Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

The non-linear impact of oil price on the oil demand

ABSTRACT

A panel smooth transition regression model was adopted to analyse the non-linear impact of oil prices on oil demand. Data for 42 countries was obtained from the International Energy Agency for the time period spanning from January 1990 to June 2017. The results indicate that a threshold value does exist. Furthermore, when the oil price was lower than this threshold value, a positive relationship between oil price and oil demand was observed. When the price of oil was higher than the threshold value, however, a negative relationship between price and demand was found.     

The Emergence of Proto-Institutions in the New Normal Business Landscape: Dialectic Institutional Work and the Dutch Drone Industry

In the current business landscape, in which technology-enabled entrepreneurship is part of the New Normal, regulatory institutional structures are in constant flux. Previous studies have framed the challenges facing entrepreneurs in mature organizational fields as avoiding the power of overbearing regulators long enough to establish the legitimacy of their ventures. In fields typified by New Normal conditions, however, regulatory frameworks for evaluating new technology-enabled ventures are often still lacking. Regulators may choose to actively reach out to entrepreneurs to arrive at a better understanding of the radical technological changes and high-frequency entrepreneurial behavioural adaptations that occur in these settings. To grasp how novel regulatory institutional structures come about in the New Normal business landscape, we conducted a processual study of the emergence of a new technology that is the Dutch remotely piloted aircraft systems (drone) industry between 2000 and 2018. Our findings show that regulatory proto-institutions result from dialectic institutional work in the form of structured interactions between entrepreneurs and regulators. Specifically, we present a process model that reveals how new regulatory structures evolve in contexts where high levels of technological and behavioural change induce systemic uncertainty, and enlarge the interdependence between entrepreneurs and regulators. We suggest that our process theory of proto-institutional emergence generalizes towards other organizational fields in which technology-enabled entrepreneurship has become the main driver of growth. Theoretically, our findings speak to the literatures on institutional work, proto-institutional emergence, and the New Normal business landscape.     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).     

GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul,Turkey

Fire stations play a central role in protection and response activities as part of emergency management services in cases of fire incidences. With the rising urban populations and city expansions, the demand for more fire services resultantly increases. It then becomes critical to effectively plan the location of emergency facilities to adequately service the population and ensure the protection of lives and infrastructure. This study, therefore proposes the use of the fuzzy extension of the Multi-Criteria Decision-Making (MCDM) method of Analytical Hierarchical Process (AHP), hence called fuzzy AHP, integrated with Geographic Information Systems (GIS) approach to optimally site new fire stations for the case of Istanbul region. This proposed fuzzy approach simulates the subjective expert judgements for the preferences of the six criteria assessed for fire station suitability mapping and thereby accounted for the uncertainty of crisp comparison values via triangular fuzzy numbers (TFNs). The criteria weights evaluated from this procedure were used in a weighted overlay analysis of the reclassified criteria map layers in ArcGIS to generate a fire station suitability map. These resultant fuzzy AHP criteria weights were validated using another MCDM technique, called Best-Worst Method (BWM) and found to be comparable and consistent. The criteria that had the strongest influence on the selection of sites for fire stations were identified to be: the density of hazardous material facilities (DHM), a high population density (HPD) and proximity to main roads (PMR) with associated weights of 33.3%, 24.4% and 15.2%, respectively. Based on a thorough assessment within the areas represented by class values ranging from 3 to 5 on the suitability map, a total of 34 new fire station sites were selected complementing the existing 121 fire stations. Further, a prioritization analysis from low, medium to high, was performed to plan the phases for the construction of new fire stations in view of competing budgetary needs and resource constraints. The methodology to achieve this was proposed and modelled for enhancing the decision-making process in urban fire station site selection studies.     

