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Purpose: As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP. Design/methodology/approach: This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis. Findings: The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP. Practical implications: The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company's collaboration and for effective management to improve NPP in the face of global competition. Originality/value: This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC. [ABSTRACT FROM AUTHOR]

Traveling in a Hakka village, the tourist can feel the culture of the Hakka in Taiwan and see traditional drama, artwork, handicrafts, and foods. The current trend in tourism planning is to incorporate online word of mouth into route design. This paper aims to examine common characteristics of Hakka village tourism development, identifying the need for planning and offering a model of the directions planning might take. It begins with big data collection of the online service and combines that with social network analysis. The results indicated that tourism planning with user's online search strategy will provide a better and more precise tour service. It is suggested that the government should set up the tour service center at the location identified as being in the structural hole of the tourism network. As cultural tourism continues to expand, big data applications will offer new opportunities and challenges to tourism planning. [ABSTRACT FROM AUTHOR]

The techniques pertaining to soft computing are the base for commencement, model and organization of intelligent systems in order to offer more perfect, economical and realistic solution which has minimal complexity levels. The concept of intelligent systems has for long supported objectives on sustainability, improvisation of efficiency and symbolized various kinds of activities like creation of jobs, earning profits, providing services and improvement in capacities in ICT. The applications based on this system are widespread in the technological market because of massive development in grid, cloud, mobile and big data applications and its corresponding connectivity advantages. ICT triggers data scientists to satisfy technical-based demands of intelligent systems around data analytics and big data applications. The major features of big data like that of reservation of designs on information and knowledge have offered the public undertaking an opportunity to enhance production levels, improved efficiency and its effectiveness. The main objective of this paper is to enhance the accuracy of predictive modelling using an optimized predictive modelling based on rough set (RS) and Elman neural network (ElNN). These advanced predictive models are designed on the basis of RS approach in initial stages and in later processes enhanced with the support of Elman-NN. RS has an excellent feature selection capability, and Elman-NN is the best at nonlinear system modelling. By integrating them, the proposed method can limit the input dimension and optimize the structure for ElNN modelling. This can reduce the mathematical computation complexity with the progress of predictive models. The experimental results indicate that through RS feature selection and the structure of Elman-NN, the predictive model can be simplified significantly with enhanced model performance. The predictive accuracy of data sets, namely air quality in Northern Taiwan, hazardous air pollutants, historical hourly weather data and US pollutants through optimization, is above 99%, and this model proves that the results of optimized predictive error are far better than those obtained by other neural networks like PCA-RBF, PCA-NN, FFNN-BP with PCA, MLR, FFNN-BP, ELM, SOM, RBF and ART2. [ABSTRACT FROM AUTHOR]

This study aims to expose the "causal core" items of LibQUAL+™ from professional librarians' views. The objective is to prioritize limited resources to improve the most influential factors affecting the library service quality. Valid LibQUAL+™ questionnaires from thirty-two university library administrators in Taiwan are collected for analysis. The Decision Making Trial and Evaluation Laboratory (DEMATEL) technique is employed to build causal maps that can clearly locate the twenty-two LibQUAL+™ question items into four quadrants: core causal, inferior causal, inferior effect, and core effect. The DEMATEL causal maps can facilitate the library administrators to prioritize the limited resources for improvement. Five "causal core" items of LibQUAL+™ are identified, including the library staff should be motivated with high willingness to help users and giving users individual attention; the library amenities should provide a quiet space for individual activities; the library Web site should allow users to locate and access information easily and individually. The survey data represent what professional librarians' think of the service quality in their libraries. The findings may serve as useful guidance to allocate limited budgets for library service improvement. This paper has pinpointed the "core causal" items of LibQUAL+™ for advancing library administration. It is the first of its kind in the literature to use the DEMATEL technique to identify the "core causal" items of LibQUAL+™ for improving library service quality. [ABSTRACT FROM AUTHOR]

Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities. [ABSTRACT FROM AUTHOR]

