Reviewerly/exHarmony huggingface dataset:
Strategi Literasi Informasi di Komunitas Belajar Qaryah Thayyibah: Pendekatan konsep literasi, teori empat wacana lacan, dan pedagogi kritis

Qaryah Thayyibah Learning Community (QTLC) is a non-formal school with an anti-mainstream method inspired by Freire and Ivan Illich, rather than adapting national-conventional curricula. In the age of information overload, in line with the emergence of digital natives in QTLC, it is important to discuss (1) how were the critical information literacy practice in this community; (2) teachers’ roles associated with the literacy; (3) and critical pedagogy perspective on the phenomenon. This research was designed using the ethnographic method with a qualitative approach. It was conducted from December 2016 until June 2017 in Kalibening village, Tingkir subdistrict, Salatiga district, Central Java province, Indonesia. The result was that the students of QTLC had been practicing critical information literacy although neither optimal nor ideal yet based on Potter’s criterion and DIKW diagram. The main supporting factors were the culture of discussion and expressing opinions that had been cultivated and ingrained in their school activities, in line with the product or works-based learning strategy. The efforts of the school committee and management to be on the "free school movement" track to some extent—compared to regular schools—freed this community to determine the most appropriate learning method with QTLC’s vision of critical pedagogy, one of which is the practice of critical information literacy. However, the interaction between students and teachers was dominated by hysteric discourse and showed very little of any analytic discourse which is an important factor to realize critical pedagogy. Despite the shortcomings that currently exist, QTLC represents a step toward the realization of critical pedagogical practices in information literacy.
Automatic Generation of News Commentary on Social Media Based on Self-Attention Mechanism

With development for social media, the technology of automatic comment generation for social media news is expected to generate great social and commercial value; so, this technology has attracted more and more attention from industry and academia. The automatic generation of social media news comments refers to the use of current popular technologies such as deep learning, NPL, and data mining to build high-performance algorithms to give machines the same ability to understand and express language as humans and to imitate human language habits to comment on social media news. Automatic generation of social media news comments is a challenging and understudied task in natural language processing. This work addresses the problem that social media news texts are too long and have complex contextual information, and existing work does not model the content structure of news well and proposes a topic encoding model. First, topic extraction and construction of news texts are carried out using the Latent Dirichlet Allocation (LDA) topic model to convert unstructured news texts into topic-sentence pairs. Second, a topic encoder is designed based on the self-attention mechanism, and topic-sentence pairs are input into the topic encoder. It goes through the sequence embedding module, the sentence encoding module, and the topic encoding module in turn to obtain the topic hidden state sequence. Finally, this paper designs a topic attention mechanism to generate diverse comments by giving different attention to each topic in the decoding part. Comprehensive and systematic experiments verify the effectiveness of this work and improve the performance of automatic generation of social media news comments.
Feedback-Based Optimization with Sub-Weibull Gradient Errors and Intermittent Updates

This paper considers a feedback-based projected gradient method for optimizing systems modeled as algebraic maps. The focus is on a setup where the gradient is corrupted by random errors that follow a sub-Weibull distribution, and where the measurements of the output -- which replace the input-output map of the system in the algorithmic updates -- may not be available at each iteration. The sub-Weibull error model is particularly well-suited in frameworks where the cost of the problem is learned via Gaussian Process (GP) regression (from functional evaluations) concurrently with the execution of the algorithm; however, it also naturally models setups where nonparametric methods and neural networks are utilized to estimate the cost. Using the sub-Weibull model, and with Bernoulli random variables modeling missing measurements of the system output, we show that the online algorithm generates points that are within a bounded error from the optimal solutions. In particular, we provide error bounds in expectation and in high probability. Numerical results are presented in the context of a demand response problem in smart power grids.
The Construction and Approximation of ReLU Neural Network Operators

