GAUGING PUBLIC INTEREST FROM SERVER LOGS, SURVEYS AND INLINKS A Multi-Method Approach to Analyze News Websites

As the World Wide Web (the Web) has turned into a full-fledged medium to disseminate news, it is very important for journalism and information science researchers to investigate how Web users access online news reports and how to interpret such usage patterns. This doctoral thesis collected and analyzed Web server log statistics, online surveys results, online reprints of the top 50 news reports, as well as external inlinks data of a leading comprehensive online newspaper (the People\u27s Daily Online) in China, one of the biggest Web/information markets in today\u27s world. The aim of the thesis was to explore various methods to gauge the public interest from a Webometrics perspective. A total of 129 days of Web server log statistics, including the top 50 Chinese and English news stories with the highest daily pageview numbers, the comments attracted by these news items and the emailed frequencies of the same stories were collected from October 2007 to September 2008. These top 50 news items’positions on the Chinese and English homepages and the top 50 queries submitted to the website search engine of the People’s Daily Online were also retrieved. Results of the two online surveys launched in March 2008 and March 2009 were collected after their respective closing dates. The external inlinks to the People’s Daily Online were retrieved by Yahoo! (Chinese and English versions), and the online reprints were retrieved by Google. Besides the general usage patterns identified from the top 50 news stories, this study, by conducting statistical tests on the data sets, also reveals the following findings. First, the editors’ choices and the readers’ favorites do not always match each other; thus content of news title is more important than its homepage position in attracting online visits. Second, the Chinese and English readers’ interests in the same events are different. Third, the pageview numbers and comments posted to the news items reflect the unfavorable attitudes of the Chinese people toward the United States and Japan, which might offer us a method to investigate the public interest in some other issues or nations after necessary modifications. More importantly, some publicly available data, such as the comments posted to the news stories and online survey results, further show that the pageview measure does reflect readers’ interests/needs truthfully, as proved by the strong correlations between the top news reports and relevant top queries. The external ininks to the news websites and the online reprints of the top news items help us examine readers\u27 interests from other perspectives, as well as establish online profiles of the news websites. Such publicly accessible information could be an alternative data source for researchers to study readers\u27 interests when the Web server log data are not available. This doctoral thesis not only shows the usefulness of Web server log statistics, survey results, and other publicly accessible data in studying Web user’s information needs, but also offers practical suggestions for online news sites to improve their contents and homepage designs. However, no single method can draw a complete picture of the online news readers’ interests. The above mentioned research methodologies should be employed together, in order to make more comprehensive conclusions. Future research is especially needed to investigate the continuously rapid growth of the “Mobile News Readers,” which poses both challenges and opportunities to the press industry in the 21st century

GAUGING PUBLIC INTEREST FROM SERVER LOGS, SURVEYS AND INLINKS

As the World Wide Web (the Web) has turned into a full-fledged medium to disseminate news, it is very important for journalism and information science researchers to investigate how Web users access online news reports and how to interpret such usage patterns. This doctoral thesis collected and analyzed Web server log statistics, online surveys results, online reprints of the top 50 news reports, as well as external inlinks data of a leading comprehensive online newspaper (the People’s Daily Online) in China, one of the biggest Web/information markets in today’s world. The aim of the thesis was to explore various methods to gauge the public interest from a Webometrics perspective. A total of 129 days of Web server log statistics, including the top 50 Chinese and English news stories with the highest daily pageview numbers, the comments attracted by these news items and the emailed frequencies of the same stories were collected from October 2007 to September 2008. These top 50 news items’positions on the Chinese and English homepages and the top 50 queries submitted to the website search engine of the People’s Daily Online were also retrieved. Results of the two online surveys launched in March 2008 and March 2009 were collected after their respective closing dates. The external inlinks to the People’s Daily Online were retrieved by Yahoo! (Chinese and English versions), and the online reprints were retrieved by Google. Besides the general usage patterns identified from the top 50 news stories, this study, by conducting statistical tests on the data sets, also reveals the following findings. First, the editors’ choices and the readers’ favorites do not always match each other; thus content of news title is more important than its homepage position in attracting online visits. Second, the Chinese and English readers’ interests in the same events are different. Third, the pageview numbers and comments posted to the news items reflect the unfavorable attitudes of the Chinese people toward the United States and Japan, which might offer us a method to investigate the public interest in some other issues or nations after necessary modifications. More importantly, some publicly available data, such as the comments posted to the news stories and online survey results, further show that the pageview measure does reflect readers’ interests/needs truthfully, as proved by the strong correlations between the top news reports and relevant top queries. The external ininks to the news websites and the online reprints of the top news items help us examine readers\u27 interests from other perspectives, as well as establish online profiles of the news websites. Such publicly accessible information could be an alternative data source for researchers to study readers\u27 interests when the Web server log data are not available. This doctoral thesis not only shows the usefulness of Web server log statistics, survey results, and other publicly accessible data in studying Web user’s information needs, but also offers practical suggestions for online news sites to improve their contents and homepage designs. However, no single method can draw a complete picture of the online news readers’ interests. The above mentioned research methodologies should be employed together, in order to make more comprehensive conclusions. Future research is especially needed to investigate the continuously rapid growth of the “Mobile News Readers,” which poses both challenges and opportunities to the press industry in the 21st century

Demands and Development Strategies for Support Services of Autonomous Learning at Chinese Universities

In recent years, autonomous learning has become one of the most popular ways for Chinese university students to obtain new knowledge and skills, which requires more support services from their affiliated institutions. However, few previous studies combined investigation of the students’ needs and learning support services. Our study conducted online survey to analyze the status quo of Chinese students’ autonomous learning and the much-needed support services from their schools. We sent out the survey in October 2019 and received 458 valid responses. All participants were undergraduate students from 195 universities/colleges in China. The following information was collected: 1. School/Grade/Major of participant; 2. Autonomous learning time/goals/methods/main concerns of these students; 3. Existing support services, e.g., spaces, resources, counseling, procedures, activities; 4. The students’ degree of satisfaction with the available support services. Chinese students showed strong and diversified needs of support services to fulfill their autonomous learning tasks, which cannot be met by their schools. We proposed a development framework and some strategies for higher education institutions in China to launch more innovative learning support services

An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD) was used to extract the features of event-related desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of “idle-state detection without training samples.” The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including “idle” task

High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.Comment: 35 pages, 5 figure

Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach

The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurement. Considering this, this paper proposes a physically informed data-driven aggregate model (AGM) for DHN, providing a concise description of the source-load relationship of DHN without exposing network details. First, we derive the analytical relationship between the state variables of the source and load nodes of DHN, offering a physical fundament for the AGM. Second, we propose a physics-informed estimator for AGM that is robust to low-quality measurement, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness. Finally, we propose a physics-enhanced algorithm to solve the nonlinear estimator with non-closed constraints efficiently. Simulation results verify the effectiveness of the proposed method