Matin Pirouz

Optimized relativity search: node reduction in personalized page rank estimation for large graphs

This paper proposes an algorithm called optimized relativity search to reduce the number of nodes in a graph when attempting to decrease the running time for personalized page rank (PPR) estimation. Even though similar estimations have been done, this method significantly increases the speed of computation, making it a feasible candidate for large graph solutions, such as search engines and friend recommendation techniques used in social media. In this study, the weighted page rank method was combined with the Monte-Carlo technique and a local update algorithm over a reduced map space; this algorithm was developed to achieve a more accurate and faster search method than FAST PPR. The experimental results showed that for nodes with a high degree of incoming nodes, the speed of estimation was twice as fast compared to FAST PPR, at the expense of a little accuracy.

An optimized approach for community detection and ranking

Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about network. Such knowledge can be utilized for target marketing or grouping similar, yet distinct, nodes. The ever-growing variety of social networks necessitates detection of minute and scattered communities, which are important problems across different research fields including biology, social studies, physics, etc. Existing community detection algorithms such as fast and folding or modularity based are either incapable of finding graph anomalies or too slow and impractical for large graphs. The main contributions of this work are twofold: (i) we optimize the Attractor algorithm, speeding it up by a factor depending on complexity of the graph; i.e. the more complex a social graph is, the better result the algorithm will achieve, and (ii) we propose a community ranker algorithm for the first time. The former is achieved by amalgamating loops and incorporating breadth-first search (BFS) algorithm for edge alignments and to fill in the missing cache, preserving a constant of time equal to the number of edges in the graph. For the latter, we make the first attempt to enumerate how influential each community is in a given graph, ranking them based on their normalized impact factor.

Vulnerability Analysis of Iframe Attacks on Websites

Clickjacking attacks are emerging threats to websites of different sizes and shapes. They are particularly used by threat agents to get more likes and/or followers in Online Social Networks (OSNs). This paper reviews the clickjacking attacks and the classic solutions to tackle various forms of those attacks. Different approaches of Cross-Site Scripting attacks are implemented in this study to study the attack tools and methods. Various iFrame attacks have been developed to tamper with the integrity of the website interactions at the application layer. By visually demonstrating the attacks such as Cross-Site scripting (XSS) and Cross-Site Request Forgery (CSRF), users will be able to have a better understanding of such attacks in their formulation and the risks associated with them.

Optimized Rank Estimator in Big Data Social Networks

In this study, FAST Personalized PageRank is utilized to find the target node set. Using the mentioned target set, the algorithm gives an estimation of the closeness of any pair of nodes in the graph. Personalized Page Vector is used to find the most popular nodes, also known as hubs, in the network. The time taken by the estimation of Personalized PageRank is directly proportional to the network size. In this work, we proposed a node reduction method to prune the graph. To decrease the entropy and reduce the number of alternate paths to the target nodes, redundant popular nodes are identified and flagged. The flagged nodes are, then, given a lower priority in the computation. After pruning the graph, estimation results achieve an improved time complexity. The proposed method achieves a twice shorter computation time as compared to FAST PPR and Local Update.

New Tighter Upper Bounds for Mining High Average-Utility Itemsets

In the past, frequent itemset mining (FIM) revealed the high-frequent patterns but ignored the more important concepts such as unit of profit and quality of the items. Recently, high-utility mining (HUIM) has caused wide public concern in the data mining field. A principal problem in HUIM is that the HUIM needs to handle the exponential search space for mining high-utility itemsets while the number of distinct items and the size of the database are both very large. High average-utility itemset mining (HAUIM) is an extension for traditional HUIM concept to provide a different measure with HUIM. It mines the average-utility value of the itemsets regarding to the length of it. Two new tighter upper-bounds, maximum following utility upper-bound (mfuub) and top-k revised transaction maximum utility upper-bound (krtmuub), are proposed in this article to further contract the size of candidate pattern set. Experiments were conducted on two benchmark datasets to show that the proposed method outperforms the previous HAUIM algorithms in terms of runtime

Optimized Label Propagation Community Detection on Big Data Networks

Identifying community structures and subnetwork patterns for complex networks provide us with great knowledge about network. Community detection has been getting lots of attention and interest in recent years. The application for such knowledge goes from target marketing to biology, social studies, and physics. The existing algorithms either lack accuracy or are too slow for Big Data graphs. Due to the rise of Big Data graphs, such solutions prove impractical for real-world datasets. In this study, we change the feed system for the Label Propagation algorithm from a random method to a degree-based system. In addition, we introduce a new convergence method that checks the membership for every node and flags them as converged when they meet the requirement. The main contributions of this work are twofold: (i) we optimize the Label Propagation algorithm, improving the accuracy by a factor of two. The results depend on the complexity of the graph; i.e. the denser a graph structure is, the better result the algorithm will achieve. (ii) We solved the inconsistency of identified communities of Label Propagation algorithm. The results are depicted using two well-known metrics known as the Normalized Mutual Information and the Adjusted Rand Index. We present that Optimized Label Propagation has better results in various real-world dataset and artificial datasets.

A Raspberry-Pi Prototype of Smart Transportation

This paper proposes a prototype of a level 3 autonomous vehicle using Raspberry Pi, capable of detecting the nearby vehicles using an IR sensor. We make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications with the nearby vehicles and road-side units. Two sets of passive and active experiments on a pair of prototypes were run, demonstrating the interconnectivity of the developed prototype. Several sensors were incorporated into an emulation based on System-on-Chip to further demonstrate the feasibility of the proposed model.