Adaptive N-Gram Classifier for Privacy Protection

We are living in a world where information is worth more than gold. Hence protecting sensitive information has become a crucial task. When telephones gave way to smartphones people not just start using them as communication tools, but to work on the go and to actively immerse in social network circles and other private communication services like chat SMS etc. Knowing each end point to the Internet is a potential risk which was a PC or laptop a while ago. Traditional methods limit the usage and somewhat the convenience of the user which dealt severely. The user knowingly or unknowingly releases sensitive information into the web which are either monitored or mined by third parties and uses them for unlawful purposes. Existing techniques mostly use data fingerprinting, exact and partial document matching and statistical methods to classify sensitive data. Keyword-based are used when the target documents are less diverse and they ignore the context of the keyword, on the other hand statistical methods ignore the content of the analyzed text. In this paper we propose a dynamic N-gram analyzer which can be used as a document classifier, we investigate the relationship of size and quality of N-grams and the effect of other feature sets like exclusion of common N-grams, grammatical words, N-gram-sizes etc. Another improvement is in the area of dynamic N-gram updater which dynamically changes the N-gram feature vectors. Our work has shown that the techniques fairly outperforms the traditional methods even when the categories exhibit frequent similarities. DOI: 10.17762/ijritcc2321-8169.150614

Dynamic Fault Analysis in Substations Based on Knowledge Graphs

To address the challenge of identifying hidden danger in substations from unstructured text, a novel dynamic analysis method is proposed. We first extract relevant information from the unstructured text, and then leverages a flexible distributed search engine built on Elastic-Search to handle the data. Following this, the hidden Markov model is employed to train the data within the engine. The Viterbi algorithm is integrated to decipher the hidden state sequences, facilitating the segmentation and labeling of entities related to hidden dangers. The final step involves using the Neo4j graph database to dynamically create a knowledge graph that visualizes hidden dangers in the substation. The effectiveness of the proposed method is demonstrated through a case analysis from a specific substation with hidden dangers revealed in the text records

CVABS: Moving Object Segmentation with Common Vector Approach for Videos

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps detecting activity of moving objects in the video. In this work, we have developed a new subspace based background modelling algorithm using the concept of Common Vector Approach with Gram-Schmidt orthogonalization. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilized as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl

The Characteristics of Postoperative Mediastinitis During the Changing Phases of Cardiac Surgery

Background. Mediastinitis is a serious complication of open heart surgery associated with high mortality, considerable health care costs, and prolonged hospital stay. We examined characteristics and incidence of mediastinitis during 29 years when indications and patient material have been in a process of change. Methods. This was a retrospective population-based study comprising all mediastinitis patients more than 16 years of age after open heart surgery between 1990 and 2018 from a population of 1.7 million. Patient records of 50 mediastinitis patients from 2004 to 2014 were reviewed and compared with 120 patients from 1990 to 1999. Results. Annual mediastinitis rate varied 0% to 1.5% with a decreasing trend-from a level exceeding 1.2% to approximately 0.3%-over the study period. In 2004 to 2014 patients with mediastinitis were older, more often smokers, and more often had diabetes mellitus and renal insufficiency than in 1990 to 1999. No difference in length of hospital treatment, antibiotic prophylaxis or treatment, intensive care unit treatment, or mortality was observed between 1990 to 1999 and 2004 to 2014. Coronary artery bypass graft surgery became less common and valve replacement and hybrid operations more common among operations leading to mediastinitis. Staphylococcus aureus increased (from 25% to 56%, p = .005) whereas coagulase-negative staphylococci (46% to 23%, P < .001) and gramnegative bacteria (18% to 12%, P = .033) decreased as causative agents. Surgery for mediastinitis remained similar except introduction of vacuum-assisted closure treatment. Conclusions. The rate of mediastinitis decreased during these 29 years. No difference in 30-day mortality in mediastinitis was seen: 0.9% in 1990 to 1999 and 2% in 2004 to 2014. (C) 2021 by The Society of Thoracic SurgeonsPeer reviewe