Compressing a JPEG image twice will greatly decrease the values of some of its DCT coefficients. This effect can be easily detected by statistics methods. To defend this forensic method, we establish a model to evaluate the security and image quality influenced by the re-compression. Base on the model, an optimized adjustment of the DCT coefficients is achieved by Genetic Algorithm. Results show that the traces of double compression are removed while preserving image quality.

We describe an evaluation system consisting of an evaluation and interpretation model to totally assess and interpret an end-user's computing capability. It includes four evaluation factors and eighteen items, the complex indicators, an evaluation process, and method. We verified the model construct was verified by factor analysis and reliability analysis through a pilot test. We confirmed the application of the developed system by applying the model to evaluating end-users in a computing business environment and presenting the results. This system contributes to developing a practical system for evaluating an end-user's computing capability and hence for improving computing capability of end-users.

This paper discusses distributed checkpointing with logging for practical applications running with limited resources. We present a discrete time model evaluating the total expected overhead per event where the number of available checkpoints that each process can hold is finite. The rollback distance is also bound to some finite interval in many actual applications. Therefore, the recovery overhead for the checkpointing scheme is described by using a truncated geometric distribution as the rollback distance distribution. Although it is difficult to analytically derive the optimal checkpoint interval, which minimizes the total expected overhead, substituting other simple probabilistic distributions instead of the truncated geometric distribution enables us to do this explicitly. Numerical examples obtained through simulations are presented to show that we can achieve almost minimized total overhead by using the new models and analyses.

In this paper, we present a model for evaluating the effectiveness of (2, 1, m) convolutional-code-based packet-level FEC, under the condition of a limited buffer size in which the number of available packets is restricted for recovery. We analytically derive the post-reconstruction receiving rate, i.e., the probability that a lost packet is received or recovered before the buffer limit is reached. We show numerical examples of the analytical results and demonstrate that the buffer size at the same level as m gives sufficient recovery performance.