Kin Choong Yow

Feature-based human face detection

Human face detection has always been an important problem for face, expression and gesture recognition. Though numerous attempts have been made to detect and localize faces, these approaches have made assumptions that restrict their extension to more general cases. We identify that the key factor in a generic and robust system is that of using a large amount of image evidence, related and reinforced by model knowledge through a probabilistic framework. In this paper, we propose a feature-based algorithm for detecting faces that is sufficiently generic and is also easily extensible to cope with more demanding variations of the imaging conditions. The algorithm detects feature points from the image using spatial filters and groups them into face candidates using geometric and gray level constraints. A probabilistic framework is then used to reinforce probabilities and to evaluate the likelihood of the candidate as a face. We provide results to support the validity of the approach and demonstrate its capability to detect faces under different scale, orientation and viewpoint.

A probabilistic framework for perceptual grouping of features for human face detection

Present approaches to human face detection have made several assumptions that restrict their ability to be extended to general imaging conditions. We identify that the key factor in a generic and robust system is that of exploiting a large amount of evidence, related and reinforced by model knowledge through a probabilistic framework. In this paper, we propose a face detection framework that groups image features into meaningful entities-using perceptual organization, assigns probabilities to each of them, and reinforce there probabilities using Bayesian reasoning techniques. True hypotheses of faces will be reinforced to a high probability. The detection of faces under scale, orientation and viewpoint variations will be examined in a subsequent paper.

Passive cached clustering routing for MANETs

This paper presents a passive caching mechanism used on clustering routing protocols for mobile ad hoc networks (MANETs). It utilizes the backbone U. Kozat et al., (2001) of the network to gather and reuse the discovered routing information more efficiently and effectively. In this paper we propose a caching mechanism based on existing clustering algorithms, we build databases of routing information on nodes along the network backbone organized by clustering algorithms. Thus, instead of generating routing packets to discover routes, ondemand routes can be found by accessing databases of routing information distributed on the backbone. Therefore, routing overhead caused by route discovery is reduced.

Mitigating DDoS attacks with transparent and intelligent fast-flux swarm network

Distributed denial of service attacks are a great threat to service availability in cloud computing. In recent years, DDoS attacks have increased tremendously in bandwidth and technique. In this article, we propose a novel approach to mitigate DDoS attacks using an intelligent fast-flux swarm network. An intelligent swarm network is required to ensure autonomous coordination and allocation of swarm nodes to perform its relaying operations. We adapted the Intelligent Water Drop algorithm for distributed and parallel optimization. The fast-flux technique was used to maintain connectivity between swarm nodes, clients, and servers. Fast-flux service networks also allow us to build a transparent service, which allows minimal modifications of existing cloud services (e.g. HTTP, SMTP). A software simulation consisting of 400,000 client nodes and 10,000 swarm nodes has shown that we can maintain 99.96 percent packet delivery ratio when the network is under attack from a similarly sized DDoS network of 10,000 dedicated malicious nodes.

Efficient Multi-Keyword Ranked Query on Encrypted Data in the Cloud

Cloud computing is becoming increasingly prevalent in recent years. It introduces an efficient way to achieve management flexibility and economic savings for distributed applications. To take advantage of computing and storage resources offered by cloud service providers, data owners must outsource their data onto public cloud servers which are not within their trusted domains. Therefore, the data security and privacy become a big concern. To prevent information disclosure, sensitive data has to be encrypted before uploading onto the cloud servers. This makes plain text keyword queries impossible. As the total amount of data stored in public clouds accumulates exponentially, it is very challenging to support efficient keyword based queries and rank the matching results on encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. The multi-keyword query problem was being considered only recently. MRSE [1] is one of the first research works to define and address the problem of effective yet secure ranked multi-keyword search over encrypted cloud data. However, the keyword dictionary used in MRSE is static and must be rebuilt when the number of keywords in the dictionary increases. It also has severe out-of-order problems in the matching results and does not take the keyword access frequencies into account, which greatly affects its usability. In this paper, we propose a novel approach, called MKQE, to address these issues. Only minor changes in the dictionary structure have to be done when extra keywords are introduced. We also introduce new trapdoor generation and scoring algorithms to make in-order query results. Furthermore, the keyword access frequency is considered so as to select an adequate matching file set. We conduct extensive simulations and the results prove that our approach performs much better than previous solutions.

Wireless Indoor Positioning System with Enhanced Nearest Neighbors in Signal Space Algorithm

With the rapid development and wide deployment of wireless local area networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common nearest neighbor in signal space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some guidelines on optimizing the parameters of our proposed enhanced algorithm.

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

Abstract The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system’s ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter’s price, without a need for predicting target prices.

Enhancing Human Face Detection Using Motion and Active Contours

Recent advances in human face detection algorithms have seen varying degrees of success using numerous approaches. We identify that a feature-based approach is able to detect faces efficiently over large viewpoint and illumination variations. In this paper, we will enhance the approach by proposing the use of active contour models to detect the face boundary, and subsequently use it to verify face candidates. We present a method to initialize the active contour model, and show how the resulting information can be used to verify true face candidates. Further verification of the face hypothesis is achieved by checking for consistency with motion. We present results of testing the system under a wide range of imaging conditions, demonstrating its capability and robustness.

Efficient Implementation of Elliptic Curve Cryptography (ECC) for Personal Digital Assistants (PDAs)

Wireless devices are characterized by low computational power and memory. In addition to this wireless environment are inherently less secure than their wired counterparts, as anyone can intercept the communication. Hence they require more security. One way to provide more security without adding to the computational load is to use elliptic curve cryptography (ECC) in place of the more traditional cryptosystems such as RSA. As ECC provides the same level of security for far less key sizes, as compared to the traditional cryptosystems, it is ideal for wireless security. In this thesis we will investigate the different ways of implementing ECC on wireless devices such as personal digital assistants (PDAs). We will present our findings and compare the different implementations. In our implementation ECC over the field Fn2 using optimal normal basis representation gives the best results.

Towards an automatic human face localization system

This paper describes a method to detect and locate human faces in an image given no prior information about the size, orientation, and viewpoint of the faces in the image. This method uses a family of Gaussian derivative filters to search and extract human facial features from the image and then group them together into a set of partial faces using their geometric relationship. A belief network is then constructed for each possible face candidate and the belief values updated by evidences propagating through the network. Different instances of detected faces are then compared using their belief values and improbable face candidates discarded. The algorithm is tested on different instances of faces with varying sizes, orientation and viewpoint and the results indicate a 91% success rate in detection under viewpoint variation.