Reviewerly/exHarmony huggingface dataset:
Comprehensive Analysis of Few-shot Image Classification Method Using Triplet Loss

Image classification task is a very important problem of a computer vision area.The first approaches to image classification tasks belong to a classic straightforward algorithm.Despite the successful applications of such algorithms a lot of image classification tasks had not been solved until machine learning approaches were involved in a computer vision area.An early successful result of machine learning applications helps researchers with extracted features classification which was not available without machine learning models.But handcrafter features were required which left the most complicated classification task impossible to solve.Recent success in deep learning allows researchers to implement automatic trainable feature extraction.This gave significant progress in the computer vision area last but not least.Processing large-scale datasets bring researchers great progress in automatic feature extraction thus combining such features with precious approaches led to groundbreaking in computer vision.But a new limitation has come -dependency on large amounts of data.Deep learning approaches to image classification task usually requires large-scale datasets.Moreover, modern models lead to unexpected behavior in distribution datasets.A few-shot learning approach of deep learning models allows us to dramatically reduce the amount of required data while keeping the same promising results.Despite reduced datasets, there is still a tradeoff between the amount of available data and trained model performance.In this paper, we implemented a siamese network based on triplet loss.Then, we investigate a relationship between the amount of available data and few-shot model performances.We compare the models obtained by metric-learning with baselines models trained using large-scale datasets.
AN IMPROVED VOGEL’S APPROXIMATION METHOD (IVAM) FOR FRAGMENT ALLOCATIONN AND REPLICATION IN DISTRIBUTED DATABASE SYSTEMS

In distributed database, allocation of fragments depends on the access time, response time, transmission cost and storage capacity.An Improved VAM mathematical model is introduced to allocate and replicate fragments in an optimal way either by maximizing the access frequency or minimizing the transmission cost.The method reduces the migration of fragments over the network by allocating fragments to the sites where the access frequency is higher.Storage capacity of sites is another essential constraint considered here.Successful retrieval ratio of fragments is taken as a performance measure to show the efficacy and the optimal behavior of the proposed algorithm.It worked on 3 fragments and 4 sites and obtained SRR is .26for allocation and .24for replication which is higher than compared works.For 3 fragments and 6 sites, SRR for allocation is .191and for replication is .17which has optimally increased the successful retrieval ratio.
Penerapan Pembelajaran Literasi di SMP Negeri 2 Sitiotio dengan Memanfaatkan Teknologi Informasi

Literacy learning needs to be experienced by school students. The goal is to optimize students' ability to understand lessons in depth. Junior High School 2 Sitiotio in Samosir regency seems to need this literacy learning experience. The application of literacy is optimized by utilizing information technology to attract students' interest in experiencing the PKM teaching and mentoring process. Based on observations, it appears that learning activities are conducive and dynamic. Teaching through Unpri's Spada and Zenius application shows that there is a new paradigm for students in literacy learning. The mentoring process also shows continuous interaction outside the classroom. The test results showed that there was an increase in student scores before and after literacy learning by 53.3% with a significance of p &lt; 0.05 and Ha was accepted. Thus, the application of literacy can significantly improve student learning outcomes by utilizing information technology.
Spatial-Frequency Redistribution-based Saliency Region Detection for Thai Text Localisation

Saliency region detection plays an important role in computer vision applications and related areas, such as human fixation, figure-ground separation, face detection, and image compression. Nevertheless, state-of-the-art methods can moderately detect the saliency regions of a psychological pattern dataset. Therefore, this paper proposes a spatial-frequency redistribution (SFR) method to improve the efficiency of the detection of the saliency regions. The proposed SFR method consists of two main procedures: (i) adaptive cosine image construction-and-implantation and (ii) saliency region detection using complete multiple-object-implanted images. The former procedure constructs an adaptive cosine image by using a cosine function based on an object structure and then individually implants it into each object detectable. This stage provides the first significant property, spatial redistribution, to the object implanted. The adaptive cosine image redistributes the objects for controllability. Then, the latter procedure transforms a complete multiple object-implanted image into the frequency domain. At this point, the second significant property, frequency redistribution, provides the simple technique for identifying and separating the target object from the unwanted objects and background. In this paper, these properties are referred to as spatial-frequency redistribution. This method was realized as a computer program, and then such program was tested with a psychological pattern dataset and a Thai text dataset. The experimental results, when compared with state-of-the-art methods, show that the proposed SFR method can achieve the clear detection of the attention region in the psychological pattern dataset. Moreover, the proposed method can achieve a F-value of 80.28% with a recall and precision of 76.32% and 85.23%, respectively, for Thai text localisation in the Thai text dataset.
IDENTIFICATION OF BACTERIAL SPOT DISEASES ON PAPRIKA LEAVES USING CNN AND TRANSFER LEARNING

