Cloud computing provides business new working paradigm with the benefit of cost reduce and resource sharing. Tasks from different users may be performed on the same machine. Therefore, one primary security concern is whether user data is secure in cloud. On the other hand, hacker may facilitate cloud computing to launch larger range of attack, such as a request of port scan in cloud with virtual machines executing such malicious action. In addition, hacker may perform a sequence of attacks in order to compromise his target system in cloud, for example, evading an easy-to-exploit machine in a cloud and then using the previous compromised to attack the target. Such attack plan may be stealthy or inside the computing environment, so intrusion detection system or firewall has difficulty to identify it. The proposed detection system analyzes logs from cloud to extract the intensions of the actions recorded in logs. Stealthy reconnaissance actions are often neglected by administrator for the insignificant number of violations. Hidden Markov model is adopted to model the sequence of attack performed by hacker and such stealthy events in a long time frame will become significant in the state-aware model. The preliminary results show that the proposed system can identify such attack plans in the real network.

We present a new detection model include monitoring network perimeter and hosts logs to counter the new method of attacking involve different hosts source during an attacking sequence. The new attacking sequence we called ¡§Scout and Intruder¡¨ involve two separate hosts. The scout will scan and evaluate the target area to find the possible victims and their vulnerability, and the intruder launch the precision strike with login activities looked as same as authorized users. By launching the scout and assassin attack, the attacker could access the system without being detected by the network and system intrusion detection system. In order to detect the Scout and intruder attack, we correlate the netflow connection records, the system logs and network data dump, by finding the states of the attack and the corresponding features we create the detection model using the Hidden Markov Chain. With the model we created, we could find the potential Scout and the Intruder attack in the initial state, which gives the network/system administrator more response time to stop the attack from the attackers.

A Mandarin keyword spotting system based on LPC, VQ, discrete-time HMM and Viterbi algorithm is proposed in the thesis. Joining with a dialogue system, this keyword spotting platform is further refined to a prototype of Taiwan Railway Natural Language Reservation System. In the reservation process, five questions: name and ID number, departure station, destination station, train type and number of tickets, and time schedule are asked by the computer-dialogue attendant. Following by the customer¡¦s speech confirmation, electronic tickets can be correctly issued and printed within 90 seconds in a laboratory environment.

Existing methods for image-based location estimation generally attempt to recognize every photo independently, and their resulting reliance on strong visual feature matches makes them most suited for distinctive landmark scenes. We observe that when touring a city, people tend to follow common travel patterns---for example, a stroll down Wall Street might be followed by a ferry ride, then a visit to the Statue of Liberty or Ellis Island museum. We propose an approach that learns these trends directly from online image data, and then leverages them within a Hidden Markov Model to robustly estimate locations for novel sequences of tourist photos. We further devise a set-to-set matching-based likelihood that treats each ``burst" of photos from the same camera as a single observation, thereby better accommodating images that may not contain particularly distinctive scenes. Our experiments with two large datasets of major tourist cities clearly demonstrate the approach's advantages over traditional methods that recognize each photo individually, as well as a naive HMM baseline that lacks the proposed burst-based observation model. / text

This paper investigates optimal portfolio strategies in a market with partial information on the drift. The drift is modelled as a function of a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from the observation of stock prices. Moreover, expert opinions in the form of signals at random discrete time points are included in the analysis. We derive the filtering equation for the return process and incorporate the filter into the state variables of the optimization problem. This problem is studied with dynamic programming methods. In particular, we propose a policy improvement method to obtain computable approximations of the optimal strategy. Numerical results are presented at the end. (author's abstract)

This thesis presents a method to investigate the extent to which articulatory based acoustic features can be exploited to reduce ambiguity in automatic speech recognition search. The method proposed is based on a lattice re-scoring paradigm implemented to integrate articulatory based features into automatic speech recognition systems. Time delay neural networks are trained as feature detectors to generate feature streams over which hidden Markov models (HMMs) are defined. These articulatory based HMMs are combined with HMMs defined over spectral energy based Mel frequency cepstrum coefficient (MFCC) acoustic features through a sequential lattice re-scoring procedure. The optimum phone strings are found by maximizing the log-linear combination of acoustic and language models likelihoods during recognition. The associated log-linear weights are estimated using a discriminative model combination approach. All the experiments are performed using the DARPA TIMIT speech database and the results are presented in terms of phone accuracies.

Thesis (Ph. D.)--University of Wyoming, 2005. / Title from PDF title page (viewed on March 10, 2008). Includes bibliographical references (p. 107-111).

Thesis (M.Eng.(Mechanical Engineering))--University of Pretoria, 2004. / Summaries in Afrikaans and English. Includes bibliographical references (leaves 80-82).

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2010. / Thesis Advisor(s): Kress, Moshe ; Szechtman, Roberto ; Second Reader: Atkinson, Michael. "June 2010." Description based on title screen as viewed on July 14, 2010. Author(s) subject terms: Lebanese Hizbullah; Lebanese Diaspora; Lebanon; Markov Decision Process; Dynamic Bayesian Network; Hidden Markov Models; Decision Analysis; Decision Theory; Decision Tree; State Tree; Influence Diagram; GeNIe; Stakeholder; State Space; Rational Actor; Action; Interest; Distribution; Forecast. Includes bibliographical references (p. 65). Also available in print.

Zugl.: Göttingen, Univ., Diss., 2009