Face recognition has become a crucial topic in recent decades, which offers important opportunities for applications in security surveillance, human-computer interaction, and forensics. However, it poses challenges, including uncontrolled environments, large datasets, and insufficiency of training data. In this paper, a face recognition system is proposed to iron out the above problems with a new framework based on a Hashing function in a three-stage filtering approach. At the first stage, candidate subjects are chosen using the Locality-Sensitive Hashing (LSH) function. We employ a voting system to select candidates via disregarding a large number of dissimilar identities considering their Local features. At the second stage, a robust image Hashing based on Discrete Cosine Transform (DCT) coefficients is used to further refine the candidate images in terms of global visual information. Finally, the test image is recognized among selected identities using other visual information, resulting in further accuracy gains. Extensive experiments on FERET, AR, and ORL datasets show that the proposed method outperforms with a significant improvement in accuracy over the state-of-the-art methods.

Image Hashing allows compression, enhancement or other signal processing operations on digital images that are usually acceptable manipulations. Cryptographic hash functions are very Sensitive to even single bit changes in image. Image Hashing is a sum of important quality features in quantized form. In this paper, we propose a novel image Hashing algorithm for authentication, which is more robust against various kinds of attacks. In the proposed approach, a short hash code is obtained using a minimum magnitude Center Symmetric Local Binary Pattern (CSLBP). The desirable discrimination power of image hash is maintained by modified Local Binary Pattern (LBP) based edge weight factor generated from gradient image. The proposed Hashing method extracts texture features using the CSLBP. The discrimination power of Hashing is increased by weight factor during the CSLBP histogram construction. The generated histogram is compressed to 1/4 of the original histogram by a minimum magnitude of CSLBP. The proposed method, has a two-fold advantage; first, it has small length, and second, it has an acceptable discrimination power. The experimental results are demonstrated by the hamming distance and the TPR, FPR, and ROC curves. Therefore, the proposed method successfully does a fair classification of content preserving and content changing images.

Embedding learning is an essential issue in Natural Language Processing (NLP) applications. Most existing methods measure the similarity between text chunks in a context using pre-trained word embedding. However, providing labeled data for model training is costly and time-consuming. So, these methods face downward performance when limited amounts of training data are available. This paper presents an unsupervised sentence embedding method that effectively integrates semantic Hashing into the Kernel Principal Component Analysis (KPCA) to construct embeddings of lower dimensions that can be applied to any domain. The experiments conducted on benchmark datasets highlighted that the generated embeddings are general-purpose and can capture semantic meanings from both small and large corpora.

Aim and Background: Thermostable DNA polymerase enzyme has been taken much into consideration due to its application in PCR and molecular biology researches and it has doubled the importance of the study on the various thermostable DNA polymerase. The purpose of this study was to assay the function and efficient production of the cold Sensitive thermostable DNA polymerase and its quick and cheap purification.Material and Methods: After synthesis of the designed gene artificially, it was cloned into the pET28 vector and then was transferred into E.coli. IPTG was used as an inducer in the study of gene expression. Initial purification of the enzyme was performed by heat treatment at 72oC and precipitation of other insoluble proteins by centrifuge. The DNA synthesis activity of crude extract was compared with commercial enzyme.Result: Recombinant cold Sensitive and thermostable Taq DNA polymerase expressed in E.coli showed a significant advantage and more desirable functionality such as activity and thermostable compared to commercial enzyme.Conclusion: With regard to importance and application level of thermostable DNA polymerase in molecular biology, the production method used in this study is practical and cost effective. Additionally, the simplicity of producing method of this enzyme and its accuracy is a good reason for artificial and Local production of highly pure cold Sensitive Taq DNA polymerase.

The central issue of the present paper is the study of the causes of the mother tongue decline in the Sahne city in Kermanshah province. The issue of language is beyond the dialect of people and is refer to the form of life and the matter of macroeconomic and political relations. From this perspective, the form of life and the language associated with it explain the praxis of the members of the society. The appropriate method of examining the problem should be able to analyze the justification of agents to their situation, that is why Grounded Theory is chosen as the method of this research and data analysis is taken from abduction reasoning. The results of the analysis introduced the categories of globalizing, modernizing, de-use of language, economic necessity and the government as the most important factors of Local language fading. In this essay the meaning of globalization is the globalizing of special type of economic relation that explain other categories role in the process of fading of languages.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of Local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of Local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

On the one hand, the development of modern medicine has made it possible to store medical images, and on the other hand, due to the daily increase in the storage of such data, it has also made their management and recovery difficult. Considering that medical images are used as a powerful tool in the early diagnosis of most diseases, providing a powerful system that can retrieve images with similar content from the growing volume of medical images is very effective in control and treatment. In this article, a medical image retrieval system based on Siamese neural network consisting of two convolutional sub-networks with 13 layers is presented. To reach the optimal subset of deep features extracted by Siamese, the Minimum Redundancy-Maximum Relevant (mRMR) technique has been used, and after binary Hashing of the features, similar images are retrieved using Hamming distance. Although the proposed model is capable of retrieving a variety of gray scale medical images, two types of lung images have been used to evaluate it, including CT scan images of Covid-19 patients in the CT-COV database and X-ray images of pneumonia patients in the Pneumonia database. The results indicate that the proposed method in the Covid database has been able to achieve an average precision of 93. 83% and 92. 73% in 5 and 10 retrieved images respectively, and an average precision of 100% in the pneumonia database, which is compared to previous methods have been able to improve the retrieval of lung images.