Design and Implementation of S-MARKS: A Secure Middleware for Pervasive Computing Applications

As portable devices have become a part of our everyday life, more people are unknowingly participating in a pervasive computing environment. People engage with not a single device for a specific purpose but many devices interacting with each other in the course of ordinary activity. With such prevalence of pervasive technology, the interaction between portable devices needs to be continuous and imperceptible to device users. Pervasive computing requires a small, scalable and robust network which relies heavily on the middleware to resolve communication and security issues. In this paper, we present the design and implementation of S-MARKS which incorporates device validation, resource discovery and a privacy module

Entrepreneurship in American Higher Education

Presents recommendations by the Kauffman Panel on Entrepreneurship Curriculum in Higher Education on making entrepreneurship a key element in the curriculum, co-curriculum activities, and university management. Includes profiles of innovative programs

Of Spiky SVDs and Music Recommendation

The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval or embedding musical items for downstream tasks. This paper investigates a curious effect that we show naturally occurring on many recommendation datasets: spiking formations in the embedding space. We first propose a metric to quantify this spiking organization's strength, then mathematically prove its origin tied to underlying communities of items of varying internal popularity. With this new-found theoretical understanding, we finally open the topic with an industrial use case of estimating how music embeddings' top-k similar items will change over time under the addition of data.Comment: Accepted for RecSys 2023 (Singapour, 18-22 September

Combing customer profiles for members' return visit rate predictions

[[abstract]]The major profit of companies in Taiwan is generated by online advertising and e-commerce. Effective advertising requires predicting how a user responds to an advertisement and then targeting (presenting the advertisement) to reflect users’ favor. As customers’ preferences may change over time, we take the different types of past behavior patterns of the registered members to capture concept drifts. Then, we combine the click preference index (CPI) and the preference drifts to propose a Behavioral Preference (BP) model, and to predict the members’ return visit rates in the specific category of the portal site. The marketers of the portal site can target the registered members with high return visit rates and design corresponding marketing strategies. The experimental results with a real dataset show that our model can effectively predict the registered members’ return visit rates.[[notice]]補正完畢[[journaltype]]國外[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]JP