Text mining with exploitation of user\u27s background knowledge : discovering novel association rules from text

The goal of text mining is to find interesting and non-trivial patterns or knowledge from unstructured documents. Both objective and subjective measures have been proposed in the literature to evaluate the interestingness of discovered patterns. However, objective measures alone are insufficient because such measures do not consider knowledge and interests of the users. Subjective measures require explicit input of user expectations which is difficult or even impossible to obtain in text mining environments. This study proposes a user-oriented text-mining framework and applies it to the problem of discovering novel association rules from documents. The developed system, uMining, consists of two major components: a background knowledge developer and a novel association rules miner. The background knowledge developer learns a user\u27s background knowledge by extracting keywords from documents already known to the user (background documents) and developing a concept hierarchy to organize popular keywords. The novel association rule miner discovers association rules among noun phrases extracted from relevant documents (target documents) and compares the rules with the background knowledge to predict the rule novelty to the particular user (useroriented novelty). The user-oriented novelty measure is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. It consists of two components: occurrence distance and connection distance. The former considers the co-occurrences of two keywords in the background documents: the more the shorter the distance. The latter considers the common connections of with others in the concept hierarchy. It is defined as the length of the connecting the two keywords in the concept hierarchy: the longer the path, distance. The user-oriented novelty measure is evaluated from two perspectives: novelty prediction accuracy and usefulness indication power. The results show that the useroriented novelty measure outperforms the WordNet novelty measure and the compared objective measures in term of predicting novel rules and identifying useful rules

Evaluating system utility and conceptual fit using CASSM

There is a wealth of user-centred evaluation methods (UEMs) to support the analyst in assessing interactive systems. Many of these support detailed aspects of use – for example: Is the feedback helpful? Are labels appropriate? Is the task structure optimal? Few UEMs encourage the analyst to step back and consider how well a system supports users’ conceptual understandings and system utility. In this paper, we present CASSM, a method which focuses on the quality of ‘fit’ between users and an interactive system. We describe the methodology of conducting a CASSM analysis and illustrate the approach with three contrasting worked examples (a robotic arm, a digital library system and a drawing tool) that demonstrate different depths of analysis. We show how CASSM can help identify re-design possibilities to improve system utility. CASSM complements established evaluation methods by focusing on conceptual structures rather than procedures. Prototype tool support for completing a CASSM analysis is provided by Cassata, an open source development

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

DETERMINANTS OF USERS’ WILLINGNESS TO USE MOBILE PAYMENT: AN EMPIRICAL STUDY IN TONGREN UNIVERISITY, CHINA

The purpose of this paper is to establish and assess the determinants of users’ willingness to use mobile payment: an empirical study in Tongren University, China. After conducting a rigorous literature review with theoretical underpinning, this research has come up with the proper methodology to move forward. According to the comprehensive guideline, the total number of Tongren College and Tongren Vocational College is more than 38,000, so the minimum sample is finally determined to be 380. Besides, after conducting Exploratory Factor Analysis (EFA), the measuring modelling was done by taking into account all the variables concurrently in order to verify the appropriateness of the overall model. All the hypotheses of this study have been tested through the application of SEM. For the overall model as a whole, the statistical result indicates a good fit. From the model, it can be seen that all the variables uphold a positive value. Findings revealed that perceived performance risk perceived financial risk and perceived privacy risk have substantial positive impacts on acceptance intention of mobile payment. Therefore, since a myriad of factors decides the attitude toward mobile payment use, further studies can also be developed by adding more constructs in the theoretical model in this paper.  Article visualizations

Attention and Task Engagement During Automated Driving

Many young drivers suffer fatal crashes each year in the United States at a rate approximately three times greater than more experienced drivers. Automated driving systems may serve to mitigate young drivers high crash rates but remain underexplored in research. This dissertation project examined the effects of levels of automation and interestingness of auditory clips on latent hazard anticipation in young drivers during simulated driving. Participants drove a vehicle at varying levels of vehicle automation (SAE Level 0, 2, or 3) in simulated scenarios, each containing a latent hazard event during which a boring, neutral, or interesting auditory clip was played. After completing all scenarios, participants completed an auditory stimuli recognition test and a questionnaire measuring the drivers’ calibration of their LHA performance. Results demonstrated that those in the L3 condition anticipated significantly fewer hazards than those in the L0 condition, corroborating previous research (Samuels et al., 2020). However, those in the L3 condition were also significantly poorer at anticipating latent hazards than those in the L2 condition, suggesting the importance of instruction on a drivers’ attentional allocation policy. A tradeoff was found between latent hazard anticipation and auditory recognition scores indicating the allocation of limited attentional resources as predicted by the Yamani and Horrey (2018) model. Interestingness of auditory stimuli had little to no effect on latent hazard anticipation. In general, automation may improve the multitasking ability of a young driver piloting L2 automation, but this benefit is lost for drivers of L3 automation. Instead, young drivers piloting L3 automation may anticipate latent hazards at rates as low as those observed in newly licensed drivers, and may be completely unaware of their failure to anticipate such hazards. The current research illustrates the criticality of user guidance when handling automated driving systems and serves as one step towards understanding the complex relationship between human drivers and automated systems

Breaking the News: First Impressions Matter on Online News

A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.Comment: The paper appears in ICWSM 201