Reviewerly/exHarmony huggingface dataset:
Cross-Modal Image Matching Based on Coupled Convolutional Sparse Coding and Feature Space Learning

Coupled sparse representation (CSR) has achieved great success in cross-modal image matching in recent years. This model not only observes a common feature space for associating cross-domain image data for recognition purpose, but also improves the accuracy of cross-modal image matching. However, it applied the locally based manner in coupled sparse coding stage, so it is difficult to extract deeper image features, which is very important to improve the accuracy of cross-modal image matching. In order to address this problem, this paper proposes an iterative cross-modal image matching algorithm based on the coupled convolutional sparse coding and feature space learning (CCSCL). In contrast to the existing CSR-base cross-modal image matching methods, this model provides a global and flexible way to overcome some limitations of CSR. By making use of convolutional sparse coding and operating on the whole image, CCSCL can grasp the correlation between the pixels well and get more accurate modal feature map. In addiction, an cross-iterative training algorithm based on common feature space and correlation analysis of coupled convolution sparse coefficient is derived to efficiently solve the optimization problem. Experimental results show that the proposed model can effectively apply to cross-domain image matching and attain 98% matching accuracy.
Kalapa : una experiencia de innovación educativa

In the present work, systematization of the educational experience of the comunidad de aprendizaje, was carried out, the purpose of which was to identify and rescue the pedagogical, didactic, and training contributions of its experience as a learning community, both individually and in groups, with to make visible the strength aspects that allow their identity and those that require rethinking and adapting, to comply with an open training at any age that looks towards the individual interests of children and young people, with methods diverse of work, whose objective is only his learning and holistic integral formation. We worked with a qualitative approach of a phenomenological and narrative nature. The collection of information was done through semi-structured interviews, participatory observation, and documentary review of the educational institution. We worked with the testimonies of the members of the Kalapa educational community and an advisor of innovative educational experiences. As result, it was found that its main strength lies in the way in which knowledge is managed through experience and practices, interpersonal relationships between members of the Kalapa community, and concern for the multidimensional development of its students. On the other hand, other aspects were made visible, in terms of opportunities for improvements, such as inter and post-discipline evaluation. “Kalapa learning community” becomes an experiential, theoretical, and curricular reference that, when placed in context, is a space for the transformation of traditional pedagogical practices to innovative educational practices. Finally, the need for reflection was demonstrated by all the groups participating in the study, to intercalate to a greater extent, caring for and respecting those aspects of formal education that are transversal with non-formal education, such as evaluation, the learning objectives, and educational processes, organized, coordinated and systematized.KEY WORDS: Educational Innovation, Systematization of experiences, educational experience, holistic education, experiential education, community, innovative educational model
Design Plant Disease Detection System Using Deep Learning Convolutional Neural Network

Indonesia is an agrarian country whose people mostly work in agriculture by contributing to the 3rd largest GDP. But on the other hand, the main problem in agriculture is the development of pests and diseases of crops. There are cases where there are crops that are attacked by diseases with less obvious symptoms for farmers. For example, in citrus plants that are attacked by CVPD. Initially, the citrus plant does not show too early symptoms of the disease, making it difficult to distinguish from healthy plants. Based on these problems early detection and identification of plant diseases are the main factors to prevent and reduce the spread of plant diseases. The study used deep learning methods with the Convolutional Neural Network (CNN) algorithm model. The dataset used comes from PlantVillage with a total of 20,639 leaf image files that have been classified based on their respective classes. The design of the model architecture is done by designing the CNN model following the DenseNet121 architecture, by changing the parameters to improve the accuracy results. Image size is 64, train shape (20639, 64, 64, 3), epoch value 50,100, and 150. The number of input layers used is 4 layers with shapes (64, 64, 3). Densenet121 shape (1024), global average pooling2D shape (1024), batch normalization 2 (1024), dropout (1024), dense (256), batch normalization 3 (256), root (Dense) (15). This research was conducted with 3 epoch iteration tests to find the best accuracy value. The training data for epoch 50,100, and 150 produces an average model accuracy of 99.38% and the average value of the model loss is 0.019% can also be seen from the testing data results for epoch 50,100, and 150 has an average model of 95.16% and can be seen also from the average value for the loss is 0.20%. Based on the algorithm that applied the resulting training accuracy of 99.58% and the accuracy of testing 96.41% then design this application is useful to accurately detect diseases in plants by using leaf imagery of the plant.
Boundary-aware Segmentation Network Using Multi-Task Enhancement for Ultrasound Image

