An Efficient Revocation Scheme with Minimal Message Length for Stateless Receivers

2003 ◽  
pp. 377-386 ◽  
Author(s):  
Yong Ho Hwang ◽  
Chong Hee Kim ◽  
Pil Joong Lee
Keyword(s):  
Message Length

Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach

2020 ◽  
pp. 333-341
Author(s):  
François Kasséné Gomis ◽  
Thierry Bouwmans ◽  
Mamadou Samba Camara ◽  
Idy Diop
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Message Length

scholarly journals Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1601
Author(s):  
Zheng Fang ◽  
David L. Dowe ◽  
Shelton Peiris ◽  
Dedi Rosadi
Keyword(s):  
Time Series ◽  
Environmental Science ◽  
Short Term Memory ◽  
Arima Model ◽  
Theoretic Approach ◽  
Arma Models ◽  
Information Theoretic ◽  
Minimum Message Length ◽  
Message Length ◽  
Real World Datasets

Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for scientists and academic researchers. Hybrid models combining neural networks and traditional Autoregressive Moving Average (ARMA) models are being used to improve the accuracy of modeling and forecasting time series. Most of the existing time series models are selected by information-theoretic approaches, such as AIC, BIC, and HQ. This paper revisits a model selection technique based on Minimum Message Length (MML) and investigates its use in hybrid time series analysis. MML is a Bayesian information-theoretic approach and has been used in selecting the best ARMA model. We utilize the long short-term memory (LSTM) approach to construct a hybrid ARMA-LSTM model and show that MML performs better than AIC, BIC, and HQ in selecting the model—both in the traditional ARMA models (without LSTM) and with hybrid ARMA-LSTM models. These results held on simulated data and both real-world datasets that we considered. We also develop a simple MML ARIMA model.

scholarly journals Minimum message length clustering: an explication and some applications to vegetation data

2001 ◽  
Vol 2 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Mike B. Dale ◽  
L. Salmina ◽  
L. Mucina
Keyword(s):  
Minimum Message Length ◽  
Message Length

scholarly journals Gambling warning labels: Differing effects of message length and message format

2020 ◽  
Author(s):  
Philip Warren Stirling Newall ◽  
Lukasz Walasek ◽  
Elliot Andrew Ludvig
Keyword(s):  
Potential Risk ◽  
Court Case ◽  
Warning Labels ◽  
Electronic Gambling Machines ◽  
Message Length ◽  
Long Run ◽  
Electronic Gambling ◽  
Objective Understanding ◽  
Message Format ◽  
Better Than

“Return-to-player” warning labels are used to display the long-run cost of gambling on electronic gambling machines in several jurisdictions. For example, a return-to-player of 90% means that for every $100 bet on average $90 is paid out in prizes. Some previous research suggests that gamblers perceive a lower chance of winning and have a better objective understanding when return-to-player information is instead restated in the “house-edge” format, e.g., “This game keeps 10% of all money bet on average.” Here we test another potential risk communication improvement: making return-to-player messages longer, by clarifying that the information applies only in the statistical long-run. It was suggested that gamblers might understand this message better than the return-to-player at the conclusion of a court case brought against an Australian casino. In this study, Australian participants (N = 603) were presented with either a standard return-to-player message, a longer “return-to-players” message, or a house-edge message. The longer return-to-players message was understood correctly more frequently than the return-to-player message, but the house-edge message was understood best of all. Participants perceived the lowest chance of winning with the longer return-to-players message. The house-edge format appears easiest for gamblers to correctly understand, but longer warning labels might be the best at warning gamblers about the long-run costs of gambling on electronic gambling machines.

Interaction in Google Wave Sends Chat Rooms Out with the Tide

2014 ◽  
pp. 639-659
Author(s):  
Linda Jones
Keyword(s):  
Second Language ◽  
World Wide ◽  
Asynchronous Communication ◽  
Cross Cultural ◽  
Chat Room ◽  
Communication Research ◽  
Chat Rooms ◽  
Message Length ◽  
L2 Learning ◽  
Innovative Learning

This chapter focuses on Google Wave, a new, emerging world-wide technology by Google that supports both synchronous and asynchronous communication. Research on this technology took place during two sessions of an advanced second language (L2) technology course whereby synchronous conversations in Google Wave were compared to synchronous conversations in Blackboard chat rooms. Students experienced both forms of technology while discussing cross-cultural and pedagogical discussions relevant to L2 learning. Structural comparisons in terms of message length, message turns, numbers of words, and clarification revealed that students were more patient and wrote lengthier, more complex posts when conversing in Google Wave as compared to the chat room. Students’ impressions further confirmed their awareness of writing and reflecting more within Google Wave. These results suggest that Google Wave will support flexible, innovative learning and will provide researchers with multiple opportunities for expanding our understanding of students’ interactions in synchronous environments.

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