본 연구의 목적은 2000년대 이후 일본 대한직접투자의 특징과 최근의 투자 부진 상황을 일본과 한국 통계자료를 중심으로 분석하고 향후 투자 확대를 위한 정책적 시사점을 모색하는 것이다. 분석 결과 일본 대한직접투자의 특징으로는, 한국은 아시아 내 피투자국으로서 다른 국가들과 경쟁을 해야 하는데 한국의 위상이 그다지 높지 않다는 점, 일본 대외직접투자에서 차지하는 한국 비중이 2% 전후로 높지 않고 그마저도 최근에는 1% 수준으로 낮아졌다는 점, 일본의 대외직접투자 전체는 비제조업 중심으로 투자가 확대되고 있는 상황에서 대한직접투자가 이루어지고 있다는 점, 2000년대 이후 일본의 투자 금액은 증가하였지만 한국 대내직접투자에서의 일본 비중은 이전 대비 낮아졌다는 점, 2015년 이후에는 그 비중이 10% 이하로 크게 하락하였다는 점, 일본의 투자에는 여전히 제조업 비중이 높다는 점 등을 들 수 있었다. 그리고 최근 일본의 투자 부진에 관해서는 최근 일본비중은 낮아졌지만 투자 금액 자체는 예전 수준을 유지하고 있다는 점, 일본의 투자가 정체 상태인 것에는 한일 정치적 갈등이 영향을 미치고 있다는 점, 투자가 정체 상태이기는 하지만 투자 내용은 긍정적이라는 점 등을 언급하였다. 나아가 이러한 분석에 근거하여 본 연구는 향후 일본 대한직접투자 유치 강화를 위한 시사점으로 양질의 일본 투자 유치를 위해서는 생산기지로서의 한국의 입지우위성을 제고시켜야 한다는 점, 서비스 분야에서는 연구개발 분야의 투자 유치를 적극 지원할 필요가 있다는 점, 한일 간 정치적 갈등을 최소화하고 민간 차원의 상호 교류를 확대할 필요가 있다는 점 등을 강조하였다. 마지막으로 본 연구의 한계로서 거시적 관점에서의 분석에 머무르고 있다는 점을 언급하였고, 이를 보완하기 위한 향후 연구과제를 제시하였다.

The purposes of this paper are to analyze the characteristics of Japanese FDI into Korea since 2000s and its sluggish pattern in recent years mainly with the data of both countries and to draw some policy implications for increasing the Japanese investment. As the result of the analysis, this paper found the following characteristics. Firstly, Korea is in a weak position as a country trying to receive Japanese FDI among Asian countries. Secondly, the proportion of FDI into Korea within the total Japanese outward FDI is not so high, around 2%, and it has fallen to around 1% recently. Thirdly, Japanese FDI into Korea has been made while Japanese FDI in general is occurring more in the sector of service industries. Fourthly, the Japanese FDI into Korea has increased since 2000s in term of investment amount, but its proportion within the total Korean inward FDI has fallen compared to the past. Fifthly, the proportion of Japanese FDI within total Korean Inward FDI has fallen to less than 10% since 2015. Sixthly, the proportion of manufacturing sector within total Japanese FDI into Korea is still high. This paper also analyzed the recent sluggish pattern of Japanese FDI into Korea and mentioned following issues. Firstly, although the proportion of Japanese FDI within total Korean inward FDI is low, the investment amount itself is still keeping the level of previous decades. Secondly, the political conflicts between Korea and Japan may have had negative influence on the Japanese FDI into Korea. Thirdly, in spite of the sluggish Japanese FDI, the quality of the investment is considered to be good. In addition, this paper emphasized the following implications for increasing Japanese FDI into Korea from the above analysis. Firstly, Korean government needs to upgrade the location advantage of Korea as an effective manufacturing site to induce more Japanese FDI. Secondly, the government needs to boost R&D related investment in the case of service sectors. Thirdly, the government needs to minimize political conflicts between Korea and Japan and to enlarge support for mutual communication in private sectors. In the final part, this paper mentioned the limitations of this study focusing mainly on macro level analysis and suggested future research topics.

We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images in the context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploits only the lowest kind of local information through the global histogram. However, such global Histograms reflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which uses local histograms of the LBP, was one of few tentative to catch higher level textural information. We believe that important local and useful information in between the two levels is just ignored by the two schemas. The newly developed method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as much local information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present in grayscale images extracted by LBP through a complex structure. The used framework is comprised of a multitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. The framework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic forms allow targeting texture directionality, which is a very useful property in texture characterization. In addition, the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared to published works including the very effective DLEP. Results show significant higher or comparable performance of GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

Artificial bee colony (ABC) algorithm has attracted significant interests recently for solving the multivariate optimization problem. However, it still faces insufficiency of slow convergence speed and poor local search ability. Therefore, in this paper, a modified ABC algorithm with bees' number reallocation and new search equation is proposed to tackle this drawback. In particular, to enhance solution accuracy, more bees in the population are assigned to execute local searches around food sources. Moreover, elite vectors are adopted to guide the bees, with which the algorithm could converge to the potential global optimal position rapidly. A series of classical benchmark functions for frequency-modulated sound waves are adopted to validate the performance of the modified ABC algorithm. Experimental results are provided to show the significant performance improvement of our proposed algorithm over the traditional version.