Complicated medical image analysis often requires a combination of disease classification, lesion detection and lesion segmentation. However, models designed for different tasks produce inconsistent or non-corresponding predictions and ignore the implicit connections between tasks. We propose a novel framework, which makes full use of the fact that segmentation and detection are mutually beneficial, boosts these three tasks in a unified framework. The proposed Information Enhancement Module uses classification information as a beneficial supplement to locate lesion quickly for segmentation. To further achieve fine segmentation with clear boundaries, we propose a Boundary-aware Loss, which dynamically adjusts supervised signal, so that our model pays more attention to boundary in later stages of training. Through experiments conducted on Thyroid Ultrasound dataset, we have demonstrated the good performance of the proposed method in joint segmentation and detection.
PENYULUHAN PEMANFAATAN APLIKASI BANDICAM & GOOGLE CLASSROOMUNTUK PEMBELAJARAN ONLINE DI SMPIT INSAN RABBANI KOTA BEKASI

Implementation of Community Service aimed at SMPIT Insan Rabbani is located at Jl. Ratuboko IV,Kav. Dutakranji, West Bekasi, Bekasi City, West Java, 17134 aims to share knowledge or transferknowledge regarding the application of video conferencing applications as a support foreducational services to teachers, education staff to understand and be able to develop online-basedlearning according to their needs. The specific target to be achieved is the understanding and abilityto apply video conferencing applications effectively and efficiently as a support for SMPIT InsanRabbani education services in order to be able to adapt to the demands and needs of educationists.The method used is in the form of counseling with practical delivery through the transfer ofknowledge and direct practice in the application so that it is truly acceptable and adds to theessence of knowledge and skills of Teachers, Education Staff on Information Technology, MicrosoftPowerpoint Applications, Video Bandicam Applications.
An Unsupervised Language-Independent Entity Disambiguation Method and its Evaluation on the English and Persian Languages

Entity Linking is one of the essential tasks of information extraction and natural language understanding. Entity linking mainly consists of two tasks: recognition and disambiguation of named entities. Most studies address these two tasks separately or focus only on one of them. Moreover, most of the state-of-the -art entity linking algorithms are either supervised, which have poor performance in the absence of annotated corpora or language-dependent, which are not appropriate for multi-lingual applications. In this paper, we introduce an Unsupervised Language-Independent Entity Disambiguation (ULIED), which utilizes a novel approach to disambiguate and link named entities. Evaluation of ULIED on different English entity linking datasets as well as the only available Persian dataset illustrates that ULIED in most of the cases outperforms the state-of-the-art unsupervised multi-lingual approaches.
MASP: Model-Agnostic Sample Propagation for Few-shot learning

Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution. These few-shot samples not only introduce high variance to the training but also may include outliers near the class boundaries. Directly feeding these samples to training algorithms can lead to unstable optimization and even incorrect gradient descent direction. In this paper, we improve the robustness to ``outliers'' by learning to propagate and refine the representations of few-shot samples to form a more compact data distribution before using them to train a classifier. We develop a mutual calibration among few-shot samples' representations by graph propagation, for which we learn an attention mechanism to build the graph and determine the propagation weights. On both clean datasets and datasets containing noisy labels, we show that our sample propagation generally improves different types of existing few-shot learning methods in multiple few-shot learning settings.
Large-sample confidence intervals of information-theoretic measures in linguistics

This article explores a method of creating confidence bounds for information-theoretic measures in linguistics, such as entropy, Kullback-Leibler Divergence (KLD), and mutual information. We show that a useful measure of uncertainty can be derived from simple statistical principles, namely the asymptotic distribution of the maximum likelihood estimator (MLE) and the delta method. Three case studies from phonology and corpus linguistics are used to demonstrate how to apply it and examine its robustness against common violations of its assumptions in linguistics, such as insufficient sample size and non-independence of data points.
TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors

Medical image segmentation is routinely performed to isolate regions of interest, such as organs and lesions. Currently, deep learning is the state of the art for automatic segmentation, but is usually limited by the need for supervised training with large datasets that have been manually segmented by trained clinicians. The goal of semi-superised and unsupervised image segmentation is to greatly reduce, or even eliminate, the need for training data and therefore to minimze the burden on clinicians when training segmentation models. To this end we introduce a novel network architecture for capable of unsupervised and semi-supervised image segmentation called TricycleGAN. This approach uses three generative models to learn translations between medical images and segmentation maps using edge maps as an intermediate step. Distinct from other approaches based on generative networks, TricycleGAN relies on shape priors rather than colour and texture priors. As such, it is particularly well-suited for several domains of medical imaging, such as ultrasound imaging, where commonly used visual cues may be absent. We present experiments with TricycleGAN on a clinical dataset of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset.
MODELING METHOD FOR PREPARING FUTURE EDUCATORS IN HIGHER EDUCATION INSTITUTIONS

The article describes the essence of the concept of model and method of modeling; its use in the educational training process of future teachers. Analysis of scientific sources gives grounds to claim that the model of formation, scientists understand the system of interrelated elements of the pedagogical process, which provide a sufficient level of formation of students of socially significant qualities of the future specialist. The article also states that the purpose of pedagogical modeling is to create a functional system of organization of mastering the future knowledge of teachers and ways of their acquisition at the level of professional, subject-specific, and cultural aspects. Modeling is a method of cognitive and management activity, which allows to adequately and fully reflect in the model representations the essence, the most important qualities and components of the system (in our case –training of masters for professional pedagogical activity), to receive information about its past, present and future status, conditions of construction, operation and development.
Features of development of electronic manuals on sewing production professions

The relevance of the study is due to: the active entry of distance and blended learning in the modern educational process, the restrictions imposed in many countries in connection with the pandemic situation; low level of readiness of the pedagogical community and students to effectively interact in conditions of a mixed or distance model of education; the conditions of the educational environment formed as a result of the introduction of blended and distance learning technologies; the need to find new approaches to the development of effective didactic and methodological tools for the formation of professional competencies of students of vocational education; creation of optimal pedagogical conditions for training working personnel for sewing industry, taking into account the rapid pace of introduction of the latest production technologies.
Perancangan Web Jasa Penyewaan Sound System Pada Eman Sounds

Eman Sounds is a business venture in the field of sound system rental services. The Eman sounds does not currently have a website application. The leasing service transaction system is still done conventionally, where if anyone wants to rent a sound system for a wedding or other official event can come directly to the Eman Sounds to see and rent it. For transaction reports sound system rental services are still recorded with paper so as to allow for data loss and errors in recording transactions. The purpose of this study is that the author will design a web sound system rental service for Eman Sounds so that it is expected to simplify the process of leasing transactions and making rental reports. The author uses the system development method, namely the waterfall model for this study. The use of PHP and HTML programming languages ​​the author uses to create web rental services and MYSQL as the database. The results of this study are expected that with a web design sound system rental service can help Eman Sounds in the process of managing rental transactions and reports.
Formation of student learning motivation

The article theoretically analyzes the motivation of students learning, identifies the prerequisites and the ways of possible increasing of the motivation's level of students' learning in an agricultural institution of higher education. This article is one of the stages to research student's youth, the level of student's motivation for learning specialized and general subjects. External and internal reasons for the decrease in the level of motivation are considered, as well as methodical recommendations are given to teachers to increase the level of motivation of students' learning.
Ancient Eruption May Change Our Understanding of Modern Volcanoes

Bubbles trapped in magma from a 1,000-year-old event reveal how scoria cones might erupt and what impact they may have on the landscape and atmosphere.
An Interactive Proof of Termination for a Concurrent $λ$-calculus with References and Explicit Substitutions

In this paper we introduce a typed, concurrent $λ$-calculus with references featuring explicit substitutions for variables and references. Alongside usual safety properties, we recover strong normalization. The proof is based on a reducibility technique and an original interactive property reminiscent of the Game Semantics approach.
PROSE-WRITER’S POETRY: A CORRELATION BETWEEN D. LESSING’S EARLY POETIC WORK AND LATE PERIOD NOVELS

The article is devoted to the study of the poetic work of D. Lessing, the Nobel laureate, better known for her prose. There is a small amount of research on this topic in foreign literary criticism. Such scholars as N. Bentley, C. Murray assess the author’s poetic work in close connection with her programmatic feminist novels ‘Golden Diary’, ‘Martha Quest’. Nevertheless, the issue of her early poetry and novels of the late period reveals common features and are promising for study, which determines the relevance of this article. The theme of creative nostalgia, longing for the lost home, exile, feeling of rootlessness is transparent for D. Lessing’s work and are present in many of her creations. The writer, experiencing ‘the trauma of migration’ somehow tries to recur in her works to the location of Africa, a long-forbidden territory for her. Such a creative ‘return’ for D. Lessing is a way of reconstructing memories, analyzing them, and comparing the past and the present. The author thematizes the problems of collective sin and guilt atonement as a way of self-reinterpretation both in her poetry and prose.
Towards Decentralized Human-Swarm Interaction by Means of Sequential Hand Gesture Recognition

