Analysis of Radon Mass Exhalation in Soil Samples from Fault Regions of Kolasib District Mizoram, India

In order to identify the distribution of radon mass exhalation soil samples from fault regions of Kolasib District were obtained. These were measured and analysed using scintillation based smart RnDuo device. The soil samples were collected from three different points in each selected fault. The exhalation rates from different locations ranges from 2.3 mBq/kg/hr – 54.19 mBq/kg/hr with an average of 20.42 mBq/kg/hr. Gamma survey measurement was also carried out which ranges between 89 nSv/hr – 157 nSv/hr with an average of 117.13 nSv/hr. The correlation graph between mass exhalation and gamma survey was plotted and a very weak correlation was obtained. Keywords: Radon, mass exhalation, soil samples, RnDuo, fault

Improving Multi-Label Learning by Correlation Embedding

In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such as high time and memory costs. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. Specifically, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we construct a multi-label classifier from the low-dimensional latent feature space to the label space, where the embedding matrix is utilized as the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE. The comparison experiment with the state-of-the-art approaches shows that the proposed method MLLCE has a competitive performance in multi-label learning.

Multilevel Intrusion Alert Post-processing for the Elimination of False Positives

Intrusion detection systems are the last line of defence in the network security domain. Improving the performance of intrusion detection systems always increase false positives. This is a serious problem in the field of intrusion detection. In order to overcome this issue to a great extend, we propose a multi level post processing of intrusion alerts eliminating false positives produced by various intrusion detection systems in the network. For this purpose, the alerts are normalized first. Then, a preliminary alert filtration phase prioritize the alerts and removes irrelevant alerts. The higher priority alerts are then aggregated to fewer numbers of hyper alerts. In the final phase, alert correlation is done and alert correlation graph is constructed for finding the causal relationship among the alerts which further eliminates false positives. Experiments were conducted on LLDOS 1.0 dataset for verifying the approach and measuring the accuracy. Keywords: Intrusion detection system, alert prioritization, alert aggregation, alert correlation, LLDOS 1.0 dataset, alert correlation graph.

Modelling of operational, physical and mechanical properties of tool steels for cold deformation dies

Correlations between the parameters of the structure of the hardened layer and the operational properties of cold deformation dies made of case-hardened steels are considered. Correlation coefficients are calculated and a correlation graph is built. A stable (95 %) correlation is shown between wear resistance, the amount and size of carbide inclusions, the effective thickness and hardness of the case-hardened layer. Keywords: correlation graph, carbonization, diffusion layer, carbides, wear resistance, hardness, strength, impact toughness. [email protected]

Keywords-Driven and Weight-aware Paper Recommendation via Paper Correlation Pattern Mining

Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.