The causes of traffic accidents are diverse, including weather, road conditions, road design, and psychological factors. With the advancement of information technology, big data on traffic accidents can be collected and analyzed more easily than before. To identify the causes of traffic accidents, we analyzed the Traffic Enforcement Case Database and Traffic Accident Database of the Traffic Division of the National Police Agency in Taiwan. The main causes of traffic accidents from 2013 to 2020 were lane drifting, overspeeding, illegal turning, running red lights, and drunk driving. The number of traffic violations has increased every year in the same period, and the number of casualties and injuries has increased since 2018. It is necessary to customize sensor technologies to monitor such violations to prevent related accidents. Advanced data mining technologies should be used to analyze the data and obtain better information to prevent violations and accidents. The results of this study provide a basis for further study related to developing preventive measures for traffic violations and accidents using advanced sensor technologies. [ABSTRACT FROM AUTHOR]

Industry 4.0, smart manufacturing and its related technologies are now becoming the leading trend in the development of the manufacturing industry. One of the key drivers of Industry 4.0 is big data analytics, which can transform large amounts of data into useful information, enabling astute and rapid decision-making strategies when combined with expert domain knowledge. The semiconductor industry is the most important high-tech industry in Taiwan, but it is also one of the most energy-consuming industries in the country. Therefore, it is critical to improve the efficiency of the manufacturing process and reduce the overall energy consumption of facility systems. This research demonstrates how to apply big data analytics in the semiconductor industry to explore the relationships of various machine parameters, develop predictive models for machine energy efficiency and apply optimisation tools to minimise energy consumption, while meeting the production demands. An empirical study is conducted in conjunction with a semiconductor corporation in Taiwan, targeting the air compressor system in its factory. The research framework is shown to be capable of assisting semiconductor fabrication plant decision-makers in optimising machine configurations, resulting in more than 10% savings on energy consumption and significantly decreased manufacturing costs. [ABSTRACT FROM AUTHOR]

Nowadays, it remains unclear whether the accumulation of Internet buzz can accurately predict customer preferences and buying intentions. We study two related perspectives with regard to what people talk about online and what they intend to do in the adoption phase to offer advice to companies aiming to excel in online marketing efforts. Drawing on the consumption value theory, we examine buyers' discussions about and intention to use wearable devices for a sample of consumers in Taiwan. A framework is advanced to explore the results of big data analysis employing text-mining techniques (i.e., what people talk about) and survey-based research using structural equation modelling (i.e., what people intend to do). Functional, emotional and conditional values surfaced as the highest Internet buzzes of wearable devices. Conversely, emotional, epistemic and functional values emerged as the most influential drivers of customers' adoption intention. Our findings suggest that different value dimensions are relevant at different points of the purchase-related decision-making process. Some values animate Internet discussions that pertain to the pre-purchase information search stage, and others appear significant during the formation of people's purchase intentions. We discuss the theoretical and practical implications of our study and provide suggestions for future research. [ABSTRACT FROM AUTHOR]

Increased medical attention is needed as the prevalence of autism spectrum disorder (ASD) rises. Both cardiovascular disorder (CVD) and hyperlipidemia are closely associated with adult ASD. Shank3 plays a key genetic role in ASD. We hypothesized that Shank3 contributes to CVD development in young adults with ASD. In this study, we investigated whether Shank3 facilitates the development of atherosclerosis. Using Gene Set Enrichment Analysis software (Version No.: GSEA-4.0.3), we analyzed the data obtained from Shank3 knockout mice (Gene Expression Omnibus database), a human population-based study cohort (from Taiwan's National Health Insurance Research Database), and a Shank3 knockdown cellular model. Shank3 knockout upregulated the expression of genes of cholesterol homeostasis and fatty acid metabolism but downregulated the expression of genes associated with inflammatory responses. Individuals with autism had higher risks of hyperlipidemia (adjusted hazard ratio (aHR): 1.39; p < 0.001), major adverse cardiac events (aHR: 2.67; p < 0.001), and stroke (aHR: 3.55; p < 0.001) than age- and sex-matched individuals without autism did. Shank3 downregulation suppressed tumor necrosis factor-α-induced fatty acid synthase expression; vascular cell adhesion molecule 1 expression; and downstream signaling pathways involving p38, Jun N-terminal kinase, and nuclear factor-κB. Thus, Shank3 may influence the development of early-onset atherosclerosis and CVD in ASD. Furthermore, regulating Shank3 expression may reduce inflammation-related disorders, such as atherosclerosis, by inhibiting tumor necrosis factor-alpha-mediated inflammatory cascades. [ABSTRACT FROM AUTHOR]

