15388195
Dynamically Weighted Hidden Markov Model for Spam Deobfuscation
Spam deobfuscation is a processing to detect obfuscated words appeared in spam emails and to convert them back to the original words for correct recognition. Lexicon tree hidden Markov model (LTHMM) was recently shown to be useful in spam deobfuscation. However, LT-HMM suffers from a huge number of states, which is not desirable for practical applications. In this paper we present a complexity-reduced HMM, referred to as dynamically weighted HMM (DW-HMM) where the states involving the same emission probability are grouped into super-states, while preserving state transition probabilities of the original HMM. DWHMM dramatically reduces the number of states and its state transition probabilities are determined in the decoding phase. We illustrate how we convert a LT-HMM to its associated DW-HMM. We confirm the useful behavior of DW-HMM in the task of spam deobfuscation, showing that it significantly reduces the number of states while maintaining the high accuracy.
5224987
Differential learning algorithms for decorrelation and independent component analysis
Decorrelation and its higher-order generalization, independent component analysis (ICA), are fundamental and important tasks in unsupervised learning, that were studied mainly in the domain of Hebbian learning. In this paper we present a variation of the natural gradient ICA, differential ICA, where the learning relies on the concurrent change of output variables. We interpret the differential learning as the maximum likelihood estimation of parameters with latent variables represented by the random walk model. In such a framework, we derive the differential ICA algorithm and, in addition, we also present the differential decorrelation algorithm that is treated as a special instance of the differential ICA. Algorithm derivation and local stability analysis are given with some numerical experimental results.
37385528
A framework for handling dependencies among web services transactions
This paper proposes an effective Web services (WS) transaction management framework to automatically manage inconsistencies occurred by relaxing isolation of WS transactions.
858957
Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making
Problem formulation is often an important first step for solving a problem effectively. In sequential decision problems, Markov decision process (MDP) ([2]; [22]) is a model formulation that has been commonly used, due to its generality, flexibility, and applicability to a wide range of problems. Despite these advantages, there are three necessary conditions that must be satisfied before the MDP model can be applied; that is, 
 
1. 
 
The environment model is given in advance (a completely-known environment). 
 
 
 
 
2. 
 
The environment states are completely observable (fully-observable states, implying a Markovian environment). 
 
 
 
 
3. 
 
The environment parameters do not change over time (a stationary environment).
19996434
A multi-material virtual prototyping system
Abstract This paper proposes a multi-material virtual prototyping system for digital fabrication of heterogeneous prototypes. It consists mainly of a topological hierarchy-sorting algorithm for processing slice contours, and a virtual simulation system for visualisation and optimisation of multi-material layered manufacturing (MMLM) processes. The topological hierarchy-sorting algorithm processes the hierarchy relationship of complex slice contours. It builds a parent-and-child list that defines the containment relationship of the slice contours, and subsequently arranges the contours in an appropriate sequence, which facilitates toolpath planning for MMLM by avoiding redundant tool movements. The virtual simulation system simulates MMLM processes and provides stereoscopic visualisation of the resulting multi-material prototypes for quality analysis and optimisation of the processes.
18315245
An Environment Model for Nonstationary Reinforcement Learning
Reinforcement learning in nonstationary environments is generally regarded as an important and yet difficult problem. This paper partially addresses the problem by formalizing a subclass of nonstationary environments. The environment model, called hidden-mode Markov decision process (HM-MDP), assumes that environmental changes are always confined to a small number of hidden modes. A mode basically indexes a Markov decision process (MDP) and evolves with time according to a Markov chain. While HM-MDP is a special case of partially observable Markov decision processes (POMDP), modeling an HM-MDP environment via the more general POMDP model unnecessarily increases the problem complexity. A variant of the Baum-Welch algorithm is developed for model learning requiring less data and time.
41968338
Group-Based Scheduling Scheme for Result Checking in Global Computing Systems
This paper considers the problem of correctness to fault-tolerance in global computing systems. Global computing system has been shown to be exposed to intentional attacks, where authentication is not relevant, network security techniques are insufficient. To guarantee correctness for computation, fault-tolerance schemes have been used to majority voting and spot-checking but these schemes intend to high computation delay because are not applied scheduling scheme for result checking. In this paper, we propose a new technique called GBSS(Group-Based Scheduling Scheme) which guarantee correctness and reduce computation delay by using fault-tolerant scheduling scheme to the result checking. Additionally, simulation results show increased usable rate of the CPU and increased performance of the system.