Video Highlight Prediction Using Audience Chat Reactions

Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.Comment: EMNLP 201

Long-Term Student Experiences in a Hybrid, Open-Ended and Problem Based Adventure Learning Program

In this paper we investigate the experiences of elementary school children over a two-year period during which they engaged with a hybrid Adventure Learning program. In addition to delineating Adventure Learning experiences, we report on educational technology implementations in ecologically valid and complex environments, while drawing inferences on the design of sustainable and successful innovations. Our research indicates that the Adventure Learning experience over the two-year period was dynamic, participatory, engaging, collaborative, and social. Students eagerly became part of the experience both inside and outside of the classroom, and it quickly became apparent that they saw themselves as valued members of the unfolding storyline that mediated their learning. Our recommendations for future research and practice include a call to evaluate "authenticity," focus on the learner experience and narrative, and consider the interplay between pedagogy, technology, and design.Center for Learning and Memor

Creating UGC Areas of Official Destination Websites: Is there a Recipe for Success? An Insight through Netnographic Research

An analysis of the existing literature has demonstrated the importance of word of mouth as a source of information for potential tourists and service consumers. The growth of the Internet and interactive websites has lead to the creation of online communities that serve as points of reference for word of mouth and in particular for independent, personal and experiential information. Recent articles have noted the growing interest of tourism companies and destinations to include UGC areas in their official websites to provide their users with these types of information and interactivity among each others. However, so far little research has been performed on the success factors of online communities. This paper wants to create a platform for further research on the topic. If destinations want to boost visits to their websites through UGS areas and create a “buzz” through positive word of mouth, it is necessary to know the correct ingredients for success. Some of these ingredients have been discovered through a netnographic analysis of an Italian virtual mountaineering community. The analysis has shown that some of the most important issues when creating online communities are the reliability of information, the ease of finding information and creating threads and posts, the constant appearance of interesting threads and discussions, the respect for other members, the passion of all of the active users for the same topics and a certain homogeneity within the users.virtual communities, netnography, electronic word of mouth, forums, information search

Global trends in large-value payments

Globalization and technological innovation are two major forces affecting the financial system and its infrastructure. Perhaps nowhere are these trends more apparent than in the internationalization and automation of payments. While the effects of globalization and technological innovation are most obvious on retail payments, the influence is equally impressive on wholesale, or interbank, payments. Given the importance of payments and settlement systems to the smooth operation and resiliency of the financial system, it is important to understand the potential consequences of these developments. This article presents ten major long-range trends in the settlement of large-value payments worldwide. The trends are driven by technological innovation, structural changes in banking, and the evolution of central bank policies. The authors observe that banks, to balance risks and costs more effectively, are increasingly making large-value payments in real-time systems with advanced liquidity-management and liquidity-saving mechanisms. Moreover, banks are settling a larger number of foreign currencies directly in their home country by using offshore systems and settling a greater number of foreign exchange transactions in Continuous Linked Settlement Bank or through payment-versus-payment mechanisms in other systems. The study also shows that the service level of systems is improving, through enhancements such as longer operating hours and standardized risk management practices that adhere to common standards, while transaction fees are decreasing. Payments settled in large-value payments systems are more numerous, but on average of smaller value. Furthermore, the overall nominal total value of large-value payments is increasing, although the real value is declining.Payment systems ; Electronic funds transfers ; Banks and banking - Automation ; Interbank market ; Globalization

The OU goes digital

Describes how the Open University is embedding electronic library resources and services into e-learning

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201