An Adaptive Image Encryption Scheme Guided by Fuzzy Models

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.Comment: Iranian Journal of Fuzzy Systems (2023

A Survey on Multi-Objective Neural Architecture Search

Recently, the expert-crafted neural architectures is increasing overtaken by the utilization of neural architecture search (NAS) and automatic generation (and tuning) of network structures which has a close relation to the Hyperparameter Optimization and Auto Machine Learning (AutoML). After the earlier NAS attempts to optimize only the prediction accuracy, Multi-Objective Neural architecture Search (MONAS) has been attracting attentions which considers more goals such as computational complexity, power consumption, and size of the network for optimization, reaching a trade-off between the accuracy and other features like the computational cost. In this paper, we present an overview of principal and state-of-the-art works in the field of MONAS. Starting from a well-categorized taxonomy and formulation for the NAS, we address and correct some miscategorizations in previous surveys of the NAS field. We also provide a list of all known objectives used and add a number of new ones and elaborate their specifications. We have provides analyses about the most important objectives and shown that the stochastic properties of some the them should be differed from deterministic ones in the multi-objective optimization procedure of NAS. We finalize this paper with a number of future directions and topics in the field of MONAS.Comment: 22 pages, 10 figures, 9 table

Deep Metric Learning with Soft Orthogonal Proxies

Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies that are assigned to different classes may end up being closely located in the embedding space and hence having a hard time to distinguish between positive and negative items. Alternatively, they may become highly correlated and hence provide redundant information with the model. To address these issues, we propose a novel approach that introduces Soft Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to be as orthogonal as possible and hence control their positions in the embedding space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an encoder to extract contextual features from images along with a DML objective. The objective is made of the Proxy Anchor loss along with the SO regularization. We evaluate our method on four public benchmarks for category-level image retrieval and demonstrate its effectiveness with comprehensive experimental results and ablation studies. Our evaluations demonstrate the superiority of our proposed approach over state-of-the-art methods by a significant margin

Multi‐objective single‐shot neural architecture search via efficient convolutional filters

Abstract This paper presents a novel approach for fast neural architecture search (NAS) in Convolutional Neural Networks (CNNs) for end‐to‐end License Plate Recognition (LPR). The authors propose a one‐shot schema that considers the efficiency of different convolutional filters to create a search space for more efficient architectures on vector processing cores. The authors’ approach utilizes a super‐network for LPR using Connectionist‐Temporal‐Cost (CTC) and ranks the importance of filters to generate a fine‐grain list of architectures. These architectures are evaluated in a multi‐objective manner, resulting in several Pareto‐optimal architectures with different computational costs and validation errors. Rather than using a single complicated building block for all layers, the authors’ method allows each stage to select a custom building block with fewer or more operations. The authors show that their super‐network is flexible to calculate filters of any required size and stride in each stage while keeping it efficient by the structural pruning. The authors’ experiments, which were performed on Iranian LPR, demonstrate that this method produces a variety of fast and efficient CNNs. Furthermore, the authors discuss the potential of this method for use in other areas of CNN application

Investigating intestinal parasitic infections with emphasis on molecular identification of Strongyloides stercoralis and Trichostrongylus colubriformis in north of Iran

Currently, parasitic infections are one of the important health problems in the world, especially in developing countries. This study aims to investigate intestinal parasites with an emphasis on molecular identification through the analysis of mitochondrial COX1 and ITS2 gene sequences of Strongyloides stercoralis (S. stercoralis) and Trichostrongylus spp. in north of Iran. Five hundred forty stool samples were collected from medical diagnostic laboratories affiliated with Mazandaran University of Medical Sciences in Sari city, north of Iran. First, all the samples were examined using direct smear, formalin-ether sedimentation, and trichrome staining technique. Suspected samples of Strongyloides larvae were cultured in agar plate. Then, DNA was extracted from samples containing Trichostrongylus spp. eggs and Strongyloides larvae. To amplify DNA, PCR was performed and the samples with a sharp band in electrophoresis were sequenced by Sanger method. Overall, the prevalence of parasitic infections in the study population was 5.4%. The highest and the lowest level of infection was observed with Trichostrongylus spp. and S. stercoralis at 3% and 0.2%, respectively. No traces of live Strongyloides larvae were seen in the culture medium of the agar plate. The six isolates obtained from the amplification of the ITS2 gene of Trichostrongylus spp. were sequenced, all of which were Trichostrongylus colubriformis. The sequencing results of COX1 gene indicated S. stercoralis. In the present study, the prevalence of intestinal parasitic infections in north of Iran has relatively decreased that its main reason can be due to the coronavirus epidemic and compliance with health principles. However, the prevalence of Trichostrongylus parasite was relatively high that it requires special attention to apply appropriate control and treatment strategies in this field