Reviewerly/exHarmony huggingface dataset:
Klasifikasi citra makanan/non makanan menggunakan metode Transfer Learning dengan model Residual Network

People's activities that often upload photos of food before eating are common in popular social media, such as Facebook, Instagram, Twitter, and Pinterest. The food image data circulating on social media can be used for business purposes, such as analyzing customer behavior patterns. However, not all of the uploaded images are food images, so that before the analysis is carried out, it is necessary to do a classification task between food images and non-food images. Therefore, the researcher proposes automatic food image classification using transfer learning method using the Residual Network model version of 18 (ResNet-18). Residual Network model is used because it has a residual connection mechanism to solve the vanishing gradient problem. In addition, transfer learning was chosen because this method leverages the features and weights that have been generated in the previous training process on large and more general data (Imagenet) and thus reduce computation time and increase accuracy. The test was carried out by comparing the capabilities of the ResNet18 model with AlexNet. In addition, the fine tuning and freeze layer methods used to improve the quality of the model were also carried out in this study. In the experiment, the data set was divided into 3,000 images for training data and 1,000 images for test data, while the evaluation used was correctness accuracy. The results obtained in the ResNet18 model, namely the fine tuning training method, produced an accuracy value of 0.981 while the freeze layer resulted in the best accuracy value of 0.988. The AlexNet model that uses the fine tuning training method produces an accuracy value of 0.970 while the freeze layer produces the best accuracy value of 0.978. It can be concluded that the mechanism with the best accuracy is found in the RestNet18 architecture using the freeze layer 1-3 with an accuracy of 0.988.
Sustainable Information Development Practices and Societal Transformation in Kenya

Demonstrate the impact of the information discipline and profession in transforming and changing the life of people through sustainable development practices and policies. Quantitative and qualitative research design collected data from information academicians and professionals. Questionnaires and interviews were used to obtain primary data while document analysis and related sources provided secondary information. Decent work and economic growth is the leading and relevant rudimentary goals of sustainable development practices that provides not only perfect employment and wealth creation opportunities but also the fundamental denominator to poverty eradication, food security, quality education, good health and well-being as well as clean water and sanitation. Post-implementation strategies for building sustainable development practices should connect the social, economic and political aspects of people with practical policies.
IDENTIFYING COMPETENCIES IN DESIGNING THE EDUCATIONAL PROGRAM «SMART CITY»

Educational program (EP) is a document where the application of an individual approach to students is ensured, so do the transformation of professional competencies from professional standards and qualification standards into learning outcomes. Innovative educational program «7MO1511(2) – Technologies – Smart City» has been developed in order to prepare masters of IT education on the basis of a multidisciplinary approach to allow the students to possess fundamental knowledge and practical skills in the fields of analysis of the development, management and services of the Internet and the use of SMART technology. However, the EP can be successful if the competencies of students are determined. In this article, the main component of educational program is discussed. The competencies were indentified under the professional standards developed by «Atameken». The concepts of competency, professional standard and educational programs are given.
Edge-Preserving Image Denoising Based on Lipschitz Estimation

The information transmitted in the form of signals or images is often corrupted with noise. These noise elements can occur due to the relative motion, noisy channels, error in measurements, and environmental conditions (rain, fog, change in illumination, etc.) and result in the degradation of images acquired by a camera. In this paper, we address these issues, focusing mainly on the edges that correspond to the abrupt changes in the signal or images. Preserving these important structures, such as edges or transitions and textures, has significant theoretical importance. These image structures are important, more specifically, for visual perception. The most significant information about the structure of the image or type of the signal is often hidden inside these transitions. Therefore it is necessary to preserve them. This paper introduces a method to reduce noise and to preserve edges while performing Non-Destructive Testing (NDT). The method computes Lipschitz exponents of transitions to identify the level of discontinuity. Continuous wavelet transform-based multi-scale analysis highlights the modulus maxima of the respective transitions. Lipschitz values estimated from these maxima are used as a measure to preserve edges in the presence of noise. Experimental results show that the noisy data sample and smoothness-based heuristic approach in the spatial domain restored noise-free images while preserving edges.
Incremental Learning with Self-labeling of Incoming High-dimensional Data