In the present paper, we construct a new type of two-hidden-layer feedforward neural network operators with ReLU activation function. We estimate the rate of approximation by the new operators by using the modulus of continuity of the target function. Furthermore, we analyze features such as parameter sharing and local connectivity in this kind of network structure.
Hierarchical Approaches to Solve Optimization Problems

Optimization is the operation of finding the most appropriate solution for a particular problem or set of problems. In the literature, there are many population-based optimization algorithms for solving optimization problems. Each of these algorithms has different characteristics. Although optimization algorithms give optimum results on some problems, they become insufficient to give optimum results as the problem gets harder and more complex. Many studies have been carried out to improve optimization algorithms to overcome these difficulties in recent years. In this study, six well-known population-based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA, and particle swarm optimization - PSO) were used. Each of these algorithms has its own advantages and disadvantages. These population-based six algorithms were tested on CEC’17 test functions and their performances were examined and so the characteristics of the algorithms were determined. Based on these results, hierarchical approaches have been proposed in order to combine the advantages of algorithms and achieve better results. The hierarchical approach refers to the successful operation of algorithms. In this study, eight approaches were proposed, and performance evaluations of these structures were made on CEC’17 test functions. When the experimental results are examined, it is concluded that some hierarchical approaches can be applied, and some hierarchical approaches surpass the base states of the algorithms.
Modelling of Factors Influencing the Citation Counts in Statistics

Citation is considered as the most popular quality assessment metric for scientific papers, and it is thus important to determine what factors promote the citation count of a paper in comparison to the others in the same field. The main aim of this study is to model the citation counts of the research published in SCI or SCI-Expanded journals of Statistics field with the growing number of scientific works in Turkey. It is well known that the right-skewed nature of the counts makes the classical regression modelling inappropriate, even if the log transformation of counts is applied [1]. Due to the fact that distribution of citation counts involves a great number of zeros, this study serves for an additional aim that is to model the counts with advanced discrete regression models for a more precise prediction [2]. Data collected for this study consist of the citation counts of all scientific papers produced by 261 Statisticians in between 2005-2017. Discrete models varying from Poisson to Zero-Inflated or Hurdle were constructed by possible influential factors, such as the publication age, the number of references, the journal category etc. Predictive performances of alternative discrete models were compared via AIC and Vuong test [3]. Results suggested that Zero Inflated Negative Binomial and Hurdle Negative Binomial mixture models are the best forms to predict the zero inflation of citation counts [4]. In addition, the influential factors of the final model were interpreted to make some suggestions to Statisticians to increase the citation counts of their work.
Children’s Incidental Learning of English Through Cartoons: An Italian Case Study

The presentation concerns a qualitative and longitudinal case study of two young Italian children acquiring English incidentally, while enjoying cartoons in a domestic environment. The data from a log kept by a participant observer over eight years are investigated to reconstruct the main steps of the process, from reception to various forms of production. Unlike
The Impact of Using Mining Technology on the Environment in the Manggarai Mine Circle Community From the Ethical Perspective of Hans Jonas

This paper focuses on the issue of the impact of the use of mining technology on the environment of the Manggarai mining circle community from the perspective of Hans Jonas responsibility ethics. Technological advances increasingly developing have a tangible impact on the entire order of human life. In addition to being very helpful to humans in everyday life, it turns out that the presence of technology negatively impacts humans, including the environment. The environment is often used as an object that provides satisfaction and benefits for humans, even so, many problems arise due to the transition of functions to nature to generate profits. The environment becomes threatened because it is polluted and damaged. The authors raised this issue using the literature method with the sources used in the form of literature from various books and journals. According to Hans Jonas, the findings of this paper are that the environment is the place and future of all living things, especially humans. Therefore, the living environment needs to be appropriately maintained, cared for, and preserved for the integrity of the existing creation and living in it.
Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised Clustering

Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.
Computer Adaptive Language Tests (CALT)

