8929565
Integrating the QMR Method with First Principles Material Science Application Code
First principles methods are used to aid the material designers and metallurgists in the investigation and design of new materials. These methods suffer from a divergent O(N{sup 3}) scaling which restricts the problem sizes that can be addressed. It will be demonstrated that the O(N{sup 3}) scaling problem can be overcome by employing real-space multiple scattering techniques to calculate the scattering properties, combined with the QMR algorithm. By incorporating the QMR method into our real-space multiple scattering code, we observed O(N) scaling for the large problem sizes of interest. This advancement will provide researchers with the necessary tools to treat large systems.
45169888
Multi-teraflops spin dynamics studies of the magnetic structure of FeMn/Co interfaces
6133036
Serial hook-ups: a comparative usability study of secure device pairing methods
Secure Device Pairing is the bootstrapping of secure communication between two previously unassociated devices over a wireless channel. The human-imperceptible nature of wireless communication, lack of any prior security context, and absence of a common trust infrastructure open the door for Man-in-the-Middle (aka Evil Twin) attacks. A number of methods have been proposed to mitigate these attacks, each requiring user assistance in authenticating information exchanged over the wireless channel via some human-perceptible auxiliary channels, e.g., visual, acoustic or tactile.
 In this paper, we present results of the first comprehensive and comparative study of eleven notable secure device pairing methods. Usability measures include: task performance times, ratings on System Usability Scale (SUS), task completion rates, and perceived security. Study subjects were controlled for age, gender and prior experience with device pairing. We present overall results and identify problematic methods for certain classes of users as well as methods best-suited for various device configurations.
40027798
Multiple Ranker Method in Document Retrieval
In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new queries. But in practice, user queries vary in large diversity, which makes the single ranker learned from a close set of data not representative. The single ranker cannot be guaranteed with the best ranking result for every single query, and this becomes the bottleneck of traditional learning to rank approaches. To address this problem, we propose a multi-ranker approach. We train multiple diverse rankers which can cover diverse categories of queries, instead of an isolated one, and take an ensemble of these rankers for final prediction. We verify the proposed multipleranker approach over real-world datasets. The experimental results indicate that the proposed approach can outperform existing ‘learning to rank’ methods significantly.
117639554
Multidimensional two-scale dilation equations
Abstract . This work involves the study of multidimensional scaling functions. These functions satisfy a two-scale dilation equation, just as the 1-D scaling functions which are used to construct 1-D wavelets. Two frameworks are presented for studying these dilation equations. Fourier methods are used to analyze existence, uniqueness and regularity of solutions. Expansions with infinite matrix products are used to derive an iterated function system (IFS) algorithm for generating the scaling function. Application to subdivision schemes for surface generation is presented, as a genuine multidimensional example where scaling functions arise and the IFS algorithm can be applied.
27638687
A latent class modeling approach to detect network intrusion
This study presents a latent class modeling approach to examine network traffic data when labeled abnormal events are absent in training data, or such events are insufficient to fit a conventional regression model. Using six anomaly-associated risk factors identified from previous studies, the latent class model based on an unlabeled sample yielded acceptable classification results compared with a logistic regression model based on a labeled sample (correctly classified: 0.95 vs. 0.98, sensitivity: 0.99 vs. 0.99, and specificity: 0.77 vs. 0.97). The study demonstrates a great potency for using the latent class modeling technique to analyze network traffic data.
6926567
Semi-Latent Dirichlet Allocation: A Hierarchical Model for Human Action Recognition
We propose a new method for human action recognition from video sequences using latent topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous latent topic models for recognition problems in computer vision is that, our model is trained in a "semi-supervised" way. Our model has several advantages over other similar models. First of all, the training is much easier due to the decoupling of the model parameters. Secondly, it naturally solves the problem of how to choose the appropriate number of latent topics. Thirdly, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification and irregularity detection results, and show improvement over previous methods.