A Rational and Efficient Algorithm for View Revision in Databases

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In this paper, we argue that to apply rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first of all, it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented, along with the concept of a generalized revision algorithm for Horn knowledge bases. We show that Horn knowledge base dynamics has interesting connection with kernel change and abduction. Finally, we also show that both variants are rational in the sense that they satisfy certain rationality postulates stemming from philosophical works on belief dynamics

A New Rational Algorithm for View Updating in Relational Databases

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.Comment: arXiv admin note: substantial text overlap with arXiv:1301.515

A Quantum Convolutional Neural Network Approach for Object Detection and Classification

This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning

Noise removal methods on ambulatory EEG: A Survey

Over many decades, research is being attempted for the removal of noise in the ambulatory EEG. In this respect, an enormous number of research papers is published for identification of noise removal, It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection and removal of an noise. More than 100 research papers have been discussed to discern the techniques for detecting and removal the ambulatory EEG. Further, the literature survey shows that the pattern recognition required to detect ambulatory method, eye open and close, varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG noise data from a various condition of EEG data

Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network

Brain tumor is a growth of abnormal cells in brain, which canbe cancerous or non-cancerous. The Brain tumor have scarce symptomsso it is very difficult to classify. Diagnosing brain tumor with histologyimages will efficiently helps us to classify brain tumor types. Sometimes,histology based image analysis is not accepted due to its variations inmorphological features. Deep learning CNN models helps to overcomethis problem by feature extraction and classification. Here proposed amethod to classify high resolution histology image. InceptionResNetV2is an CNN model, which is adopted to extract hierarchical features with-out any loss of data. Next generated deep spatial fusion network to ex-tract spatial features found in between patches and to predict correct fea-tures from unpredictable discriminative features. 10-fold cross-validationis performed on the histology image. This achieves 95.6 percent accu-racy on 4-class classification (benign, malignant, Glioblastoma, Oligo-dendroglioma). Also obtained 99.1 percent accuracy and 99.6 percentAUC on 2-way classification (necrosis and non-necrosis)

Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network

Brain tumor is a growth of abnormal cells in brain, which canbe cancerous or non-cancerous. The Brain tumor have scarce symptomsso it is very difficult to classify. Diagnosing brain tumor with histologyimages will efficiently helps us to classify brain tumor types. Sometimes,histology based image analysis is not accepted due to its variations inmorphological features. Deep learning CNN models helps to overcomethis problem by feature extraction and classification. Here proposed amethod to classify high resolution histology image. InceptionResNetV2is an CNN model, which is adopted to extract hierarchical features with-out any loss of data. Next generated deep spatial fusion network to ex-tract spatial features found in between patches and to predict correct fea-tures from unpredictable discriminative features. 10-fold cross-validationis performed on the histology image. This achieves 95.6 percent accu-racy on 4-class classification (benign, malignant, Glioblastoma, Oligo-dendroglioma). Also obtained 99.1 percent accuracy and 99.6 percentAUC on 2-way classification (necrosis and non-necrosis)

Searching for S-boxes with better Diffusion using Evolutionary Algorithm

Over the years, a large number of attacks have been proposed against substitution boxes used in symmetric ciphers such as differential attacks, linear attacks, algebraic attacks, etc. In the Advanced Encryption Standard (AES) Block cipher, the substitution box is the only nonlinear component and thus it holds the weight of the cipher. This basically means that if an attacker is able to mount a successful attack on the substitution box of AES, the cipher is compromised. This research work aims to provide a solution for increasing cryptographic immunity of S-boxes against such attacks. A genetic algorithm based approach has been proposed to search for 8 × 8 balanced and bijective S-boxes that exhibit values of differential branch number, non-linearity, differential uniformity, count and length of cycles present and distance from strict avalanche criterion that are similar to or better than the AES S-box. An S-Box evaluation tool is also implemented to evaluate any S-boxes generated. S-box of AES is resistant to the crypt-analytic attacks. S-boxes constructed by the proposed algorithm have better cryptographic properties so they are also resistant to the crypt-analytic attacks. The strict avalanche criterion[11], which is based on completeness[22] and diffusion[5], is an essential property for any 8 × 8 S-box. Good diffusion means that a small change in the plaintext may influence the complete block after a small number of rounds. Therefore, a lower DSAC value is desirable to prevent vulnerabilities to attacks such as differential attacks. The DSAC is therefore used as the primary fitness criterion in this research work to search for S-boxes with better diffusion