Key predistribution scheme using finite fields and reed muller codes

Resource constraint sensors of a Wireless Sensor Network (WSN) cannot afford the use of costly encryption techniques like public key while dealing with sensitive data. So symmetric key encryption techniques are preferred where it is essential to have the same cryptographic key between communicating parties. To this end, keys are preloaded into the nodes before deployment and are to be established once they get deployed in the target area. This entire process is called key predistribution. In this paper we propose one such scheme using unique factorization of polynomials over Finite Fields. To the best of our knowledge such an elegant use of Algebra is being done for the first time in WSN literature. The best part of the scheme is large number of node support with very small and uniform key ring per node. However the resiliency is not good. For this reason we use a special technique based on Reed Muller codes proposed recently by Sarkar, Saha and Chowdhury in 2010. The combined scheme has good resiliency with huge node support using very less keys per node.

Hierarchical semi-markov conditional random fields for recursive sequential data

Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.

A nanoscale SQUID operating at high magnetic fields

A washer-free Nb nanoSQUID has been developed for measuring magnetization changes from nanoscale objects. The SQUID loop is etched into a 250 nm wide Au/Nb bilayer track and the diameter of the SQUID hole is ~ 70 nm. In the presence of a magnetic field perpendicular to the plane of the SQUID, vortex penetration into the 250 nm wide track can be observed via the critical current–applied field characteristic and the value at which vortex first penetrates is consistent with the theoretical prediction. Upon removing the applied field, the penetrated vortices escape the track and the critical current at zero field is restored.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Epoxy nanocomposites with aligned carbon nanofillers by external electric fields

This paper presents systematic studies on aligning carbon nanofillers in epoxy by external fields, either electric fields or magnetic fields, to create nanocomposites with greatly improved mechanical and electrical properties. Carbon nanofibers (CNFs) and graphene nanoplatelets (GnPs) were observed to align along the field direction in the epoxy resin. Compared to the unmodifed epoxy and those with randomly-oriented carbon nanofillers, the nanocomposites with aligned carbon nanofillers showed significantly higher fracture toughness and electrical conductivity along the direction of the external field. Compared with randomly-oriented nanofillers, aligned GnPs and CNFs produced 40% and 27% improvement in fracture energy at 1.0 wt%, bringing the total increase in fracture energy over the neat polymer to more than 10 times. Several key toughening mechanisms were identified through fractographic analysis, which was used to develop predictive models to quantify the increases in the value of GIc as a result of 1-D and 2D carbon nanofillers. The present findings suggest that aligning carbon nanofillers presents a very promising technique to create multi-scale reinforcement with greatly increased electric conductivity and fracture toughness.