Reviewerly/exHarmony huggingface dataset:
Statistics Related with Ewens Sampling Formula

For the Ewens sampling formula, the number of distinct components is written as a sum of one and independent Bernoulli random variables almost surely. Therefore, as approximation to the distribution of the number, we consider shifted Poisson and binomial distributions. We recommend the shifted binomial distribution with the almost same first two moments as the number of components and give its two applications. Next, we consider the order statistics of a sample from the GEM distribution on the positive integers. The gaps among the order statistics are independent and have geometric distributions. Therefore, the frequency distribution of order statistics is the Donnelly–Tavaré–Griffiths formula. The number of the largest values has the Yule distribution asymptotically. According to the Erdős–Turán law, log of the order of random permutation is asymptotically normal. This asymptotic property was generalized for the Ewens sampling formula by Barbour and Tavaré. For the formula with random parameter having a continuous distribution, the asymptotic distribution of the law is not normal and related with the distribution of the random parameter.
PELATIHAN PERHITUNGAN PAJAK PPH PASAL 21, PASAL 23, DAN PPH PASAL 4 AYAT 2 KEPADA KEPALA SEKOLAH, BENDAHARA, ADMINISTRASI SMP KOTA TANGERANG SELATAN GUGUS 02

The purpose of the PKM activities is to maximize the performance of principal, treasurer, administration of SMP Kota Tangerang Selatan cluster 02. The focus is on school treasurer tasked with handling the use of BOS funds. BOS Treasurer has a tax obligation that is somewhat different than the government treasurer in general. Procedure of calculation of PPh article 21 on employee's salary is derived from calculating the entire gross income of a month which includes basic salary and allowances, then reduced by the existing deduction, then calculated net income a year. The PKM research object is the principal, treasurer, administration of SMP Kota Tangerang Selatan cluster 02. The problem is derived from the lack of knowledge and the school principal, treasurer related to the tax problems in the use of BOS funds (school operational Assistance). The training Program that will be provided by experts in the field of tax is expected to provide knowledge related to tax calculation of article 21, article 23, and PPh article 4 paragraph 2 to the principal, treasurer, SMP administration of Tangerang City South Cluster 02.Keywords: Training, tax calculation of article 21, article 23, and PPh article 4 paragraph 2
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing

Depth data provide geometric information that can bring progress in RGB-D scene parsing tasks. Several recent works propose RGB-D convolution operators that construct receptive fields along the depth-axis to handle 3D neighborhood relations between pixels. However, these methods pre-define depth receptive fields by hyperparameters, making them rely on parameter selection. In this paper, we propose a novel operator called malleable 2.5D convolution to learn the receptive field along the depth-axis. A malleable 2.5D convolution has one or more 2D convolution kernels. Our method assigns each pixel to one of the kernels or none of them according to their relative depth differences, and the assigning process is formulated as a differentiable form so that it can be learnt by gradient descent. The proposed operator runs on standard 2D feature maps and can be seamlessly incorporated into pre-trained CNNs. We conduct extensive experiments on two challenging RGB-D semantic segmentation dataset NYUDv2 and Cityscapes to validate the effectiveness and the generalization ability of our method.
Referring Expression Comprehension: A Survey of Methods and Datasets

Referring expression comprehension (REC) aims to localize a target object in an image described by a referring expression phrased in natural language. Different from the object detection task that queried object labels have been pre-defined, the REC problem only can observe the queries during the test. It thus more challenging than a conventional computer vision problem. This task has attracted a lot of attention from both computer vision and natural language processing community, and several lines of work have been proposed, from CNN-RNN model, modular network to complex graph-based model. In this survey, we first examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to encode the visual and textual modalities. In particular, we examine the common approach of joint embedding images and expressions to a common feature space. We also discuss modular architectures and graph-based models that interface with structured graph representation. In the second part of this survey, we review the datasets available for training and evaluating REC systems. We then group results according to the datasets, backbone models, settings so that they can be fairly compared. Finally, we discuss promising future directions for the field, in particular the compositional referring expression comprehension that requires longer reasoning chain to address.
Implementasi Algoritma Neural Network Berbasis Particle Swarm Optimazation untuk Penentuan Kredit