Creating a test to adequately assess reading, speaking, listening, and writing proficiency in a foreign language has many challenges. Traditionally, such tests have been paper-based or done by an evaluator in a face-to-face mode. The increasing use of technology in language education has recently shifted the way assessment can be performed. This paper will develop
Discord : An all in one messaging application (Case Study)

Discord was started to solve a big problem: how to communicate with friends around the world while playing games online.it’s a home for communities of any size,but it’s most widely used by small and active groups of people who talk regularly. Discord is used by everyone from local hiking clubs, to art communities, to study groups. Discord has millions of people creating places for their friends and communities, talking for upwards of 4 hours per day on the platform. Discord is now where the world talks, hangs out, and builds relationships. Discord lets anyone create a space to find belonging—just like it did for Jason and Stan.People love discord because it’s a home for all their communities and groups of friends.it’s a place where they can be themselves and spend time with other people who share their interests and hobbies.In this Case study we are trying to resolve the following cases such as Why discord keeps on the widely acceptable chance of global and local communication and How discord manages the large crowd and their activities.
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.
Experimental quantum state discrimination using the optimal fixed rate of inconclusive outcomes strategy

The problem of non-orthogonal state discrimination underlies crucial quantum information tasks, such as cryptography and computing protocols. Therefore, it is decisive to find optimal scenarios for discrimination among quantum states. We experimentally investigate the strategy for the optimal discrimination of two non-orthogonal states considering a fixed rate of inconclusive outcomes (FRIO). The main advantage of the FRIO strategy is to interpolate between unambiguous and minimum error discrimination by solely adjusting the rate of inconclusive outcomes. We present a versatile experimental scheme that performs the optimal FRIO measurement for any pair of generated non-orthogonal states with arbitrary a priori probabilities and for any fixed rate of inconclusive outcomes. Considering different values of the free parameters in the FRIO protocol, we implement it upon qubit states encoded in the polarization mode of single photons generated in the spontaneous parametric down-conversion process. Moreover, we resort to a newfangled double-path Sagnac interferometer to perform a three-outcome non-projective measurement required for the discrimination task, showing excellent agreement with the theoretical prediction. This experiment provides a practical toolbox for a wide range of quantum state discrimination strategies using the FRIO scheme, which can greatly benefit quantum information applications and fundamental studies in quantum theory.
A Hybrid Method for Assessment with Linguistic Grades

Assessment is an important component of all human and machine activities, because it helps to determine possible mistakes and to improve performance with respect to the corresponding activity. A hybrid method is developed in this paper for assessment with linguistic grades, which uses closed real intervals and soft sets as tools. Soft sets, introduced by Molodstov in 1999, are used for a qualitative assessment in a parametic manner and the closed real intervals enable us to quantify the qualitative assessment achieved with the help of soft sets. Two examples for chess players’ and machines’ performance assessment are also presented illustrating our results.
ANALYSIS OF THE VACCINATION'S IMPACT ON THE INCREASE IN COVID-19’S DAILY NEW AND RECOVERED CASES USING THE VECTOR AUTOREGRESSIVE (VAR) MODEL (CASE STUDY: WEST KALIMANTAN)

One of the efforts to suppress the increasing number of COVID-19 cases is the government's provision of a COVID-19 vaccine. This study examines the effect of the number of people who have been vaccinated, the first dose of vaccine, on the addition of new cases and cured cases. The three variables were analysed simultaneously using the help of the Vector Autoregressive (VAR) model. The data is on the number of new, recovered cases and people vaccinated per day from January 13 to December 30, 2021, in West Kalimantan Province. The main steps in this study are order identification, parameter estimation, and interpretation of the results. In this study, the order selection of the VAR model is limited to a maximum of the fourth order. Parameter estimation uses the Ordinary Least Square (OLS) method from several possible orders. Furthermore, the model selection is based on the smallest AIC and BIC values. The result is that the second-order VAR model has the smallest AIC and BIC values, so this model is said to be the best model. The interpretation of the equation obtained is that 74.1% of the factors adding new cases, the number of people being vaccinated, and the addition of cured cases at one and two last times affect the addition of new cases on that day. Meanwhile, the addition of new cases today was only influenced by 42.2% by new cases, the number of people being vaccinated, and the addition of recovered cases in the previous one and two days.
TEXT CLUSTERING ONLINE LEARNING OPINION DURING COVID-19 PANDEMIC IN INDONESIA USING TWEETS