Many incoming data chunks are being produced each day continuously at high speed with soaring dimensionality, and in most cases, these chunks are unlabeled.Our study combines incremental learning with self-labeling to deal with these incoming data chunks.We first search for the best data dimensionality reduction algorithm, leading to the optimal low-dimensional space for all the incoming chunks.The incremental classifier is then adapted gradually with chunks that are optimally reduced and self-labeled.Using a highly-dimensional and multi-class dataset, we conduct several experiments to demonstrate our incremental learning approach's efficacy and compare it with incremental learning using human-annotated labels.
Universal Semantic Tagging for English and Mandarin Chinese

Universal Semantic Tagging aims to provide lightweight unified analysis for all languages at the word level.Though the proposed annotation scheme is conceptually promising, the feasibility is only examined in four Indo-European languages.This paper is concerned with extending the annotation scheme to handle Mandarin Chinese and empirically study the plausibility of unifying meaning representations for multiple languages.We discuss a set of language-specific semantic phenomena, propose new annotation specifications and build a richly annotated corpus.The corpus consists of 1100 English-Chinese parallel sentences, where compositional semantic analysis is available for English, and another 1000 Chinese sentences which has enriched syntactic analysis.By means of the new annotations, we also evaluate a series of neural tagging models to gauge how successful semantic tagging can be: accuracies of 92.7% and 94.6% are obtained for Chinese and English respectively.The English tagging performance is remarkably better than the state-ofthe-art by 7.7%.
DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, wepropose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly adapt to heterogeneous tumors. We introduce a simple but effective Exponential Distance Transform (ExpDT) that converts user interactions into guide maps regarded as the spatial and appearance prior. Comparing with popular Euclidean and geodesic distances, ExpDT is more robust to various image sizes, which reserves the distribution of interactive inputs. Furthermore, to enhance the tumor-related features, we design a deep interactive module to propagate the guides into deeper layers. We train and evaluate DINs on three MRI data sets from NF1 patients. The experiment results yield significant improvements of 44% and 14% in DSC comparing with automated and other interactive methods, respectively. We also experimentally demonstrate the efficiency of DINs in reducing user burden when comparing with conventional interactive methods.
Prototype of A Monitoring And Prevention System For People Vulnerable To Cardiac Arrest And Covid-19 Using IoT And SAP Cloud Platform

In the present work of investigation due to the worldwide conjuncture is being based on a system of monitoring and prevention for people vulnerable to cardiac arrests and covid -19 by means of the implementation of Arduino plates linked with an oximeter to measure the oxygenation in the blood that will help to monitor and to prevent cases of mortality in Peru. For the development of the project, we used the Scrum methodology and helped to develop an orderly manner and with daily communication and the advantages that this methodology provides are that it is incremental and iterative so that we can update, add or remove certain sprints that may not be useful. On the other hand, for the development of the prototype, we used builme, a tool for design with a code generator that is exclusively from SCP, where the interfaces are included as real-time information to monitor patients either from anywhere in the world to prevent early death. © 2021 Seventh Sense Research Group®.
Students’ attitude to learning using virtual reality programs

This article presents an empirical study of the interest of higher educational institution students in supplementing educational activities with virtual reality technologies. The article substantiates the relevance of using VR technologies in teaching University students. The purpose of the study is to determine the feasibility of using virtual reality technologies in the learning process. The review of literature sources devoted to virtual reality and its application in the educational sphere, including the training of highly specialized professionals. Specific examples of the use of virtual reality technologies in the educational process are presented. Classifications of virtual reality technologies are given depending on the necessary means to enter the virtual environment, as well as on the type of virtual reality technologyVR, depending on the degree of user immersion. The possibility and feasibility of using each type of virtual reality in the educational process in modern educational institutions is evaluated. The empirical part of the research is devoted to conducting a survey of University students on their awareness of VR, interest in learning through the use of VR technologies, satisfaction with virtual reality technologies already introduced into the educational process if they have experience, and preferences in the way VR is used in training. The main empirical results of the study are presented in the form of tables based on the results of a survey among students. The results of the research can be used in pedagogy when developing new educational programs, when making decisions about the introduction of innovative technologies in the educational process, when improving the skills of teachers as a theoretical material that introduces the concept of virtual reality and the prospects for its use in education.
Listening Activities in EFL Textbooks: An Analytical Study

