Recapping: effective pedagogy to ensure inclusivity and optimise learning and teaching experiences

Recaps have long been used in the media industry, where they have been found to be effective in supporting viewers’ understanding and recall of information. More recently, recaps have been explored in educational settings. This study examines whether recapping can support inclusivity and help to optimise learning and teaching experiences in the context of a business school in a widening participation university. We applied a mixed-method approach to collect data from both students and instructors. We used questionnaires to capture quantitatively their perceptions about recapping and semi-structured interviews to explore their opinions in more depth. Our findings indicate that recapping is perceived as an effective pedagogical approach that can improve learning experience of students, teaching experience of teachers and ensure a more inclusive environment. The research makes both theoretical and methodological contributions to the literature

On the I/O Costs of Some Repair Schemes for Full-Length Reed-Solomon Codes

Network transfer and disk read are the most time consuming operations in the repair process for node failures in erasure-code-based distributed storage systems. Recent developments on Reed-Solomon codes, the most widely used erasure codes in practical storage systems, have shown that efficient repair schemes specifically tailored to these codes can significantly reduce the network bandwidth spent to recover single failures. However, the I/O cost, that is, the number of disk reads performed in these repair schemes remains largely unknown. We take the first step to address this gap in the literature by investigating the I/O costs of some existing repair schemes for full-length Reed-Solomon codes.Comment: Accepted by the ISIT'1

RFおよびマイクロ波応用のためのメタマテリアル伝送線路に関する研究

【学位授与の要件】中央大学学位規則第4条第1項【論文審査委員主査】白井　宏　（中央大学理工学部教授）【論文審査委員副査】趙　晋輝（中央大学理工学部教授）、小林　一哉（中央大学理工学部教授）、宇野　亨（東京農工大学工学部教授）博士（工学）中央大

Recapping effective pedagogy to ensure inclusivity and optimise learning and teaching experiences

Recaps have long been used in the media industry, where they have been found to be effective in supporting viewers’ understanding and recall of information. More recently, recaps have been explored in educational settings. This study examines whether recapping can support inclusivity and help to optimise learning and teaching experiences in the context of a business school in a widening participation university. We applied a mixed-method approach to collect data from both students and instructors. We used questionnaires to capture quantitatively their perceptions about recapping and semi-structured interviews to explore their opinions in more depth. Our findings indicate that recapping is perceived as an effective pedagogical approach that can improve learning experience of students, teaching experience of teachers and ensure a more inclusive environment. The research makes both theoretical and methodological contributions to the literature

Predicting Performances of Mutual Funds using Deep Learning and Ensemble Techniques

Predicting fund performance is beneficial to both investors and fund managers, and yet is a challenging task. In this paper, we have tested whether deep learning models can predict fund performance more accurately than traditional statistical techniques. Fund performance is typically evaluated by the Sharpe ratio, which represents the risk-adjusted performance to ensure meaningful comparability across funds. We calculated the annualised Sharpe ratios based on the monthly returns time series data for more than 600 open-end mutual funds investing in listed large-cap equities in the United States. We find that long short-term memory (LSTM) and gated recurrent units (GRUs) deep learning methods, both trained with modern Bayesian optimization, provide higher accuracy in forecasting funds' Sharpe ratios than traditional statistical ones. An ensemble method, which combines forecasts from LSTM and GRUs, achieves the best performance of all models. There is evidence to say that deep learning and ensembling offer promising solutions in addressing the challenge of fund performance forecasting.Comment: 16 pages, 4 figures, 4 table

On the Security of Rate-limited Privacy Pass

The privacy pass protocol allows users to redeem anonymously issued cryptographic tokens instead of solving annoying CAPTCHAs. The issuing authority verifies the credibility of the user, who can later use the pass while browsing the web using an anonymous or virtual private network. Hendrickson et al. proposed an IETF draft (privacypass-rate-limit-tokens-00) for a rate-limiting version of the privacy pass protocol, also called rate-limited Privacy Pass (RlP). Introducing a new actor called a mediator makes both versions inherently different. The mediator applies access policies to rate-limit users’ access to the service while, at the same time, should be oblivious to the website/origin the user is trying to access. In this paper, we formally define the rate-limited Privacy Pass protocol and propose a game-based security model to capture the informal security notions introduced by Hendrickson et al.. We show a construction from simple building blocks that fulfills our security definitions and even allows for a post-quantum secure instantiation. Interestingly, the instantiation proposed in the IETF draft is a specific case of our construction. Thus, we can reuse the security arguments for the generic construction and show that the version used in practice is secure