市场环境、企业社会责任与基金安全投资转移

当市场环境不好以及不确定性增加时，基金进行安全投资转移。企业积极履行社会责任所形成的声誉资本具有“类保险”作用，在面临负面事件或不确定性环境冲击时能够防止股价大幅下跌和市值损失；基金是否会在市场环境不好时将投资组合转向社会责任绩效更高的股票进行安全投资转移呢？使用2010—2017年开放式基金样本，实证检验了企业社会责任、市场环境与安全投资转移之间的关系。研究发现，在市场环境表现不佳时，基金避险需求增加，倾向将投资组合更多地转向社会责任绩效高的股票进行安全投资转移；进一步研究发现基金和基金经理特征影响基金的安全投资转移行为，净资金流入较少、历史业绩差、经理选股能力差的基金根据企业社会责任进行安全投资转移的程度大。研究还发现，基金的安全投资转移行为对基金业绩和基金收益波动产生了积极影响。     

Happiness sentiments and the prediction of cross-border country exchange-traded fund returns

This research examines whether social media (Twitter) happiness sentiment and country-level happiness sentiment indices predict cross-border ETF returns. To account for complicated associations between happiness sentiment and ETF returns, we use a quantile regression approach and find that Twitter and trading market (U.S.) happiness sentiments are strong predictors of future ETF returns, for which both have far greater predictive power than those of their home countries. Home country happiness indices exhibit asymmetric impacts across quantiles, suggesting the importance of trading country (U.S.) and Twitter happiness sentiments. Higher U.S. and home countries’ freedom to make life choices, absence of corruption perception, and confidence in national government precede higher ETF returns, while U.S. GDP, social support, health life expectancy, positive affect, and negative affect precede lower (abnormal) returns. We find that higher return quantile country ETFs provide a safe haven for U.S. investors during a U.S. bear market.     

Do fiscal rules cause better fiscal balances? A new instrumental variable strategy

This paper estimates the causal effect of fiscal rules on fiscal balances in a panel of 142 countries over the period 1985–2015. Our instrumental variable strategy exploits the geographical diffusion of fiscal rules across countries. The intuition is that reforms in neighboring countries may affect the adoption of domestic reforms through peer pressure and imitational effects. We find that the mere existence of fiscal rules correlates with lower deficits, but the positive link disappears when endogeneity is correctly addressed. However, when considering the strength of rules through a continuous index of fiscal rules’ design, we show that well-designed rules have a statistically significant impact on fiscal balances. We conduct several robustness tests and show that our results are generally robust and not affected by weak instrument problems.     

Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Bankruptcy prediction is still important topic receiving notable attention. Information about an imminent bankruptcy threat is a crucial aspect of the decision-making process of managers, financial institutions, and government agencies. In this paper, we utilize a newly acquired dataset comprising financial parameters derived from the annual reports of small- and medium-sized companies. The data, which reveal the true ratio between bankrupt and non-bankrupt companies, are severely imbalanced and only contain a small fraction of bankrupt companies. Our solution to overcome this challenging scenario of imbalanced learning was to adopt three one-class classification methods: a least-squares approach to anomaly detection, an isolation forest, and one-class support vector machines for comparison with conventional support vector machines. We provide a comprehensive analysis of the financial attributes and identify those that are most relevant to bankruptcy prediction. The highest prediction performance in terms of the geometric mean score is 91%. The results are validated on two datasets from the manufacturing and construction industries.     

Trade tariff,wage gap and public spending

This paper studies the interplay between wage gap and government spending in a small open economy facing a shock in trade policy. We consider a specific factor model with an export sector, which uses skilled labour, and an import-competing sector, which uses unskilled labour. We find the conditions under which there exists an inverse (direct) relation between trade liberalization (protection), which increases (decreases) the skilled-unskilled wage gap, and the level of government expenditure. We also show how either an unbalanced distribution of political bargaining power, or tariff revenue co-financing public spending may break this inverse relation. Moreover, the direct relation between tariff protection and public goods provision can be strenghtened by progressive taxation and weakened by regressive taxation.