This study aims to analyze the content of the Pupils' Book of Action Pack 5 in terms of the listening activities. In this study, the researchers handled these activities using a content analysis sheet. The study followed the descriptive methodology in analyzing the content of the listening activities. This study also aims to provide a comprehensive and clear answer to the extent to which listening activities in Pupil’s Book in Action Pack 5 are included and frequent and the extent to which listening activities in Action Pack 5 do correspond to the specific outcomes related to listening organiser in the General Guidelines. The findings showed that the listening activities included in Action Pack 5 are consistent with the general guidelines of the listening skills. It also showed that the results of frequency of the listening activities based on the criteria came in favor of achieving the proficiency level of the organiser. As a result, this research is beneficial in two areas: first, development of the curricula; and second, planning the instruction of the listening skill.
Translate and Classify: Improving Sequence Level Classification for English-Hindi Code-Mixed Data

Code-mixing is a common phenomenon in multilingual societies around the world and is especially common in social media texts.Traditional NLP systems, usually trained on monolingual corpora, do not perform well on code-mixed texts.Training specialized models for code-switched texts is difficult due to the lack of large-scale datasets.Translating code-mixed data into standard languages like English could improve performance on various code-mixed tasks since we can use transfer learning from state-of-the-art English models for processing the translated data.This paper focuses on two sequence-level classification tasks for English-Hindi code mixed texts, which are part of the GLUECoS benchmark -Natural Language Inference and Sentiment Analysis.We propose using various pre-trained models that have been fine-tuned for similar English-only tasks and have shown state-of-the-art performance.We further finetune these models on the translated codemixed datasets and achieve state-of-the-art performance in both tasks.To translate English-Hindi code-mixed data to English, we use mBART, a pre-trained multilingual sequenceto-sequence model that has shown competitive performance on various low-resource machine translation pairs and has also shown performance gains in languages that were not in its pre-training corpus.
Semiparametric two-sample admixture components comparison test: The symmetric case

In this paper, we consider admixture models which are two-component mixture distributions having one known component. This is the case when a gold standard reference component is well known, and when a population contains such a component plus another one with different features. When two populations are drawn from such models, we propose a penalized χ2-type testing procedure allowing a pairwise comparison of the unknown components, i.e. to test the equality of their residual features densities, under a symmetry condition. A numerical study is carried out from a large range of simulation setups to illustrate the asymptotic properties of our test. Moreover the testing procedure is applied on a real-world case: galaxy velocities datasets, where stars heliocentric velocities mixed with the Milky Way are compared.
A Methodology for Reliable Code Plagiarism Detection Using Complete and Language Agnostic Code Clone Classification

Code clone detection plays a vital role in both industry and academia.Last three decades have seen more than 250 clone detection techniques with lack of single framework that can detect and classify all 4 basic types of code clones with high precision.This serious lack of clone classification impacts largely on the universities and online learning platforms that fail to validate the projects or coding assignments submitted online.In this paper, we propose a complete and language agnostic technique to detect and classify all 4 clone types of C, C++, and Java programs.The method first generates the parse tree then extracts the functional tree to eliminate the need for the preprocessing stage employed by previous clone detection techniques.The generated parse tree contains all the necessary information for detecting code clones.We employ TF-IDF cosine similarity for the proper classification of clone types.The proposed technique achieves incredible precision rate of 100% in detecting the first two types of clones and 98% precision in detecting type-3 and type-4 clones for small codes of C, C++, and Java containing an average line count of 5.The proposed technique outperforms the existing tree-based clone detection tools by providing the average precision of 98.07% on the C, C++, and Java programs crawled from Github with an average line count of 15 which signifies that cosine similarity measure on ANTLR functional tree accurately detects all 4 types of small clones and act as proper validation tools for identifying the learning level in the submitted programming assignment.
New Applications of the Monte-Carlo Tree Search to Computer Daihinmin