Fingerprint-based biometric identification is one of the most interesting automatic systems for identifying individuals. Owing to the poor sensing environment and poor quality of skin, biometrics remains a challenging problem. The main contribution of this paper is to propose a new approach to recognizing a person's fingerprint using the fingerprint's local characteristics. The proposed approach introduces the barycenter notion applied to triangles formed by the Delaunay triangulation once the extraction of minutiae is achieved. This ensures the exact location of similar triangles generated by the Delaunay triangulation in the recognition process. The results of an experiment conducted on a challenging public database (i.e., FVC2004) show significant improvement with regard to fingerprint identification compared to simple Delaunay triangulation, and the obtained results are very encouraging.

The purpose of the high-speed railway construction is to better satisfy passenger travel demands. Accordingly, the design of the train working plan must also take a full account of the interests of passengers. Aiming at problems, such as the complex transport organization and different speed trains coexisting, combined with the existing research on the train working plan optimization model, the multiobjective bi-level programming model of the high-speed railway passenger train working plan was established. This model considers the interests of passengers as the center and also takes into account the interests of railway transport enterprises. Specifically, passenger travel cost and travel time minimizations are both considered as the objectives of upper-level programming, whereas railway enterprise profit maximization is regarded as the objective of the lower-level programming. The model solution algorithm based on genetic algorithm was proposed. Through an example analysis, the feasibility and rationality of the model and algorithm were proved.

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

Aiming at the problem of service reliability in resource reservation in cloud computing environments, a model of dynamic cloud resource reservation based on trust is proposed. A domain-specific cloud management architecture is designed in which resources are divided into different management domains according to the types of service for easier management. A dynamic resource reservation mechanism (DRRM) is used to test users' reservation requests and reserve resources for users. According to user preference, several resources are chosen to be candidate resources by fuzzy cluster analysis. The fuzzy evaluation method and a two-way trust evaluation mechanism are adopted to improve the availability and credibility of the model. An analysis and simulation experiments show that this model can increase the flexibility of resource reservation and improve user satisfaction.

Digital forensics is a vital part of almost every criminal investigation given the amount of information available and the opportunities offered by electronic data to investigate and evidence a crime. However, in criminal justice proceedings, these electronic pieces of evidence are often considered with the utmost suspicion and uncertainty, although, on occasions are justifiable. Presently, the use of scientifically unproven forensic techniques are highly criticized in legal proceedings. Nevertheless, the exceedingly distinct and dynamic characteristics of electronic data, in addition to the current legislation and privacy laws remain as challenging aspects for systematically attesting evidence in a court of law. This article presents a comprehensive study to examine the issues that are considered essential to discuss and resolve, for the proper acceptance of evidence based on scientific grounds. Moreover, the article explains the state of forensics in emerging sub-fields of digital technology such as, cloud computing, social media, and the Internet of Things (IoT), and reviewing the challenges which may complicate the process of systematic validation of electronic evidence. The study further explores various solutions previously proposed, by researchers and academics, regarding their appropriateness based on their experimental evaluation. Additionally, this article suggests open research areas, highlighting many of the issues and problems associated with the empirical evaluation of these solutions for immediate attention by researchers and practitioners. Notably, academics must react to these challenges with appropriate emphasis on methodical verification. Therefore, for this purpose, the issues in the experiential validation of practices currently available are reviewed in this study. The review also discusses the struggle involved in demonstrating the reliability and validity of these approaches with contemporary evaluation methods. Furthermore, the development of best practices, reliable tools and the formulation of formal testing methods for digital forensic techniques are highlighted which could be extremely useful and of immense value to improve the trustworthiness of electronic evidence in legal proceedings.

Climate change has become a major challenge for sustainable development of human society. This study is an attempt to analyze existing literature to identify economic indicators that hamper the process of global warming. This paper includes case studies based on various countries to examine the nexus for environment and its relationship with Foreign Direct Investment, transportation, economic growth and energy consumption. Furthermore, the observations are analyzed from the perspective of China-Pakistan Economic Corridor (CPEC) and probable impact on carbon emission of Pakistan. A major portion of CPEC investment is allocated for transportation. However, it is evident that transportation sector is substantial emitter of carbon dioxide (CO2) gas. Unfortunately, there is no empirical work on the subject of CPEC and carbon emission for vehicular transportation. This paper infers that empirical results from various other countries are ambiguous and inconclusive. Moreover, the evidence for the pollution haven hypothesis and the halo effect hypothesis is limited in general and inapplicable for CPEC in particular. The major contribution of this study is the proposal of an energy efficient transportation model for reducing CO2 emission. In the end, the paper suggests strategies to climate researchers and policymakers for adaptation and mitigation of greenhouse gases (GHG).