Real estate appraisal is an important tool for evaluating property values when purchasing, selling, insuring, lending or taxing on residency properties. The traditional way to do real estate appraisal is to analyse recent sales data with basic information, such as bedrooms, bathrooms, land-size, and suburb to estimate house price prediction by comparing and selecting similar properties to the property that is being evaluated. Real estate agents are widely using this method for their clients to help them determine a price to list when selling a home or a price to offer when buying a home. However, the traditional method is very subjective and depends heavily on the experience and knowledge of real estate agents and two different agents might come up with different estimate prices. Today, machine learning and deep learning techniques provide an objective, advanced and robust option, making the joint analysis of tabular data combined with visual content possible. We are interested in estimating the real estate prices like real estate agents. In this paper, we examine to use deep learning combining with extreme Gradient Boosting (XGBoost) for real estate appraisal, by analysing historical sale records together with visual content, with online house pictures, and by scoring each image from an aesthetic point of view to make a house price prediction. Our experiment shows that an improvement in performance of house price prediction accuracy, with replacing the last output layer with XGBoost.

Passwords are the first line of defense for many computerized systems. The quality of these passwords decides the security strength of these systems. Many studies advocate using password entropy as an indicator for password quality where lower entropy suggests a weaker or less secure password. However, a closer examination of this literature shows that password entropy is very loosely defined. In this paper, we first discuss the calculation of password entropy and explain why it is an inadequate indicator of password quality. We then establish a password quality assessment scheme: password quality indicator (PQI). The PQI of a password is a pair λ = (D, L), where D is the Levenshtein's editing distance of the password in relation to a dictionary of words and common mnemonics, and L is the effective password length. Finally, we propose to use PQI to prescribe the characteristics of good quality passwords.

This paper presents a new senior health monitoring system using the Kinect device to monitor elderly people and detect when they are likely to fall by measuring their gait, and analyzing change in posture when they change from sitting to standing or vice versa. Support vector machine is used to analyze the gait and posture data obtained from the Kinect device. Several experiments were performed to evaluate the proposed system and experimental results as well as research experience on using the Kinect device will be presented.

Electroencephalography (EEG) signal has been used widely in health and medical fields. It is also used in brain-computer interface (BCI) systems for humans to continuously control mobile robots and wheelchairs. Recently, the research communities successfully explore the potential of using EEG as a new type of biometrics in user authentication. EEG-based user authentication systems have the combined advantages of both password-based and biometric-based authentication systems, yet without their drawbacks. In this paper, we propose to take the advantage of rich information, such as age and gender, carried by EEG signals for user authentication in multi-level security systems. Our experiments showed very promising results for the proposed multi-factor EEG-based authentication method.