An Estimator Algorithm For Learning Automata With Changing Number Of Actions

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

A Hierarchical System of Learning Automata

A learning automaton operating in a random environment updates its action probabilities on the basis of the reactions of the environment, so that asymptotically it chooses the optimal action. When the number of actions is large the automaton becomes slow because there are too many updatings to be made at each instant. A hierarchical system of such automata with assured c-optimality is suggested to overcome that problem.The learning algorithm for the hierarchical system turns out to be a simple modification of the absolutely expedient algorithm known in the literature. The parameters of the algorithm at each level in the hierarchy depend only on the parameters and the action probabilities of the previous level. It follows that to minimize the number of updatings per cycle each automaton in the hierarchy need have only two or three actions.

A method of fraud & intrusion detection for e-payment systems in mobile

The need for paying with mobile devices has urged the development of payment systems for mobile electronic commerce. In this paper we have considered two important abuses in electronic payments systems for detection. The fraud, which is an intentional deception accomplished to secure an unfair gain, and an intrusion which are any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. Most of the available fraud and intrusion detection systems for e-payments are specific to the systems where they have been incorporated. This paper proposes a generic model called as Activity-Event-Symptoms(AES) model for detecting fraud and intrusion attacks which appears during payment process in the mobile commerce environment. The AES model is designed to identify the symptoms of fraud and intrusions by observing various events/transactions occurs during mobile commerce activity. The symptoms identification is followed by computing the suspicion factors for event attributes, and the certainty factor for a fraud and intrusion is generated using these suspicion factors. We have tested the proposed system by conducting various case studies, on the in-house established mobile commerce environment over wired and wire-less networks test bed.

Transcriptional switching in Escherichia coli during stress and starvation by modulation of Sigma 70 activity

During active growth of Escherichia coli, majority of the transcriptional activity is carried out by the housekeeping sigma factor (Sigma 70), whose association with core RNAP is generally favoured because of its higher intracellular level and higher affinity to core RNAP. In order to facilitate transcription by alternative sigma factors during nutrient starvation, the bacterial cell uses multiple strategies by which the transcriptional ability of Sigma 70 is diminished in a reversible manner. The facilitators of shifting the balance in favour of alternative sigma factors happen to be as diverse as a small molecule (p)ppGpp (represents ppGpp or pppGpp), proteins (DksA, Rsd) and a species of RNA (6S RNA). Although 6S RNA and (p)ppGpp were known in literature for a long time, their role in transcriptional switching has been understood only in recent years. With themelucidation of function of DksA, a new dimension has been added to the phenomenon of stringent response. As the final outcome of actions of (p)ppGpp, DksA, 6S RNA and Rsd is similar, there is a need to analyse hese mechanisms in a collective manner. We review the recent trends in understanding the regulation of Sigma 70 by (p)ppGpp, DksA, Rsd and 6S RNA and present a case for evolving a unified model of RNAP redistribution during starvation by modulation of Sigma 70 activity in E. coli.

Solving MDPs using two-timescale simulated annealing with multiplicative weights

We develop extensions of the Simulated Annealing with Multiplicative Weights (SAMW) algorithm that proposed a method of solution of Finite-Horizon Markov Decision Processes (FH-MDPs). The extensions developed are in three directions: a) Use of the dynamic programming principle in the policy update step of SAMW b) A two-timescale actor-critic algorithm that uses simulated transitions alone, and c) Extending the algorithm to the infinite-horizon discounted-reward scenario. In particular, a) reduces the storage required from exponential to linear in the number of actions per stage-state pair. On the faster timescale, a 'critic' recursion performs policy evaluation while on the slower timescale an 'actor' recursion performs policy improvement using SAMW. We give a proof outlining convergence w.p. 1 and show experimental results on two settings: semiconductor fabrication and flow control in communication networks.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Experimental and Analytical Investigations of the actions taking place in a Synchronous machine during the starting period when the starting Is effected by means of alternating current

The literature on the subject of the present investigation is somewhat meagre. A rotary converter or synchronous motor no! provided with any special starting devices forms, when started from the alternating current side, a type of induction motor whoso Htator is provided with a polyphase winding, and whoso rotor has a single-phase (or single magnetic axis) winding.

Protumorigenic actions of S100A2 involve regulation of PI3/Akt signaling and functional interaction with Smad3

S100 family of calcium-binding proteins is commonly upregulated in a variety of tumor types and is often associated with tumor progression. Among several S100 members, altered expression of S100A2 is a potential diagnostic and prognostic marker in cancer. Several reports suggest a role for S100A2 in metastasis. Earlier, our studies established regulation of S100A2 by transforming growth factor- (TGF-) and its involvement in TGF--mediated cancer cell invasion and migration. However, the molecular mechanisms of S100A2 protumorigenic actions remain unexplored. In the present study, we demonstrate that overexpression of S100A2 in A549 lung cancer cells induced epithelialmesenchymal transition (EMT) followed by increased invasion, loose colony morphology in soft agar and enhanced Akt phosphorylation (Ser-473). Furthermore, overexpression of S100A2 led to increased tumor growth in immunocompromised mice. In agreement, immunohistochemical examination of resected xenograft tumors established inverse correlation between S100A2 and E-cadherin expression together with activated Akt signaling. Interestingly, our study demonstrates a strong dependence of S100A2 and Smad3 in TGF--induced Hep3B cell EMT and invasion. Most importantly, we demonstrate that these effects of S100A2 are manifested through functional interaction with Smad3, which is enhanced in the presence of high calcium and TGF-. S100A2 stabilizes Smad3 and binds to its C-terminal MH2 domain. Additionally, loss of S100A2 attenuates the transcription of TGF-/Smad3 target genes involved in tumor promotion, such as PA1-1 and vimentin. Collectively, our findings present the first mechanistic details of S100A2 protumorigenic actions and its involvement in TGF--mediated cancer cell invasion and EMT.

A Cause and Effect Analysis of Motion Trajectories for Modeling Actions

An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.