Using Artificial Neural Networks to forecast Exchange Rate,including VAR‐VECM residual analysis and prediction linear combination

The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision‐makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short‐term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post‐processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long‐term non‐linear components of a time series, which VECM and VAR models are unable to do. Post‐processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post‐processing increases forecasting accuracy.     

人工智能时代专利制度的实践挑战与应对策略

人工智能系统日益提高的复杂度、自主决策能力,以及在更广泛领域的应用,已经对现行法律制度和监管体系构成重大挑战。特别是在专利等知识产权领域,从技术方案到创造性审查,从辅助决策到自主发明创造,整个生态系统都面临前所未有的挑战。侧重从实践角度探寻解决人工智能与专利制度冲突的手段和途径,首先,阐述现阶段人工智能专利制度的实践特征;然后,着重从人工智能的技术、应用两个层面梳理、分析和探讨实践中面临的困境与挑战;最后,结合相关专利制度、规则、程序提出针对性的应对策略和建议。     

Comparison of Russian （FSB and Old KGB） and Turkish Secret Services （MIT） as an Intelligence Dimension

In this study Russian Federation＇s current president Vladimir Putin period has been investigated from all aspects. Russian-Turkish relations have a high impact factor from all dimensions for these two countries. Also the secret services of Turkey as Turkish National Intelligence Organization （MIT） and Russia＇s old Committee for State Security （KGB） and today＇s Federal Security Service （FSB） are really famous intelligence services as well. KGB was a famous and super power Union of Soviet Socialist Republics （USSR）＇s intelligence service until 1991. After 1991 USSR was collapsed and then new Russian secret service by the name of FSB has been created. Today＇s Russia＇s president Vladimir Putin was a technological intelligence agent in the Democratic Germany in the USSR period with the colonel degree. In this article we have investigated and researched until the USSR＇s collapsing date and since that date until today those two countries： Turkey and Russia＇s secret service＇s comparison. Also we should take away that the USSR was an empire and Turkey was just a third the Middle East as a North Atlantic Treaty Organization （NATO） member clarified from all aspects country as a gendarmerie of USA＇s in This strategic collaboration has been     

A new investor sentiment indicator (ISI) based on artificial intelligence: A powerful return predictor in China

This paper utilizes deep learning approach widely documented in artificial intelligence, and proposes an investor-sentiment indicator (ISI) that is consistent with the purpose of forecasting stock market returns. We find that ISI is positively correlated with future stock market returns at a monthly frequency, but negatively associated with subsequent returns over a longer horizon. Moreover, ISI outperforms other well-recognized predictors both in and out of sample, and can predict cross-sectional stock returns sorted by industry. We also show a positive association between monthly ISI and dividend growth rate, which indicates that investors’ expectations about future cash flows may contribute to the return predictability of ISI.     

基于SOFM网络的城镇土地定级研究——以深圳宝安区为例

本文运用人工神经网络(ANN)的理论与方法,构建了应用于城镇土地定级的SOFM网络模型,将"聚类"思想应用于定级评价中,克服了传统方法因子权重过于主观的缺点。本文以深圳市宝安区为例进行了方法验证,并与传统方法进行了比较,结果表明,基于SOFM的定级模型在空间聚合性和准确性方面优化了定级结果,进一步表明了此类人工智能类方法在评价领域的适应性。     

新冠疫情蔓延突显数字经济独特优势

本文认为,以数字经济为核心的虚拟经济形式在当下中国经济发展过程中具有主导性作用,其根本特征就是人与人之间可以超越时空发生非接触性的经济关系。据此评估当下“人传人”的新冠疫情所带来的经济损失,发现人与人接触性较强的实体经济与实体国际贸易遭受的损失较大,而人与人接触性较弱的数字经济却获得一定发展,并在防控新冠疫情蔓延、保障社会供给等方面发挥了重要作用。进而提出,应对新冠疫情长期发展战略和短期应对措施都将是着力发展数字经济,尤其是发展人工智能产品。     

Predicting airline passengers’ loyalty using artificial neural network theory

The study explores a model for predicting airline loyalty using the antecedents indicated in previous studies. Data was collected using a questionnaire distributed to 614 domestic air passengers using the snowball sampling method. The measurement tool had 16 scale items constructed on the recommendations of previous studies. Passenger satisfaction, airline service quality, passenger perceived value, and airline image are identified as determinants for airline loyalty. The predictive analytical approach of Artificial Neural Network theory and covariance-based Structural Equation Modelling for determining causality is employed in the study. The artificial neural network model predicts airline loyalty with 89% accuracy. Sensitivity analysis suggests passenger satisfaction as the most significant predictor of airline loyalty. The causal study supports that passenger satisfaction mediates the relationship between airline service quality and airline loyalty.     

Artificial neural network models for airport capacity prediction

This paper proposes artificial neural network models to predict the arrival/departure capacity of airports. Multilayer perceptron (MLP), recurrent neural networks (RNN), and long short-term memory (LSTM) models have been trained using capacity and meteorological data from Hartsfield–Jackson Atlanta International Airport (ATL) from 2013 to 2017. The models’ predictive performances were validated against the observed capacity of ATL in 2018. The qualitative and quantitative analysis of the trained models confirmed that the artificial neural networks approach is effective in predicting airport capacity. In addition, the transferability of the models for Boston Logan International Airport (BOS) is examined. Capacity prediction performance for BOS measures the transferability of the models trained with the ATL data. MLP showed good transferability without taking any other measures, and RNN and LSTM were able to predict the BOS capacity well after fine-tuning.     

Should humans work?

Should humans work? A simple question at a time when the advent of AI, automation and robotics claims a privileged position in the future of work. The question is perplexing and confusing however once we enquire into the meaning of technology and work as such: some claim that human jobs and well-being might be threatened by technological advances while others predict an increase in high skilled demand.In short, we claim that standard debates obfuscate the question. That is, while animating the debate, standard arguments are instrumental to avoiding confrontation with the fundamental relation between humans, technology and work, namely, our position regarding what to do with technology and work, i.e. humans are “absent” in an “advancing” environment. We propose that focusing debates on the advantages and disadvantages of the development of advanced technology with the potential to do any human task does not necessarily clarify the situation, but rather unveils the obscurity of our relationship with technology by illuminating the lack of clarification.     

Ethics of autonomous weapons systems and its applicability to any AI systems

Most artificial intelligence technologies are dual-use. They are incorporated into both peaceful civilian applications and military weapons systems. Most of the existing codes of conduct and ethical principles on artificial intelligence address the former while largely ignoring the latter. But when these technologies are used to power systems specifically designed to cause harm, the question must be asked as to whether the ethics applied to military autonomous systems should also be taken into account for all artificial intelligence technologies susceptible of being used for those purposes. However, while a freeze in investigations is neither possible nor desirable, neither is the maintenance of the current status quo. Comparison between general-purpose ethical codes and military ones concludes that most ethical principles apply to human use of artificial intelligence systems as long as two characteristics are met: that the way algorithms work is understood and that humans retain enough control. In this way, human agency is fully preserved and moral responsibility is retained independently of the potential dual-use of artificial intelligence technology.