Paprika, often called bell peppers, is a plant with the Latin name Capsicum annuum var. gross. Paprika in Indonesia has a high selling value, so the opportunity for cultivating the paprika plant itself is enormous. However, the cultivation of this plant cannot be separated from the threat of disease that can affect the yield of paprika. Bacterial spot is one of them, and it is a disease that is very dangerous for paprika plants because the disease infects all parts of the plant. In this case, early detection is needed to carry out appropriate treatment to minimize the effects caused by bacterial spots. Detection of bacterial spots on paprika can be done by direct observation or conducting laboratory tests, but this requires people who have the appropriate knowledge and experience. Based on the above problems, the identification system can be an option in identifying bacterial spot disease in paprika. This research chose the Convolutional Neural Network (CNN) algorithm in the identification system. Because CNN is one of the algorithms that can receive output in the form of an image which is very suitable for the case of bacterial spots on peppers, this research dataset is divided into healthy leaves and leaves infected with bacterial spots. In this study, the implementation of CNN with transfer learning obtained results from a test accuracy of 90%, training accuracy 97% with a loss of 8.5%, validation accuracy of 97.5% with a loss of 6.9%.
Conditions for the Development of Psychological and Pedagogical Competence of Teachers of Vocational (Professional and Technical) Education

The purpose of the article is to determine and study the conditions of development of psychological and pedagogical competence of teachers of vocational education in the system of continuing education. The leading empirical method is a questionnaire that incorporates a significant number of respondents in different regions of Ukraine in order to analyze the conditions for the development of psychological and pedagogical competence of teacher. The article determines motivation of professional activity and readiness for an innovative activity as main conditions for the development of the said competence. The article substantiates an innovative model of teachers’ professional development - the introduction of pedagogical coaching technologies in the system of continuous vocational education. The article considers the Pedagogical Coaching School program and its activity as an innovative model for training of teachers. The article provides results of monitoring studies and assessment of customers’ satisfaction level with educational services, quantitative and qualitative indicators of satisfaction among students of the Pedagogical Coaching School, and the analysis of the results obtained.
Text Extraction and Language Translation for Health Care and Clinical Records Using Deep Learning

Abstract Language conversion is a technique that has tremendous use worldwide. With the multitude of languages available, it is not feasible for an average person to learn languages he/she will not use. However, everyone faces situations where knowing a certain language would help in a certain city or even during work. This problem can be solved using the latest technologies in deep learning and computer vision. A system that can identify the text from images and convert them into a well-known language like English has tremendous use everywhere especially in the medical field. The medical field is one where communication and exchange of information is critical. In case of a medical emergency during a tourists visit, the inability to understand what the doctor is trying to say or what type of medication is being prescribed would lead only to more stress and panic. There are systems that have tried to solve this problem and there will be more in the future. One of the glaring issues faced by contemporary systems is the lack of image processing before getting the text of a signboard using computer vision algorithms. Blurred images or images where the characters of the text are too close to each other can hamper the system’s performance to a great extent. The proposed system provides a way by which the text present in an image can be extracted and displayed back to the user in a universal language like English. The system incorporates an optical character recognition tool called pytesseract to get the text from the image and it is converted into a different language using deep learning.
Missing Child Identification Using Deep Learning and LBPH Algorithm

Abstract—Crimes are at rise and turning into hard for police to become aware of and rescue the Missing Persons. Our Proposed System will use Face Recognition Algorithms and could have the capability for IRIS recognition as well to detect Missing Persons. Face Recognition begins with extracting the coordinates of features such as width of mouth, width of eyes, pupil and comparing the result with the measurement stored in the database and returning the closest record. Nowadays, face recognition techniques are growing around the world. We will be using advanced algorithms like LBPH for our system and also compare to other older algorithms to prove higher accuracy of our system. We will be building a web based system integrated with Backend Machine Learning server. The Backend ML system will handle all the search, detection and recognition using our face recognition and Iris Recognition Model and all the data stored in the database.
Learning Optimal Flows for Non-Equilibrium Importance Sampling