Bad credit is one of the credit risks faced by financial and banking industry players. Bad credit occurs if in the long run, financial institutions or banks cannot withdraw credit loans within the allotted time. Bad credit has a bad impact for credit providers in the form of risk of loss. Of course this should not be allowed to drag on and must be resolved. However, to guarantee accuracy in determining creditworthiness, an accurate algorithm is needed. Neural Network is an algorithm that can map and classify data input and can be used for the prediction of several variables that are input into this algorithm. To be able to add accuracy in predicting the determinants of credit worthiness, this algorithm is optimized with Particle Swarm Optimazation. The results of the study in the form of confusion matrix prove that the accuracy of Neural Networks based on Particle Swarm Optimazation has an accuracy rate of 96.67% and AUC results of 0.965
Domain Expansion in DNN-based Acoustic Models for Robust Speech Recognition

Training acoustic models with sequentially incoming data -- while both leveraging new data and avoiding the forgetting effect-- is an essential obstacle to achieving human intelligence level in speech recognition. An obvious approach to leverage data from a new domain (e.g., new accented speech) is to first generate a comprehensive dataset of all domains, by combining all available data, and then use this dataset to retrain the acoustic models. However, as the amount of training data grows, storing and retraining on such a large-scale dataset becomes practically impossible. To deal with this problem, in this study, we study several domain expansion techniques which exploit only the data of the new domain to build a stronger model for all domains. These techniques are aimed at learning the new domain with a minimal forgetting effect (i.e., they maintain original model performance). These techniques modify the adaptation procedure by imposing new constraints including (1) weight constraint adaptation (WCA): keeping the model parameters close to the original model parameters; (2) elastic weight consolidation (EWC): slowing down training for parameters that are important for previously established domains; (3) soft KL-divergence (SKLD): restricting the KL-divergence between the original and the adapted model output distributions; and (4) hybrid SKLD-EWC: incorporating both SKLD and EWC constraints. We evaluate these techniques in an accent adaptation task in which we adapt a deep neural network (DNN) acoustic model trained with native English to three different English accents: Australian, Hispanic, and Indian. The experimental results show that SKLD significantly outperforms EWC, and EWC works better than WCA. The hybrid SKLD-EWC technique results in the best overall performance.
Pengembangan Media Pembelajaran Kartu Pintar Berbasis Android untuk Pengenalan Shalat Fardhu di Sekolah Dasar

The purpose of this study was to test the feasibility of an android-based learning media with Augmented Reality technology in learning fard prayer in elementary schools. The method used in this research is Research and Development (R&D) with the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The research subjects are Grade II students of SD Muhammadiyah 16 Surakarta. The results of this study obtained results from media experts with a percentage of eligibility of 89.06% included in the very feasible category, material experts with 94.44% results included in the very feasible category, then student trials with 86.14% results included in the very feasible category of student learning outcomes namely pre-test and post-test results obtained an increase of 22.89%.
Active Learning under Label Shift

We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling and propose their combined usage in active learning. Our method is known as Mediated Active Learning under Label Shift (MALLS). It balances the bias from class-balanced sampling and the variance from importance weighting. We prove sample complexity and generalization guarantees for MALLS which show active learning reduces asymptotic sample complexity even under arbitrary label shift. We empirically demonstrate MALLS scales to high-dimensional datasets and can reduce the sample complexity of active learning by 60% in deep active learning tasks.
A NEW APPROACH TO BUILDING A POST-QUANTUM SCHEME OF ELECTRONIC DIGITAL SIGNATURE

Cryptographic information security techniques are essential in building a modern cybersecurity infrastructure. Recently, there have been new challenges and threats to cryptographic transformation. In particular, the emergence and rapid development of the latest quantum computing technologies necessitates the urgent need for the development and research of new methods of post-quantum cryptographic transformations, that is, those that will be sustainable even if quantum cryptanalysis is possible. This article is devoted to the analysis of possibilities of implementation of digital signature schemes based on using error-correcting codes. This approach allows cryptographers to build schemes that are resistant to both classic cryptanalysis and cryptanalysis which uses quantum computing. The article describes the principles of the classic digital signature scheme which is named CFS and built using a Niederreiter-like transform, and also we propose a new approach that enables an implementation of signature according to the McEliece transformations. This approach preserves the advantages of its predecessor and provides additional protection against special attacks. Also, a comparative analysis and characterization of the considered schemes according to the criteria of resistance to classic and quantum cryptanalysis, complexity of necessary transformations and length of generated signatures are made. The results show that reliable and secure cryptographic transformations can be built, in particular, electronic digital signature algorithms that are code-based and secure even in the case of quantum cryptanalysis. However, it should be noted that the drawback of code-based signature schemes is the large amount of key data required by the algorithm, as well as the difficulty in creating a signature due to the need for multiple decryption of the syndrome, which remains a topical topic and needs further research
Spatio-Temporal Event Segmentation and Localization for Wildlife Extended Videos

