Video-Based Face Recognition with New Classifiers

An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.

The information content of rules and rule sets and its application

The information content of rules is categorized into inner mutual information content and outer impartation information content. Actually, the conventional objective interestingness measures based on information theory are all inner mutual information, which represent the confidence of rules and the mutual information between the antecedent and consequent. Moreover, almost all of these measures lose sight of the outer impartation information, which is conveyed to the user and help the user to make decisions. We put forward the viewpoint that the outer impartation information content of rules and rule sets can be represented by the relations from input universe to output universe. By binary relations, the interaction of rules in a rule set can be easily represented by operators： union and intersection. Based on the entropy of relations, the outer impartation information content of rules and rule sets are well measured. Then, the conditional information content of rules and rule sets, the independence of rules and rule sets and the inconsistent knowledge of rule sets are defined and measured. The properties of these new measures are discussed and some interesting results are proven, such as the information content of a rule set may be bigger than the sum of the information content of rules in the rule set, and the conditional information content of rules may be negative. At last, the applications of these new measures are discussed. The new method for the appraisement of rule mining algorithm, and two rule pruning algorithms, λ-choice and RPClC, are put forward. These new methods and algorithms have predominance in satisfying the need of more efficient decision information.

A novel mapping scheme and modified decoding algorithm for BICM-ID

This paper proposes a novel mapping scheme for bit-interleaved coded modulation with iterative decoding(BICM-ID).The symbol mapping is composed of two QPSK with different radiuses and phases,called cross equalization-8PSK-quasi-semi set partitioning(CE-8PSK-Quasi-SSP).Providing the same average power,the proposed scheme can increase the minimum squared Euclidean distance(MSED)and then improve the receiving performance of BICM-ID compared with conventional symbol mapping schemes.Simultaneously,a modified iteration decoding algorithm is proposed in this paper.In the process of iteration decoding,different proportion of the extrinsic information to the systematic observations results in distinct decoding performance.At high SNR(4~9dB),the observation information plays a more important role than the extrinsic information.Simulation results show that the proportion set at 1.2 is more suitable for the novel mapping in BICM-ID.When the BER is 10^(-4),more than 0.9dB coding gain over Rayleigh channels can be achieved for the improved mapping and decoding scheme.

Improved Information Set Decoding Algorithms over Galois Ring in the Lee Metric

The security of most code-based cryptosystems relies on the hardness of the syndrome decoding(SD) problem.The best solvers of the SD problem are known as information set,decoding(ISD) algorithms.Recently,Weger,et al.(2020) described Stern’s ISD algorithm,s-blocks algorithm and partial Gaussian elimination algorithms in the Lee metric over an integer residue ring Z_(pm),where p is a prime number and m is a positive integer,and analyzed the time complexity.In this paper,the authors apply a binary ISD algorithm in the Hamming metric proposed by May,et al.(2011)to solve the SD problem over the Galois ring GR(p^(m),k) endowed with the Lee metric and provide a detailed complexity analysis.Compared with Stern’s algorithm over Zpmin the Lee metric,the proposed algorithm has a significant improvement in the time complexity.