Effect of plant growth regulators on growth and yield of chili (Capsicum annuum L.)

Chili (Capsicum annuum L.) is an important food additive with high medicinal value. To investigate the effect of plant growth regulators on chili, seedlings of chili were collected from the local market and grown in the experimental field of the University of Barishal, Bangladesh. Foliar spray of different degrees of plant growth regulators, Gibberellin (50 mg/l, 100 mg/l, 250mg/l, 350 mg/l GA3) and Cytokinin (50 mg/l, 100 mg/l, 250mg/l, 350mg/l Kn) were applied from 15 days of germination. Data on different growth and yield parameters were collected and analyzed statistically. The result reveals that there is a significant difference in growth and yield related traits in chili due to the application of plant growth regulators. An optimum level of PGRs application shows better performance compare with control. Plant height particularly influenced by GA3 whereas other attributes like the number of leaves, branches, flowers and fruits are greatly influenced by the application of kinetin

Facebook Report on Privacy of fNIRS data

The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting.Comment: 15 pages, 5 figures, 3 table

Auto DP-SGD: Dual Improvements of Privacy and Accuracy via Automatic Clipping Threshold and Noise Multiplier Estimation

DP-SGD has emerged as a popular method to protect personally identifiable information in deep learning applications. Unfortunately, DP-SGD's per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility. To enhance the model's utility, researchers proposed various adaptive DP-SGD methods. However, we examine and discover that these techniques result in greater privacy leakage or lower accuracy than the traditional DP-SGD method, or a lack of evaluation on a complex data set such as CIFAR100. To address these limitations, we propose an Auto DP-SGD. Our method automates clipping threshold estimation based on the DL model's gradient norm and scales the gradients of each training sample without losing gradient information. This helps to improve the algorithm's utility while using a less privacy budget. To further improve accuracy, we introduce automatic noise multiplier decay mechanisms to decrease the noise multiplier after every epoch. Finally, we develop closed-form mathematical expressions using tCDP accountant for automatic noise multiplier and automatic clipping threshold estimation. Through extensive experimentation, we demonstrate that Auto DP-SGD outperforms existing SOTA DP-SGD methods in privacy and accuracy on various benchmark datasets. We also show that privacy can be improved by lowering the scale factor and using learning rate schedulers without significantly reducing accuracy. Specifically, Auto DP-SGD, when used with a step noise multiplier, improves accuracy by 3.20, 1.57, 6.73, and 1.42 for the MNIST, CIFAR10, CIFAR100, and AG News Corpus datasets, respectively. Furthermore, it obtains a substantial reduction in the privacy budget of 94.9, 79.16, 67.36, and 53.37 for the corresponding data sets.Comment: 25 pages single column, 2 figure

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PD

Investigation of cellular level of water in plant-based food material

Water in plant tissue is generally distributed in three different spaces namely, intercellular water, intracellular water, and cell wall water. For hygroscopic material, these three water states should be considered for understanding heat and mass transfer during drying. However, to the authors’ best knowledge, the proportion of these three types of water in plant-based food tissue has not yet been investigated. The present study was performed to investigate the proportion of intercellular water, intracellular water, and cell wall water inside plant-based food material. In this study, experiments were performed for two different plant-based food tissues namely, granny smith apple and potato. H1-NMR relaxation measurement offers a unique method for investigating the physical state of tissue water in compartments by using T2 relaxometry. The different water environments were calculated by using multicomponent fits of the T2 relaxation curves. The experimental results confirmed that plant-based food materials contain about 80 to 92 % LBW, 6 to 16 % free water and only about 1 to 6 % SBW. An attempt was made to establish the relationship between physical properties of fruits and vegetables and the proportion of different water environments. Interestingly, it was found that SBW strongly depends on the proportion of solid in the sample tissue, whereas FW depends on the porosity of the material

Security at the Physical Layer over GG Fading and mEGG Turbulence Induced RF-UOWC Mixed System

This work was supported in part by the National Research Foundation of Korea grant funded by the Korean Government (Ministry of Science and ICT) under Grant 2019R1A2C1083988, in part by the Ministry of Science and ICT, South Korea, under the Information Technology Research Center Support Program supervised by the Institute for Information and Communications Technology Planning and Evaluation, under Grant IITP-2021-2016-0-00313, and in part by Sejong University through its Faculty Research Program under Grant 20202021.Peer reviewedPublisher PD

Enhancing security of TAS/MRC-based mixed RF-UOWC system with induced underwater turbulence effect

Post commercial deployment of fifth-generation (5G) technologies, the consideration of sixth-generation (6G) networks is drawing remarkable attention from research communities. Researchers suggest that similar to 5G, 6G technology must be human-centric where high secrecy together with high data rate will be the key features. These challenges can be easily overcome utilizing PHY security techniques over high-frequency free-space or underwater optical wireless communication (UOWC) technologies. But in long-distance communication, turbulence components drastically affect the optical signals, leading to the invention of the combination of radio-frequency (RF) links with optical links. This article deals with the secrecy performance analysis of a mixed RF-UOWC system where an eavesdropper tries to intercept RF communications. RF and optical links undergo η−μ and mixture exponential generalized Gamma distributions, respectively. To keep pace with the high data rate of the optical technologies, we exploit the antenna selection scheme at the source and maximal ratio combining diversity at the relay and eavesdropper, while the eavesdropper is unaware of the antenna selection scheme. We derive closed-form expressions of average secrecy capacity, secrecy outage probability, and probability of strictly positive secrecy capacity to demonstrate the impacts of the system parameters on the secrecy behavior. Finally, the expressions are corroborated via Monte Carlo simulations

Support directional shifting vector: A direction based machine learning classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm