve over time and evaluate the determinants of long-run misinformation and polarization in the network. To that end, we construct a synthetic network calibrated to Twitter and simulate the information exchange process over a long horizon to quantify the bots’ ability to spread fake news. A key insight is that significant misinformation and polarization arise in networks in which only 15% of agents believe fake news to be true, indicating that network externality effects are quantitatively important. Higher bot centrality typically increases polarization and lowers misinformation. When one bot is more influential than the other (asymmetric centrality), polarization is reduced but misinformation grows, as opinions become closer the more influential bot’s preferred point. Finally, we show that threshold rules tend to reduce polarization and misinformation. This is because, as long as agents also have access to unbiased sources of information, threshold rules actually limit the influence of bots.     

机器学习在人力资源管理领域中的应用研究

本研究关注的是机器学习在人力资源管理领域的应用,通过目前已有研究的梳理,以了解在人力资源管理领域中人工智能/机器学习的应用程度和研究热点。同时,基于管理实践,本研究提出了人力资源管理者如何利用算法,以有价值的方式处理和解释数据,将其真正应用于人力资源管理的六大模块工作中,以降低管理成本,提供不断增长的优势和潜力。最后,在现有学术研究和管理实践的基础上,本研究列举了机器学习在人力资源管理领域应用可能面临的挑战,以及未来的研究方向,以期为后续的研究提供一定的参考。     

中国机构投资者的学习机制研究——理性贝叶斯还是简单强化式学习

理论文献通常假设机构投资者遵循理性贝叶斯法则更新其信念,在此学习模式下,盈亏经验本身并不能直接影响机构行为。但该假设的合理性尚未得到实证研究的充分支持。中国新股发行中的抽签分配制度为检验上述命题提供了比较理想的随机实验机会。本文基于新股抽签分配数据,系统检验了随机的盈亏经验对机构投资行为的影响,结果发现:(1)机构投资者显著受制于幼稚的强化式学习机制的影响,即通过随机抽签在前期获得新股分配的机构(处置组)相对未获配机构(控制组),其下期参与新股申购的概率显著提高,并且前期收益率能够有效地强化这种盈利经历与参与概率之间的正向关系。(2)盈亏经验能够显著改变专业机构的估值信念,即前期通过随机抽签获得高收益体验的机构相对未获配机构,在后续新股询价过程中给出了显著更高的报价上调水平。(3)盈亏经验对机构行为产生影响的一种可能渠道是借助机构投资管理人的强化式学习过程,基于基金经理个人特征变量的调节机制研究表明,丰富的长期从业经验、高学历的教育水平以及多位基金经理的相互竞争都能在一定程度上缓解盈亏经历对基金行为的影响。本文基于随机实验的设计为经验与行为之间的因果关系提供了可信证据,证实了即使是被奉为理性投资者代表的专业机构也会受制于简单强化式学习的显著影响。     

Gender differences in the first course in accounting: An achievement goal approach

We examine whether gendered patterns can be observed in first-year students' achievement goals in an introductory accounting course; a question largely overlooked by prior literature. This investigation is motivated by perceptions of accounting as a masculine field involving gender role stereotypes and business schools as competitive and performance-oriented environments. Our findings suggest that male students tend to adopt performance-approach goal, implying that they are more competitive than female students, and that their performance is thus driven by a desire to outperform others. Our findings further suggest that male students' expectations of learning accounting are higher than those of female students. The expectations explain the gender differences in the performance-approach goal. Finally, we find that this performance-approach goal mediates gender differences in course performance depending on the mode of assessment; male students received higher grades for exams but not for teamwork. Overall, our study highlights the importance of considering contextual aspects related to competitiveness, masculinity, and the mode of assessment on an accounting course when addressing students’ achievement goals and expectations of learning accounting. We thus contribute to the understanding of how learning environment, accounting pedagogy, and the broader field of professional accounting intersects with individual student attributes, creating differential learning outcomes.     

Forecasting small area populations with long short-term memory networks

Local and state governments depend on small area population forecasts to make important decisions concerning the development of local infrastructure and services. Despite their importance, current methods often produce highly inaccurate forecasts. Recent years have witnessed promising developments in time series forecasting using Machine Learning across a wide range of social and economic variables. However, limited work has been undertaken to investigate the potential application of Machine Learning methods in demography, particularly for small area population forecasting. In this paper we describe the development of two Long-Short Term Memory network architectures for small area populations. We employ the Keras Tuner to select layer unit numbers, vary the window width of input data, and apply a double training and validation regime which supports work with short time series and prioritises later sequence values for forecasts. These methods are transferable and can be applied to other data sets. Retrospective small area population forecasts for Australia were created for the periods 2006–16 and 2011–16. Model performance was evaluated against actual data and two benchmark methods (LIN/EXP and CSP-VSG). We also evaluated the impact of constraining small area population forecasts to an independent national forecast. Forecast accuracy was influenced by jump-off year, constraining, area size, and remoteness. The LIN/EXP model was the best performing method for the 2011-based forecasts whilst deep learning methods performed best for the 2006-based forecasts, including significant improvements in the accuracy of 10 year forecasts. However, benchmark methods were consistently more accurate for more remote areas and for those with populations <5000.     

The era of Omni-learning: Frameworks and practices of the expanded human resource development

According to the ever-changing organizational environment, we also adopt an ever-expanding HRD in contents and scope. Focusing on the drivers of the recent HRD reforms, the growing demand for organizational agility and holistic capabilities of human resources is driving the need for change, and the pandemic crisis is pushing the revolutionary changes of HRD. Such trends of the expanded HRD can be characterized as a ‘march toward Omni-learning’. In specific, there are at least four noticeable and intertwined waves of HRD reforms toward Omni-learning: (1) embracing holistic capabilities such as benchmarking, modeling, forecasting, and backcasting (BMFB); (2) integrating working and learning by promoting on-the-job learning (OJL), on-the-life learning (OLL), and on-the-life training (OLT); (3) standardizing communication tools such as LMF (logic tree; multi-dimensional matrix/map; flowchart) and EEOSP (everything/everyone on the same page); and (4) diversifying communication space-time across diverse places (close; remote) and times (synchronized; a-synchronized). And all the HRD waves are commonly facilitated and promoted by technological breakthroughs of artificial intelligence (AI) and the metaverse. Beyond the current innovations of HRD, no one would be certain about the answer to the question “What’s next?”. But what is certain is that HRD will continue to be deepened and widened as long as human resources are needed to respond to the ever-changing organizational environment.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

Explainable Artificial Intelligence (XAI) in auditing

Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention regarding their potential applications in auditing. One major challenge of their adoption in auditing is the lack of explainability of their results. As AI/ML matures, so do techniques that can enhance the interpretability of AI, a.k.a., Explainable Artificial Intelligence (XAI). This paper introduces XAI techniques to auditing practitioners and researchers. We discuss how different XAI techniques can be used to meet the requirements of audit documentation and audit evidence standards. Furthermore, we demonstrate popular XAI techniques, especially Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), using an auditing task of assessing the risk of material misstatement. This paper contributes to accounting information systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of AI applications applied to auditing tasks.