Using offline training schemes, researchers have tackled the event segmentation problem by providing full or weak-supervision through manually annotated labels or self-supervised epoch-based training. Most works consider videos that are at most 10's of minutes long. We present a self-supervised perceptual prediction framework capable of temporal event segmentation by building stable representations of objects over time and demonstrate it on long videos, spanning several days. The approach is deceptively simple but quite effective. We rely on predictions of high-level features computed by a standard deep learning backbone. For prediction, we use an LSTM, augmented with an attention mechanism, trained in a self-supervised manner using the prediction error. The self-learned attention maps effectively localize and track the event-related objects in each frame. The proposed approach does not require labels. It requires only a single pass through the video, with no separate training set. Given the lack of datasets of very long videos, we demonstrate our method on video from 10 days (254 hours) of continuous wildlife monitoring data that we had collected with required permissions. We find that the approach is robust to various environmental conditions such as day/night conditions, rain, sharp shadows, and windy conditions. For the task of temporally locating events, we had an 80% recall rate at 20% false-positive rate for frame-level segmentation. At the activity level, we had an 80% activity recall rate for one false activity detection every 50 minutes. We will make the dataset, which is the first of its kind, and the code available to the research community.
SELECTION OF NEW BIKE IN THE MARKET USING AHP-TOPSIS TECHNIQUE

New stylish Bikes are being launched by manufacturing companies is recently developing quickly alongside the improvement of innovation and science. Similarly, makers need to improve their products, i.e., the bikes, to meet customers requirement. This study tried to evaluate bike selection by utilizing ten criteria that are considered by the customers when they are willing to purchase the bikes, namely, Price, Displacement, Maximum Power, Maximum Torque, Number of Cylinders, Number of Gears, KMPL, Ground Clearance, Kerb/Wet Weight and Fuel Tank Capacity. The analytical hierarchy process (AHP) is integrated with technique for order reference by similarity to ideal solution (TOPSIS) to meet the objective of this study. Firstly, the weights of each criterion are to determine using AHP. These weights will be used in TOPSIS method to select the best alternative. A case study to exhibit the applicability of the methods was conducted to evaluate ten types of bikes which are available in the market having a price less than one lakh. The AHP-TOPSIS result showed that the Honda CB Hornet 160R is the best bike among the ten alternatives.
Non-Contact Vibration Detection Software and Hardware Design with Image Processing Method to Reveal Substantial Changes in Structures and Systems

Many types of equipment are used for vibration detection in buildings and systems.However, these systems have their own problems.In this article, a combined software and hardware system is designed for the solution of these problems.The system has been designed with the Motion Magnification Analysis (MMA) method, which stands out in the literature studies.C # language and .NET environment have been used as software.The Accord library was used for the image processing process.These libraries were also used for the analysis of subspaces and components of the image.During the study, three new algorithms that have not been applied in the past and not in the literature have been developed.The first of these is the derivative of the method known as Color Magnification, which analyzes the color space.The second is the derivative of the Motion Magnification method, which magnifies the vibration to the visible level.And the third one is the Frequency Determination algorithm, which enables frequency measurement.With this solution, it is provided to make an analysis of the images taken with devices with relatively slow cameras such as mobile phones/tablets as well as fast cameras.All results were compared with a touchable vibration analyzer for benchmarking and showed that the proposed method was successful.
LibGuides: Open Journal System: Working with Digital Assets

This guide aims to familiarize faculty, student, and staff users to the open journal system (OJS) through a series of tutorials, videos, and links to helpful information on the Web.
Graph Neural Networks for Node-Level Predictions

The success of deep learning has revolutionized many fields of research including areas of computer vision, text and speech processing. Enormous research efforts have led to numerous methods that are capable of efficiently analyzing data, especially in the Euclidean space. However, many problems are posed in non-Euclidean domains modeled as general graphs with complex connection patterns. Increased problem complexity and computational power constraints have limited early approaches to static and small-sized graphs. In recent years, a rising interest in machine learning on graph-structured data has been accompanied by improved methods that overcome the limitations of their predecessors. These methods paved the way for dealing with large-scale and time-dynamic graphs. This work aims to provide an overview of early and modern graph neural network based machine learning methods for node-level prediction tasks. Under the umbrella of taxonomies already established in the literature, we explain the core concepts and provide detailed explanations for convolutional methods that have had strong impact. In addition, we introduce common benchmarks and present selected applications from various areas. Finally, we discuss open problems for further research.
New insights on temporal and spatial evolution of Yellowstone earthquake swarms: a multidisciplinary geological-seismological approach

