MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

GHG Emissions of Italian Unlisted Firms and Bank Debt

In this paper I investigate the effect of the greenhouse gas emissions of a sample of 478 Italian unlisted firms on their bank debt,in the context of the growing attention and awareness for climate change issue.My findings show that unlisted firms generating low levels of greenhouse gas emissions borrow less long-term,short term,and total bank debt than unlisted firms generating high levels of greenhouse gas emissions,to finance their growth opportunities.Being the first work analyzing this topic,to the best of my knowledge,further empirical research could also investigate the impact of greenhouse gas emissions on the bank financing of unlisted firms of different countries,representing specific cultural and political approaches towards climate change problems.

Absorptive capacity and R&D strategy of labor-managed firms

Taking absorptive capacity effects on research spillovers into consideration, this paper focuses on the R＆D investment decisions and the output decisions of labor-managed firms. Based on the general model of the cost-reducing R＆D, the strategic interactions of output and R＆D investment between labor-managed firms in a duopoly are analyzed. Moreover, the impact of absorptive capacity effects on optimal output in the production stage is discussed. In the R＆D stage, the impacts of absorptive capacity effects on the equilibrium R＆D investment in cooperative and non-cooperative R＆D are analyzed. Finally, the R＆D strategy of labor-managed firms is compared with the behavior of profit-maximizing firms. The results show that equilibrium R＆D investment is always higher than that in the exogenous spillover rate, which is similar to the behavior of the profit-maximizing firms. However, unlike the profit-maximizing firms, the impact of the absorptive capacity that affects the relationship between the optimal output and its own（rival＇ s）R＆D is shown to be dependent upon a return-to-scale of the production.

Security investment and information sharing for complementary firms with heterogeneous monetary loss

Two complementary firms＇ information sharing and security investment are investigated. When two complementary firms with heterogeneous assets are both breached, it is assumed that they suffer different losses which are associated with their information assets. Some insights about optimal strategies for the firms and the attacker are obtained by the game theory, which forms a comparison with those derived from substitutable firms, and those derived from complementary firms with homogenous loss. In addition, both the unit transform cost of investment and the extent of firms＇loss affect the optimal strategies.Assuming that firms can control information sharing, security investments and both of them, respectively, the effect of the social planner is further analyzed on the information sharing, firms＇ aggregate defence, the aggregate attack and social total cost. Finally, some policy advice is provided through numerical simulation. Results show that firms are willing to choose security investment centrally rather than individually, but an intervention in information sharing by the social planner may not necessarily be preferable.

Knowledge-Based Classification in Automated Soil Mapping

A machine-learning approach was developed for automated building of knowledgebases for soil resources mapping by using a classification tree to generate knowledge from trainingdata. With this method, building a knowledge base for automated soil mapping was easier than usingthe conventional knowledge acquisition approach. The knowledge base built by classification tree wasused by the knowledge classifier to perform the soil type classification of Longyou County,Zhejiang Province, China using Landsat TM bi-temporal images and CIS data. To evaluate theperformance of the resultant knowledge bases, the classification results were compared to existingsoil map based on a field survey. The accuracy assessment and analysis of the resultant soil mapssuggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

How Chinese Financial Centers Integrate into Global Financial Center Networks:An Empirical Study Based on Overseas Expansion of Chinese Financial Service Firms

The increasing globalization of the Chinese economy has been enabled by both Chinese financial institutions operating globally as well as international firms operating within China. In geographical terms, this has been organized through a number of strategic cities serving as gateways for the exchange of financial functions, products and practices between China and the global economy. Drawing on location data of financial service firms in China listed on stock exchanges in Shenzhen, Shanghai and Hong Kong, this paper shows that Chinese financial firms are expanding globally and how Chinese financial centers are positioned and connected in the urban networks shaped by these financial service firms. It is found that Hong Kong, China, holds strategic positions in the integration of Chinese cities into global financial center networks, and that establishing a foothold in global financial centers such as New York and London has been a priority for Chinese financial institutions. The increasing capital flows directed by Chinese financial institutionssuggests a shifting global financial geography, with numerous Chinese cities playing increasingly important roles within global financial center networks.

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

Identification of Behavior of Voluntary Traceability and Analysis of Its Determinants: A Case Study of Hog Slaughtering and Processing Firms in Zhejiang Province,China

In recent years, the Chinese government has highlighted the importance of adopting hog safety/quality traceability, and a growing amount of research continues to entice firms to adopt traceability systems. In this study, a survey was conducted on a sample of pig slaughtering and processing firms in Zhejiang, China through personal interviews and emails. The aim of this study was to examine the determinants of firm behavior on the implementation of voluntary traceability systems with more stringent standards and controls than those of the mandatory system in China. The results revealed that motivation based on product quality improvement, capital ability and role perception （business type） had significantly positive relationships with a firm＇s voluntary traceability. Other incentives, such as operation improvement, recall risk reduction, reduced occurrence of safety issues, and technical strength were not found to be supportive in our study. This study provides an opportunity to better understand the determinants of firm behavior on voluntary traceability, particularly in light of the fact that some Chinese firms are facing the threat of criminal action for the use of illegal additives and the abuse of Clenbuterol. Policy recommendations on encouraging the implementation of pork safety voluntary traceability by hog slaughtering and processing firms are also discussed.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Structural Topology Design of Container Ship Based on Knowledge-Based Engineering and Level Set Method