Many applications in computational sciences and statistical inference require the computation of expectations with respect to complex high-dimensional distributions with unknown normalization constants, as well as the estimation of these constants. Here we develop a method to perform these calculations based on generating samples from a simple base distribution, transporting them by the flow generated by a velocity field, and performing averages along these flowlines. This non-equilibrium importance sampling (NEIS) strategy is straightforward to implement and can be used for calculations with arbitrary target distributions. On the theory side, we discuss how to tailor the velocity field to the target and establish general conditions under which the proposed estimator is a perfect estimator with zero-variance. We also draw connections between NEIS and approaches based on mapping a base distribution onto a target via a transport map. On the computational side, we show how to use deep learning to represent the velocity field by a neural network and train it towards the zero variance optimum. These results are illustrated numerically on benchmark examples (with dimension up to $10$), where after training the velocity field, the variance of the NEIS estimator is reduced by up to $6$ orders of magnitude than that of a vanilla estimator. We also compare the performances of NEIS with those of Neal's annealed importance sampling (AIS).
Studies that have had a significant impact on the modern education system

In this article, the author discusses research that has had a significant impact on the modern education system. Shows the strengths and weaknesses of countries that have implemented education reforms. Analyzes the views of Michael Barber and Mona Murshed, McKinsey,Michael Fullan, professors at Harvard University. Meta-analysis seeks to identify similarities or differences by combining the results of several studies with statistical methods. Teachers say that students' learning outcomes cannot be improved without improving the way they teach their subjects. The most important thing in the pedagogical profession is that every teacher's desire to constantly improve their professional skills is the only way to success. He also stressed the importance of using digital learning technologies, which have been introduced into the education system since March 2020.
A universal black-box optimization method with almost dimension-free convergence rate guarantees

Universal methods for optimization are designed to achieve theoretically optimal convergence rates without any prior knowledge of the problem's regularity parameters or the accurarcy of the gradient oracle employed by the optimizer. In this regard, existing state-of-the-art algorithms achieve an $\mathcal{O}(1/T^2)$ value convergence rate in Lipschitz smooth problems with a perfect gradient oracle, and an $\mathcal{O}(1/\sqrt{T})$ convergence rate when the underlying problem is non-smooth and/or the gradient oracle is stochastic. On the downside, these methods do not take into account the problem's dimensionality, and this can have a catastrophic impact on the achieved convergence rate, in both theory and practice. Our paper aims to bridge this gap by providing a scalable universal gradient method - dubbed UnderGrad - whose oracle complexity is almost dimension-free in problems with a favorable geometry (like the simplex, linearly constrained semidefinite programs and combinatorial bandits), while retaining the order-optimal dependence on $T$ described above. These "best-of-both-worlds" results are achieved via a primal-dual update scheme inspired by the dual exploration method for variational inequalities.
Q-linear Convergence of Distributed Optimization with Barzilai-Borwein Step Sizes

The growth in sizes of large-scale systems and data in machine learning have made distributed optimization a naturally appealing technique to solve decision problems in different contexts. In such methods, each agent iteratively carries out computations on its local objective using information received from its neighbors, and shares relevant information with neighboring agents. Though gradient-based methods are widely used because of their simplicity, they are known to have slow convergence rates. On the other hand, though Newton-type methods have better convergence properties, they are not as applicable because of the enormous computation and memory requirements. In this work, we introduce a distributed quasi-Newton method with Barzilai-Borwein step-sizes. We prove a Q-linear convergence to the optimal solution, present conditions under which the algorithm is superlinearly convergent and validate our results via numerical simulations.
Tableaux for some deontic logics with the explicit permission operator

In this paper we present a tableau system for deontic logics with the operator of explicit permission. By means of this system the decidability of the considered logics can be proved. We will sketch how these logics are semantically defined by means of relating semantics and how they provide a simple solution to the free choice permission problem. In short, these logics employ relating implication and a certain propositional constant. These two are in turn used to define deontic operators similarly as in Andersonian-Kangerian reduction, which uses different intensional implications and constants.
Redundancy Reduction Twins Network: A Training framework for Multi-output Emotion Regression