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

In urban areas, public transport can improve sustainable transport by reducing vehicles and congestion, and improving accessibility. Mass rapid transit (MRT) is especially important for large cities, such as Taipei City, Taiwan. In particular, MRT aims to improve mobility and reliability, but has limitations in providing first and last mile accessibility to final destinations or origins. Taipei City, Taiwan has introduced a public bike sharing scheme to service this gap. However, few studies have addressed how a public bike sharing scheme assists first and last mile accessibility. This study uses Taipei City as a case study to investigate this issue by comparing the demand and supply of the public bike sharing scheme, YouBike, at a detailed spatial scale. The supply of YouBike is represented by the time saving compared to walking for each identified origin and destination pair in the study area (i.e., Point of Interests (POIs) and MRT stations). The demand for YouBike is total trips from each YouBike station to a MRT station using public transport smart card data. By comparing the demand and supply for over 400 zones or villages, service gaps and areas of unbalanced service can be identified. The results show that YouBike does provide first and last mile service for the MRT network with some evidence of service mismatch in the study area, i.e., high service for low demand and vice versa. The conclusions of the paper can help cities wanting to introduce a bike sharing schemes to improve first and last mile transport. [ABSTRACT FROM AUTHOR]

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data. [ABSTRACT FROM AUTHOR]

Existing studies on the urban heat island (UHI) effect tend to focus on the planar scale. However, the UHI effect has obvious three-dimensional properties. This research focuses on the vertical structure of UHI. The study combined big data from sounding balloon and surface measurements in the Database for Atmospheric and Hydrologic Research, Taiwan. Data organization and classification were conducted using the Python programming language, the NumPy mathematical function library, and the Pandas data manipulation library. Results confirmed the existence of the atmospheric boundary layer and the canopy. Furthermore, the temperature inversion of the atmospheric boundary layer fell between heights of 130 m and 667 m. The height of the inversion is affected by the wind speed and cloudiness. Thereby demonstrating that the inversion fluctuates with weather scenario changes. The research results can be applied as the direction of urban planning thinking for urban heat island countermeasures. [ABSTRACT FROM AUTHOR]

How can governing digital health for infectious disease outbreaks be enhanced? In many ways, the COVID-19 pandemic has simultaneously represented both the potential and marked limitations of digital health practices for infectious disease outbreaks. During the pandemic's initial stages, states along with Big Data and Big Tech actors unleashed a scope of both established and experimental digital technologies for tracking infections, hospitalisations, and deaths from COVID-19 – and sometimes exposure to the virus SARS-CoV-2. Despite the proliferation of these technologies at the global level, transnational and cross-border integration, and cooperation within digital health responses to COVID-19 often faltered, while digital health regulations were fragmented, contested, and uncoordinated. This article presents a critiquing reflection of approaches to conceptualising, understanding, and implementing digital health for infectious disease outbreaks, observed from COVID-19 and previous examples. In assessing the strengths and limitations of existing practices of governing digital health for infectious disease outbreaks, this article particularly examines 'informal' digital health to build upon and consider how digitised responses to addressing and governing infectious disease outbreaks may be reconceptualised, revisited, or revised. [ABSTRACT FROM AUTHOR]

This study aims to incorporate the application of RS technology with management strategies and proposes the Remote Sensing Knowledge-Based Decision Support System (RS-KBDSS) framework. It is not only focusing on considering RS technology for discussing the accuracy of estimation but also conducting strategic thinking that is needed for keeping the inventory and monitoring task running by obtaining knowledge and decision support. This study offers more flexible and accurate estimation models for different resolution images and different kinds of models. The results obtained by the study is satisfied with the practical application. [ABSTRACT FROM AUTHOR]

This study investigates whether the existence or strength of any misreaction in the options market is affected by investor sophistication and investor sentiment. Based on a unique data set of the complete history of all transactions in the Taiwan options market, we find that individual investors exhibit significant misreaction to information and that this misreaction becomes stronger during periods of high investor sentiment. In addition, more active or aggressive individual investors always exhibit misreaction and do not learn from their past mistakes. Our empirical results are robust to alternative measures of investor sentiment and definitions of long- and short-term horizons. [ABSTRACT FROM PUBLISHER]