Knowledge-Based Engineering （KBE） is introduced into the ship structural design in this paper. From the implementation of KBE, the design solutions for both Rules Design Method （RDM） and Interpolation Design Method （IDM） are generated. The corresponding Finite Element （FE） models are generated. Topological design of the longitudinal structures is studied where the Gaussian Process （GP） is employed to build the surrogate model for FE analysis. Multi-objective optimization methods inspired by Pareto Front are used to reduce the design tank weight and outer surface area simultaneously. Additionally, an enhanced Level Set Method （LSM） which employs implicit algorithm is applied to the topological design of typical bracket plate which is used extensively in ship structures. Two different sets of boundary conditions are considered. The proposed methods show satisfactory efficiency and accuracy.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

A Knowledge-based System for the Analysis of the Ability of Paying back Loans

This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.

Effect of family control on corporate dividend policy of firms in Pakistan

This study examines the impact of family control on the dividend policy of firms in Pakistan,covering the period from 2009 to 2016.It also investigates whether family control moderates the impact of firm-specific factors on the dividend policy.The GMM model for panel data estimation is used.The mean difference univariate analysis shows that family firms differ from nonfamily firms based on financial characteristics.The multivariate analysis shows that family firms pay lower dividends than nonfamily firms.Besides,firm size inversely affects the dividend policy,whereas tangibility positively affects it.Moreover,family control does not moderate the impact of all firm-specific factors on the dividend policy.Overall,family control,size,and tangibility are found to be the main determinants of the dividend policy in Pakistan.

Strategic HR Director Position: Implementation of SHRM in Firms

This study argues theoretical framework for strategic human resource management(SHRM),and implementation of SHRM in firms.HRM field is related to skill development and performance improvement.So,theoretical framework of SHRM is established by performance and skills.HR systems improve knowledge,skills,and abilities(KSAs)of employees,which affect individual-level performance.Individual-level performance makes employee go up to executive positions,and talents at the up affect firm-level performance via their managerial decisions.Secondly,a firm may apply SHRM through three factors:strategic HR director,HR systems,and star employees.

Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

Determinants of Capital Structure in the Nigerian Listed Insurance Firms

The paper investigates the determinants of capital structure in Nigerian listed insurance firms using data obtained from annual report of the sampled firms for the period of 2001-2010. It used five explanatory variables to measure their effects on debt ratio. Multiple regression is employed as a tool of analysis. The result reveals that all the explanatory variables have statistically and significantly influenced the explained variable. The results approve the prediction of pecking order theory in the case of profitability and trade-off theory in case of tangibility variables. The growth variable supports the agency theory hypothesis whereas size variable confirms to the asymmetry of information theory. It is therefore recommended that the management of listed insurance firms in Nigeria should always consider their positions using these capital structure determinants as important inputs before embarking on debt financing decision.

After-market liquidity and IPOs underpricing： Evidence from Shari＇ah and non Shari＇ah-based firms

The underpricing of initial public offerings （IPOs） is generally explained with asymmetric information and risk. We complement these traditional explanations with a new theory proposed by Ellul and Pagano （2006） where investors worry also about the after-market illiquidity that may result from asymmetric information after the IPO. The less liquid the after-market is expected to be, the larger will be the IPO underpricing. The samples are the 41 IPOs carried out between 2001-2005. The samples are 7 Shari＇ah-based firms and 34 non Shariah-based firms. Shariah-based firms are those included in Jakarta Islamic Index （JII）, at least one period （one semester）. Regression results show that the relationship between after-market liquidity and underpricing is insignificant unless we use trading frequency as proxy for liquidity for non Shariah-based firms.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Factors Affecting Financial Performance of Tourism Destination Firms Listed on Stock Exchanges in China

Purpose:the aim of this research is to test the effect of financial ratio on the financial performance of tourism destination firms listed on stock exchange in China.The research selected ratios:current ratio(CR)as a dimension of liquidity,total asset turnover ratio(TATR)as a dimension of asset utilization,debt ratio(DE)as a dimension of leverage,natural logarithm of total asset(LNTA)as a dimension of firm size,GDP growth rate as a dimension of economic prosperity,and effective tax rate as a dimension of effective tax.This research will use return on asset(ROA),return on sales(ROS),return on equity(ROE)and sales growth(SG)to determine the financial performance.Since stock exchange founded in China,tourism destination firm developed very fast.However tourism destination listed firms have weakness financial performance.Design/methodology/approach:the research data collected from quarterly financial report,from 2012 Q1 to 2018 Q4.The secondary data has been analyzed by multiple regression.Finding:the result indicate that CR,TATR,GDP growth rate have positive impact on financial performance.While DE has negative impact on financial performance.And LNTA has a mix result with financial performance.Originality/value:This study led to the effect of financial ratios on tourism's financial performance since past researches with this aim were difficult to identify and certain references were not specifically linked to the topic.