Key management system maintains the confident of secret information from unauthorized users and verifying the integrity of exchanged messages and authenticity. But recent advances in electronics and computer technologies create the complexity of key management in wireless sensor networks (WSN). Additionally, the traditional key management systems are not up to the mark due to limited resources like memory, and energy constraints.In this paper, we propose an optimal cluster based key management system (OC-KMS) for WSNs. The proposed system consist of two contributions, in first, we perform the energy efficient clustering using modified animal Diaspora (MAD)optimization algorithm and cluster head (CH) selection using JAYA trust model. In second contribution, we propose the certificate less signcryption algorithm, which generates and distributes the public and private keys for each node in sensor networks. The proposed system resists various network layer attacks without affecting the network performance. The simulation resultdescribes that the proposed system perform very efficient than existing in terms of both performance and security wise.
In this article we use a combination of neural networks with other techniques for the analysis of orthophotos. Our goal is to obtain results that can serve as a useful groundwork for interactive exploration of the terrain in detail. In our approach we split an aerial photo into a regular grid of segments and for each segment we detect a set of features. These features depict the segment from the viewpoint of a general image analysis (color, tint, etc.) as well as from the viewpoint of the shapes in the segment. We perform clustering based on the Formal Concept Analysis (FCA) and Non-negative Matrix Factorization (NMF) methods and project the results using effective visualization techniques back to the aerial photo. The FCA as a tool allows users to be involved in the exploration of particular clusters by navigation in the space of clusters. In this article we also present two of our own computer systems that support the process of the validation of extracted features using a neural network and also the process of navigation in clusters. Despite the fact that in our approach we use only general properties of images, the results of our experiments demonstrate the usefulness of our approach and the potential for further development.
The Self-Organizing Map model considers the possibility of 1D and 3D map topologies. However, 2D maps are by far the most used in practice. Moreover, there is a lack of a theory which studies the relative merits of 1D, 2D and 3D maps. In this paper a theory of this kind is developed, which can be used to assess which topologies are better suited for vector quantization. In addition to this, a broad set of experiments is presented which includes unsupervised clustering with machine learning datasets and color image segmentation. Statistical significance tests show that the 1D maps perform significantly better in many cases, which agrees with the theoretical study. This opens the way for other applications of the less popular variants of the self-organizing map.
Never before in history data has been generated at such high volumes as it is today. It is estimated that every year about 1 Exabyte (= 1 Million Terabyte) of data are generated, of which a large portion is available in digital form. Exploring and analyzing the vast volumes of data becomes increasingly difficult. This paper describes system Vitamin-S that aims to help when analyzing very large data sets.
VPS-30-En is a small lexical resource that contains the following 30 English verbs: access, ally, arrive, breathe,
claim, cool, crush, cry, deny, enlarge, enlist, forge, furnish, hail, halt, part, plough, plug, pour, say, smash, smell, steer, submit, swell,
tell, throw, trouble, wake and yield. We have created and have been using VPS-30-En to explore the interannotator agreement potential
of the Corpus Pattern Analysis. VPS-30-En is a small snapshot of the Pattern Dictionary of English Verbs (Hanks and Pustejovsky,
2005), which we revised (both the entries and the annotated concordances) and enhanced with additional annotations. and This work has been partly supported by the Ministry of
Education of CR within the LINDAT-Clarin project
LM2010013, and by the Czech Science Foundation under
the projects P103/12/G084, P406/2010/0875 and
P401/10/0792.
VPS-GradeUp is a collection of triple manual annotations of 29 English verbs based on the Pattern Dictionary of English Verbs (PDEV) and comprising the following lemmas: abolish, act, adjust, advance, answer, approve, bid, cancel, conceive, cultivate, cure, distinguish, embrace, execute, hire, last, manage, murder, need, pack, plan, point, praise, prescribe, sail, seal, see, talk, urge . It contains results from two different tasks:
1. Graded decisions
2. Best-fit pattern (WSD) .
In both tasks, the annotators were matching verb senses defined by the PDEV patterns with 50 actual uses of each verb (using concordances from the BNC [2]). The verbs were randomly selected from a list of completed PDEV lemmas with at least 3 patterns and at least 100 BNC concordances not previously annotated by PDEV’s own annotators. Also, the selection excluded verbs contained in VPS-30-En[3], a data set we developed earlier. This data set was built within the project Reviving Zellig S. Harris: more linguistic information for distributional lexical analysis of English and Czech and in connection with the SemEval-2015 CPA-related task.