Facing the COVID-19 pandemic, Taiwan demonstrated resilience at the initial stage of epidemic prevention, and effectively slowed down its spread. This study aims to document public epidemic awareness of COVID-19 in Taiwan through collecting social media- and Internet-based data, and provide valuable experience of Taiwan's response to COVID-19, involving citizens, news media, and the government, to aid the public in overcoming COVID-19, or infectious diseases that may emerge in the future. The volume of Google searches related to COVID-19 and face masks was regarded as an indicator of public epidemic awareness in the study. A time-series analysis was used to explore the relationships among public epidemic awareness and other COVID-19 relevant variables, which were collected based on big data analysis. Additionally, the content analysis was adopted to analyze the transmission of different types of fear information related to COVID-19 and their effects on the public. Our results indicate that public epidemic awareness was significantly correlated with the number of confirmed cases in Taiwan and the number of news reports on COVID-19 (correlation coefficient:.33–.56). Additionally, the findings from the content analysis suggested that the fear of the loss of control best explains why panic behavior occurs among the public. When confronting the highly infectious COVID-19, public epidemic awareness is vital. While fear is an inevitable result when an emerging infectious disease occurs, the government can convert resistance into assistance by understanding why fear arises and which fear factors cause excessive public panic. Moreover, in the digitalization era, online and social media activities could reflect public epidemic awareness that can e harnessed for epidemic control. [ABSTRACT FROM AUTHOR]

This study is based on the situation of Taiwan listed companies as derivative financial products from 2015 to 2017, analyzing the relationship between the hedging of derivative financial products and characteristics of enterprises and the factors that affect the hedging decision-making of companies. It is found that even after the announcement of Taiwan's No. 34 and No. 36 bulletins, there are still some problems that are needed to improve in the disclosure of derivative financial product investment information by Taiwan's listed companies, at least in the disclosure of the reasons for this conduct which is still insufficient. In this study, two-stage regression analysis method is applied to empirical analysis, and it is found that hedging activities are related to corporate characteristics, such as expected financial crisis costs, corporate size, equity issues, growth investment opportunities, and information asymmetry. In the investment of derivative financial products, enterprises should evaluate their own financial characteristics as a reference for the risk avoidance decision. At the same time, it is necessary to investigate different natures of hedging tools used in appropriate risk categories, so as to fully achieve the hedging effect and maximize the hedging benefits. This study also found that companies with higher growth investment opportunities, larger size, and higher financial crisis costs will tend to use derivative financial products for hedging. As for the impact of other industries, it is found that the electronic and electrical machinery industries are more active than other industries in hedging behaviors of undertaking derivative financial products' transaction. [ABSTRACT FROM AUTHOR]

This study assessed the severity of building fires in 17 villages that comprise Taishan District, New Taipei City, Taiwan. A literature review was performed to discuss the impact of fire severity assessment criteria in order to develop items and factors for the analytic hierarchy process (AHP). We identified six items for the building fire severity assessment: rescue response time, narrow road density, water-sacristy area density, building risk, hazardous materials' place density, and fire safety inspection-regulated premises density. Big data analysis and remote sensing were employed to facilitate devising the AHP structure with items and factors. We also compared the annual average burned area from 2005 to 2015 through the building fire severity assessment to validate assessment accuracy. The actual yearly average burned area in each village of Taishan District was used to verify the building fire severity assessment, and the compliance rate of the rating (i.e., high, moderate, and low) was, respectively, 60%, 67%, and 67%. The proposed assessment is evidently feasible and can act as a reference for quantitative analyses for assessments of building fire severity. [ABSTRACT FROM AUTHOR]

This article examines the reasons behind Taiwan's effective COVID‐19 response. While some have argued that Taiwan's success with COVID‐19 is based on its experience with SARS, I argue that we should not attribute Taiwan's effective response solely to its SARS experience. The country's success mainly lies in three factors: (1) reliance on the mask policy as the main disease prevention measure and the ability to quickly expand mask production capacity; (2) use of big data and technology to enhance effective implementation of disease prevention and detection measures; and (3) strong state‐society relations favoring transparency, communication, and collaboration. The first two factors can trace their roots to the country's developmental state model. Democracy provides the institutional underpinning for a vibrant civil society and the synergy between state and civil society, strengthening Taiwan's crisis governance legitimacy and increasing citizens' voluntary compliance. [ABSTRACT FROM AUTHOR]

