Payone : incentive for epidemic protocol-based anonymity system

Anonymity systems maintain the anonymity of communicating nodes by camouflaging them, either with peer nodes generating dummy traffic or with peer nodes participating in the actual communication process. The probability of any adversary breaking down the anonymity of the communicating nodes is inversely proportional to the number of peer nodes participating in the network. Hence to maintain the anonymity of the communicating nodes, a large number of peer nodes are needed. Lack of peer availability weakens the anonymity of any large scale anonymity system. This work proposes PayOne, an incentive based scheme for promoting peer availability. PayOne aims to increase the peer availability by encouraging nodes to participate in the anonymity system by awarding them with incentives and thereby promoting the anonymity strength. Existing incentive schemes are designed for single path based approaches. There is no incentive scheme for multipath based or epidemic based anonymity systems. This work has been specifically designed for epidemic protocols and has been implemented over MuON, one of the latest entries to the area of multicasting based anonymity systems. MuON is a peer-to-peer based anonymity system which uses epidemic protocol for data dissemination. Existing incentive schemes involve paying every intermediate node that is involved in the communication between the initiator and the receiver. These schemes are not appropriate for epidemic based anonymity systems due to the incurred overhead. PayOne differs from the existing schemes because it involves paying a single intermediate node that participates in the network. The intermediate node can be any random node that participates in the communication and does not necessarily need to lie in the communication path between the initiator and the receiver. The light-weight characteristics of PayOne make it viable for large-scale epidemic based anonymity systems.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Comparison of the effects of inquiry-based cooperative learning and demonstrations in science education

The reported research project involved studying how teaching science using demonstrations, inquiry-based cooperative learning groups, or a combination of the two methods affected sixth grade students’ understanding of air pressure and density. Three different groups of students were each taught the two units using different teaching methods. Group one learned about the topics through both demonstrations and inquirybased cooperative learning, whereas group two only viewed demonstrations, and group three only participated in inquiry-based learning in cooperative learning groups. The study was designed to answer the following two questions: 1. Which teaching strategy works best for supporting student understanding of air pressure and density: demonstrations, inquirybased labs in cooperative learning groups, or a combination of the two? 2. And what effect does the time spent engaging in a particular learning experience (demonstrations or labs) have on student learning? Overall, the data did not provide sufficient evidence that one method of learning was more effective than the others. The results also suggested that spending more time on a unit does not necessarily equate to a better understanding of the concepts by the students. Implications for science instruction are discussed.