Reviewerly/exHarmony huggingface dataset:
ANALISIS KESALAHAN BAHASA DALAM BIDANG MORFOLOGI PADA SURAT KABAR KOMPAS.COM (EDISI NOVEMBER-DESEMBER 2021)

The issue to be addressed in this study is the investigation of morphological language errors in the November-December 2021 edition of Kompas.com. Language errors are common in situations or professions that need consistency in language standards, particularly language usage, which prioritizes things other than communication as the end consequence of language practices. Morphology is the development of words according to their shape and meaning in expressions. According to the Great Indonesian Dictionary, newspapers are sheets of paper on which news organized into columns is printed daily or regularly. This research information comes from the newspaper Kompas.com. The data collection methods for this study are: (a) collecting the November-December 2021 edition of the Kompas.com newspaper, and (b) reading the November-December 2021 edition of the Kompas.com newspaper. The study found 81 data from 31 news articles. The investigation of language errors in morphology covers affix selection errors, word repetition errors, compound word compile errors, and word choice errors. This study concludes that word choice errors are more frequently reported.
An Exploratory Data Analysis for Synchronous Online Learning Based on AFEA Digital Images

The spread of COVID-19 throughout the world has affected the education sector. In some higher education institution, such as Polytechnic Caltex Riau (PCR), it is mandatory for students to participate in synchronous or asynchronous learning activities via virtual classroom. Synchronous online learning is usually supported by video conferencing media such as Google Meeting or Zoom Meeting. The communication between lecturers and students is captured as an image as evidence of students’ interaction and participation in certain learning subjects. These images can provide information for lecturers in determining students’ internal feelings and measuring students’ interest through facial emotions. Taking this reason into account, the current research aims to analyze the emotions detected in facial expression through images using automatic facial expression analysis (AFEA) and exploratory data analysis (EDA), then visualize the data to determine the possible solution to improve the educational process’ sustainability. The AFEA steps applied were face acquisition to detect facial parts in an image, facial data extraction and representation to process feature extraction on the face, and facial expression recognition to classify faces into emotional expressions. Thus, this paper presents the results obtained from applying machine learning algorithms to classify facial expressions into happy and unhappy emotions with mean values of 5.58 and 2.70, respectively. The data were taken from the second semester of 2020/2021 academic year with 1,206 images. The result highlighted the fact that students showed the facial emotion based on the lecture types, hours, departments, and classes. It indicates that there are, in fact, several factors contributing to the variances of students’ facial emotions classified in synchronous online learning.
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse

Transformers have achieved remarkable success in several domains, ranging from natural language processing to computer vision. Nevertheless, it has been recently shown that stacking self-attention layers - the distinctive architectural component of Transformers - can result in rank collapse of the tokens' representations at initialization. The question of if and how rank collapse affects training is still largely unanswered, and its investigation is necessary for a more comprehensive understanding of this architecture. In this work, we shed new light on the causes and the effects of this phenomenon. First, we show that rank collapse of the tokens' representations hinders training by causing the gradients of the queries and keys to vanish at initialization. Furthermore, we provide a thorough description of the origin of rank collapse and discuss how to prevent it via an appropriate depth-dependent scaling of the residual branches. Finally, our analysis unveils that specific architectural hyperparameters affect the gradients of queries and values differently, leading to disproportionate gradient norms. This suggests an explanation for the widespread use of adaptive methods for Transformers' optimization.
PERAMALAN HARGA BITCOIN DENGAN METODE SMOOTH TRANSITION AUTOREGRESSIVE (STAR)

One of the spectacular advances in technology in the economic field is the cryptocurrency it created. The fluctuating price of Bitcoin, is widely used as a means of making profit. The time series forecasting method that can be used for the case of nonlinear time series data such as Bitcoin data is the smooth transition autoregressive (STAR) model. STAR is an extension of the autoregressive model for nonlinear time data. The purpose of this study is to obtain the results of forecasting Bitcoin price data for the next 2 two months using the STAR method. The data used in this study is Bitcoin daily price data from September 2017 to April 2021. To estimate the STAR model, several things that must be determined are the autoregressive model, transition variables, and transition functions. If the STAR model has been estimated, forecasting will be carried out for the next 2 months, which results in the forecast for the highest Bitcoin price falling on June 30, 2021 and the lowest Bitcoin price falling on May 1, 2021.
Human Activity Discovery with Automatic Multi-objective Particle Swarm Optimization Clustering with Gaussian Mutation and Game Theory

