SchedMail: Sender-Assisted Message Delivery Scheduling to Reduce Time-Fragmentation

Although early efforts aimed at dealing with large amounts of emails focused on filtering out spam, there is growing interest in prioritizing non-spam emails, with the objective of reducing information overload and time fragmentation experienced by recipients. However, most existing approaches place the burden of classifying emails exclusively on the recipients' side, either directly or through recipients' email service mechanisms. This disregards the fact that senders typically know more about the nature of the contents of outgoing messages before the messages are read by recipients. This thesis presents mechanisms collectively called SchedMail which can be added to popular email clients, to shift a part of the user efforts and computational resources required for email prioritization to the senders' side. Particularly, senders declare the urgency of their messages, and recipients specify policies about when different types of messages should be delivered. Recipients also judge the accuracy of sender-side urgency, which becomes the basis for learned reputations of senders; these reputations are then used to interpret urgency declarations from the recipients' perspectives. In order to experimentally evaluate the proposed mechanisms, a proof-of-concept prototype was implemented based on a popular open source email client K-9 Mail. By comparing the amount of email interruptions experienced by recipients, with and without SchedMail, the thesis concludes that SchedMail can effectively reduce recipients' time fragmentation, without placing demands on email protocols or adding significant computational overhead

Combining content and social features in a deep learning approach to Vietnamese email prioritization

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386

Making 'The Daily Me': Technology, economics and habit in the mainstream assimilation of personalized news

The mechanisms of personalization deployed by news websites are resulting in an increasing number of editorial decisions being taken by computer algorithms — many of which are under the control of external companies — and by end users. Despite its prevalence, personalization has yet to be addressed fully by the journalism studies literature. This study defines personalization as a distinct form of interactivity and classifies its explicit and implicit forms. Using this taxonomy, it surveys the use of personalization at 11 national news websites in the UK and USA. Research interviews bring a qualitative dimension to the analysis, acknowledging the influence that institutional contexts and journalists’ attitudes have on the adoption of technology. The study shows how: personalization informs debates on news consumption, content diversity, and the economic context for journalism; and how it challenges the continuing relevance of established theories of journalistic gate-keeping

Understanding the significance of reward and threat triggers : practitioners\u27 perspectives

This study examined organization development (OD) practitioners\u27 perspectives on the relative importance of the five domains of a neuroscience-based motivation framework that categorizes common issues that trigger toward or away responses in the brain. The SCARF Model\u27s five domains include Status, Certainty, Autonomy, Relatedness, and Fairness (Rock, 2008). This study sought to understand if practitioners\u27 perspectives are in line with existing research and ultimately to identify the most effective practices that provide the highest level of benefit relative to reducing threat responses and increasing rewards. The first phase of this study employed an online survey using pairwise comparison, or forced choice, of each domain on a weighted scale. This methodology required explicit choices be made among each of the SCARF domains in order to answer a single question: Active management of which reward/threat trigger poses the greater benefit to a change effort, and by how much? The survey methodology resulted in a prioritization by 48 OD practitioner respondents that depicts the magnitude of each domain\u27s benefit and ultimately implies that active management of the highest ranking domain (Fairness) offers significantly greater benefit than the other four. The second phase of this study included interviews of eight OD practitioners during which the survey results were presented. This phase of the study discovered a dominant theme of communication as a means of threat trigger mitigation and reward trigger maximization for all of the SCARF domains

Project Testbed: Argument Mapping and Deliberation Analytics

One key goal of the Catalyst project was to design metrics that could capture and represent aspects of the conversation’s structural quality, to assist harvesters and moderators. Many such metrics, alerts and visualizations were developed in the course of the project, but initial user testing has shown that users find it difficult to interpret abstract signals. Following that, we have both introduced new analytics that we felt could be more directly useful, and improved the representation of existing ones. We evaluated their usefulness in a smaller conversation and in experimental settings

Social selling strategies in the IT industry

CEMSThe present work project provides research and conclusions over three main topics related to social selling strategies in the IT industry being those 1) internal and external social selling best practices; 2) the best countries and industries to invest in social selling activities; and 3) the measurement of the ROI for social selling. A mixed approach is taken being both primary research (in-depth interviews and questionnaires) and secondary research used. Some evidence of the benefit of investing in social selling as well as recommendations regarding countries, industries and ROI are provided