In this paper, we propose the Redundancy Reduction Twins Network (RRTN), a redundancy reduction training framework that minimizes redundancy by measuring the cross-correlation matrix between the outputs of the same network fed with distorted versions of a sample and bringing it as close to the identity matrix as possible. RRTN also applies a new loss function, the Barlow Twins loss function, to help maximize the similarity of representations obtained from different distorted versions of a sample. However, as the distribution of losses can cause performance fluctuations in the network, we also propose the use of a Restrained Uncertainty Weight Loss (RUWL) or joint training to identify the best weights for the loss function. Our best approach on CNN14 with the proposed methodology obtains a CCC over emotion regression of 0.678 on the ExVo Multi-task dev set, a 4.8% increase over a vanilla CNN 14 CCC of 0.647, which achieves a significant difference at the 95% confidence interval (2-tailed).
EEML: Ensemble Embedded Meta-learning

To accelerate learning process with few samples, meta-learning resorts to prior knowledge from previous tasks. However, the inconsistent task distribution and heterogeneity is hard to be handled through a global sharing model initialization. In this paper, based on gradient-based meta-learning, we propose an ensemble embedded meta-learning algorithm (EEML) that explicitly utilizes multi-model-ensemble to organize prior knowledge into diverse specific experts. We rely on a task embedding cluster mechanism to deliver diverse tasks to matching experts in training process and instruct how experts collaborate in test phase. As a result, the multi experts can focus on their own area of expertise and cooperate in upcoming task to solve the task heterogeneity. The experimental results show that the proposed method outperforms recent state-of-the-arts easily in few-shot learning problem, which validates the importance of differentiation and cooperation.
A Neural Network Based Method with Transfer Learning for Genetic Data Analysis

Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing. However, this technique is largely ignored in application to genetic data analysis. In this paper, we combine transfer learning technique with a neural network based method(expectile neural networks). With transfer learning, instead of starting the learning process from scratch, we start from one task that have been learned when solving a different task. We leverage previous learnings and avoid starting from scratch to improve the model performance by passing information gained in different but related task. To demonstrate the performance, we run two real data sets. By using transfer learning algorithm, the performance of expectile neural networks is improved compared to expectile neural network without using transfer learning technique.
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data

Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achieving state-of-the-art performance on mainstream benchmarks. Recently, many of these datasets, e.g., MS-Celeb-1M and VGGFace2, are retracted due to credible privacy and ethical concerns. This motivates this work to propose and investigate the feasibility of using a privacy-friendly synthetically generated face dataset to train face recognition models. Towards this end, we utilize a class-conditional generative adversarial network to generate class-labeled synthetic face images, namely SFace. To address the privacy aspect of using such data to train a face recognition model, we provide extensive evaluation experiments on the identity relation between the synthetic dataset and the original authentic dataset used to train the generative model. Our reported evaluation proved that associating an identity of the authentic dataset to one with the same class label in the synthetic dataset is hardly possible. We also propose to train face recognition on our privacy-friendly dataset, SFace, using three different learning strategies, multi-class classification, label-free knowledge transfer, and combined learning of multi-class classification and knowledge transfer. The reported evaluation results on five authentic face benchmarks demonstrated that the privacy-friendly synthetic dataset has high potential to be used for training face recognition models, achieving, for example, a verification accuracy of 91.87\% on LFW using multi-class classification and 99.13\% using the combined learning strategy.
THE ANALYSIS OF PERFORMANCE, INFORMATION, ECONOMY, CONTROL, EFFICIENCY, AND SERVICE (PIECES) IN-LIBRARY MANAGEMENT INFORMATION SYSTEMS POSTGRADUATE UIN ALAUDDIN MAKASSAR

This article elaborates the PIECES analysis of the management information system evaluated in terms of performance, information, control, efficiency, and services resulting from the applied library management information system. The type of research used is descriptive research with a qualitative approach. This research is a phenomenological approach where the purpose of this approach is to study in-depth the state of the postgraduate library at UIN Alauddin Makassar. The results of this research show the description of the library management information system applied by the postgraduate library of UIN Alauddin Makassar, where the software used has been integrated into the core activities of the library, namely the management of library materials, circulation services, search engine services, membership services, and reporting on library activities. The use of Slims 7 Candana software has met the needs of library management. However, the control section for reporting the number of visitors Postgraduate Library of UIN Alauddin Makassar does not apply visitor attendance at the library either manually or automatically by the management information system (visitor counter service). This section has no data input process, so the output process for reporting visitors neither exists.