&amp;lt;p&amp;gt;The Yellowstone volcanic field, in western United States, is well known for intense seismic activity, abundant geothermal features and a violent volcanic history that includes a caldera-forming eruption 640 ka ago. Even though the recentmost eruption dates back to 70 ka ago, a very high seismicity, quasi-continuous surficial deformation through uplift and subsidence stages (at rates of up to 70 mm/yr) and intense hydrothermal activity are clear evidences of a still very active volcanic field. Thanks to a recently improved seismic network, here we analyze the rate of occurrence of 19&amp;amp;#8217;538 relocated earthquakes belonging to the temporal window between 1988 and 2016. Starting from this dataset, we identify and characterize the seismic swarm activity occurring in the study area after 2007. We also evaluate the analogies and differences of their seismic behavior through the analysis of frequency-magnitude distribution of seismic events. We investigate the identified seismic swarms clustered in space and time, their relation with active volcanic and tectonic processes and stress field variations caused by the migration of magmatic and hydrothermal fluids. Calculated b-values associated with the recentmost seismic swarms have been related to past swarms that occurred in the area, thus revealing the temporal and spatial evolution of such phenomena. Our study gives new crucial insights to understand the relation between seismic and magmatic activity in the Yellowstone volcanic plateau, with important implications for a better comprehension of the local seismic and volcanic hazards.&amp;lt;/p&amp;gt;
Eficacia del dispositivo didáctico REI en el aprendizaje desde las voces de los protagonistas

Esta investigacion da a conocer el cambio que se produce en un curso universitario al utilizar el dispositivo didactico Recorrido de Estudio e Investigacion (REI) propuesto por la Teoria Antropologica de lo Didactico (TAD) lo cual permite enfrentar el monumentalismo en las aulas, tomando en cuenta que en la mayoria de estas aun se siguen realizando clases de forma tradicional. Se establece su eficacia en el procesamiento de la informacion y tipos de aprendizajes. El inicio del REI, es una pregunta generatriz de la cual se derivan preguntas derivadas, usadas para abordar los contenidos de un curso de estadistica. La investigacion se enmarca en el metodo mixto, se emplea un diseno cuasi experimental de dos grupos con pre y post test. En el grupo experimental se realiza una intervencion utilizando el REI, mientras que el grupo control trabaja bajo monumentalismo. Los resultados obtenidos permiten establecer la influencia del dispositivo didactico en las estrategias de aprendizaje y tipos de aprendizajes, como indicadores de aprendizaje estables, se presentan dos tramas: una correspondiente al uso de dispositivo didactico, incorporacion de la pregunta y la segunda es la aplicacion de entrevista. Los estudiantes se muestran motivados a trabajar en forma colaborativa y opinan favorablemente acerca de la implementacion. Palabras clave: utilizacion de preguntas, condiciones de aprendizajes, desarrollo cognitivo, ensenanza superior. EFFICACY OF THE DIDACTIC DEVICE CSR IN THE LEARNING FROM THE VOICES OF THE PROTAGONISTS ABSTRACT This research intends to make known the change that takes place in the university classroom by using the didactic device Study and Research Course (SRC) proposed by the Anthropological Theory of the Didactic (ATD) which allows to face the monumentalism in the classrooms, taking in mind that in most of these classes are still carried out traditionally. It establishes its effectiveness in the processing of information and types of learning. The starting. The purpose of the research is to describe the levels of mathematical knowledge acquired by students in the course of working with SRC. The research is part of the mixed method, using a quasi-experimental design of two groups with pre and post-test. In the experimental group an intervention is performed with an SRC, while the control group works with traditional methodology.The SRC is designed so that its implementation promotes learning from the student's own activities. Activities that constitute the proposal of class work are presented where students will be responsible for finding the ways to solve the question presented, while the role of the teacher is to guide and guide the activities of their students. The obtained results allow to establish the influence of the didactic device in the learning strategies and types of learning, as indicators of lasting learning, the students are motivated to work in a cooperative way and they think favorably about its implementation. Key words:use of questions, learning conditions, cognitive development, team teaching, higher education.
The Edit Distance to $k$-Subsequence Universality

A word $u$ is a subsequence of another word $w$ if $u$ can be obtained from $w$ by deleting some of its letters. The word $w$ with alph$(w)=Σ$ is called $k$-subsequence universal if the set of subsequences of length $k$ of $w$ contains all possible words of length $k$ over $Σ$. We propose a series of efficient algorithms computing the minimal number of edit operations (insertion, deletion, substitution) one needs to apply to a given word in order to reach the set of $k$-subsequence universal words.