6293846
Cryptographic Key Generation from Biometric Data Using Lattice Mapping
Crypto-biometric systems are recently emerging as an effective process of key management to address the security weakness of conventional key release systems using pass-codes, tokens or pattern recognition based biometrics. This paper presents a lattice mapping based fuzzy commitment method for cryptographic key generation from biometric data. The proposed method not only outputs high entropy keys, but also conceals the original biometric data such that it is impossible to recover the biometric data even when the stored information in the system is open to an attacker. Simulated results have demonstrated that its authentication accuracy is comparable to the well-known k-nearest neighbour classification
12496830
A Fast Algorithm for Color Image Segmentation
Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering in the lower layer (higher resolution). Experiments have shown that the proposed algorithm is significantly faster than the original K-means algorithm while producing comparable segmentation results
7583260
Simultaneous MAP estimation of inhomogeneity and segmentation of brain tissues from MR images
Intrascan and interscan intensity inhomogeneities have been identified as a common source of making many advanced segmentation techniques fail to produce satisfactory results in separating brains tissues from multi-spectral magnetic resonance (MR) images. A common solution is to correct the inhomogeneity before applying the segmentation techniques. This paper presents a method that is able to achieve simultaneous semi-supervised MAP (maximum a-posterior probability) estimation of the inhomogeneity field and segmentation of brain tissues, where the inhomogeneity is parameterized. Our method can incorporate any available incomplete training data and their contribution can be controlled in a flexible manner and therefore the segmentation of the brain tissues can be optimised. Experiments on both simulated and real MR images have demonstrated that the proposed method estimated the inhomogeneity field accurately and improved the segmentation.
2713110
Orthographic case restoration using supervised learning without manual annotation
One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model excluding case-related features. This paper presents an alternative two-step approach whereby a preprocessing module (Step 1) is designed to restore case-sensitive form which is subsequently processed by the original system (Step 2). Step 1 is mainly implemented as a Hidden Markov Model trained on a large raw corpus of case sensitive documents. It is demonstrated that this approach (i) outperforms the feature exclusion approach for named entity tagging, (ii) leads to limited degradation for parsing, relationship extraction and case insensitive question answering, (iii) reduces system complexity, and (iv) has wide applicability: the restored text can be used in both statistical model and rule-based systems.
14087384
Rapid development of Hindi named entity recognition using conditional random fields and feature induction
This paper describes our application of conditional random fields with feature induction to a Hindi named entity recognition task. With only five days development time and little knowledge of this language, we automatically discover relevant features by providing a large array of lexical tests and using feature induction to automatically construct the features that most increase conditional likelihood. In an effort to reduce overfitting, we use a combination of a Gaussian prior and early stopping based on the results of 10-fold cross validation.
2727371
Constructivist learning systems: a new paradigm
The educational enterprise is in the midst of a philosophical shift from a behaviorist to a constructivist paradigm. If constructivism is to be taken seriously as a new educational paradigm in the information age, learning technology R&D must pay more attention to learners instead of teachers. A constructivist learning environment (CLE) is a technology-rich, open place where a learner can use a variety of tools and information resources in his pursuit of learning goals and problem-solving activities. Wherein the learner can draw upon information resources and tools to actively construct knowledge, generate a diverse array of ideas, develop multiple modes of representation, engage in social interaction, and solve authentic problems. Four major elements are bound together by their roles in the establishment of a CLE: public knowledge system; distributed instructional system; learning community; constructivist learning system.
18404050
AntWeb - the adaptive Web server based on the ants' behavior
We present the AntWeb system, developed under the research area of Web Intelligence (WI). Our approach to AntWeb application is inspired by the ant colonies foraging behavior, to adaptively mark the most significant links, by means of the shortest route to arrive to target pages. We consider the Web users as artificial ants, and use the ant theory as a metaphor to guide user's activity in the Web site. We describe the ant's theory in which AntWeb is based on. We also present the AntWeb system, its implementation and a case study with some experiments. The database in AntWeb stores a vast amount of information related to the users' visit to Web sites, which can be useful for further Web mining.
2550376
LDV Remote Voice Acquisition and Enhancement
Laser Doppler vibrometers (LDVs) have been widely used in industry inspection. One of the superior characteristics of an LDV is that it can detect and measure extremely tiny vibration of a target at a large distance, with sensitivity on the order of 1 mum/s. On the other hand, we have found that most objects nearby audio sources can be vibrated by the audio waves. These two aspects motivate our research in a new application of the LDVs, namely remote voice detection from surrounding vibrated objects. However, the detected speech signals may be corrupted by many noise sources, such as laser photon noises, target movements, and background acoustic noises (wind, engine sound, etc.). Therefore, speech enhancement algorithms based on Gaussian bandpass and Wiener filters are designed to effectively improve the intelligibility of the noisy voice signals detected by the LDV system. Experimental results show that remote voice detection via an LDV is very promising, when choosing appropriate targets close to human subjects and using the proposed enhancement techniques