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to tackle the problem of human activity discovery fully unsupervised and map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm does not require any prior knowledge of the number of activities to be discovered and can find the optimal number. Multi-objective optimization problems typically cannot have a single optimal solution. To solve this issue, Nash Equilibrium (NE) is applied to the pareto front to choose the best solution. NE does not just look for the best solution, but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. Experiments on five challenging datasets demonstrate that the proposed approach is the most efficient to achieve better accuracy in human activity discovery and also can determine the optimal number of clusters.
Association Rule Mining using FP-Growth and An Innovative Artificial Neural Network Techniques

Abstract: With the prodigious proliferation of ginormous-scale data depots, a need for incorporating the empirical techniques of Data Mining (DM) with the effectiveness of commensurate systems to intuitively manage cosmic volumes of data has now risen. To quell these obstructions of managing data efficiently, in this document we present a new algorithm based on ANN for DM activities, which overcomes the problems in the current available algorithms of mining apropos of their execution time and interestingness and to prove its efficiency the new algorithm will be compared to a popular mining algorithm: FP Growth. The following sections of the paper are sorted as – section I exhibits the introduction, section II exhibits the previously done related works, section III is exemplifies of the methodologies, section IV exhibits the experimental setup of the analogous work, section V inculcates the experimental result and finally section VI depicts the conclusion of the respective work. Keyword: Data Mining, Association Rule Mining, FP Growth algorithm, Artificial Neural Networks.
A statistical analysis of an image classification problem

The availability of massive image databases resulted in the development of scalable machine learning methods such as convolutional neural network (CNNs) filtering and processing these data. While the very recent theoretical work on CNNs focuses on standard nonparametric denoising problems, the variability in image classification datasets does, however, not originate from additive noise but from variation of the shape and other characteristics of the same object across different images. To address this problem, we consider a simple supervised classification problem for object detection on grayscale images. While from the function estimation point of view, every pixel is a variable and large images lead to high-dimensional function recovery tasks suffering from the curse of dimensionality, increasing the number of pixels in our image deformation model enhances the image resolution and makes the object classification problem easier. We propose and theoretically analyze two different procedures. The first method estimates the image deformation by support alignment. Under a minimal separation condition, it is shown that perfect classification is possible. The second method fits a CNN to the data. We derive a rate for the misclassification error depending on the sample size and the number of pixels. Both classifiers are empirically compared on images generated from the MNIST handwritten digit database. The obtained results corroborate the theoretical findings.
Formation of the Lened W-(Be) Skarn Deposit by Neutralization of a Magmatic Fluid—Evidence from H3BO3-Rich Fluids

Magmatic-hydrothermal systems, especially those causing the formation of tungsten deposits, may be enriched in boron, as is suggested by the presence of hydrothermal tourmaline. This study examines the boron and metal (including tungsten) concentrations of quartz-hosted fluid inclusions in the Lened W-(Be) deposit of the Canadian Cordillera and resolves (i) the analytical challenges involved during fluid salinity calculations of B-rich fluids and (ii) the relationship between fluid chemical composition and ore-forming processes involved at Lened. The aqueous fluid inclusions from this study have high CO2 and boron contents, indicated by the presence of a carbonic phase and sassolite crystals (H3BO3) in fluid inclusions. The boron content of the aqueous liquid phase (0.5 wt. %) was determined using microthermometric and Raman spectroscopic analyses. Boron was judged the most appropriate internal standard for quantifying the LA-ICP-MS data from these inclusions after calculation of salinity in the H2O-NaCl-H3BO3 system (3.5 to 5 wt. % NaCleq). Trace element data of the fluids show relatively high concentrations of Li (40 to 474 ppm), Al (56 to 1003 ppm), As (36 to 490 ppm) and Cs (68 to 296 ppm); and lower concentrations of Rb (3.6 to 77 ppm), Sr (0.4 to 23 ppm), Sb (1 to 32 ppm), Ba (0.6 to 163 ppm), Mg (6.9 to 7.6 ppm) and other metals, such as Be (2.4 to 10.2 ppm), W (2.4 to 27 ppm) and Cu (5.1 to 73 ppm). The high Cs and Li concentrations suggest a magmatic origin of the metals, while the moderate concentrations in Sr and Ba are indicative of fluid–rock interaction with the surrounding limestone. The presence of sassolite suggests that these fluids were highly acidic. The neutralization of this fluid through interaction with the surrounding limestone is the most probable trigger for scheelite precipitation. The presence of such high boron content in the magmatic fluid at Lened indicates the potential role in the enrichment of the source melt before fluid exsolution.
Differentiable Invariant Causal Discovery

Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are non-identifiable, turning this problem into a continuous optimization task prone to data biases. In many real-life situations, data is collected from different environments, in which the functional relations remain consistent across environments, while the distribution of additive noises may vary. This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions. Specifically, DICD aims to discover the environment-invariant causation while removing the environment-dependent correlation. We further formulate the constraint that enforces the target structure equation model to maintain optimal across the environments. Theoretical guarantees for the identifiability of proposed DICD are provided under mild conditions with enough environments. Extensive experiments on synthetic and real-world datasets verify that DICD outperforms state-of-the-art causal discovery methods up to 36% in SHD. Our code will be open-sourced.
Assessing Information Security Continuous Monitoring in the Federal Government

To confront the relentless and increasingly sophisticated cyber assaults from cybercriminals, nation-state actors, and other adversaries, the U.S. Federal Government must have mechanisms to reduce or eliminate compromise and debilitating consequences. Information Security Continuous Monitoring (ISCM) leverages technology to rapidly detect, analyze, and prioritize vulnerabilities and threats and deliver a data-driven, risk-based approach to cybersecurity. Although monitoring information system security became a requirement for government agencies over 20 years ago and billions of dollars are being spent annually for cybersecurity, ISCM remains at a low maturity level across the Federal Government. This research framework presented is part of ongoing doctoral research. The research seeks to identify the challenges achieving an effective ISCM program and inform measures needed to optimize ISCM. The research involves conducting an ISCM Program Assessment in a Department of Defense (DoD) organization using the recently published National Institute of Standards and Technology (NIST) ISCM Assessment (ISCMA) methodology and the companion assessment tool ISCMAx. An ISCM doctrine placement is presented, derived from the NIST ISCM assessment elements, to more clearly articulate and visualize the doctrine of a well-designed and well-implemented ISCM program. This research will also contribute to the knowledge base for assessing ISCM in the Federal government and the functionality of the ISCMAx tool.
PAGER: Progressive Attribute-Guided Extendable Robust Image Generation

This work presents a generative modeling approach based on successive subspace learning (SSL). Unlike most generative models in the literature, our method does not utilize neural networks to analyze the underlying source distribution and synthesize images. The resulting method, called the progressive attribute-guided extendable robust image generative (PAGER) model, has advantages in mathematical transparency, progressive content generation, lower training time, robust performance with fewer training samples, and extendibility to conditional image generation. PAGER consists of three modules: core generator, resolution enhancer, and quality booster. The core generator learns the distribution of low-resolution images and performs unconditional image generation. The resolution enhancer increases image resolution via conditional generation. Finally, the quality booster adds finer details to generated images. Extensive experiments on MNIST, Fashion-MNIST, and CelebA datasets are conducted to demonstrate generative performance of PAGER.
VIRTUAL AND AUGMENTED REALITY IN PRODUCTION ENGINEERING

Augmented Reality is a combination of a real and a computer-generated or virtual world. It is achieved by augmenting computer-generated images on real world. It is of four types namely marker based, marker less, projection based and superimposition based augmented reality. It has many applications in the real world. AR is used in various fields such as medical, education, manufacturing, robotics and entertainment. Augmented reality comes under the field of mixed reality. It can be considered as an inverse reflection of Virtual Reality. They both have certain similarities and differences. This paper gives information about Augmented Reality and how it started. It analyses various types of augmented reality, its applications and its advantages and disadvantages. This paper also gives us knowledge regarding those major threats that augmented reality will face in the near future and about its current and future applications. It gives us a comparison between the two related topics, Augmented reality and Virtual reality. The following paper also helps us know about the effect of Augmented Reality on the human lif
Development of Thematic Digital Comics for Healthy Children During the Covid-19 Pandemic for PJOK Learning Class V Elementary School Students in Palembang

