Data expansion with Huffman codes

The following topics were dealt with: Shannon theory; universal lossless source coding; CDMA; turbo codes; broadband networks and protocols; signal processing and coding; coded modulation; information theory and applications; universal lossy source coding; algebraic geometry codes; modelling analysis and stability in networks; trellis structures and trellis decoding; channel capacity; recording channels; fading channels; convolutional codes; neural networks and learning; estimation; Gaussian channels; rate distortion theory; constrained channels; 2D channel coding; nonparametric estimation and classification; data compression; synchronisation and interference in communication systems; cyclic codes; signal detection; group codes; multiuser systems; entropy and noiseless source coding; dispersive channels and equalisation; block codes; cryptography; image processing; quantisation; random processes; wavelets; sequences for synchronisation; iterative decoding; optical communications

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by mainly redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone

Tree Sampling for Detection of Information Source in Densely Connected Networks

We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these approaches result in low detection performance in densely-connected networks, since there is a substantial number of nodes that are infected through the non-shortest path. In this work, we take a two-step approach to the source detection problem in densely-connected networks. By introducing the concept of detour nodes, we first sample trees that the infection process likely follows and effectively compare the probability of the sampled trees. Our solution has low complexity of O(n2logn, where n denotes the number of infected nodes, and thus can be applied to large-scale networks. Through extensive simulations including practical networks of the Internet autonomous system and power grid, we evaluate our solution in comparison with two well-known previous schemes and show that it achieves the best performance in densely-connected networks

Malicious Information Source Detection in Social Networks

The paper is devoted to investigation of the problem of malicious information source detection among the users of popular social network. The advantages and disadvantages of existing algorithms of rumor source detection were analyzed. The practice-oriented algorithm of malicious source detection have been proposed, it differs from the existing ones with additional verification procedure of the authorship. Appropriate software have been developed

Byzantine Modification Detection in Multicast Networks With Random Network Coding

An information-theoretic approach for detecting Byzantine or adversarial modifications in networks employing random linear network coding is described. Each exogenous source packet is augmented with a flexible number of hash symbols that are obtained as a polynomial function of the data symbols. This approach depends only on the adversary not knowing the random coding coefficients of all other packets received by the sink nodes when designing its adversarial packets. We show how the detection probability varies with the overhead (ratio of hash to data symbols), coding field size, and the amount of information unknown to the adversary about the random code