The Monte Carlo tree search is popular in computer programs that play games. In this study, we present two new applications of the Monte Carlo method for the card game Daihinmin: as a method for setting good evaluation values in repeated plays, and as a method for increasing or decreasing the score of a target player.
Bayes estimators of Weibull -Lindley Rayleigh distribution parameters using Lindley's approximation

For The Weibull Lindley Rayleigh distribution (WLRD) we have obtained the Bayes Estimators of scale and shape parameters using Lindley's approximation (L-approximation) under square error loss function. The proposed estimator have been compared with the corresponding MLE for their risks based on corresponding simulated samples.
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, episodic memory banks. Our proposed strategy has two benefits: 1) convenient object-level resampling without significant extra computation, and 2) implicit feature-level augmentation from model updates. We show that image-level and object-level resamplings are both important, and thus unify them with a joint resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones.
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation

While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples.However, manually annotating data is expensive and time-consuming.Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model.In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process.Our proposed approach, Active 2 Learning (A 2 L), actively adapts to the deep learning model being trained to eliminate such redundant examples chosen by an AL strategy.We show that A 2 L is widely applicable by using it in conjunction with several different AL strategies and NLP tasks.We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by ≈ 3 -25% on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.
DESIGNING HEDGE ALGEBRAIC CONTROLLER AND OPTIMIZING BY GENETIC ALGORITHM FOR SERIAL ROBOTS ADHERING TRAJECTORIES

In recent years, the application of hedge algebras in the field of control has been studied. The results show that this approach has many advantages. In additions, industrial robots are being well-developed and extensively used, especially in the industrial revolution 4.0. Accurate control of industrial robots is a class of problems that many scientists are interested in. In this paper, we design a controller based on hedge algebra for serial robots. The control rule is given by linguistic rule base system. The goal is to accurately control the moving robot arm which adheres given trajectories. Optimization of fuzzy parameters for the controller is done by genetic algorithms. The system has been simulated on the Matlab-Simulink software. The simulation results show that the orbital deviation is very small. Moreover, the controller worked well with correct control quality. This result once presents the simplicity and efficiency of the hedge algebras approach to control.
Dynamics of Perception Distortions of Human Body Physical Dimensions in Virtual Reality

Controlling characters in a virtual reality (VR) environment can lead to the interiorization of their body dimensions by the recipients. The possible preservation of these distortions in their psyche will indicate a high degree of psychological impact of a VR on a person and the potential danger of developing depersonalization of the recipients and their dependence on such stimulation. The study of the stability of these distortions is necessary in the context of ensuring the safety of the impact of VR environments on the human psyche. The main focus of the study is on the perception distortions of human body dimensions, as they are sensed by people immersed in a VR environment, and their dynamics depending on the number of immersions. The impact of the virtual reality environment was simulated using the Freedom Locomotion VR application. One virtual reality immersion session took 15 minutes. To obtain psychometric indicators of the subjects perception of their own body dimensions, the technique Measurements according to M. Feldenkrais was used. All the participants (N = 45, three experimental groups) underwent a mandatory preliminary measurement using this technique (several hours before exposure) and a final measurement (one day after the last exposure). At the same time, the results of preliminary measurements were taken as indicators of the subjects habitual perception of their own body dimensions and were considered in each data processing as a comparison group. Free movement in a VR environment leads to distortions in the subjects perception of their own body dimensions. In all the experimental groups, there was a tendency to exaggerate body dimensions immediately after immersions, which indicates the qualitative similarity of these distortions. The effect of repetitive immersion in a VR environment on the perception of body dimensions is that it increases awareness in perceiving body parts that are least active at the time of immersion. Controlling an anthropomorphic character in a VR leads to an increase in the subjective significance of the recipients own body perception and an increased concentration of attention on the parameters least involved in immersion. The results of the study show that an increased level of awareness in perceiving their own bodies is characteristic of the subjects who have experience of repetitive immersions in a VR environment in the guise of an anthropomorphic character. Controlling a bodily projection in a VR headset does not cause its long-term interiorization, but has a positive effect on the formation of personal corporeality.