The research aims to: 1) develop thematic digital comics for healthy children during the Covid-19 pandemic for learning PJOK for Class V Elementary School students in Palembang, 2) to determine the effectiveness of product implementation. The research method uses quantitative research with the ADDIE development model approach. The research subjects consisted of 1 physical education learning technology expert, 1 learning psychology expert, 3 PJOK teachers and 30 elementary school fifth grade students. Quantitative data were collected through questionnaire instruments and observation guidelines. Data analysis used descriptive quantitative. The results of the research are 1) it has produced a thematic digital comic model for healthy children during the Covid-19 pandemic for Grade V Elementary School Students in Palembang, 2) the percentage of effectiveness is 87.73% so it is very feasible to use. The hope is that it will become an alternative choice of learning media for physical education, sports and health (PJOK)
Stereo matching algorithm based on combined matching cost computation and edge preserving filters

The stereo matching &lt;span&gt;process is one of the key areas that impact the stereo vision technologies which are commonly used in the application of three-dimensional reconstructions. The accuracy of the depth information used in three-dimensional reconstruction is directly proportional to the accuracy of the disparity obtained from stereo matching. The challenging issue in the stereo matching process is to determine the accurate corresponding point between the left image and right image, especially for image pairs that have different exposure such as different illumination and image pair with less texture region. In order to increase the accuracy of disparity value, a new stereo matching algorithm is proposed based on the combination of Sum of absolute different and census transform at matching cost computation. guided filter was used in the matching cost aggregation in order to remove noise and preserve the edge of the image. In the optimization step, the winner take all strategy is used to select the minimum matching cost. Finally, a median filter is applied to the initial disparity map for refinement purposes. The experimental results show that the algorithm is effective in reducing the error and improving the accuracy of the disparity map in different illumination regions, less textured regions and different environmental exposure.&lt;/span&gt;
DIAGNOSIS OF STRUCTURE FORMATION KNOWLEDGE OF A FUTURE TEACHER

The questions related to forming and diagnostics of "normative" structure o f knowledges are examined in the article. Under "normative" knowledges knowledges, which respond to nufuest, which are subject to the obligatory mastering and subsequent control from the side of educational establishments state educational standard, are understood. On the basis of the conducted researches of grounded, that the test control is the most objective form of diagnostics of the formed structure of knowledges.
Computing the Variance of Shuffling Stochastic Gradient Algorithms via Power Spectral Density Analysis

When solving finite-sum minimization problems, two common alternatives to stochastic gradient descent (SGD) with theoretical benefits are random reshuffling (SGD-RR) and shuffle-once (SGD-SO), in which functions are sampled in cycles without replacement. Under a convenient stochastic noise approximation which holds experimentally, we study the stationary variances of the iterates of SGD, SGD-RR and SGD-SO, whose leading terms decrease in this order, and obtain simple approximations. To obtain our results, we study the power spectral density of the stochastic gradient noise sequences. Our analysis extends beyond SGD to SGD with momentum and to the stochastic Nesterov's accelerated gradient method. We perform experiments on quadratic objective functions to test the validity of our approximation and the correctness of our findings.
Early Transferability of Adversarial Examples in Deep Neural Networks

This paper will describe and analyze a new phenomenon that was not known before, which we call "Early Transferability". Its essence is that the adversarial perturbations transfer among different networks even at extremely early stages in their training. In fact, one can initialize two networks with two different independent choices of random weights and measure the angle between their adversarial perturbations after each step of the training. What we discovered was that these two adversarial directions started to align with each other already after the first few training steps (which typically use only a small fraction of the available training data), even though the accuracy of the two networks hadn't started to improve from their initial bad values due to the early stage of the training. The purpose of this paper is to present this phenomenon experimentally and propose plausible explanations for some of its properties.
The Effectiveness of Snakes and Ladders Game on Mother's Activeness in Participating of Pregnant Women's Classes in South Amanatun District, Timor Tengah Selatan

Pregnant Women Class was a means learning together about health for pregnant women in the form of face-to-face in groups, which aims to increase the knowledge and skills of mothers regarding pregnancy, prenatal care, childbirth, postpartum care, newborn care, myths, comorbidities. The lack of learning media in pregnant women's classes can have an impact on the activeness of mothers taking classes for pregnant women. Based on the initial study conducted by the researcher, there was a gap between expectations and reality, namely that the activeness of mothers could reach (100%) but in reality, it only reached (50%), because the delivery media in the pregnant women class only used leaflets, flip sheets and KIA books as well as the delivery method in the mother class.