Using comments made in discussion threads on the social media aggregation website Reddit, the topics of conversation were identified using the probabilistic topic model Latent Dirichlet Allocation (LDA). Employing these topics as features for a neural network, several different neural network frameworks were trained on the data to serve as models to identify which threads a given user would be interested in contributing to based on their previously shown interests. This was done on a sample set of 30 users using 10 different initial random weights for each framework. The ideal model for each user was identified as being the one that scored the highest F2-Score, the harmonic mean of precision and recall with a bias towards recall, on a development set. Testing these ideal models on a test set, they achieved an average F2-Score of 0.825, as well as an average precision of 0.542 and an average recall of 0.956 for a sample set of users.

Wireless sensor networks consist of hundreds or thousands of nodes and are broadly used for collecting data from the environment. In this type of network nodes communicate with each other and send their data to the base station. Since sensor nodes are battery limited, it is crucial to minimize the amount of energy they dissipate for communication. The LEACH protocol is an elegant clustering algorithm for sending information to base station. It uses clustering, therefore each node only communicates with their cluster head nodes and then those cluster heads communicate with base station. In this paper we improved the LEACH algorithm and proposed a weighted energy efficient clustering algorithm (WEEC) algorithm for wireless sensor networks. We take into consideration the location of each node while clusters are forming. The simulation result proves that our proposed scheme noticeably increases the life time of the network.

The purpose of this work is to reduce the delivery time for the initial portion of multimedia objects over HTTP protocol. The multimedia files we consider are those embedded in web pages. By prefetching embedded media from server to client, we are able to overcome the effects of network latency.