Earticle

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.

SNS is perceived as an effective tool for sharing news and enabling news content to reach many more users than before. And some users think that SNS is an important source to get news. This study’s purpose is to understand the key factors contributing to behavior of news consumption on social network sites in Cambodia and its influence. We identified three key factors including convenience, recency, and variety; however, recency showed less significant effect on news consumption on SNS. Besides the key factors, it also seeks to understand the impact of news consumption on social engagement and government’s transparency in Cambodia. The analytical results achieved through the Partial Least Squares (PLS) approach.

Smartphone is affecting not only our everyday life but also our business, directly causing diverse side-effects. But up until now, many researches that have been conducted on smartphone were focused on continuance intention for smartphone use and user satisfaction. Thus, the objective of this research is to investigate the effects that smartphone dependency has on digital dementia which is one of negative effects of smartphone. For the purpose, we have reviewed studies that are related to perceived characteristics of existing smartphones. As perceived characteristics variables, we have adopted perceived ease of use and perceived usefulness. And to present our research model, we have adopted smartphone dependency as psychological attitude variable and digital dementia as consequence variable. To implement an empirical analysis of our research model, we have conducted a survey with college student groups as research target who use smartphone most.

We propose statistical methods for evaluating the efficiency of CAPTCHA. Most people unfairly assumed that machines are not capable at reading precisely. This fact leads to the invention of CAPTCHA, a distorted word or short phase, which is designed to thwart computers and separate human from machines. However, advances in image recognition technologies mean that machines are constantly getting better at recognizing CAPTCHA. This forces CAPTCHA designers to design even more difficult CAPTCHAs to prevent their systems from being gamed by malicious bots. However, this arm race has an unintended side effect on the common users. Many CAPTCHAs are now so hard that many people are unable to read them. This obviously conflicts with the original purpose that CAPTCHA was invented in the first place. Our analysis shows that some CAPTCHAs are more users friendly. In particular, Yahoo-style CAPTCHA is the most friendliness. This suggests that a good CAPTCHA could be a simple text with some distortion that prevents machines from correctly segmenting characters.

This study reports a preliminary finding of the types and numbers of graphs being presented in the annual reports of about thirty top listed companies trading publicly in the stock markets of three countries- Thailand (SET), Malaysia (BM), and Singapore (SGX)-that were chosen based on their inclusion in the ASEAN Stars Index under the ASEAN Trading Link project. A total of 6,753 graphs from nineteen sectors were extracted and examined. Banking, real estate, and telecommunications are ranked the three most condense sectors, accounting for 50.2% of the total number of graphs observed. The three most used graphs are the Conservative Bar, Donut graph and Stack Bar. Less than one percent of Infographic type graphs were used. The five most depicted graphed variables are Asset, Revenue, Net profit, Liability, and Dividend. Using rudimentary framework to detect distorted or misleading statistical graphs, the study found 60.6% of the graphs distorted across the three markets, SET, BM, and SGX. BM ranked first in percentages of graphs being distortedly presented (73%). The other two markets, SET and SGX, have about the same proportions, 53.88% and 53.03%, respectively. Likewise, the proportions of Well-designed versus Inappropriatedesigned graphs of the latter two markets are a little over one time (SET = 1 : 1.17; SGX = 1 : 1.13), whereas the proportion is almost triple for the BM market (BM = 1 : 2.70). In addition, the trend of distorted graphs found is slightly increasing as the longevity of the ASEAN Stars Index increases. One possible explanation for the relatively equal proportion of inappropriate graphs found is that SET is the smallest market and SGX, though the largest, is the most regulated market. BM, on the other hand, may want to present their financial data in the most attractive manner to prospective investors, thus, regulatory constraints and governance structure are still lenient.

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining—extracting meaningful information from unstructured text data—has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co- occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

This paper presents a fast Time Difference of Arrival (TDOA) estimation for sound source localization. TDOA is the time difference between the arrival times of a signal at two sensors. We propose a partial cross correlation method to increase the speed of TDOA estimation for sound source localization. We do this by predicting which part of the cross correlation function contains the required TDOA value with the help of the signal energies, and then we compute the cross correlation function in that direction only. Experiments show approximately 50% reduction in the cross correlation computation time thereby increasing the speed of TDOA computation. This makes it very relevant for real world surveillance.

Although the telecommunications service industry has become a basic infrastructure component of the information and communication technology (ICT) industry, it is now losing its reputation as a cash cow due to achieving a saturation of service subscribers, especially in South Korea. With the exception of improving network speeds, network operators are experiencing difficulties in creating new innovations. Therefore, new innovations for the telecommunications service industry should be identified in conjunction with government policies for industry promotion. To examine the innovative capabilities of a specific industry, innovation system theories were used as a framework for research. However, existing innovation systems are limited with regard to explaining the openness of relationships and user participation which are general properties of the ICT industry. Moreover, as sources of innovative activity, additional values such as financial support and collaboration are more appropriate. This study presents a new innovation system based on innovation values. We analyze the telecommunications service industry and prioritize the importance of telecommunication policies within South Korea. An in-depth interview with experts based on the innovation system framework is conducted first. Next, innovation factors derived from the interview are applied within an analytic hierarchy process (AHP), leading to a prioritization of innovation factors for the telecommunications service industry.

The purpose of this study is to provide an explanation for the association between the characteristics of a network and the type of innovation by considering the effect of absorptive capacity. To do so, this study examined the moderating effects of absorptive capacity on the characteristics of network-innovation relationship in a technical-driven venture company. In order to obtain a better understanding about consequences caused by interfirm network, information was obtained from 169 Korean IT venture companies. Results confirmed that the network diversity is positively associated with exploration. Our results also suggested that the network strength is positively associated with exploitation. Finally, we found a positive two-way interaction between absorptive capacity and the network diversity-exploration relationship. Then, we discussed implications and directions for future research.

This paper addresses the issue how the total life cycle cost may be minimized and how the cost should be allocated to the acquirer and developer. This paper differentiates post life cycle change (PLCC) endeavors from PLCC activities, rigorously classifies PLCC endeavors according to the result of PLCC endeavors, and rigorously defines the life cycle cost of a software product. This paper reviews classical definitions of software ‘maintenance’ types and proposes a new typology of PLCC activities too. The proposed classification schemes are exhaustive and mutually exclusive, and provide a new paradigm to review existing literatures regarding software cost estimation, software ‘maintenance,’ software evolution, and software architecture from a new perspective. This paper argues that the long-term interest of the acquirer is not protected properly because warranty period is typically too short and because the main concern of warranty service is given to removing the defects detected easily. Based on the observation that defects are caused solely by errors the developer has committed for software while defects are often induced by using for hardware (so, this paper cautiously proposes not to use the term ‘maintenance’ at all for software), this paper argues that the cost to remove defects should not be borne by the acquirer for software.