In this work, we present preliminary work on a novel method for Human-Swarm Interaction (HSI) that can be used to change the macroscopic behavior of a swarm of robots with decentralized sensing and control. By integrating a small yet capable hand gesture recognition convolutional neural network (CNN) with the next-generation Robot Operating System \emph{ros2}, which enables decentralized implementation of robot software for multi-robot applications, we demonstrate the feasibility of programming a swarm of robots to recognize and respond to a sequence of hand gestures that capable of correspond to different types of swarm behaviors. We test our approach using a sequence of gestures that modifies the target inter-robot distance in a group of three Turtlebot3 Burger robots in order to prevent robot collisions with obstacles. The approach is validated in three different Gazebo simulation environments and in a physical testbed that reproduces one of the simulated environments.
A Review On Spotting and Segmenting The Tumor Using Mammography

Bosom malignant growth is practically a typical type of disease for ladies. Mammography is one of the best techniques to identify breast cancer in the Medical image. The precision rate is higher in the bosom disease in mammography relies upon the exactness in dividing a picture. Advanced picture handling helps in decreasing the mistake and humanizing the order exactness to the issue of bosom malignant growth identification. The real any expectation of this document is to audit a current methodology for characteristics and dividing the growth in mammographic pictures.
Using disney’s theme song to improve the students’ listening skill at eleventh grade of SMAN 13 Bone

The main concern of this research was aimed at improving the listening skill of the grade XI students of SMAN 13 Bone in the academic year of 2020/2021. The researcher used WhatsApp group as a media to communicate with students, face to face cannot be implemented because of the Covid-19 pandemic. The technique of analyzing data was applied by using quantitative data. The quantitative data was taken from the test. The result shows there was a positive effect of students’ listening skill using Disney’s theme song. It showed from the mean of the Pre-test was 66,43 and the mean score of post-test was 78,0357. From the data, it indicated that using Disney’s theme song in Listening skill was effective. The advantages of using song are: Song could become a media introduction of new languages, songs improve students’ pronunciation, song could be give good contribution in increasing students’ motivation in Listening, and song can strengthen memory of students.
Deploying Azure Arc-Enabled PostgreSQL Hyperscale

While Chapter 5 was handling SQL Managed Instance, this chapter will guide through the necessary steps when it comes to working with PostgreSQL Hyperscale instead.
Language Model Compression and implications to Search

BERT like transformer language models are getting complex and large. Industry and research are training large general-purpose models that can do a multitude of natural language tasks such as mapping text in a semantic similarity space or doing query intent recognition etc. These models are then later fine-tuned to specific uses, which means the need to operationalize several versions of these models. As things grow and spiral out of proportion with respect to compute and cost, there is a direct impact on operationalization. As beneficiaries of this research and the models thus produced, there is a need for reliable model compression techniques to effectively use these models in downstream tasks. Current research proposes methods such as tree pruning, knowledge distillation, quantization, parameter size reduction that have different uses and downstream implications. We would like to propose an analytical framework for measuring the quality of model compression, especially with respect to application to downstream tasks and share the cost implications of some of these techniques as applied to Lexis Answers DCG.
Generating SQL Command Syntax Using MySQL Based on Typing Command Sentence

Information retrieval system is a system that is widely used to retrieve information. This research will discuss how the system finds back the information stored in database tables. Tables in the database are arranged to store all forms of data entered by database users so that later the stored data can be used again. Re-accessing the database's information must go through a mechanism known as a database management system (DBMS). One of the most widely used DBMS is MySQL. By using a DBMS, information and data can be manipulated according to user needs. Data manipulation in the database is done in a special language, namely SQL (Structure Query Language). Mastery of SQL commands is an obligation for database users so that the manipulated data can produce the required information. However, many database users still do not understand how the actual SQL command syntax manages and manipulates data into information. This is, of course, very risky if the solution is not immediately sought because it will hinder the process of retrieving information from the data stored in the database. For this problem to be resolved, it is necessary to design a system that can help database users translate their wishes into SQL command syntax. This paper will discuss how a command in Indonesian can be translated into SQL command syntax. The method used to solve this research problem is rule-based. There are two stages in the main process: the pre-processing stage, which consists of a word tokenization process, and a translation stage, including a keyword grouping process. This keyword grouping process consists of the keyword group analysis phase, table and column analysis, identification of SQL commands, and mapping of SQL commands. From all stages that have been passed and testing of 7 scenarios with ten (10) commands for each scenario, the accuracy is 81.42%. The inaccuracy in the testing process is more a problem of displaying data from two or more tables, for example, using the join table command. This problem can be addressed by adding new rules for the use of table joins.