To prevent the spread of corona virus, restriction of social activities are implemented including school activities which reaps the pros and cons in community. Opinions about online learning are widely conveyed mainly on Twitter. Tweets obtained can be used to extract information using text clustering to group topics about online learning during pandemic in Indonesia. K-Means is often used and has good performance in text clustering area. However, the problem of high dimensionality in textual data can result in difficult computations so that a sampling method is proposed. This paper aims to examine whether a sampling method to cluster tweets can result to an efficient clustering than using the whole dataset. After pre-processing, five sample sizes are selected from 28300 tweets which are 250, 500, 2500, 10000 and 20000 to conduct K-Means clustering. Results showed that from 10 iterations, three main cluster topics appeared 90%-100% in sample size of 2500, 10000 and 20000. Meanwhile sample size of 250 and 500 tend to produced 20%-60% appearance of the three main cluster topics. This means that around 8% to 35% of tweets used can yield representative clusters and efficient computation which is four times faster than using entire dataset.
Edge Computing Resource Optimal Allocation Method Based on Blockchain Technology

An edge computing resource optimal allocation approach based on blockchain technology is suggested to achieve efficient and safe allocation of edge computing resources. The edge computing resource optimal allocation approach based on priority task is utilized in the method to reduce calculation time and energy consumption while achieving resource optimal allocation based on work priority. In the resource allocation, the resource privacy protection method based on private blockchain is adopted to ensure the resource security in the resource allocation for edge computing. The proposed strategy considerably improves the efficiency of edge computing in resource allocation, according to the experimental results. In comparison with the results before using the method, the energy consumption after using the method is reduced by at least 13 J. The safety coefficient of the optimal allocation of the edge computing resources is as high as 0.95. The time consumption of edge computing resource allocation of this method is shorter. Therefore, the method can quickly adapt to the increase of data volume in comparison with the similar methods, so as to adjust the allocation for edge computing resources at the fastest speed, and achieve the optimal allocation of edge computing resources.
Automatic Handwritten Digit Recognition Model

The Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it seems. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits. However, it turns into a challenging task for computers to recognize digits. Simple hunches about how to recognize digits become difficult to express algorithmically. Moreover, there is a significant variation in writing from person to person, which makes it immensely complex. The MNIST digit recognition system is the working of a machine to train itself so that it can recognize digits from different sources like emails, bank cheques, papers, images, etc. Convolutional Neural Networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, kernel size for CNN-based handwritten digit recognition. Our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy, along with reduced operational complexity and cost.
HUMAN ACTIVITY DETECTION SYSTEM

The purpose of this project is to develop a model that would take the image input from different sources and detect actions that are being performed in them by the people also commonly referred to as Human actions recognition. The project starts by analyzing the procedures and models that already exist to perform this activity along with the assessment of their advantages and disadvantages respectively. There were many approaches to build this project that mainly used sensors and various modelling procedures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks(RNN), Deep Neural Networks(DNN) and few Image Processing techniques. The goal is to come up with a model that is able to detect a selection of activities on which the model was trained during the development phase. The designed model helps to overcome the disadvantage of the previously built models that relied on the use of sensors. With the elimination of sensors, we eliminate the cost associated with respect to the sensors and the errors that can be caused due to the use of sensors. The vision-based HAR(human activity recognition) has produced fruitful findings with the advantage of high optical sensor resolution and the rapidly evolving computer vision techniques.