Shri Rai

Distinguishing games and simulation games from simulators

The advanced computational capabilities in modern personal computers have made it possible for consumers to experience simulations with a high degree of verisimilitude through simulation games (a.k.a. Sims). In recent years, the cross-boundary technology exchange between game and simulation technology, along with other factors, has contributed to the confusion as to what makes a simulation game and what makes a simulator. This article provides a users and designers perspective on a definitive comparison of the similarities and differences between games in general, simulation games, and simulators. It also introduces a method that can be easily used to distinguish games and simulation games from simulators by using observable design characteristics. On the other hand, the convergence of functionality and technology in simulation games and simulators has created new applications of simulation. One such application is in serious games. Serious games and simulation games are confusingly similar in many ways. However, they greatly differ in functionality. This article also provides a method to distinguish serious games from simulation games, to clarify the strict categorization between these two applications of simulation.

Static and dynamic difficulty level design for edutainment game using artificial neural networks

When designing a game, one of the major tasks is to design a game of exciting and challenging difficulty levels to maintain the interest level of a player throughout the game. This is especially important when designing an educational game. This paper proposes the use of Artificial Neural Networks (ANNs), specifically the Backpropagation Neural Networks (BPNNs) for handling the gaming experience. The BPNNs can provide targeted learning experience for the user or the student. This will achieve personalized learning that is an important issue for student relationship management. The proposed frameworks will provide motivation for the student as the difficulty level progresses and adjusts to suit individual users.

The use of interactive simulations to affect driving behaviour

Many studies have shown that no matter what is done to try to get drivers to improve their driving behaviour there will always be some who would not see the benefit of modifying their behaviour. This paper reports on work in progress using a specially built simulator to convince drivers of the benefit of having good driving behaviour. The system uses Interactive Simulations in a Virtual Reality environment to immerse drivers in various road situations.

An Innovative Fingerprint Feature Representation Method to Facilitate Authentication Using Neural Networks

Authentication systems enable the verification of claimed identity; on computer systems these are typically password-based. However, such systems are vulnerable to numerous attack vectors and are responsible for a large number of security breaches. Biometrics is now commonly investigated as an alternative to password-based systems. There are numerous biometric characteristics that can be used for authentication purposes, each with different levels of accuracy and positive and negative implementation factors. The objective of the current study was to investigate fingerprint recognition utilizing Artificial Neural Networks ANNs as a classifier. An innovative representation method for fingerprint features was developed to facilitate verification by ANNs. For each participant, the method required the alignment of their fingerprint samples based on extracted local features, and the selection of 8 of these aligned features common to their samples. The six attributes belonging to each of the selected features were used for ANN input. Unlike the common usage, each participant had one dedicated ANN trained to recognize only their fingerprint samples. Experimental results returned a false acceptance rate FAR of 0.0 and a false rejection rate FRR of 0.0022, which were comparable to and in some cases, slightly better than other research efforts in the field.

Applications of Soft Computing for Musical Instrument Classification

In this paper, a method for pitch independent musical instrument recognition using artificial neural networks is presented. Spectral features including FFT coefficients, harmonic envelopes and cepstral coefficients are used to represent the musical instrument sounds for classification. The effectiveness of these features are compared by testing the performance of ANNs trained with each feature. Multi-layer perceptrons are also compared with Time-delay neural networks. The testing and training sets both consist of fifteen note samples per musical instrument within the chromatic scale from C3 to C6. Both sets consist of nine instruments from the string, brass and woodwind families. Best results were achieved with cepstrum coefficients with a classification accuracy of 88 percent using a time-delay neural network, which is on par with recent results using several different features.

Complex game design modeling

This paper looks at the game design and engineering approach to model the game design. The game modeling framework discussed in this paper could be a systematic alternative for implementing in the game engine architecture. The suggested game modeling framework incorporates structural game component, temporal game component and boundary game component frameworks. It is suitable to model most complex games and game engines.

A usability evaluation of Neuromender's upper limb game-based rehabilitation system for stroke survivors

Game-based technologies have been widely used as part of stroke rehabilitation. The Neuromender system utilises game-based technologies and consists of serious games that are designed and developed for the purpose of rehabilitation of stroke survivors. In this paper, one of the modules in the Neuromender system which is the “upper limb” module is described and tested for its usability. The upper limb module primarily focuses on the rehabilitation of the upper body extremities of stroke survivors. An experimental study is designed to test the usability of the upper limb module. Various metrics including the optimal distance between the 3D depth sensor device and the survivor, the optimal position of the 3D depth sensor with respect to the survivor, and the response time of the gestures made by the survivors based on their distance to the sensor are evaluated. At the end of the experiments, the optimal distance and optimal position for the survivors to utilise the upper limb module is determined.

Measures of Similarity in Memory-Based Collaborative Filtering Recommender System: A Comparison

Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the e-commerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity.

Cooperative feature level data fusion for authentication using neural networks

In traditional research, data fusion is referred to as multi-sensor data fusion. The theory is that data from multiple sources can be combined to provide more accurate, reliable and meaningful information than that provided by a single data source. Applications in this field of study were originally in the military domain; more recently, investigations for its application in various civilian domains (eg: computer security) have been undertaken. Multi-sensor data fusion as applied to biometric authentication is termed multi-modal biometrics. The objective of this study was to apply feature level fusion of fingerprint feature and keystroke dynamics data for authentication purposes, utilizing Artificial Neural Networks (ANNs) as a classifier. Data fusion was performed adopting the cooperative paradigm, a less researched approach. This approach necessitates feature subset selection to utilize the most discriminatory data from each source. Experimental results returned a false acceptance rate (FAR) of 0.0 and a worst case false rejection rate (FRR) of 0.0006, which were comparable to—and in some cases, slightly better than—other research using the cooperative paradigm.

Agent-based simulation for infrastructure protection and emergency evacuation training

Simulators have been used for some time to provide training to people in a number of occupations. Simulation systems enable what-if questions to be posed and the consequences of user actions to be studied in a cost-effective and safe manner. Simulations also enable detailed user behavior to be logged for later study to extract data that may be difficult to capture in real life. This paper describes the development of a multi-user simulation platform that enables certain emergency events to be simulated. All user behavior is logged for later analysis so that user behavior under certain stressful events can be studied. Software agents in the simulation system can be used to model crowds or agents with a particular intent. The system can be used to provide emergency evacuation training. The system can also be used to test the security of building designs to find out how the security of these buildings can be compromised.