SUMMARY: The Annual SHOT Report was published on July 12 at the Annual Symposium. This was preceded by a 2‐day meeting of the International Haemovigilance Network (IHN). The IHN meeting provides an opportunity for haemovigilance experts to network with one another and share presentations, which this year included those from China and Taiwan. Reviews of pulmonary complications were highlighted since the definitions of both transfusion‐related acute lung injury and transfusion‐associated circulatory overload are undergoing revision. The seminar provided an opportunity to present some UK data to an international group (the INTERVAL donor study, the value of big data and work on genomics and human factors). SHOT reports for incidents reported in 2017 demonstrate that, overall, 85·5% are caused by errors. Key recommendations from SHOT are: (i) All staff involved in transfusion must be trained in and know ABO group compatibility. Clinical staff must not just rely on the laboratory staff to get this right. (ii) IT systems have the potential to increase transfusion safety by minimising human factors and should be considered for all transfusion steps. (iii) A formal risk assessment for transfusion‐associated circulatory overload should be undertaken wherever possible. [ABSTRACT FROM AUTHOR]

Most recent discussion of the adaptive reuse of school land has focused almost exclusively on repurposing or redeploying vacant school space rather than comprehensively re-planning and constructing the entire school land for the overall needs of society and urban development. The relevant government agencies for school land reuse in Taiwan, such as the Ministry of Education and municipal governments, mostly provide subjective regulations or revitalization provisions for the sustainable development of school resources; however, no specific scientific assessment or a planning procedure has been proposed to revitalize school land. Therefore, constructing a scientific, quantitative, and objective planning framework and procedure is necessary for the adaptive reuse of school land based on the needs of overall society and urban development in order to replace the existing and outdated planning philosophy and to correct prominent shortcomings of past planning operations that were solely in accordance with the qualitative judgment and decision making of official agencies. In this study, we mainly adopted the analytic network process (ANP) and big data, including demographics, facility usage, and social welfare indicators, to assist the Taipei City government to construct or reform land reuse strategies for junior high and elementary schools facing immediate or future closure, consolidation, or downsizing. To take a more realistic approach to improve final decision making, the investigation of expert questionnaires through the ANP was based on the consideration of future trends that were objectively evaluated by big datasets. The novel planning philosophy and concise decision framework for reuse strategies we designed are expected to improve public decision-making transparency, adaptive reuse effectiveness, and quality of urban life. Ultimately, our proposed strategies and suggestions can not only assist local public sectors to promote the policy of adaptive reuse of surplus school lands but also serve as an appropriate blueprint of urban sustainability for the central government in the near future. [ABSTRACT FROM AUTHOR]

As information technology continues to evolve rapidly, programming skills become increasingly crucial. To be able to construct superb programming skills, the training must begin before college or even senior high school. However, when developing comprehensive training programmers, the learning and teaching processes must be considered. In order to improve the students' learning outcome and engagement in programming course, this study applied learning analytics into the proposed massive online open courses (MOOCs) enabled collaborative programming course. Through the proposed learning activity, instructors receive a monthly report that explains which students are at risk and in need of timely intervention. This study conducted an experiment to evaluate the effectiveness of the proposed learning activity. Students in the experimental group received learning interventions from an instructor according to the result of learning analytics, and students in the control group received interventions according to the instructor's observation. The data for this study were collected over 10 weeks at a university in Taiwan. The result indicated that the proposed programming course with learning analytics improved students' learning outcomes and levels of engagement. [ABSTRACT FROM PUBLISHER]

This article examines the accident externality from driving in terms of loss probability and severity by using a unique individual-level data set with more than 3 million observations from Taiwan. Two types of accident externality are, respectively, measured: the average number of kilometers driven per month per vehicle and the total number of speeding tickets per month. For both variables, we find significant evidence to support the existence of the accident externality. Moreover, we find that the accident externality is heterogeneous in terms of the vehicles' characteristics. [ABSTRACT FROM AUTHOR]

With the growth of big data and open data in recent years, the importance of data anonymization is increasing. Original data need to be anonymized to prevent personal identification from being revealed before being released to the public. There is a growing variety of de-identification methods which have been proposed to reduce the privacy issues, however, there is still much to be improved. The purpose of this study is to demonstrate the possibilities of re-identification from masked data, and to compare the pros and cons of different de-identification methods. A set of electronic toll collection data from Taiwan was used and we successfully re-identified vehicles with specific patterns. Four de-identification methods were performed and finally we compared the strengths and weaknesses of these methods and evaluated their appropriateness. [ABSTRACT FROM AUTHOR]